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Transcriptional regulation in nephron progenitor cells
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1
Transcriptional Regulation in Nephron Progenitor Cells
by
Qiuyu Guo
A dissertation submitted in conformity with the requirements
for the degree of Doctor of Philosophy
in Developmental and Stem Cells Biology, Regenerative Medicine
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
May 2019
© Copyright by Qiuyu Guo 2019
2
Preface
A stem cell population is characterized by self-renewal and multi-potency. The fate of
stem cells is regulated transcriptionally through intrinsic transcription factors (TF) and
environmental signaling pathways. In mammals, nephron progenitor cells (NPCs) are a distinct
population that self-renew and give rise to all cells of the nephron. NPCs are present in the
embryonic and early post-natal kidney. A balance between self-renewal and differentiation is
required to generate the proper number of nephrons. An understanding of the mechanisms at play
will facilitate regenerative nephrology.
The first chapter of this thesis reviews the literature on NPCs, from the perspective of
signaling pathways and downstream transcriptional mechanisms. This knowledge has
contributed to the establishment of in vitro systems to maintain NPCs and to derive NPCs, and
subsequently nephron/kidney organoids, from pluripotent stem cells.
The second chapter describes my studies characterizing cistromes of a few important
transcription factors in NPC with ChIP-Seq, from which a set of cis-regulatory modules (CRMs)
was identified that harbors regulatory input from multiple factors. To evaluate the functional
significance of these CRMs, mouse models were generated with CRMs deletions (Six2 distal
enhancer and Wnt4 distal enhancer). Analyzing phenotypic and transcriptional features revealed
some interesting underlying logic of developmental transcriptional regulation.
The last chapter of my thesis stemmed from our interest in systematically characterizing
the transcriptome and epigenome of NPCs in self-renewal and differentiation states. Due to the
limited availability of material in vivo, we employed a protocol that efficiently isolated NPCs
from embryonic mouse kidney and maintained their self-renewal in a chemically defined culture.
Elevating levels of the Wnt pathway agonist, CHIR99021, induced a rapid and robust
differentiation program. I generated transcriptomic and epigenomic profiles utilizing this model.
The data indicate a switch in the engagement of Lef/Tcf factors and enhanced β-catenin
association at CRMs accompanying CHIR99021-mediated induction of NPCs differentiation.
In summary, by integrating genomics, genetics and biochemical data, this thesis provides
insights on transcriptional regulatory mechanisms of NPCs by transcription factors and
environmental signaling pathways. What is revealed here may reflect some general principles in
transcriptional regulation in various stem cell systems in general.
3
Acknowledgments
I am grateful for being a student of Dr. Andy McMahon. What Andy has provided me
was much more than just opportunities to learn and master cutting-edge techniques, to write in a
scientific fashion, to think with a scientist’s logic, and to communicate and make friends with
other scientists, all of which are what most people can expect most from a PhD mentor; he also
ignited my love for science through encouragement and his own elegant acts as an extraordinary
scientist. He helped me to learn and define what I am. He is my role model.
I want to specially thank Dr. Anton Valouev, who served as my co-mentor from 2012 to
2015. He got me started with bioinformatics and genomics. It would have been much more
difficult for me if he were not there.
I want to thank all past and present McMahon laboratory members, especially Dr. Lori
O’Brien, Dr. Albert Kim, Tracy Tran, Dr. Nils Lindstrom, Dr. Cheng Song and Helena Bugacov,
and to colleagues outside the McMahon laboratory in particular Xi Chen, Xizi Wang and Haoze
Yu. I have learnt various skills and specific insights from them, which resulted in productive
collaborations.
I want to thank Dr. Peggy Farnham, Dr. Neil Sigil, Dr. Michael Stallcup and Dr. Ruchi
Bajpai, who kindly served as my committee members, providing constructive criticism and
helpful suggestions at my annual research appraisal meetings, and whenever a specific question
arose. I’d like to extend my gratitude to Dr. Justin Ichida, with whom I did one of my rotations. I
have learnt from him the dedication and endurance to achieve great things. Importantly, I want to
thank Dr. Deborah L Johnson, who was my mentor when I was in the Biochemistry Master’s
program prior to joining PIBBS. She was the first person who acknowledged my talent,
encouraged and helped me to get in the track of acdemia.
I want to thank Dr. Charles Nicholet, Dr. David Ruble, Gigi Ostrow and Bernadette
Masinsin for their high-quality technical support during the years of my work. The bulk of data
generation in my thesis would not have been possible without them.
Finally, I want to thank my parents, who cultivated me as one with respect and curiosity
of knowledge, and provided the necessary support for me to pursue my career in science in
another country. I also need to thank my wife Ning, who was also a scientist and has been ever
supportive to my lifestyle as a scientist. I am in debt to them for all the time I could have spent
with them but was put in my work.
4
Table of Contents
Acknowledgments ................................................................................................................... 3
List of Tables .......................................................................................................................... 7
List of Figures ......................................................................................................................... 8
List of Appendices ................................................................................................................. 10
Chapter 1 Transcriptional Regulation of NPCs: Implications for Specification and
Maintenance of NPCs and Differentiating Kidney Cell Types ................................................ 11
1.1 Abstract ............................................................................................................................ 11
1.2 Perspective ........................................................................................................................ 11
1.3 Overview of mammalian kidney development ................................................................. 12
1.4 Nephron progenitor cells and transcriptional regulation by nephron lineage-specific
transcription factors ..................................................................................................................... 13
1.4.1 Paired box family (Pax2 and Pax8) ..................................................................................................... 13
1.4.2 Odd-skipped related transcription factor 1 (Osr1) ............................................................................... 13
1.4.3 Wilms tumor 1 (Wt1) .......................................................................................................................... 14
1.4.4 Spalt like transcription factor 1 (Sall1) ............................................................................................... 15
1.4.5 Hox family transcription factors .......................................................................................................... 15
1.4.6 Sine oculis-related homeobox transcription factors (Six1, Six2) ........................................................ 16
1.4.7 EYA transcriptional coactivator and phosphatase 1 (Eya1) ................................................................ 17
1.4.8 Forkhead box transcription factors (Foxc1, Foxc2) ............................................................................ 17
1.4.9 LIM homeobox protein 1 (Lhx1) ........................................................................................................ 18
1.4.10 Others ............................................................................................................................................. 18
1.4.11 Summary ........................................................................................................................................ 18
1.5 Transcriptional regulation of nephron progenitor cells by signaling pathways ............... 19
1.5.1 Regulation of NPC maintenance and differentiation by Wnt signaling .............................................. 19
1.5.2 Regulation of NPCs by Bmp signaling pathway ................................................................................. 21
1.5.3 Regulation of NPC by Fgf signaling pathway ..................................................................................... 23
1.5.4 Regulation of NPCs by Fat4 and signals from interstitial progenitor cells ......................................... 25
1.5.5 Regulation of NPCs by Notch signaling ............................................................................................. 26
1.5.6 Summary ............................................................................................................................................. 27
1.6 Regulation of NPCs in vitro .............................................................................................. 29
1.6.1 2D cultures of NPC ............................................................................................................................. 29
1.6.2 3D NPC cultures ................................................................................................................................. 31
1.6.3 Development of kidney organoids from pluripotent stem cells ........................................................... 32
1.7 Addendum: The canonical Wnt signaling pathway and its function in other stem cell
systems .......................................................................................................................................... 37
1.7.1 The molecular mechanisms downstream of canonical Wnt signaling ................................................. 37
1.7.2 Wnt signaling regulates hair follicle stem cells (HFSCs) .................................................................... 37
1.7.3 Wnt signaling regulates intestinal stem cells (ISCs) ........................................................................... 39
Chapter 2 Epigenetic and Genetic Analysis of Nephron Progenitor Cells .............................. 41
2.1 Abstract ................................................................................................................................... 41
2.2 Introduction ............................................................................................................................ 42
2.3 Result ...................................................................................................................................... 43
5
2.3.1 A novel transgenic strategy to generate nephron progenitor-specific ChIP-Seq data ............................... 44
2.3.2 Identification of nephron progenitor-specific transcription factor interaction sites .................................. 46
2.3.3 Six2, Hoxd11, Osr1 and Wt1 co-bound sites predict key enhancers and targets of the nephron
progenitors ......................................................................................................................................................... 52
2.3.4 Transcription factor co-binding is preferentially associated with genes active in differentiating structures
and reveals novel targets ................................................................................................................................... 59
2.3.5 Deletion of the Six2 and Wnt4 distal enhancers reveals their roles in modulating target gene expression
........................................................................................................................................................................... 63
2.3.6 The Br mouse is the result of an inversion altering the Six2 regulatory landscape .................................. 69
2.4 Discussion ................................................................................................................................ 74
2.4.1 Transcriptional hierarchy of nephron progenitors .................................................................................... 75
2.4.2 Transcriptional factors: activator, repressor and interactions ................................................................... 76
2.4.3 Genomic co-localization of transcription factors in nephron progenitor cells .......................................... 77
2.4.4 Target gene functions in nephron progenitors .......................................................................................... 77
2.4.5 Deletion of regulatory hotspots ................................................................................................................ 78
2.4.6 Topological rearrangements in the Br mutant and cis regulation of Six2 ................................................. 79
2.5 Materials and methods ............................................................................................................ 80
2.5.1 Mouse strains ............................................................................................................................................ 80
2.5.2 ChIP-seq ................................................................................................................................................... 83
2.5.3 4C-seq ....................................................................................................................................................... 83
2.5.4 ChIP-seq data analysis .............................................................................................................................. 84
2.5.5 DNA sequence motif analysis .................................................................................................................. 85
2.5.6 Genomic Regions Enrichment of Annotations Tool (GREAT) Analysis ................................................. 86
2.5.7 Region-based enrichment analysis ........................................................................................................... 87
2.5.8 Fluorescence-Activated Cell Sorting ........................................................................................................ 88
2.5.9 RNA-seq analysis ..................................................................................................................................... 88
2.5.10 qPCR ...................................................................................................................................................... 89
2.5.11 In situ hybridization ................................................................................................................................ 89
2.5.12 Electrophoretic mobility shift assay (EMSA) ......................................................................................... 90
2.5.13 Immunoprecipitation .............................................................................................................................. 90
2.5.14 Immunofluorescence .............................................................................................................................. 91
2.5.15 Br sequencing and mapping ................................................................................................................... 91
Chapter 3 A β-catenin driven switch in Tcf/Lef transcription factor binding to DNA targets
sites promotes commitment of mammalian nephron progenitor cells ..................................... 92
3.1 Abstract ................................................................................................................................... 92
3.2 Introduction ............................................................................................................................ 93
3.3 Results ..................................................................................................................................... 94
3.3.1 NPEM supplemented with higher level of CHIR manifested signs of early differentiation of mouse
nephron progenitor cells within a day ............................................................................................................... 94
3.3.2 CHIR-mediated induction modifies the epigenomic profile of NPCs .................................................... 100
3.3.3 Differential expression and DNA binding of Tcf family members in the regulation of NPC programs . 103
3.3.4 β-catenin uses both pre-established and de novo enhancer-promoter loops to drive NPC differentiation
program ........................................................................................................................................................... 113
3.4 Discussion .............................................................................................................................. 116
3.4.1 Summary of major findings .................................................................................................................... 116
3.4.2 Function and regulation of distinct Tcf/Lef factors in NPC and other stem cell systems ....................... 116
3.4.3 Elevated level of β-catenin leads to activation of Tcf/Lef-bound enhancers .......................................... 117
3.4.4 Pre-establishment of enhancer-promoter loops contributes to potency of stem cells to destined fates .. 118
3.4.5 Future Studies ......................................................................................................................................... 119
6
3.5 Materials and Methods ......................................................................................................... 121
3.5.1 mRNA-Seq and data analysis ................................................................................................................. 121
3.5.2. ChIP-Seq ............................................................................................................................................... 122
3.5.3 ChIP-Seq data analysis ........................................................................................................................... 123
3.5.4 ATAC-Seq and data analysis .................................................................................................................. 124
3.5.5 Hi-C data generation and analysis .......................................................................................................... 124
3.5.6 Single-cell RNA-Seq and data analysis .................................................................................................. 125
3.5.7 Reverse transcription followed by qPCR (RT-qPCR) ............................................................................ 125
3.5.8 Immunofluorescence staining ................................................................................................................. 126
3.5.9 Immunoblots ........................................................................................................................................... 127
References .......................................................................................................................... 128
Appendices .......................................................................................................................... 144
7
List of Tables
Table 1 Nephron progenitor cell culture conditions.
8
List of Figures
Figure 1. Gene regulatory network model of mouse NPC.
Figure 2. Workflows of derivation of nephron or kidney organoid from pluripotent stem cells.
Figure 3. Identification of Six2, Hoxd11 and Osr1 binding sites in nephron progenitors by ChIP-
seq.
Figure 4. Additional statistics of Six2, Hoxd11, and Osr1 ChIP-seq data.
Figure 5. Validation of ChIP-seq identified binding motifs by EMSA.
Figure 6. Regulatory hotspots in nephron progenitors defined by co-binding of Six2, Hoxd11,
Osr1 and Wt1.
Figure 7. ChIP-seq reveals Wt1-mediated regulatory programs in the developing kidney.
Figure 8. Six2, Hoxd11, Osr1, and Wt1 binding sites are enriched near nephron progenitor
specific genes and those associated with differentiation programs.
Figure 9. Deletion of the Six2 distal enhancer leads to reduction in Six2 levels and concomitant
loss of a Six2 allele results in severe renal hypoplasia.
Figure 10. E18.5 phenotypes of Six2
ΔDE/GCE
compared to Six2
GCE/GCE
mutants.
Figure 11. Deletion of the distal enhancer for Wnt4 results in reduced expression of Wnt4
specifically in renal vesicles and smaller kidneys.
Figure 12. The Six2 regulatory landscape is altered in the Br mouse leading to reduced Six2
expression and ectopic Six3 expression in the kidney.
Figure 13. Localization of the predicted topologically associating domains around Six2 and Six3
and further characterization of the Br allele.
Figure 14. NPEM supplemented with differential levels of CHIR99021 models nephron
progenitor cell maintenance or differentiation in a plate.
9
Figure 15. Supplementary RNA-Seq data analysis.
Figure 16. High dosage of CHIR99021 triggered change of NPC epigenome.
Figure 17. Supplementary ATAC-Seq data analysis.
Figure 18. Differential expression of Tcf family transcription factors in NPC in response to
distinct level of CHIR.
Figure 19. Supplementary evidence for differential expression of Tcf/Lef factors.
Figure 20. Increased CHIR dosage induces a switch of Tcf/Lef factors binding to the genome.
Figure 21. Supplementary ChIP-Seq data analysis.
Figure 22. Analysis of Tcf7l1 binding in low CHIR.
Figure 23. β-catenin activates gene expression through both stable and de novo enhancer-
promoter loops.
10
List of Appendices
Publications and contributions
11
Chapter 1 Transcriptional Regulation of NPCs: Implications for
Specification and Maintenance of NPCs and Differentiating Kidney Cell
Types
1.1 Abstract
As in other stem/progenitor systems, in NPCs environmental signals regulate a collection
of transcription factors leading to transcriptional programs that determine NPC states. In this
review, I discuss the role of signaling pathways and transcription factors in the specification and
maintenance of NPCs focusing on mammalian genetic studies. Insights here have led to
protocols that support the culture and expansion of NPCs in vitro, and the de novo formation of
NPCs and differentiated NPC derivatives from pluripotent stem cells. I review the rationale for
selecting key components that underlie the effectiveness of these procedures. Wnt signaling is
particularly important and the central pathway of focus in my thesis. Given Wnt-directed
mechanisms are a major theme, I expand beyond the kidney to consider the function of Wnt
signaling in some well-studied mammalian stem cell systems.
1.2 Perspective
The mammalian metanephric kidney maintains fluid homeostasis. The number of
individuals afflicted with kidney disease is on the rise, and reduced nephron number has been
associated with disease outcomes (Bertram et al., 2011). In the mouse, genetic studies have
demonstrated that nephrons are generated from a Six2+ progenitor pool in a regulatory process
requiring the transcriptional action of Six2 for progenitor maintenance (Self et al., 2006a).
Human SIX2 shows an expression and activity similar to its murine counterpart suggesting that
mouse Six2 and human SIX2 have similar functions (O'Brien et al., 2016). Consistent with this
view, human mutations in SIX2 are associated with renal hypoplasia and the malignant
transformation of progenitor cells in Wilms’ tumor, a pediatric nephroblastoma (Walz et al.,
2015; Weber et al., 2008; Wegert et al., 2015). There is an increasing interest in the relationship
between nephron progenitors, their output, and congenital and acquired kidney disease (Bertram
et al., 2011; Bertram et al., 2016). Further, new approaches to modulate nephron progenitor
outputs to generate kidney structures in vitro call for a better understanding of regulatory
12
processes at play in vivo (Morizane et al., 2015; Taguchi et al., 2014; Takasato et al., 2015). To
this end, my thesis work has focused on transcriptional mechanisms regulating nephron
progenitors.
1.3 Overview of mammalian kidney development
The mammalian kidney arises from the intermediate mesoderm (IM). In mouse, the
precursor, nephric ducts emerge in pairs at rostral somite levels around embryonic day 8.75
(e8.75). These ducts migrate posteriorly, inducing the non-functional mesonephric tubular
structures until reaching the hindlimb level around e10.5. Here, nephric ducts interact with the
adjacent, pre-specified metanephric mesenchyme, marking the initiation of kidney
morphogenesis. The nephric duct, induced by the mesenchymal cells, forms bilateral outgrowths,
the ureteric buds. The metanephric mesenchyme condenses and encapsulates the ingrowing
uretric bud tips. By e11.5, a ureteric but initiates a first branching event forming a T-shaped
structure (McMahon, 2016); 12 cycles of iterative branching that are complete shortly after birth
establishes the entire network of the urine transporting collecting system (Short et al., 2014).
Branching growth is driven by signals secreted from the adjacent capping mesenchyme while
ureteric branch tips signal to expand and differentiate progenitor populations in the capping
mesenchyme, including NPCs. At each branching event, a subset of NPCs cells stream beneath
the ureteric branch tips forming a tight cluster, the pretubular aggregate, presaging nephron
formation (Lindstrom et al., 2018). Subsequently, the pretubular aggregate epithelializes to form
a cyst-like renal vesicle. Each renal vesicle gives rise to a single nephron. Nephrogenesis ends 2-
4 days after birth in the mouse with the complete depletion of NPCs.
A series of morphological changes follows from the formation of the renal vesicle.
During this process, the developing nephron establishes a ‘proximal-to-distal’ pattern clearly
evident by the S-shape body stage. The distal-most proportion of the S-shaped body connects to
the collecting duct, plumbing a network that enables fluid trafficking in the future kidney. At the
proximal-most end, the S-shaped body generates a cleft to which endothelial cells are recruited,
while adjacent cells in the nephron anlagen are specified to a podocyte-forming fate. Podocyte-
endothelial cell interactions, together with mesangial cell types derived from interstitial
progenitors in the capping mesenchyme, results in assembly of the renal filter in the renal
corpuscle. The remainder of the S-shaped body forms other nephron segments. Based on the
nephron-collecting duct network, vasculogenesis and angiogenesis build up the elaborate renal
13
vascular network. Interstitial cell types organize around the vasculature and tubules, and neurons
project in to innervate various target cell types
1.4 Nephron progenitor cells and transcriptional regulation by nephron lineage-
specific transcription factors
NPCs in the capping mesenchymal cells are positioned immediately adjacent to the
branching ureteric tips. The progenitors receive multiple signaling inputs from both ureteric tips
and interstitial progenitor cells that regulate both the self-renewal and differentiation of NPCs
(McMahon, 2016). A number of transcriptional regulatory factors including Six1/2, Hoxa/c/d11,
Osr1, Wt1, Sall1, Eya1, Pax2, Myc and Sox4 have been shown to play a critical role in NPC
actions. In this section, I provide an overview of the role of key regulatory factors.
1.4.1 Paired box family (Pax2 and Pax8)
Pax2, a paired-box transcription factors, present in the pronephros, mesonephric and
metanephric kidney. In the metanephros, Pax2 is expressed in the ureteric epithelium, NPCs and
developing nephrons (Bouchard et al., 2000). Pax2 mutant mice exhibit kidney agenesis as a
result of a failure of metanephric development (Torres et al., 1995). The combined loss of Pax2
and the closely related paralog Pax8 results in the complete loss of the entire kidney lineage
(Bouchard et al., 2002). Pax2 is required for ureteric bud invasion regulating Gdnf production, a
key signal deriving from NPCs controlling branching morphogenesis of the ureteric network
(Brophy et al., 2001). Specific depletion of Pax2 in Six2+ NPCs suggests Pax2 is required for
NPC specification: mutants display a complete loss of NPCs, at least in part through a trans-
differentiation to interstitial cell types (Naiman et al., 2017).
1.4.2 Odd-skipped related transcription factor 1 (Osr1)
Osr1 is a zinc-finger transcription factor broadly expressed through the kidney forming
intermediate mesoderm. After the initiation of kidney development, Osr1 expression is
progressively restricted to NPCs by the time of ureteric bud branching (James et al., 2006). Early
formed Osr1+ cells give rise to ureteric component of the kidney, later specified Osr1+ cells
form the nephron lineage (Mugford et al., 2008b; Taguchi et al., 2014; Taguchi and
14
Nishinakamura, 2017). The nephric duct fails to migrate to the metanephric mesenchyme
forming region and the analysis of key metanephric-specific genes including Six2, Eya1, Gdnf,
Pax2 and Sall1, all of which are absent in Osr1 mutants; the metanephric mesenchyme went
through a rapid apoptosis at E10.5 in the mutants (James et al., 2006). Within the capping
mesenchyme, Osr1 is specifically expressed in uncommitted Cited1+/Six2+ NPCs (Mugford et
al., 2009). Removing Osr1 in Six2+ progenitor cells results in renal hypoplasia following a
premature loss of the nephron progenitor pool. Osr1 and Six2 synergistically regulate key NPC
promoting genes associated with self-renewal (Eya1, Cited1, Wt1). Osr1 represses expression the
differentiation-required Wnt4 possibly through direct engagement on the Wnt4 enhancer, as loss
of Osr1 lead to expanded activity of the Wnt4 enhancer-driven reporter (Xu et al., 2014a).
1.4.3 Wilms tumor 1 (Wt1)
Wt1 was initially identified as the tumor suppressor mutated in an inherited pediatric
kidney cancer, Wilm’s tumor (Haber et al., 1990). Wt1 encodes a zinc-finger transcription factor.
Mutagenesis study have confirmed Wt1 is essential for the formation of the kidney and other
organs sharing an intermediate mesoderm origin such as the gonads. Wt1 mutants develop a
metanephric mesenchyme, the ureteric bud fails to invade, and the metanephric mesenchyme is
rapidly lost through apoptosis (Kreidberg et al., 1993), highlighting a critical role for Wt1 in the
normal specification and maintenance of the metanephric mesenchyme. Wt1 is expressed in
intermediate mesoderm as early as E9.5 (Xu et al., 1999). After the initiation of kidney
development, Wt1 is expressed in NPCs, interstitial progenitor cells and some of their
differentiated derivatives notably the podocyte lineage from the earliest stage of podocyte
specification in the nephron anlagen. Kidney-specific (Essafi et al., 2011) or tamoxifen-
controlled (Hu et al., 2011) ablation of Wt1 blocks formation of the vascular endothelial cell-
derived glomerulus, consistent with Wt1 action in adjacent podocyte precursors. Directly
knocking down Wt1 activity in Six2+ cells with Six2-Cre (Kann et al., 2015a) resulted in
progressive loss of nephron progenitors and renal hypoplasia, suggesting an additional role of
Wt1 in NPC maintenance. This role of Wt1 is likely mediated by direct DNA binding and
regulation of Gas1 expression (Kann et al., 2015a). Deciphering the function of Wt1 is
complicated by the presence of multiple splice isoforms, and DNA and RNA binding activities
(Hohenstein et al., 2006). The +KTS isoform contains three extra amino acids (KTS) at the end
of exon 9, between zinc fingers 3 and 4. Mouse mutants that cannot produce the +KTS isoform
15
show reduced nephrogenic zone size and gain of stroma, indicating shrinkage of the NPC pool,
as well as defects in glomerular development and kidney development is compromised (Hammes
et al., 2001), suggesting the +KTS isoform play a specific role in maintaining NPC.
1.4.4 Spalt like transcription factor 1 (Sall1)
Sall1 is the murine homolog of the human SALL1 gene, mutations in SALL1 cause
Townes-Brocks syndrome (Kohlhase et al., 1998). During embryonic development, Sall1 is
expressed initially in the nephrogenic primordium, then mesonephric ducts and finally in
metanephric mesenchyme (both the NPCs and interstitial cells) and differentiating nephron
derivatives (Nishinakamura et al., 2001). Sall1-deficient mice shows either kidney agenesis or
severe hypoplasia; a failure of ureteric invasion reflecting reduced Gdnf production was a likely
explanation for the phenotype (Sanchez et al., 1996) (Nishinakamura et al., 2001). However, a
later study showed that ureteric invasion is not affected in the Sall1 mutant, but the ureteric tip
does not branch, and branching is not restored by adding Gdnf to Sall1 mutant kidney organ
cultures (Kiefer et al., 2010). Analysis suggests an altered gene expression within the ureteric
branch tip. Examining NPCs in Sall1 mutants showed a loss of expression of the NPC regulatory
genes Six2, Eya1, and Osr1, and the NPC specific marker, Cited1, as early as E12.5. By E13.5,
NPCs were lost and ectopic renal vesicle-like structures suggesting a role for Sall1 in NPC
maintenance (Basta et al., 2014). Consistent with this view, removal of Sall1 activity specifically
in Six2+ cells prematurely depleted NPCs with a concomitant expansion of the renal vesicle
domain; subsequently, renal vesicles formed nephron structures suggesting nephrogenesis does
not require Sall1 (Kanda et al., 2014). ChIP-Seq data points to Sall1 binding near progenitor self-
renewal genes, overlapping with Six2, consistent with Sall1-Six2 co-operating in sustaining the
progenitor transcriptional program (Kanda et al., 2014). Sall4, forms a heterodimer with Sall1
that is likely to play a role with Sall1 as compound heterozygous mutants (Sall1+/-; Sall4+/-)
shows a similar phenotype to Sall1-/- (Sakaki-Yumoto et al., 2006).
1.4.5 Hox family transcription factors
Up to 30 Hox genes are expressed during mammalian kidney development (Di-Poi et al.,
2007). Of these, Hox11 (Hoxa11/Hoxc11/Hoxd11) paralogs are exclusively expressed in the
metanephric kidney, their expression is first observed in specified metanephric mesenchyme
16
prior to ureteric bud ingrowth (Mugford et al., 2008a). Triple-knockouts of all three Hox11
paralogs resulted in complete failure of kidney morphogenesis. Early analysis demonstrated
metanephric mesenchyme formed, Wt1, Pax2 and Eya1 are detectable in the mutant
mesenchyme; however, no Six2 or Gdnf was detected suggesting an incomplete specification of
NPCs (Wellik et al., 2002). Ectopic expression of Hoxd11 in mesonephric cells leads to
enhanced activation of Six2, and downstream features of the metanephric specific program in
differentiating NPCs suggesting Hox11 factors are both required and sufficient for the
metanephric program. Mechanistically, regulatory regions proximal to Six2 and Gdnf
transcription start sites both respond to elevated levels of Hoxa11, Eya1 and Pax2 in an additive
manner (Gong et al., 2007), suggesting these three factors possibly regulate Six2 and Gdnf
expression co-operatively. Taken together, the data suggest Hox11 paralogs are critical for
specifying a renewable source of NPCs, activating a program within NPCs that directs ureteric
bud outgrowth, and the later differentiation of nephron structures such as the loop of Henle that
distinguish mesonephric and metanephric nephrons.
1.4.6 Sine oculis-related homeobox transcription factors (Six1, Six2)
Six genes encode transcription factors distinguished by a distinct class of homeobox-
DNA binding motif shared amongst Six family members. Six1 is expressed transiently in
uninduced metanephric mesenchyme prior to ureteric invasion (before e10.5). Loss of Six1 leads
to failure of ureteric bud invasion and death of metanephric mesenchyme cells resulting in renal
agenesis. Eya1, Pax2, Wt1 and Gdnf are expressed but Six2 and Sall1 expression are absent in
Six1-/- metanephric mesenchyme (Xu et al., 2003). Another Six family member, Six4, shows a
similar domain of expression with Six1 as far as E10.5. Six1/Six4 double knockout resulted in an
exacerbated phenotype, with loss of Gdnf, Sall1 and Pax2 expression in the putative metanephric
mesenchyme, suggesting the two genes functionally reinforce each other in kidney development
(Kobayashi et al., 2007). A third member Six2 is expressed in the capping mesenchyme after
ureteric invasion until depletion of progenitors shortly after birth in mice. Loss of Six2 leads to
premature differentiation of the progenitors (Kobayashi et al., 2008; Self et al., 2006a). Six2
blocks progenitor differentiation through countering the transcriptional programs induced by
Wnt/β-catenin (Kobayashi et al., 2008; Park et al., 2012), which will be discussed in detail in the
next session of this review.
17
1.4.7 EYA transcriptional coactivator and phosphatase 1 (Eya1)
Mammalian Eya1 is a homologue of the Drosophila gene eyes absent (eya). Eya1
encodes a transcriptional co-activator which interacts with the homeodomain So/Six2 proteins
and a phosphatase (Pignoni et al., 1997). Mutation of this gene in human leads to defects in
craniofacial and renal defects (Abdelhak et al., 1997). In mice embryogenesis, Eya1 is expressed
as early as E8.75 in intermediate mesoderm, then in caudal mesonephric tubules and finally
becomes restricted to metanephric mesenchyme. In the developing kidney, Eya1 is expressed
exclusively in uncommitted nephron progenitors (Xu et al., 2014b). Eya1 mutant kidneys fail to
undergo ureteric bud invasion resulting in an accompanying apoptosis of the metanephric
mesenchyme. Interestingly, Pax2 and Wt1 expression are not affected while Six2 is not activated
in the mutant (Xu et al., 1999). Inactivating Eya1 specifically in Six2+ NPCs lead to a loss of
Six2 expression, premature differentiation and hypoplastic kidneys resembling Six2 mutants,
consistent with the scenario that Eya1 forming a complex with Six2 to autoregulate Six2
expression (Park et al., 2012; Xu et al., 2014b). Eya1 was shown to interact with both c-Myc and
N-Myc, both of which are expressed in nephron progenitors at E13.5. Loss of Eya1 correlates
with increased phosphorylation of c-Myc at T58, a modification associated with Myc
degradation (Xu et al., 2014b), which may be explained by the absence of Eya1 phosphatase
activity (Rebay et al., 2005). Myc promotes progenitor proliferation and kidney growth (Bates et
al., 2000; Couillard and Trudel, 2009). Thus, the loss of a Myc-Eya1 regulatory axis is predicted
to reduce NPC proliferation and nephron numbers.
1.4.8 Forkhead box transcription factors (Foxc1, Foxc2)
Foxc1, Foxc2 and Foxd2 share similar expression patterns during kidney development:
they are present in mesonephric tubules and later in metanephric mesenchyme, restricting
expression to capping mesenchyme (Kume et al., 2000a; Kume et al., 2000b). As yet, mutational
analysis has failed to identify a direct role for these genes in nephron progenitor specification or
maintenance; however, phenotypes have been observed in the ureteric network including ectopic
ureter outgrowths in Foxc1+/-;Foxc2+/- mutants (Kume et al., 2000a; Kume et al., 2000b) and
hydro-ureter and hydronephrosis. Further, zebrafish studies indicate a role of Foxc1 in podocyte
development where Foxc1/c2 are also expressed in the mouse (He et al., 2014; O'Brien et al.,
2011).
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1.4.9 LIM homeobox protein 1 (Lhx1)
The LIM-class homeodomain transcription factor, Lhx1, is expressed in intermediate
mesoderm at E7.5 and the nephric duct and mesonephric tubules by E10.5. In the metanephric
kidney, Lhx1 is expressed in the ureteric epithelium and in the early renal vesicle resolving to
distal regions by the S-shaped body stage (Kobayashi et al., 2005). Gonads and kidneys fail to
form in Lhx1 mutant mice reflecting an earlier failure in nephric duct formation (Shawlot and
Behringer, 1995). Six2Cre-driven inactivation of Lhx1 does not block early nephrogenesis but
there is a failure of glomerular development. Moreover, Lhx1-/- cells are specifically excluded
from proximal proportion of developing nephrons which give rise to podocytes in chimeric
embryos composed of wild-type and Lhx1-/- cells (Kobayashi et al., 2005). Thus, Lhx1 likely
plays a key role in proximal nephron patterning specifying podocytes.
1.4.10 Others
Sox4 encodes an Sry-related high-mobility group (HMG) box family transcription factors
that is broadly expressed in the nephrogenic zone including the capping mesenchyme. Although
Six2-Cre removal of Sox4 specifically in NPCs did not result in an overt phenotype, kidneys
show signs of injury after birth and altered podocyte gene activity (Huang et al., 2013). Whether
this relates to earlier Sox4 activity specifically in NPCs is not clear.
The basic helix-loop-helix/B-zipper proteins Myc and Mycn overlap in nephron
progenitors. These factors also show differential activity in differentiated derivatives of NPCs
and ureteric branch tips (Xu et al., 2014b). Various mutations (Couillard and Trudel, 2009)
(Bates et al., 2000) including the specific removal of Myc and/or Mycn in Six2+ NPCs
(McMahon laboratory unpublished data) indicate both factors are required for normal expansion
of the NPC pool, and likely the normal growth of developing nephrons.
1.4.11 Summary
Genetics studies have highlighted three major developmental events in nephron
development that are targets of concerted transcriptional activity of factors expressed within
NPCs or the early forming NPC aggregates initiating nephrogenesis. The first is the
establishment of a normal metanephric NPC population a process that requires the activity of
Pax2, Osr1, Wt1, Sall1, Hox11, Eya1 and Six1. Subsequently, Six2, Osr1, Myc, Mycn, Sall1 and
19
Eya1 are required for maintaining and/or expanding NPCs. Stable nephron differentiation is
linked to actions of Wt1, Lhx1 and Sox4.
Figure 1 represents a non-comprehensive attempt to summarize key regulatory circuitry
using BioTapestry (REF) informed by genetic analysis and direct DNA binding studies utilizing
ChIP-seq and other approaches for: Six2 (Kanda et al., 2014; O'Brien et al., 2016; Park et al.,
2012), Wt1 (Motamedi et al., 2014) O'Brien et al., 2018); Sall1 (Kanda et al., 2014); Hoxd11
(O'Brien et al., 2018); Osr1 (O'Brien et al., 2018). These studies have identified specific
enhancer modules for target genes regulating the NPC state (e.g. CRMs directing Six2
expression) or inducing the commitment of NPCs (Kanda et al., 2014; O'Brien et al., 2016; Park
et al., 2012). These strategies have employed fluorescence activated cell sorting (FACs) with
genetic strains that label NPCs in vivo or a magnetic cell isolation to enrich for NPCs from non-
transgenic kidneys (Brown et al., 2015). The latter enables rapid, bulk isolation of cells reducing
the reliance on specific transgenic mouse strains and avoiding FACs induced cell stress.
Subsequent functional studies deleting enhancer modules have provided strong evidence
supporting a critical role for predicted CRMs regulating Six2 and Wnt4 expression (O'Brien et
al., 2018).
1.5 Transcriptional regulation of nephron progenitor cells by signaling pathways
The nephrogenic niche surrounding each ureteric branch tip comprises progenitor
populations contributing to nephrons (NPCs), interstitial fibroblast-like cell types (interstitial
progenitor cells), the collecting system (epithelial progenitors in branch tips) and the vascular
network (endothelial progenitor cells). The maintenance and differentiation of NPCs is supported
by activities of a number of signaling pathways activated by ligands from these distinct cellular
sources. Wnt, Bmp, Fgf, Notch and atypical cadherins play a central role in NPC actions
(McMahon, 2016).
1.5.1 Regulation of NPC maintenance and differentiation by Wnt signaling
The first Wnt ligand identified as regulator of nephrogenesis was Wnt4 (Stark et al.,
1994). Wnt4 activation in the pre-tubular aggregate is one of the first indicators of NPC
commitment to the nephrogenic program. Wnt4 null mice do not develop renal vesicles, resulting
in severely hypoplastic kidneys supporting an autocrine role for Wnt4 in progression of the
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nephrogenic program (Stark et al., 1994). Wnt4 is activated by, and dependent on, Wnt9b
secreted by adjacent ureteric epithelial cells through a ß-catenin-dependent, canonical, Wnt
signaling pathway that will be discussed in depth later (Carroll et al., 2005; Park et al., 2007).
Though Wnt4 and Wnt9b can both activate Wnt4, Pax8 and Cdh1 indicative of similar regulatory
activities (Kispert et al., 1998), evidence suggests a dynamic signaling program downstream of
Wnt4 (Park et al., 2007). This requires a shift from canonical to non-canonical driven Wnt4 to
direct the mesenchyme to epithelial transition establishing the renal vesicle (Tanigawa et al.,
2011).
Loss of Wnt9b from the ureteric epithelium, or β-catenin from NPCs also results in the
early depletion of the nephron progenitor pool, and down-regulation of a number of genes
expressed in NPCs (Karner et al., 2011). This has led to the proposal that Wnt9b/ ß-catenin
signaling has a dual role in the maintenance and differentiation of NPCs (Karner et al., 2011).
However, whether there is a direct transcriptional role for Wnt9b/ß-catenin is less clear for
progenitor maintenance. As discussed earlier, Six2 inactivation leads to premature differentiation
of NPCs. This effect also depends on the presence of Wnt9b, as differentiation does not happen
in Wnt9b null background (Kobayashi et al., 2008). How Six2 action opposes Wn9b signaling
normally is not clear.
Whereas there may be other factors engaged in vivo, BIO (an inhibitor of GSK3)
treatment of NPCs in vitro is sufficient to activate transcriptional features of early induced NPCs.
BIO-mediated inhibition of GSK3b leads to the accumulation of β-catenin activating the
canonical Wnt pathway and treatment correlates with β-catenin engagement on enhancers
activated in renal vesicles (Park et al., 2012). Further, enrichment of Lef/Tcf binding sites in β-
catenin ChIP-seq datasets, and functional mutational studies of identified enhancers in transgenic
analyses, indicate Wnt/β-catenin-driven NPC differentiation most likely acts through Lef/Tcf
DNA binding factors (Park et al., 2012). Interestingly, chemically defined NPEM medium also
requires a GSK3b inhibitor CHIR22091 (CHIR) for effective NPC expansion (Brown et al.
2015), while higher CHIR levels induce NPC differentiation similar to BIO. Thus in vitro results
replicate in vivo requirements for Wnt/β-catenin action in maintenance and differentiation of
NPCs.
Six2 represses Wnt4 genetically (Self et al., 2006a), and auto-activates its expression
through multiple enhancers (O'Brien et al., 2018; Park et al., 2012). Six2 and β-catenin co-
localize on enhancers promoting Six2 (NPC maintenance) and Wnt4 (NPC differentiation),
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expression (Park et al., 2012). Currently, it is not possible to reconcile all published data into a
compelling model of differential Wnt/β-catenin activities with the interplay and actions of Six2.
1.5.2 Regulation of NPCs by Bmp signaling pathway
Bmp7 is broadly expressed in the nephron lineage and ureteric network; expression of
Bmp2, Bmp3 and Bmp4 overlaps Bmp7 in developing nephrons, but not in NPCs (Dudley and
Robertson, 1997). In Bmp7-null mutants, the ureteric bud invades the metanephric mesenchyme,
but embryos develop severely hypoplastic kidneys (Dudley et al., 1995; Luo et al., 1995). From
E14.5, there is a marked reduction of Pax2 and Wt1 expression in the nephrogenic kidney cortex,
as well as Wnt4 and Pax8 in early developing nephrons. Thus, both NPC maintenance and
differentiation appear to be impaired (Dudley et al., 1995; Luo et al., 1995). In the latter,
glomeruli are lost, but nephron tubules form in the Bmp7 mutant. LiCl, a canonical Wnt pathway
agonist, can induce Bmp7 reporter activity in cultured metanephric mesenchyme, suggesting that
Bmp7 can be regulated by canonical Wnt signaling (Godin et al., 1998). Another Bmp family
member, Bmp4, is expressed in ureteric epithelium and required for the proper development of
the ureter, but there is no overt evidence suggesting its direct involvement in nephron
development (Miyazaki et al., 2000). Both Bmp4 and Bmp6 can functionally substitute for Bmp7
(Oxburgh et al., 2005), suggesting that the distinct requirement of Bmp7 in nephron development
is most likely a result of its specific expression pattern.
Bmp receptor activation leads to the phosphorylation of Smad1, 5 and 8, and the nuclear
translocation of these DNA-binding factors together with the co-SMAD Smad4 DNA binding
partner. Inactivation of Smad4 in the ureteric epithelium with Hoxb7Cre does not result in an
obvious phenotype, while loss of Smad4 in the nephron lineage, driven by Bmp7Cre, leads to
hypoplastic kidneys (Oxburgh et al., 2004). Similar to Bmp7 null mutants, Pax2 and Wt1
expression is dramatically reduced in the developing nephrons of Bmp7Cre; Smad4-/- mutant
kidneys by E14.5, but not in adjacent NPCs; glomeruli and nephron tubules are present, though
in reduced numbers (Oxburgh et al., 2004). The data supports a specific role for Smad4 post
NPC differentiation. A Smad-responsive element derived from the Id1 promoter region has been
used to drive a reporter to detect Bmp responsiveness. In the nephron lineage, activity of the
reporter is not detected in NPCs or renal vesicles, but is detected shortly after in comma-shaped
and S-shaped body stages (Blank et al., 2008). Overall, results are consistent with the detection
of activated phospho-SMAD (p-SMAD) (Brown et al., 2013), supporting the view that NPCs do
22
not respond to Bmp7, or alternatively, that signaling utilizes a non-canonical, Smad-independent
mechanism.
Bmp signaling has been reported to occur through a Smad-independent, Alk3-Tak1-Jnk-
Jun pathway (Muthukrishnan et al., 2015). Loss of the Alk3 BMP receptor specifically in
intermediate mesoderm results in a significant down-regulation of Six2 and Osr1 by E12.5,
resulting in a mild renal hypoplasia. NPCs differentiate into Wnt4+ renal vesicles, but kidneys
form fewer glomeruli and nephrons, reflecting the reduced kidney size (Di Giovanni et al.,
2011). The activated forms of Jun and ATF2 are detected in NPCs and renal vesicles in the
nephrogenic zone. Further, the presence of activated forms of ATF and Jun islargely lost in
Bmp7 -/- kidneys (Blank et al., 2009). Conditional loss of Jun, or Tak1 that is upstream of Jun,
results in a reduced NPC number, decreased cell proliferation and mild renal hypoplasia. In NPC
culture, Bmp significantly promotes G1 to S transition, suggesting that the Bmp-Jnk-Jun
pathway contributes to NPC proliferation (Muthukrishnan et al., 2015).
The Bmp7/Smad pathway has also been shown to modulate Wnt-induced NPC
differentiation. The NPC niche comprises Cited1+/Six2+ and Cited1-/Six2+ NPC subsets; the
former represents the most uncommitted progenitors. Cited1+/Six2+ cells are more refractory to
Wnt/β-catenin-induced differentiation in vitro than Cited1-/Six2+ cells; treatment with Bmp7
promotes differentiation of Cited1+/Six2+ cells (Brown et al., 2013). In line with this, pSMAD
was detected in the Cited1-/Six2+ cells and more differentiated renal vesicles in vivo. However,
this finding is in contrast with earlier data from the same group that the Id1 reporter does not
show activity before S-shaped body stage (Blank et al., 2008). Moreover, the Cited1-/Six2+
component is largely lost in Bmp7 null kidneys (Brown et al., 2013). The evidence supports a
signaling role for Bmp/Smad in the initial differentiation of NPCs. Trps1 encodes a Bmp7-
dependent transcription factor expressed in capping mesenchyme, early developing nephrons and
ureteric buds. Trps1 mutants preserve the nephrogenic zone, forming markedly reduced numbers
of glomeruli and nephron tubules, suggestive of a Bmp-dependent differentiation of NPCs
through Trps1 (Gai et al., 2009).
A number of genes influence Bmp signaling in kidney development. Mammalian Bmper
is closely related to Drosophila kielin/Chd, Bmp-interacting proteins that regulate Bmp activity.
Bmper is specifically expressed in NPCs and very early differentiated progenitors. Bmper
knockout kidneys are hypomorphic, with reduced numbers of progenitor niches and glomeruli
(Ikeya et al., 2006), though there is no direct evidence of Bmper/Bmp interactions in the
23
mammalian kidney. Grem1 produces a protein that binds Bmps and antagonizes their signaling
action (Hsu et al., 1998). Grem1 null mice display kidney agenesis due to a failure of ureteric
invasion, which correlates with a loss of Gdnf expression in the metanephric mesenchyme
(Michos et al., 2004). Treatment with either Grem1 or Gdnf protein is sufficient to induce
ureteric branching in Grem1-null kidneys in culture. Interestingly, heterozygous loss of Bmp7 on
a Grem1-null background completely restores kidney development (Michos et al., 2007). These
results suggest that inhibition of Bmp7 by Grem1 is required for normal ureteric invasion, and
that mesenchymal expression of Gdnf regulates this inhibition. Clearly, Bmp signaling regulates
both the initial induction of ureteric branching and the regulation of NPCs after initiating kidney
development. Interestingly, Bmp has been linked to the transcription factor Wt1; Wt1 binds at
the Bmper promoter in the embryonic kidney. Wt1 morphilino-mediated knockdown in kidney
organ culture activated p-Smad with a loss of Six2 expression, while treatment with Gremlin
protein on top of the morpholino restored Six2 expression in NPCs (Motamedi et al., 2014).
These data suggest both Bmper and Grem1 may repress Bmp-Smad signaling in NPCs.
1.5.3 Regulation of NPC by Fgf signaling pathway
The role of Fgf signaling in kidney development was first discovered by genetic studies
of Fgf receptors. At E11.5, Fgfr1 is expressed in metanephric mesenchyme, while Fgfr2 is
expressed in both ureteric bud and metanephric mesenchyme. Double conditional knockout of
Fgfr1 and Fgfr2 in mesenchyme, driven by Pax3Cre, leads to kidney agenesis, accompanied by
loss of Six2, Sall1, Gdnf and Pax2 expression, as well as widespread apoptosis in metanephric
mesenchyme by E11.5 (Poladia et al., 2006). This was the first evidence that Fgf signaling
activation is required to initialize ureteric invasion and kidney development. Another Fgf
receptor, Fgfrl1, is expressed broadly in nephron lineage cells. The Fgfrl1 null mutant shows
severely hypoplastic kidneys: nephrogenic differentiation is never initiated, although ureteric
invasion is not disturbed, and NPCs can persist until E14.5. Moreover, decreased proliferation
and increased apoptosis in metanephric mesenchyme is also observed in mutant kidneys (Gerber
et al., 2009). Mutation of Fgf7 and Fgf10, or their major receptor Fgfr2-IIIb results in slightly
smaller kidneys but normal-looking nephrons (Ohuchi et al., 2000; Qiao et al., 1999; Revest et
al., 2001). This evidence suggests that Fgf signaling is involved in both initial ureteric
bud/mesenchymal interaction and nephron progenitor differentiation.
24
To screen for signaling molecules that promote NPC maintenance and proliferation in
vitro, the Oxburgh lab developed a nephrogenic zone cell culture (similar to an embryonic renal
cortex cell culture) and used Cited1 expression as a marker to measure the activity of individual
molecules. In the screen, specific Fgf family members, including Fgf2, Fgf9 and Fgf20, showed
significant effects in maintaining Cited1 expression. Consistent with Fgf receptors being receptor
tyrosine kinases (RTKs), induced transgenic expression of Spry1, an RTK inhibitor protein,
resulted in the dramatic decrease of Cited1 and Six2 expression. Microarray analysis showed that
the NPCs are accessible to a variety of Fgf ligands, including Fgf9 expressed in ureteric bud and
Fgf1, Fgf10 and Fgf20 expressed in NPCs (Brown et al., 2011). In fact, removal of Fgf20 and
Fgf9 activity results in additive NPC loss, culminating in kidney agenesis in Fgf20-/-;Fgf9-/-
embryos. Progenitor commitment seems unaffected in those cases, as manifested by the
expression of NPC differentiation markers (Barak et al., 2012).
Fgf8 is expressed specifically in renal vesicles in the and distal segments of S-shaped
bodies. Fgf8 is required for initial differentiation of NPCs. Inactivation of Fgf8 in metanephric
mesenchymal cells, driven by Pax3Cre, leads to the loss of Wnt4 and Lhx1 expression, and
ultimately to the absence of both nephric tubules and podocytes; nephron development does not
pass the renal vesicle stage in the mutant (Grieshammer et al., 2005). Similar results were
observed in T-Cre driven inactivation of Fgf8, where T-Cre activity affects the entire mesoderm
(Perantoni et al., 2005). There might also be a secondary role of Fgf8 in nephron development
post progenitor commitment. Mice with hypomorphic alleles of Fgf8 do not develop nephric
tubules, although glomerulus formation is normal (Grieshammer et al., 2005). This could be due
to the specific presence of Fgf8 in distal regions of S-shaped bodies, which give rise nephric
tubules.
Despite functioning in opposite ways, Fgf9/20 and Fgf8 activate the same set of Fgf
receptors, although with different preferences (Ornitz and Itoh, 2015). Distinct cellular outcomes
of the two sets of Fgfs suggest differential intracellular signaling in NPCs versus induced NPCs.
While there is evidence suggesting that Fgf9/20 in NPCs signals through the RAS-MAPK and
PI3K-AKT axis (Brown et al., 2011), no data exists about the downstream molecular events of
Fgf8 upon NPC induction. The recently developed NPC culture (Brown et al., 2015) can
potentially be used to study such mechanisms.
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1.5.4 Regulation of NPCs by Fat4 and signals from interstitial progenitor cells
Fat4 is an atypical cadherin. In Drosophila, Ft (mammalian homologue: Fat4), through
binding to Ds (mammalian homologue: Dchs) cadherins on the surfaces of the adjacent cells,
works through the Hippo/Yki pathway to reduce cell proliferation and regulate organ size (Blair
and McNeill, 2018). In order to identify Fat4’s role in regulating NPCs, Dr. Anindita Das and her
colleagues studied the interaction between interstitial progenitor cells (IPCs) and NPCs. When
IPCs are depleted with Foxd1Cre-driven diphtheria toxin A (DTA), the NPC pools expand
dramatically, with a significant reduction in nephrogenic differentiation (Das et al., 2013),
suggesting that a signal from IPCs limits NPC proliferation. Das’ team identified the signal as
Fat4, supported by the fact that Fat4 mutation phenotypically mimics the loss of IPCs (Das et al.,
2013). Two subsequent independent studies from Bagherie-Lachidan’s and Mao’s groups
demonstrated how the loss of Fat4 affects the regulation of the NPC pool (Bagherie-Lachidan et
al., 2015; Mao et al., 2015). These studies showed that Fat4 represses NPC expansion through its
binding partner Dchs1/2, which is expressed on NPCs, and the Six2Cre-driven Dchs1 mutant
shows an NPC expansion phenotype similar to that of the Fat4 mutant (Bagherie-Lachidan et al.,
2015; Mao et al., 2015). Das’ study linked Fat4’s function to Yap activity in NPCs, and showed
Yap to be localized to the NPC nucleus upon the loss of IPCs; the Six2Cre-driven loss of Yap
and its homolog Taz (both mammalian homologs of Drosophila Yki) resulted in reduced NPC
pools, suggesting that Yap/Taz promotes NPC expansion (Das et al., 2013). However, the
Bagherie-Lachidan and Mao studies did not report nuclear localization of Yap in Fat4 mutants
(Bagherie-Lachidan et al., 2015; Mao et al., 2015). Moreover, a study led by Dr. Antoine
Reginensi demonstrated that the removal of Yap in NPCs did not lead to any detectable
difference in the progenitor cells, but rather to a defect in glomeruli and to stunted nephron
tubule development (Reginensi et al., 2013). The Reginensi team then generated data from a case
of Yap hyper-activation, which clarified why their outcome differed from the Das study. Lats
(Drosophila Wts) are kinases that phosphorylate and deactivate Yap; when both Lats1 and Lats2
are inactivated, the NPCs develop into interstitium-like cells rather than nephron cells, and the
NPC pool shrinks, as revealed by lineage tracing (McNeill and Reginensi, 2017). Therefore,
current evidence contradicts the idea that Fat4 regulates NPCs through Yap signaling, although
Fat4 does restrict NPC proliferation. The molecular events downstream of Fat4/Dchs still require
investigation in NPCs.
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1.5.5 Regulation of NPCs by Notch signaling
According to the general paradigm, Notch signaling starts with the interaction of ligand
(in mammals, Dll1, 3, 4 and Jag1 ,2) and receptor (in mammals, Notch1, 2, 3, 4), which triggers
cleavage by γ-secretase and the nuclear translocation of the Notch intracellular domain (ICD).
Notch ICD, as a transcriptional co-activator, binds to the transcription factor Rbpj in the nucleus
to activate target gene expression (Bigas and Espinosa, 2018). In the embryonic kidney, only
ligands Dll1 and Jag1, and receptors Notch1, 2 and 3 are expressed in developing nephrons post
NPC differentiation (Leimeister et al., 2003; Piscione et al., 2004). Initially, scientists observed
abnormal glomeruli in a Notch2 hypomorphic mutant, revealing the role of Notch signaling in
glomerular development (McCright et al., 2001). Treating cultured embryonic kidney with
DAPT, an inhibitor of γ-secretase, blocked both proximal tubule and podocyte development
(Cheng et al., 2003). Removing Notch2, but not Notch1, in metanephric mesenchyme with
Pax3Cre is sufficient to block the development of the proximal tubule and podocytes; nephron
stop developing before the S-shaped body stage (Cheng et al., 2007). A similar phenotype was
observed by inactivating Rbpj with Rarb-Cre (Bonegio et al., 2011). These studies suggest the
critical functions of Notch signaling in specifying the proximal fate of nephrons. In a recent
work, both Notch1 and Notch2 were inactivated with Wnt4Cre in early differentiated NPCs,
disrupting S-shaped body structures and triggering the loss of all nephron lineage cells, including
podocytes and proximal and distal tubules (Chung et al., 2017). In addition, when Rbpj or
Notch1/Notch2 is inactivated with Six2Cre, more Six2+ NPCs seem to accumulate in the cap
mesenchyme, likely a result of blocked differentiation (Chung et al., 2016). Because both Notch
receptors were inactivated, this study provides more conclusive evidence that Notch signaling is
required to generate all nephron segments. In line with this, ectopic activation of Notch signaling
by expressing Notch2 ICD specifically in NPCs (through Six2-Cre) did not produce more
proximal nephron segments, such as glomeruli and proximal tubules, but rather led to the
premature differentiation and depletion of all NPCs; kidneys are severely hypoplastic and cystic
in this case (Fujimura et al., 2010). Moreover, ubiquitous activation of Notch1 ICD in kidney
explants led to epithelialization of NPCs in the absence of Wnt4 or Wnt9b (Boyle et al., 2011).
Interestingly, in the cases above, Notch1 ICD+ cells develop almost exclusively into proximal
tubule cells on a wildtype background, but into both podocytes and proximal tubules on Wnt4-/-
or Wnt9b-/- backgrounds.
27
Figure.1 Gene regulatory network built based on published mouse genetics studies regarding
regulation of NPC by transcription factors and signaling pathways.
1.5.6 Summary
Current evidence supports the dichotomous roles of Wnt, Bmp and Fgf signaling in
regulating both NPC self-renewal and differentiation (Fig. 1), while Fat4 promotes
differentiation. It is relatively clear that Bmp signaling promotes NPC proliferation and
differentiation using same ligands acting through different downstream pathways, i.e.,
Tak1/Jnk/Jun for proliferation (Muthukrishnan et al., 2015) and Smad for differentiation (Brown
et al., 2013; Oxburgh et al., 2004). Fgf utilizes ligands and receptors with distinct functions, i.e.,
Fgf9/20 (Barak et al., 2012) and Fgfr1/2 (Poladia et al., 2006) to sustain NPC proliferation, and
Fgf8 (Grieshammer et al., 2005) and Fgfrl1 (Gerber et al., 2009) to trigger differentiation. While
it has been shown that Fgf9/20 uses a classical RTK-RAS-MAPK axis to relay its signal, the
Fgf8’s downstream molecular events remain unclear. The ureteric Wnt9b is required and
sufficient to initiate NPC differentiation through the canonical nuclear β-catenin/Tcf pathway
(Carroll et al., 2005; Park et al., 2012; Park et al., 2007); NPC proliferation also requires Wnt9b
28
and β-catenin (Karner et al., 2011), but the downstream mechanism is elusive and likely to be
Tcf-independent (McMahon lab, unpublished). Fat4 signaling in NPCs is relatively unexplored.
One study argued that Yap activation mediates Fat4 signaling at the interstitial progenitor-NPC
junction (Das et al., 2013); however, the supporting evidence was not reproduced by other
groups (Bagherie-Lachidan et al., 2015; Reginensi et al., 2013). Therefore, the signaling
components in NPCs that mediate Fat4 input still require investigation.
A general model of stem cell maintenance involves collaborative regulation by a core set
of co-expressed transcription factors, discussed in the previous section. Modulating
environmental signals would change stem cell transcriptional programs regulated by these core
transcription factors. In the case of NPCs, it has been shown that either a high level of Wnt
stimulation (Brown et al., 2015; Park et al., 2012) or ectopic activation of Notch (Boyle et al.,
2011; Fujimura et al., 2010) is sufficient to trigger either a larger or smaller scale differentiation
program, concomitant with the loss of expression of some core transcription factors. In contrast,
relatively little is known about the interactions between signaling pathways and core
transcription factors in NPC maintenance. Bmp7 was shown to be required for Pax2 and Wt1
expression (Dudley et al., 1995; Luo et al., 1995); Fgf signaling is required for Pax2, Six2 and
Sall1 expression (Poladia et al., 2006). Bmp (Muthukrishnan et al., 2015), Fgf (Barak et al.,
2012) and Wnt (Karner et al., 2011) are all required for NPC proliferation, but this
transcriptional program seems independent of regulation by the core transcription factors.
Furthermore, the downstream transcriptional components responsible for such regulation, and
their potential interaction with the core transcription factors, are unknown; the only exception is
the data showing an interaction between Six2 and β-catenin in regulating a number of genes
involved in either self-renewal or differentiation (Park et al., 2012). Arguably, our knowledge of
downstream transcriptional components was previously limited by the difficulty in collecting
nephron progenitor cells to perform epigenomics analysis, such as ChIP-Seq, ATAC-Seq and
HiC, which now can be alleviated by the new methods for efficiently harvesting and culturing
NPCs (Brown et al., 2015).
Despite our limited understanding of the molecular mechanisms underlying
environmental signaling inputs, genetic and cell biology studies in mice have led to the
development of a few in vitro systems for maintaining and expanding NPCs (Brown et al., 2015),
as well as differentiating human or mouse embryonic stem (ES) or pluripotent stem (PS) cells
toward kidney lineages. These will be discussed in detail in the next section.
29
1.6 Regulation of NPCs in vitro
1.6.1 2D cultures of NPC
Based on the growing understanding of the roles of Fgf, Bmp and Wnt in maintaining
NPCs, the nephron progenitor expansion medium (NPEM) was developed (Brown et al., 2015).
A Matri-gel-coated 2D culture was established with the following core components in the
medium (Table 1): FGF9 (200 ug/ml), BMP4 (30 ng/ml), BMP7 (30 ng/ml), LDN-193189 (125
nM) and CHIR99021 (1.25 uM). LDN-193189 is a SMAD inhibitor, through which Bmp/Smad-
induced differentiation is inhibited, while the pro-self-renewing function of Bmp is retained.
CHIR99021 is a GSK3 inhibitor, which is intended to activate β-catenin. Consistent with the
requirement for Wnt/β-catenin in NPC maintenance in vivo, the addition of CHIR99021
promotes proliferation. Notably, a higher level of CHIR99021 triggers morphological changes,
the activation of epithelial markers and the transcriptional signatures of differentiation
(McMahon lab, unpublished). This is also consistent with precocious differentiation of NPCs in
vivo when β-catenin is constitutively activated (Park et al., 2007).
NPEM culture is capable of maintaining NPCs isolated from E13.5 embryos through P1
neonates in a Cited1+ state for 6–10 passages, culminating in ~1 billion fold expansion.
Following this protocol, we were able to expand E16.5 NPCs for 3 passages over the course of 2
weeks; however, each passage increased the chance of spontaneous differentiation (McMahon
lab, unpublished). The NPCs maintained in NPEM displayed nephrogenic potential, as
aggregation of the cultured cells and induction with a higher level of CHIR (3 uM) activated
expression of epithelial marker (Ecad) and proximal tubule marker (LTL); however, a podocyte
signature was not induced, which is consistent with the fact that a high level of Wnt/β-catenin
promotes distal (tubular) fate choice in the developing nephron (Lindstrom et al., 2015).
Although the timeframe for NPC expansion is unknown, NPEM culture could also be used to
study NPCs’ response to the modulation of signaling pathways and their underlying mechanisms.
Moreover, the NPEM protocol also introduced a magnetic-activated cell sorting (MACS)-based
method for NPC purification from embryonic mouse kidneys, which enables isolation of millions
of NPCs from E15.5–P0 mouse embryos without a transgenic reporter. Therefore, NEPM
permits biochemical experiments with NPCs, such as immuno-precipitation, ChIP and HiC,
which all require relatively large numbers of cells.
30
Tanigawa et al. developed a culture condition (Tanigawa et al., 2016) where NPCs are
aggregated for 48 hours, then cultured in a defined condition on top of a fibronectin matrix. The
core components of the medium (Table 1) are leukemia inhibitory factor (LIF), the Rho kinase
inhibitor Y27632, FGF2 and TGF-α. LIF was employed due to its pro-proliferation effect on rat
metanephric mesenchyme culture (Plisov et al., 2001); Y27632 was employed since its addition
increased the size of mouse metanephric mesenchymal cell colonies (Osafune et al., 2006).
Intriguingly, in rat metanephric mesenchymal culture, LIF induces the expression of
epithelization markers through the activation of Jnk, and Y27632 inhibits Jnk activation while
retaining the pro-proliferation effect of LIF (Tanigawa et al., 2015). This somewhat contradicts
the role of Jun, downstream of Jnk, in vivo in promoting NPC proliferation without affecting
differentiation (Muthukrishnan et al., 2015). FGF2 (Perantoni et al., 1995) and TGF-α (Rogers et
al., 1992) were used for their pro-survival function. In addition, the Notch inhibitor DAPT was
introduced to inhibit Notch-induced NPC differentiation. CHIR99021 and BMP7 were also
present in the culture for similar reasons that they are in NPEM (Brown et al., 2015).
It’s worth noting that this protocol only expands E11.5 Six2-GFP+ cells (close to 2,000
fold) but does not efficiently expand NPCs isolated from later-stage embryos. NPCs can be
passaged 3 times in a 19-day period while retaining their nephrogenic potential, reflected by their
ability to express podocyte (nephrin) and tubular (Cdh1 and Cdh6) signatures induced by spinal
cord. Furthermore, NPCs derived from mouse ES cells or human iPS cells can be maintained and
retain their nephrogenic potential in this culture, although their expansion rate is poor (Tanigawa
et al., 2016).
Table 1A. Mouse nephron progenitor cell culture
Brown et al., 2015 (NPEM) Tanigawa et al., 2016 Li et al., 2016 (NPSR)
Culture
conditio
n
Format 2D (Matrigel coating) 2D (iMatrix coating) 3D
basal
medium APEL DMEM/F12 DMEM/F12
specific
factors
Factor
Final
Concentration Factor
Final
Concentration Factor
Final
Concentration
BMP4 30ng/ml BMP7 5 ng/ml BMP7 50ng/ml
BMP7 30ng/ml
CHIR9902
1 1 uM
CHIR9902
1 1uM
CHIR99021 1.25uM DAPT 2.5 uM FGF2 200ng/ml
31
FGF9 200ng/ml
FGF2/FGF
9 50 ng/ml Heparin 1ug/ml
Heparin 1ug/ml LIF 5 ng/ml LIF 10ng/ml
IGF1 20ng/ml TGFα 10 ng/ml Y27632 10uM
IGF2 2ng/ml Y27632 10 uM
LDN19318
9 125nM
Y27632 10uM
culture period up to 10 passages 3 passages, 19 days 110 passages, 17 months
cell source E13.5, E17.5, P1 E11.5 E11.5, E13.5, E16.5, P1
Table 1B. Human nephron progenitor cell culture
Brown et al., 2015 (NPEM) Tanigawa et al., 2016 Li et al., 2016 (NPSR)
Culture
condition
Format 2D (Matrigel coating) 2D (iMatrix coating) 3D
basal
medium APEL DMEM/F12 DMEM/F12
specific
factors
Factor
Final
Concentration Factor
Final
Concentration Factor
Final
Concentration
BMP4 30 ng/ml BMP7 5 ng/ml A83-01 0.05-0.5 uM
BMP7 30 ng/ml
CHIR9902
1 1 uM BMP7 50 ng/ml
CHIR99021 1.25 uM DAPT 2.5 uM CHIR99021 1 uM
FGF9 200 ng/ml
FGF2/FGF
9 50 ng/ml FGF2 200 ng/ml
Heparin 1 ug/ml LIF 5 ng/ml Heparin 1 ug/ml
IGF1 20 ng/ml TGFα 10 ng/ml
LDN19318
9 10-100 nM
IGF2 2 ng/ml Y27632 10 uM LIF 10 ng/ml
LDN19318
9 125 nM Y27632 10 uM
Y27632 10 uM
culture period 2 passages P0 (8 days w/o passage) 50 passages, 7 months
cell source
hESC differentiation
(unpurified) (Takasato et al.,
2014)
hiPSC differentiation
(unpurified) (Taguchi et al.,
2014)
primany human fetal kidney
(week 9-17) FACS with
EpCAM-/NGFR+
1.6.2 3D NPC cultures
Neither of the 2D systems discussed above sustain the long-term culture of NPCs. A
breakthrough was made in this regard utilizing a 3D model. Similar to the initial procedure in
32
Kanigawa et al. 2016, the procedure described by Li et al. starts with aggregating mouse NPCs,
which are kept as aggregates in low-attachment wells, instead of being cultured in matrix-coated
plates (Li et al., 2016). The components of the medium (termed “NPSR”) are similar to
Kanigawa et al. 2016, with varied concentrations and exclusion of DAPT (Table 1).
Astonishingly, Li et al. showed that they can maintain this 3D culture for up to 110 passages
over the course of 17 months without losing the expression of NPC signature genes. The NPC
aggregate can differentiate into nephron lineages including podocytes (PODXL+, WT1+),
proximal tubules (LTL+, AQP+) and distal tubules (DBA+, CDH1+). Moreover, with the
addition of inhibitors of Smad1/5/8 (LDN193189) and Smad2/3 (A83-01), the NPSR (this
version is termed hNPSR) can support the culture of human NPCs isolated by EpCAM-/NGFR+
selection with an efficiency similar to mouse NPCs and preserve nephrogenic potential.
The almost indefinite propagation capability of NPSR makes it possible to model
nephron-related diseases coupled with gene editing. In fact, the Li paper demonstrated highly
efficient gene targeting with CRISPR/Cas9 in NPC lines (Li et al., 2016). Of particular interest,
the authors demonstrated the self-organization of cultured NPCs when combined with Six2-
kidney cells. By combining human NPCs, ureteric progenitors and progenitors from other
lineages (interstitial, endothelial, vascular, etc.), it is reasonable to speculate that NPSR could
enable the assembly of an in vitro kidney with the right structures; ESC/iPSC derivation would
then become optional.
Collectively, the current NPC culture protocols, enabled by genetic studies in mice,
resulted in significant advances in the creation of in vitro models for NPC regulation,
nephrogenic cell derivation and disease modeling. Clearly the NPSR system has the most
exciting potential applications, but requires independent verification from other labs. Once there
are valid protocols for deriving and maintaining progenitors for other kidney lineages, we can
expect to grow human kidneys in a dish.
1.6.3 Development of kidney organoids from pluripotent stem cells
Since the derivation of human embryonic stem cells (Thomson et al., 1998) and induced
pluripotent stem cells (Takahashi et al., 2007), using human pluripotent stem cells to study
human organ development and model diseases has become a clear possibility. Several protocols
(Morizane et al., 2015; Taguchi et al., 2014; Taguchi and Nishinakamura, 2017; Takasato et al.,
33
2014; Takasato et al., 2015) have been developed for deriving kidney organoids from pluripotent
stem cells, as will be discussed in detail below (Fig 16).
The design of these protocols falls into two categories. Approaches in the first category
simultaneously direct hPSCs to differentiate into both nephron and ureteric bud (UB) lineages.
Previous work attempted to derive the nephrogenic intermediate mesoderm (IM) by conditioning
an environmental signal to trigger the expression of the marker gene OSR1 (Mae et al., 2013).
However, OSR1 expression is not specific to IM during development. The first kidney organoid
protocol (Takasato et al., 2014) optimized the induction of IM from hES/iPS cells by inducing
the co-expression of IM markers OSR1, PAX2 and LHX1 through treatment with FGF9
following CHIR99021 (CHIR). Further conditioning with FGF9, BMP4 and retinoic acid (RA)
facilitated the induction of metanephric mesenchyme (MM), which includes ~20% SIX2+
putative NPCs. After 22 days in 2D culture, the induced hESC culture spontaneously expressed
the signature genes for podocytes (SYNPO, NPHS1, WT1), proximal tubules (AQP1, SLC3A1)
and collecting ducts (AQP2, SCNNB1). However, there were no structures similar to their in
vivo counterparts. If the induced culture was dissociated, aggregated and left with no growth
factors at day 18, it transformed into a 3D structure and formed lumen-like structures similar to
ureteric epithelium (PAX2+, AQP2+) or proximal tubules (AQP1+, SLC3A+), although
glomerulus-like structures were absent, potentially indicating a problem.
An update of the aforementioned protocol made a few changes. First, to enhance
induction of the metanephric mesenchymal lineage, the Wnt agonist, CHIR, was initially applied
for 4 days instead of 2 days; monolayer culture in this condition eventually (at day 18)
manifested expression of marker genes for both metanephric mesenchyme (SIX2, SIX1,
HOXD11) and ureteric buds (GATA3, KRT8). Secondly, the culture was transferred to 3D
earlier (day 7 vs. day 18) and was given a CHIR pulse followed by a 5-day FGF9 conditioning,
before growth factors were removed. The resulting organoid (at day 18) demonstrated proper
segmented expression of markers for the collecting duct (GATA3+, ECAD+), early distal tubule
(GATA3-, LTL-, ECAD+), early proximal tubule (LTL+, ECAD-) and glomerulus (WT1+).
However, the presence and efficiency of NPCs in the 3D organoid were not shown, posing a
question. Renal interstitium lineage cells—ranging from early FOXD1+/MEIS1+ progenitors
(day 11) to later mesangial cells and pericytes (PDGFRA+) (day 18)—surrounded the putative
glomerulus, consistent with in vivo observations. A network of endothelial cells (CD31+, KDR+,
SOX17+) also formed, wrapping and invading NPHS+ putative podocytes. The LTL+ proximal
34
tubule cells could uptake dextran from the medium, and the mature (LTL+, ECAD+) part
underwent apoptosis in response to the proximal tubule toxicant cisplatin, indicating the
functionality of these proximal tubule-like structures in the organoid.
The second category of kidney organoid derivation strategies seeks to generate individual
lineages (nephron, ureteric epithelium and stroma) in the kidney, before generating an organ that
contains multiple lineages. In order to induce the nephron lineage that differentiates from the
ureteric epithelium lineage, the first group (Taguchi et al., 2014) relied on insights from mouse
development. Using lineage tracing, they elegantly demonstrated that the nephrogenic
metanephric mesenchyme is specifically derived from the posterior part of E8.5 intermediate
mesoderm, while the anterior part is predicted to contain precursors of ureteric epithelium. They
subsequently optimized the condition to induce metanephric mesenchyme from the E8.5
posterior IM with a 3-step combination of Bmp, CHIR, RA, activin and FGF9. The same
induction procedure for posterior E8.5 IM was applied to posterior nascent mesoderm (T+,
Cdx2+, Tbx6+) derived from mouse ES cells, and this approach achieved a culture with 65%
nephron progenitors (quantified as Osr1+, Itga8+, Pdgfra-), with expression of Wt1, Pax2, Sall1
and Six2 and very few stroma cells. When the induced metanephric mesenchyme culture was
exposed to spinal cord tissue, nephron lineage structures formed spontaneously, including
podocytes (Wt1+, nephrin+, podocin+), proximal tubules (Cdh6+, LTL+, Aqp1+, Jag1+,
Megalin+) and distal tubules (Ecad+, Brn1+, NCC+). When transplanted beneath a mouse’s
kidney capsule, the induced metanephric mesenchyme/embryonic spinal cord combination
vascularizes and formes similar nephron structures as observed in vitro. These data indicate the
nephrogenic capacity of the induced metanephric mesenchyme. A longer treatment with the
same protocol was applied to human iPS cells, and this induced the formation of structures
similar to those observed in mouse cell culture. In summary, the method developed by Taguchi
et al. 2014 was efficient in generating specific metanephric mesenchyme lineage cells, including
a high percentage of NPCs. The caveats are that 1) the protocol is relatively complicated and
difficult to reproduce and 2) spinal cord induction is required to initiate nephrogenesis. In a
subsequent paper, the group developed a method to induce ureteric epithelium progenitors from
iPSCs (Taguchi and Nishinakamura, 2017). When combined with the previously developed
iPSC-derived NPCs, the resulting structure resembled the developing kidney, with branching
ureteric buds capped at the tips by NPCs. Data was shown for both mouse and human cultures.
However, this complex protocol may not robust or convenient enough for widespread use.
35
Subsequently, another group developed a protocol (Morizane et al., 2015) for inducing a
nephron-specific organoid. Borrowing insights from Tahuchi’s work, this new protocol also
carefully derived the nephrogenic posterior primitive streak (WT1+, HOXD11+, PAX2-,
LHX1-) with CHIR followed by activin treatment. Afterwards, a low dose of FGF9 was applied,
efficiently inducing a 90% SIX2+ population of putative NPCs, which also expressed SALL1,
WT1, PAX2 and EYA1. To induce nephrogenesis, the culture was re-plated in 3D with a 2-day
stimulation of CHIR and a constant supply of FGF9, before withdrawing growth factors 5 days
later. After 21 days of differentiation, the protocol produced segmented nephron-like structures,
i.e., podocyte clusters (NPHS+, PODXL+, WT1+) connected to proximal tubules (LTL+,
AQP1+), descending limbs of Henle (CDH1+, AQP1+), thick ascending limbs of Henle
(CDH1+, UMOD+) and distal convoluted tubules (CDH1+, UMOD-). Functionally, the organoid
displayed a specific injury response to drugs toxic to nephric tubules (gentamicin and cisplatin).
In general, this protocol provides a quicker, simpler and chemically defined option for inducing
nephron organoids.
36
Figure 2. Workflows of published protocols to derive nephron or kidney organoid from
pluripotent stem cells.
hESC/hiPSC
primitive
streak
intermediate
mesoderm
metanephric
mesenchyme
ureteric
epithelium
stroma
BMP4 30 ng/ml
activin 10 ng/ml
OR CHIR 7 uM
FGF9 200 ng/ml
FGF9 200 ng/ml
BMP7 50 ng/ml
RA 0.1 uM
MIXL1
T
OSR1
PAX2
LHX1
D0 D2 D6
D17
SIX2
WT1
GDNF
HOXD11
C-RET
HOXB7
GATA3
FOXD1
podocyte
proximal
tubule
collecting
duct
D22
SYNPO
NPHS1
WT1
AQP1
SLC3A1
AQP2
SCNNB1
proximal
tubule
ureteric
epithelium
WT1
PAX2
AQP1
SLC3A1
AQP2
PAX2
metanephric
mesenchyme
JAG1
ECAD
renal
vesicles
2D
3D
hESCs/hiPSC
glomerulus
collecting
duct
mesangial/
pericyte
CHIR 8 uM FGF9 200 ng/ml
D0 D4
D21
GATA3+
ECAD+
PDGFRA+
CHIR pulse
D10
3D
D15
No GF
proximal
tubule
distal
tubule
WT1+
LTL+
ECAD-
LTL-
ECAD+
GATA3-
endothelial
CD31+
KDR+
SOX17+
Takasato et al., 2014
Takasato et al., 2015
hESCs/hiPSC
embryonic
bodies
epiblast
BMP4 0.5 ng/ml
Y27632 10 uM
activin 1 ng/ml
FGF2 20 ng/ml
BMP4 1 ng/ml
CHIR 10 uM
D0 D1 D3
D22
Taguchi et al., 2014
nascent
mesoderm
D5
T
CDX2
TBX6
BMP4 1 ng/ml
CHIR 10 uM
posterior
nascent
mesoderm
D9
T
CDX2
TBX6
HOX11
activin 10 ng/ml
BMP4 1 ng/ml
CHIR 3 uM
RA 0.1 uM
D11
OSR1
WT1
HOX11
posterior
intermediate
mesoderm
CHIR 1 uM
FGF9 5 ng/ml
D14
SIX2
WT1
SALL1
PAX2
metanephric
mesenchyme
podocyte
proximal
tubule
distal
tubule
NPHS1
WT1
SALL1
CDH6
ECAD
3D
embryonic
spinal cord
Morizane et al., 2015
hESCs/hiPSC
late primitive
streak
posterior
intermediate
mesoderm
CHIR 8 uM activin 10 ng/ml FGF9 10 ng/ml
D0 D4 D7
NPC
D9
pre-tubular
aggregates
D11
PAX8
LHX1
FGF9 10 ng/ml
D14
PAX8
LHX1
LAM
BRN1
HNF1B
renal
vesicles
No GF
3D
OR CHIR 10 uM
noggin 5 ng/ml
T
TBX6
OSR1
WT1
HOX11
SIX2
WT1
SALL1
PAX2
EYA1
CHIR 3 uM
FGF9 10 ng/ml
D28
podocyte
proximal
tubule
distal
tubule
NPHS1
PODXL
LTL
CDH2
CDH1
UMPD
BRN1
SIX2: 10-20%
SIX2: 60%
SIX2: 74-90%
Strategy 1: multi-lineage induction
Strategy 2: nephron lineage induction
37
1.7 Addendum: The canonical Wnt signaling pathway and its function in other
stem cell systems
1.7.1 The molecular mechanisms downstream of canonical Wnt signaling
Wnt signaling is categorized into a canonical pathway and multiple non-canonical ones,
depending on the downstream mechanisms. Canonical Wnt signaling acts through β-catenin and
Tcf/Lef family transcription factors. Detailed reviews of the precise molecular mechanisms can
be found elsewhere (Mosimann et al., 2009; Tortelote et al., 2017). Briefly, in the absence of the
Wnt ligand, the transcriptional co-activator β-catenin is phosphorylated by the cytoplasmic β-
catenin destruction complex consisting of Apc, Axin, Gsk3b, Csnk1a1 (casein kinase 1) and
Ppa2ca (protein phosphatase 2A), and subsequently degraded through a ubiquitination pathway.
Inside the nucleus, the HMG box family Tcf transcription factors are bound by co-repressors
(Tle family members, Ctbp, etc.) and repress Wnt target gene expression. Upon Wnt ligand
binding to Fzd receptor/Lrp co-receptor on the cell surface, Axin in the β-catenin destruction
complex is sequestered by the activated receptor protein complex, enabling β-catenin to escape
degradation. The overall effect is an increase in the level of β-catenin protein in the cell, with
part of the protein entering the nucleus, binding to Tcf/Lef transcription factors and activating
Wnt target gene transcription. While evidence suggests that all four mammalian Tcf/Lef family
members are able to functionally interact with both Tle family co-repressors and β-catenin
(Brantjes et al., 2001), Tcf7l1 and Tcf7l2 more often act as transcriptional repressors, and Tcf7
and Lef1 typically function as transcriptional activators (Lien and Fuchs, 2014). One in vitro
study suggested that, compared to Tcf7 and Lef1, Tcf7l1 and Tcf7l2 bind to Tle with much
higher affinity, correlating with less efficient transcriptional activation in the presence of β-
catenin (Chodaparambil et al., 2014). In addition, the domain that interacts with Ctbp, another
transcriptional co-repressor, is present in Tcf7l1 and Tcf7l2 only (Lien and Fuchs, 2014). This
evidence explains the distinct behaviors of Tcf/Lef family members in the biological context.
1.7.2 Wnt signaling regulates hair follicle stem cells (HFSCs)
Mammalian hair follicles are unique in their life cycle: growth (anagen), regression
(catagen), and rest (telogen). This cycle of hair replenishment relies on HFSCs, which are
generally activated during the anagen phase and quiescent during the telogen phase (Yi, 2017).
38
HFSCs were initially identified as slow-cycling cells residing in the bulge of adult mammalian
hair follicles (Cotsarelis et al., 1990; Tumbar et al., 2004). During embryogenesis, the HFSC
population is marked by Sox9, which is required for the formation of this cell population and for
the proper development of hair follicles (Nowak et al., 2008). Gene expression analysis and
lineage tracing experiments suggest that these cells, marked by expression of Sox9, Lhx2,
NFATc1, Tcf7l1, Lgr5 and Krt15 (Liu et al., 2003), give rise to various cell types in hair
follicles. In adult mice, HFSCs respond to the underlying cluster of mesenchymal cells, called
the dermal papilla, and grow downward until they mature and produce hair.
Canonical Wnt pathway components are critical for hair follicle development and
function. Several Wnt ligands are expressed in the hair follicle placode, including Wnt10b (Andl
et al., 2002; St-Jacques et al., 1998) and Wnt3a (Millar et al., 1999), although it’s not clear
whether any particular ligand is critical. Notably, β-catenin is specifically expressed in the hair
placodes of skin during mouse embryonic development (Andl et al., 2002; Huelsken et al.,
2001). Conditional ablation of β-catenin in epidermis and hair follicles with Krt14Cre results in
hair loss due to the failure of HFSCs to differentiate into follicular keratinocytes (Huelsken et al.,
2001). Conversely, constitutively activated β-catenin leads to the expansion of the hair follicle
fate during development (Zhang et al., 2008) and de novo formation of hair follicles in adults
(Gat et al., 1998). Lef1-deficient mice lack whiskers and body hair, in addition to other organ
abnormalities (Genderen et al., 1994). Ectopic expression of Dickkopf1 (Dkk), a diffusible
inhibitor of Wnt, in mouse skin, leads to the complete failure of hair follicle formation (Andl et
al., 2002).
Before terminal differentiation, HFSCs first give rise to hair germ (HG) cells. There is
some interesting interplay between β-catenin and Tcf/Lef factors in regulating HFSC
differentiation. Tcf7l1 (Tcf3) is preferentially expressed in HFSCs (Lien et al., 2014; Merrill et
al., 2001), while Lef1 is preferentially expressed in HG cells (Lien and Fuchs, 2014; Merrill et
al., 2001; Zhou et al., 1995). A transcriptional response to Wnt/β-catenin is activated specifically
in the Lef1+ domain, but not in the Tcf7l1+ domain, as measured by TOPGAL reporter activity
(Merrill et al., 2001), suggesting that Lef1 mediated activation of Wnt/β-catenin targets while
Tcf7l1 does not. In addition, when β-catenin is constitutively activated in the entire skin lineage
in the mice carrying Krt14Cre-driven N-terminal deleted β-catenin alleles, Lef1 is activated in
both HFSCs and HG cells (Merrill et al., 2001), suggesting Lef1 itself is a target of β-catenin. A
subset of Tcf7l1 binding sites in HFSCs are occupied by Lef1 in temporarily amplifying cells
39
(TACs), associated with activation of genes expressed in TACs (Adam et al., 2018); this is
consistent with the notion that Tcf7l1 is a transcriptional repressor, while Lef1 is a
transcriptional activator. However, the remainder of Tcf7l1-bound sites in HFSCs that are not
replaced by Lef1 in TACs are associated with preferential expression in HFSCs. In one example,
the Tcf/Lef motif in the enhancer is required for its activity (Adam et al., 2018), suggesting some
cases where Tcf7l2/Tcf7l2 can positively regulate gene expression. The interplay between
Tcf/Lef factors has a significant functional consequence. Tcf7l1 and Tcf7l2 are required to
suppress the precocious proliferation of HFSCs, while Lef1 and Tcf7 are required for HFSC
differentiation and hair regeneration (Adam et al., 2018).
1.7.3 Wnt signaling regulates intestinal stem cells (ISCs)
The small intestine is a rapidly proliferating organ in adult mammals: almost all of its
cells are regenerated every 4–5 days. Cells in the villi protruding into the intestinal lumen are
continuously shed while being replaced by differentiating ISCs (Clevers et al., 2014). Initial
efforts to characterize ISCs led to the identification of Lgr5+ cells, part of the crypt base
columnar (CBC) cells located at the bottom of intestinal crypts and interspaced between Paneth
cells. Lgr5+ cells are highly proliferative and give rise to all cells in the small intestine, as well
as the colon (Barker et al., 2007). Shortly after the identification of Lgr+ cells, a largely distinct
Bmi1+ population residing at the +4 position above the crypt were identified with similar stem
cell characteristics (Sangiorgi and Capecchi, 2008). Notably, the Bmi1+ population is required
for crypt homeostasis, as demonstrated by an induced cell death experiment. A later study further
supported the stemness of Bmi1+ cells, but not of Lgr5+ cells, by showing that Lgr5+ cells are
descendants of Bmi1+ cells and dispensable for intestinal homeostasis (Tian et al., 2011).
However, another report indicated that the two lineages are interconvertible (Takeda et al.,
2011). The Lgr5+ cells are fast-proliferating, while the Bmi1+ cells are largely quiescent; once
the Lgr5+ cells are lost due to irradiation, Bmi1+ cells, which are refractory to irradiation,
replenish them (Yan et al., 2012). The issue was further resolved by a more recent study showing
that Bmi1+ cells normally give rise to Paneth and enteroendocrine lineages, and only become
Lgr5+ cells in response to injury (Buczacki et al., 2013). Taken together, these studies indicate
that Lgr5+ cells are the ISCs responsible for intestinal crypt homeostasis, and that these cells can
be replenished by quiescent Bmi1+ cells after injury.
40
ISCs’ function largely depends on Wnt signaling. Tcf7l2 (Tcf4) (Korinek et al., 1998;
van Es et al., 2012) and β-catenin (Fevr et al., 2007) are required for maintaining ISC
proliferation. Tcf7l2 (Tcf4), with β-catenin, functions as a transcriptional activator in ISCs.
Conversely, administration of R-spondin leads to increased intestinal proliferation (Kim et al.,
2005). Another Tcf/Lef family member, Tcf7 (Tcf1) is upregulated upon the loss of Apc,
suggesting upregulation by high levels of β-catenin; interestingly, mice with a null Tcf7 mutation
develop mammary gland adenoma and intestinal neoplasm, suggesting that Tcf7’s function in
intestinal development is distinct from that of Tcf7l2 (Roose et al., 1999).
Importantly, mutations related to the Wnt signaling pathway are associated with colon
cancer. APC, a component of the β-catenin destruction complex, is frequently mutated in colon
cancer cases (Kinzler 1991; Nishisho 1991; Kinzler 1996; Wood 2007). In colon carcinoma with
an inactivating mutation of APC, TCF7L2 transactivates TOPFLASH reporters (which are
Tcf/Lef response elements-driven) in a β-catenin-dependent manner (Korinek et al., 1997).
Notably, in normal colon cells, a dominant negative form of Tcf7 is expressed and equally
distributed between nuclear and cytoplasmic compartments; in colon cancer cells, Tcf7 is
predominantly cytoplasmic (Najdi et al., 2009). Other Wnt-activating mutations have also been
associated with colorectal cancer, including those affecting Axin2 (Lammi et al., 2004), β-
catenin (Morin et al., 1997) and R-spondins (Seshagiri et al., 2012). Moreover, among colon
cancer clones, the ones with high Wnt-driven transcriptional activity develop a cancer stem cell
phenotype, i.e., high tumorigenicity, while their low Wnt-response counterparts are poor at
initiating tumors (Vermeulen et al., 2010). The transcriptional program specific to ISCs was
found to be enriched in a subset of aggressive colorectal cancer cases (Merlos-Suarez et al.,
2011). Collectively, this evidence connects the reactivation of a Wnt-driven stem cell program to
the carcinogenic transformation of the colon.
41
Chapter 2 Epigenetic and Genetic Analysis of Nephron Progenitor Cells
Disclaimer:
Dr. Lori O’Brien and I are the co-first authors on the paper (O'Brien et al., 2018) which
developed from the studies discussed in this chapter. I performed most of the bioinformatics
analysis (except for mapping structural mutation of Br/Br mice) and part of the experiments
(Wt1 and Ctcf ChIP-Seq, Six2GFP+, Cited1RFP+ RNA-Seq, generation of Wnt4ΔDE/ΔDE
mice, histological and RT-qPCR analysis of Wnt4ΔDE/ΔDE and Six2ΔDE/ΔDE mice, 4C-Seq
and EMSA assays). Dr. Lori O’Brien contributed to initial design of the experiments and part of
the experiments (Six2, Six2BF, Hoxd11BF and Osr1BF ChIP-Seq, generation of Six2ΔDE/ΔDE
mice, histological and RT-qPCR analysis of Br/Br mice). A number of other McMahon
laboratory members and outside collaborators have also contributed to this project.
2.1 Abstract
Nephron progenitor number determines nephron endowment; a reduced nephron count is
linked to the onset of kidney disease. Several transcriptional regulators including Six2, Wt1,
Osr1, Sall1, Eya1, Pax2, and Hox11 paralogues are required for specification and/or maintenance
of nephron progenitors. However, little is known about the regulatory intersection of these
players. Here, we mapped nephron progenitor-specific transcriptional networks for Six2,
Hoxd11, Osr1, and Wt1. We identified 373 multi-factor associated ‘regulatory hotspots’ around
genes closely associated with progenitor programs. To examine their functional significance, we
deleted ‘hotspot’ enhancer elements for Six2 and Wnt4. Removal of the distal enhancer for Six2
leads to a ~40% reduction in Six2 expression. When combined with a Six2 null allele, progeny
display a premature depletion of nephron progenitors. Loss of the Wnt4 enhancer led to a
significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney, a
phenotype also enhanced in combination with a Wnt4 null mutation. To explore the regulatory
landscape that supports proper target gene expression, we performed CTCF ChIP-seq to identify
insulator-boundary regions. One such putative boundary lies between the Six2 and Six3 loci.
Evidence for the functional significance of this boundary was obtained by deep sequencing of the
radiation-induced Brachyrrhine (Br) mutant allele. We identified an inversion of the Six2/Six3
locus around the CTCF-bound boundary, removing Six2 from its distal enhancer regulation, but
placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where
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Six3 is normally expressed. Six3 is now predicted to fall under control of the Six2 distal
enhancer. Consistent with this view, we observed ectopic Six3 in nephron progenitors. 4C-seq
supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys.
Together, these data expand our view of the regulatory genome and regulatory landscape
underpinning mammalian nephrogenesis.
2.2 Introduction
The mammalian metanephric kidney maintains fluid homeostasis. The number of
individuals afflicted with kidney disease is on the rise, and reduced nephron number has been
associated with disease outcome (Bertram et al., 2011). In the mouse, genetic studies have
demonstrated that nephrons are generated from a Six2+ progenitor pool in a regulatory process
requiring the transcriptional action of Six2 for progenitor maintenance (Self et al., 2006a).
Human SIX2 shows an expression and activity similar to its murine counterpart suggesting that
mouse Six2 and human SIX2 likely have similar functions (O'Brien et al., 2016). Consistent with
this view, human mutations in SIX2 are associated with renal hypodysplasia and the malignant
transformation of progenitor cells in Wilms’ tumor, a pediatric nephroblastoma (Walz et al.,
2015; Weber et al., 2008; Wegert et al., 2015). There is an increasing interest in the relationship
between nephron progenitors, their output, and congenital and acquired kidney disease (Bertram
et al., 2011; Bertram et al., 2016). Further, new approaches to modulate nephron progenitor
outputs to generate kidney structures in vitro call for a better understanding of regulatory
processes at play in vivo (Morizane et al., 2015; Taguchi et al., 2014; Takasato et al., 2015).
Nephron progenitor specification and nephron progenitor maintenance are dependent on a
number of additional transcriptional regulatory factors including Hoxa/c/d11, Osr1, Wt1, Sall1,
Eya1, Pax2, and Six1. Previous studies of mouse mutants in these genes suggest complex
hierarchical interactions amongst these factors (Bouchard et al., 2002; James et al., 2006;
Kreidberg et al., 1993; Li et al., 2003; Mugford et al., 2008a; Mugford et al., 2008b;
Nishinakamura et al., 2001; Ohmori et al., 2015; Patterson et al., 2001; Sajithlal et al., 2005;
Torres et al., 1995; Wang et al., 2005; Wellik et al., 2002; Xu et al., 2014a; Xu and Xu, 2015; Xu
et al., 1999; Xu et al., 2003). Identification of their genomic targets and target regulatory
mechanisms are essential to determine the nephrogenic regulatory network.
Direct nephron progenitor ChIP-seq studies have identified a broad range of potential
transcriptional targets of Six2/SIX2 action in the mouse and human kidney, respectively, and
43
verified predicted enhancer modules for several of these targets (Kanda et al., 2014; O'Brien et
al., 2016; Park et al., 2012). Six2 interacts at cis-regulatory modules of genes expressed both in
the nephron progenitors and their committed nephron-forming descendants through enhancers
co-engaged by differentiation-inducing transcriptional complexes formed in response to
canonical Wnt signaling (Kanda et al., 2014; Park et al., 2012). Interestingly, a potential role for
Hox11 paralogs within Six2-predicted cis-regulatory modules is suggested by the strong
enrichment of AT-rich homeobox motifs in Six2 ChIP-seq peaks (Park et al., 2012). The
genomic targets of Wt1 have also been analyzed by ChIP experiments of embryonic mouse
kidneys [30-32]. Though the approach was not specific to nephron progenitors, these studies
revealed the interplay with many genes expressed in, and critical for, nephron progenitors,
including Fgf and Bmp family members (Hartwig et al., 2010; Kann et al., 2015b; Motamedi et
al., 2014). Sall1 ChIP-seq has also shed light on its active roles in nephron progenitors and
repressive actions on development of nascent nephrons, respectively [29]. Interestingly, a subset
of Six2- and Sall1-bound regions overlap suggesting these factors co-associate and target
analysis predicts genes regulating the nephron progenitor population (Kanda et al., 2014).
With a working model that multi-factor binding will highlight key regulatory nodes of the
nephron progenitor pathway (Ballester et al., 2014; Boyer et al., 2005; Gerstein et al., 2012; He
et al., 2011; Zinzen et al., 2009), we utilized ChIP-seq analysis to identify a subset of putative
regulatory elements associated with multiple transcription factors. gRNA/Cas9-mediated
ablation of ‘regulatory hotspots’ adjacent to Six2 and Wnt4 highlight the significance of these
enhancer elements in regulating target gene expression. Additional analyses of the regulatory
landscape surrounding Six2 identified insulator-bound elements which constrain enhancer
function. In support of this finding, deep sequencing of the Br mutant mouse identified an
inversion of Six2 and Six3 loci altering enhancer specificity. These studies highlight the critical
role of multi-factor input and proper enhancer context for directing appropriate target gene
expression.
2.3 Result
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2.3.1 A novel transgenic strategy to generate nephron progenitor-specific ChIP-Seq
data
To extend our understanding of the transcriptional regulatory networks operating within
mouse nephron progenitors, we developed a transgenic approach to overcome the limited
availability and inconsistency of working antibodies for key transcriptional components, and
complications that arise from the diverse expression of regulatory factors elsewhere in the
kidney. In this transgenic strategy, an epitope-tagged transcription factor-of-interest is expressed
exclusively within the nephron progenitor compartment using a Six2 distal enhancer (DE)
previously shown to recapitulate Six2-like, nephron progenitor restricted expression (Fig 3A
(Park et al., 2012)). This approach obviates the need to enrich for the progenitor population in
whole kidney samples simplifying ChIP procedures and avoiding potential artifacts introduced
by tissue dissociation and fluorescence-activated cell sorting (FACS). We also took advantage of
established tagging methods which have been utilized to successfully isolate protein:DNA
complexes (Hojo et al., 2016; Mazzoni et al., 2011; Wang et al., 2009; Zhang et al., 2013). Each
transcription factor-of-interest is appended with a BioTag-FLAG (BF) epitope at the C-terminus
of the target protein. Co-production of an EGFP-BirA enzyme on the transgene through an IRES
element also allows both ready visualization of transgenic kidneys and biotinylation of the
biotin-recognition motif (BioTag) enabling an additional mode of isolation of factor-associated
DNA or protein complexes through streptavidin affinity purification (Fig 3A). Though the biotin
tagging strategy proved successful (Fig 4C) and provided a secondary purification option, we did
not utilize it for any ChIP experiments as anti-FLAG antibodies were sufficient for all of the
studies presented here.
To rigorously assess the efficacy of this strategy and to develop a protocol for whole
kidney ChIP, we first generated Six2-BF
tg
mice to determine whether Six2 ChIP-seq generated
with the transgenic line (Six2-BF) replicates Six2 ChIP-seq using a Six2-specific antibody (Six2-
ab). Six2-BF was restricted to the Six2+ nephron progenitors as indicated by specific detection
of the anti-FLAG epitope (Fig 3C). FLAG ChIP-seq from Six2-BF+ kidneys identified 6808
Six2-associated regions in the Six2-BF data, with 90% of these peaks overlapping with Six2-ab
peaks (Fig 3B). The two datasets were relatively correlated (R
2
=0.69) and, as expected,
overlapping peaks were ranked higher than Six2-BF unique peaks indicating the variability in the
data reflects marginal peak calls (Fig 4A). The most enriched motif discovered from the top 1000
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peaks in the Six2-BF ChIP-seq dataset (‘TCANGTTTCA’, 47%, p-value=10
-1190
, Fig 3D)
matched and agreed with the identified Six2 motif from our previous ChIP studies (Fig 4B;
(Kanda et al., 2014; O'Brien et al., 2016; Park et al., 2012)). The motif was relatively centered
within the peaks suggesting direct binding of Six2 to the motif (Fig 3D). Additionally,
electrophoretic mobility shift assays (EMSA) utilizing recombinant Six2 and a Six2 motif
identified within the Six2-DE showed a strong interaction of Six2 protein with its DNA target
(Fig 5A). Mutational analysis on this Six2-binding site demonstrated that the most conserved
bases in the consensus (1T, 6T and 9C) were critical individually for effective protein-DNA
interaction (Fig 5A). Wt1 and bHLH recognition motifs were also significantly enriched in Six2
binding regions (Fig 4F) consistent with an expected role for Wt1 within the progenitor
compartment (Essafi et al., 2011; Motamedi et al., 2014), and an unidentified role for a bHLH
factor. To interrogate the regulatory functions of Six2-BF, we performed GREAT Gene
Ontology (GO) analysis (McLean et al., 2010) on Six2-BF peaks. Six2-BF peaks were highly
enriched near genes associated with kidney development as reflected by the top GO term
‘ureteric bud development’ (Fig 3E).
In summary, the FLAG transgenic strategy robustly reproduced Six2-ab ChIP-seq data
generated from wild-type kidneys identifying expected Six2 target and gene associations. These
whole kidney-derived datasets significantly extend the depth of Six2 ChIP-seq peaks identified
from earlier reports ( (Park et al., 2012): 3907 peaks, (Kanda et al., 2014): 4306 peaks). While
our transgenic strategy is useful for targets for which there are no working antibodies or when a
progenitor-specific ChIP is desired, expression levels of the tagged protein or affinities of the
FLAG antibody versus protein-specific antibodies (if one exists) may affect the number of
relevant peaks discovered. Interestingly, although peaks identified uniquely with the Six2-ab
showed lower levels of enrichment, these peaks still enriched for the Six2 motif at a similar level
(46%) and were linked to kidney development GO terms suggesting a biological relevance to the
interactions (Fig 4A). As nearly all Six2-BF peaks are contained within the larger Six2-ab
dataset (~90%, Fig 3B), for a more complete analysis of Six2 bound target regions we used the
latter dataset for subsequent analyses.
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2.3.2 Identification of nephron progenitor-specific transcription factor interaction
sites
Having validated the transgenic strategy for generation of nephron progenitor specific
ChIP-seq data, we established additional transgenic mouse lines to identify regulatory
interactions mediated by other transcriptional regulators in nephron progenitors. Viable and
phenotypically normal founders were generated for Hoxd11 (Hoxd11-BF
tg
) and Osr1 (Osr1-
BF
tg
). Immunostaining with anti-FLAG antibodies confirmed the restriction of Hoxd11-BF and
Osr1-BF to Six2+ nephron progenitors and validated the use of both transgenic lines for ChIP-
seq analyses (Fig 3C). We also attempted to generate transgenic lines for Wt1, Hoxa11, Pax2,
Sall1, and Eya1 but were unsuccessful in producing any founder animals. Further, we were not
able to obtain transgenic progeny which survived past birth from the original Hoxd11-BF
founder. These observations suggest transgene and/or transgenic line dependent lethality (see
Discussion).
To map Hoxd11- and Osr1-associated genomic regions within nephron progenitors, we
performed FLAG ChIP-seq on E16.5 Hoxd11-BF
tg/+
and Osr1-BF
tg/+
kidneys identifying 7776
Hoxd11-BF and 5032 Osr1-BF associated regions (Fig 3D). Osr1-BF protein levels were
markedly lower and this may account for the lower number of target sites identified (Fig 3C).
Both Hoxd11-BF and Osr1-BF peaks showed typical enhancer features: similar to the Six2-BF
dataset the majority of the peaks were located >5kb from the transcription start site (TSS) within
intronic (Six2-BF:46.6%, Hoxd11-BF: 46.2%, Osr1-BF: 44.7%) or intergenic regions (Six2-BF:
46.5%, Hoxd11-BF: 48.7%, Osr1-BF: 43.6%) (Fig 4D, E). GREAT analysis identified an
enrichment for both factors near genes associated with processes related to metanephric kidney
development (Fig 3E).
Using the same workflow adopted above for analysis of Six2 interactions, we identified
the top DNA motif enriched in Hoxd11-BF (‘TTTATGG’, 38%, p-value=10
-1033
, Fig 3D) and
Osr1-BF datasets (‘GCTNCTG’, 45%, p-value = 10
-1438
, Fig 3D). Both motifs were well-
centered within each peak dataset (Fig 3D). Multiple Hox factors are expressed in nephron
progenitors and each may exhibit distinct binding preferences. While the predicted Hoxd11 motif
has a prominent AT-rich Hox factor consensus feature, the motif differs from that identified
through protein-DNA binding microarray (PBM) studies in vitro (‘TTTACGA’, (Badis et al.,
2009), Fig 5B). EMSA analysis confirmed Hoxd11 binding and the relative importance of the
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bases 2T and 4A which are conserved in both the PBM and ChIP-seq based predictions, while
the 1T and the 5T/C positions, which differed between the two predicted motifs, were not
important for binding in vitro (Fig 5B). The Osr1 motif identified from our ChIP-seq data closely
resembled that predicted from PBM studies (‘GCTACTG’, (Badis et al., 2009)) though no strong
preference for the 4
th
nucleotide position was seen in the in vivo motif. EMSA demonstrated
Osr1 bound to the predicted Osr1 binding site within the Six2-DE (GCTGCTG). Interestingly,
substituting an A in the 4G position to more closely reflect the PBM motif enhanced the Osr1
interaction (Fig 5C). These findings suggest that in vivo regulatory processes may prefer weaker
binding, potentially adding greater flexibility to transcriptional interactions. Wt1 and bHLH
motifs were also enriched in each peak dataset, as was observed for Six2-BF peaks (Fig 4F).
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Figure 3. Identification of Six2, Hoxd11 and Osr1 binding sites in nephron progenitors by
ChIP-seq. (A) Schematics shows characteristics of the transgenic mice used to generate nephron
progenitor-specific ChIP-seq data: the Six2 distal enhancer (Six2-DE) drives nephron
progenitor-specific expression of a FLAG-tagged transcription factor in the embryonic kidney.
The IRES allows co-expression of GFP-BirA. Transgenic founders are utilized for FLAG ChIP-
seq to identify progenitor specific programs. (B) Venn diagram shows overlap of peaks from
FLAG ChIP-seq generated from Six2-BF
tg/+
(Six2-BioTagFLAG) mouse kidneys and from the
Six2 antibody (Six2-ab) ChIP-seq. (C) Immunostain for Six2 and FLAG at E16.5 shows the
overlap of the two proteins for each transgenic line. (D) Motifs identified from Six2-BF, Hoxd11-
BF, and Osr1-BF peaks with MEME (Multiple EM for Motif Elicitation; +/- 50 bp window).
Coverage and p-values were calculated with FIMO (Find Individual Motif Occurrences) results.
Smoothened histogram indicates distribution of motif-peak distance. Predicted TF= predicted
transcription factor binding the discovered motif. (E) Functional annotation of Six2-BF,
Hoxd11-BF, and Osr1-BF peaks performed using GREAT (Genomic Regions Enrichment of
Annotations Tool). The top over-represented gene ontology terms belonging to the two
categories are shown. ‘Obs.’, number of peaks associated with genes annotated with
corresponding term; ‘Exp.’, number of peaks expected to be associated with genes annotated
with corresponding term by chance. The bar plot indicates the binomial p-values measuring
over-representation of the corresponding terms.
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50
Figure 4. Additional statistics of Six2, Hoxd11, and Osr1 ChIP-seq data. (A) (Top left) Boxplot
shows distribution of QuEST scores of Six2-BF peaks overlapping with Six2-ab peaks and the
peaks unique to Six2-BF. (Top right) Scatter plot shows correlation of ChIP-seq reads within
150 bp window of Six2-ab and Six2-BF ChIP-seq peaks. (Middle) Enrichment of the Six2 motif in
shared versus Six2-ab unique peaks. (Bottom) GREAT gene ontology analysis of shared peaks
and peaks unique to the Six2-ab ChIP-seq. (B) The most enriched motif identified from top Six2-
FACS or Six2-ab peaks, its enrichment, and distribution. (C) Immunostaining of E15.5 kidneys
for Streptavadin 568 on Hoxd11-BF kidneys to highlight the biotinylation of the BioTag
component of the BioTagFLAG (BF). (D) Histograms shows distribution of Six2-BF (top),
Hoxd11-BF (middle) and Osr1-BF (bottom) peaks’ distance to the nearest TSS. (E) Pie charts
show distribution of Six2-BF (top), Hoxd11-BF (middle) and Osr1-BF (bottom) peaks in the
genome. (F) Wt1 and bHLH motifs identified from Six2-BF, Hoxd11-BF or Osr1-BF peaks with
MEME. Coverage and p-values were calculated with FIMO results. Smoothened histogram
indicates distribution of motif-peak distance. (G) Venn diagrams show overlap of peaks
identified from Six2-ab and Osr1-BF replicates ChIP-seq data sets.
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52
Figure 5. Validation of ChIP-seq identified binding motifs by EMSA. (A) (1) Weblogo of Six2
motif and probe sequences, with red bases indicating mutation made in the corresponding
probes. WT=Wildtype, M=mutant (2) EMSA result shows binding of recombinant GST-tagged
Six2 protein (Six2) or GST control (G) to the indicated probes. (3) EMSA result shows effect of
the GST or Six2 antibodies on Six2 protein binding to probes. (4) EMSA result shows binding of
Six2 to the WT probe in the presence of the indicated competitor probe. (B) (1) Weblogo of
Hoxd11 motif and probe sequences, with red bases indicating mutation made in the
corresponding probes. (2) EMSA result shows binding of recombinant GST-tagged Hoxd11
protein (Hoxd11) or GST (G) to the indicated probes and effect of antibody on the binding. (3)
EMSA result shows effect of competitors on Hoxd11 protein binding to the probe. (4) Weblogo of
published PBM Hoxd11 motif. (C) (1) Weblogo of Osr1 motif and probe sequences, with red
bases indicating mutation made in the corresponding probes. UP=UniProbe (PBM) motif,
O2=Osr2 motif [S1]. (2) EMSA result shows binding of recombinant GST-tagged Osr1 protein
(O) or GST control (G) to the indicated probes. W=water control. (3) EMSA result shows effect
of antibody on protein binding to the indicated probe. (4) EMSA result shows effect of
competitors on Osr1 binding to the indicated probe. (5) The published Osr1 motif.
2.3.3 Six2, Hoxd11, Osr1 and Wt1 co-bound sites predict key enhancers and
targets of the nephron progenitors
A Wt1-like binding motif was predicted within all three datasets suggesting Wt1 co-
regulation within Six2, Hoxd11 and Osr1 transcriptional networks. Other groups have published
Wt1 ChIP from the whole embryonic kidney or glomerulus (Hartwig et al., 2010; Kann et al.,
2015b; Motamedi et al., 2014) but no nephron progenitor-specific Wt1 data has been generated.
We attempted to generate a viable Wt1-BF transgenic line but failed, so we adopted a recently
developed protocol for enriching nephron progenitors by magnetic-activated cell sorting
(MACS) (Brown et al., 2015), and performed ChIP-seq with a Wt1-specific antibody on E16.5
nephron progenitors (Wt1-NP, Fig 7A).
Compared to a Wt1 ChIP from the whole kidney (Wt1-kidney) which we generated from
the same stage (Fig 7A), the recovered motif from the Wt1-NP dataset, ‘CCTCCCCCNC’,
closely matches the motif identified in our own whole kidney dataset, and published non-
nephron progenitor-restricted Wt1 kidney ChIP data (Fig 7B, (Hartwig et al., 2010; Kann et al.,
2015b; Motamedi et al., 2014)). The motif also matched the predicted Wt1 motif that was highly
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enriched in the earlier Six2, Osr1, and Hoxd11 datasets (Fig 4F). The motif was centered in the
ChIP peak dataset supporting direct DNA binding (Fig 7B). The nephron progenitor-specific
Wt1 ChIP shared >50% of peaks with our whole kidney dataset. Shared target genes with roles
in kidney development were focused on genes involved nephron progenitor maintenance and
differentiation, while those unique to the whole kidney also targeted genes associated with
podocytes (Fig 7F). This suggests that our Wt1-NP ChIP is representative of regulatory functions
for Wt1 within nephron progenitors. The majority of peaks showed an intergenic (35%) and
intronic (33%) distribution (Fig 7D). However, Wt1 showed significant enrichment near
promoters within 5kb of the TSS (25%, Fig 7C, D Figs), significantly more promoter enrichment
than observed with the other factors (between 3.1 and 5.3%, Fig 4), potentially reflecting Wt1’s
binding preference to a cytosine-rich motif and GC enrichment at promoters. This result is in
contrast to the Wt1 ChIP-seq performed by Motamedi et al., who found peaks to be enriched
more distally (Motamedi et al., 2014). However, if we performed GREAT analysis with the
‘basal plus extension’ parameter which includes larger regulatory domains compared to the more
restricted ‘single nearest gene’ parameter which was utilized in all of our analyses, we observe a
greater enrichment for Wt1-NP peaks 50-500kb from the TSS (Fig 7C). Importantly, the GREAT
parameters recovered ‘ureteric bud development’ and ‘metanephric nephron morphogenesis’
terms which are consistent with Wt1 kidney functions (Fig 7E).
To investigate potential co-operative actions of Six2, Hoxd11, Osr1, and Wt1 in nephron
progenitors, we analyzed all pairwise overlaps of transcription factor binding sites, and evaluated
the statistical significance of such two-factor overlap. Not all genome fractions are accessible to
transcription factor binding, and binding of many transcription factors correlates with open
chromatin (Thurman et al., 2012). For simplicity, our statistical analysis is built on the
assumption that only open chromatin, identified by utilizing the Assay for Transposase
Accessible Chromatin with high-throughput sequencing (ATAC-seq) within nephron progenitors
(see Methods for details of the approach and access to data), is accessible to any of the DNA
binding factors analyzed in the current study. We found that the greatest significance of co-
binding is observed between Six2 and Hoxd11 (-log10p=320 at all Six2 sites where Hoxd11 is
bound and -log10p=361 at all Hoxd11 sites where Six2 is bound), and Six2 and Wt1 (-log10p=106
at all Six2 sites where Wt1 is bound and -log10p=123 at all Wt1 sites where Six2 is bound)
interacting regions. The weakest co-association is between Wt1 and Hoxd11 (-log10p=6 for each
pairwise association), although still significant (Fig 6A). Potential target genes for each factor
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(based on GREAT analysis, (McLean et al., 2010)) were also subjected to pairwise comparisons.
Hoxd11, Osr1, and Wt1 share the majority of their target genes with Six2 (ratio greater than 0.60
or 60%, Fig 6A and B). Hoxd11 shows the greatest overlap with Six2 (0.80 or 80%), although all
pairwise overlaps showed that nearly half of the comparators target genes are shared with any
one factor. These results suggest that these factors likely cooperate in regulatory actions within
nephron progenitors.
Next, we overlapped all four datasets to identify sites where all factors converge in the
potential regulation of target genes. We recovered 373 putative cis-regulatory modules where
Six2, Hoxd11, Osr1, and Wt1 associated within 1kb of each other (Fig 6B, S1 Table). Regions
co-bound by all four factors displayed the strongest Six2 binding. In addition, Six2 peaks bound
by any three-factor combination were on average stronger than two-factor combinations, while
Six2 peaks bound by any factor in combination with Six2 were stronger than Six2-only peaks
(Fig 4H).
We refer to regions co-bound by all four factors as ‘regulatory hotspots’ hypothesizing
that these may play a key role in nephron progenitor programming. Consistent with this view,
regulatory hotspots were enriched around genes annotated to developmental processes such as
‘ureteric bud development’ (Fig 6C). Further, two regulatory hotspots are known from published
studies to drive transgenic reporters with expression profiles reflecting the putative target genes:
a region ~60kb upstream of Six2 which corresponds to the Six2-DE used in our transgenic
strategy and the Wnt4-DE 50kb upstream of Wnt4 (Fig 6D, (Park et al., 2012)). Sall1 is also
bound at the Six2 distal enhancer but not at the Wnt4 enhancer site(Kanda et al., 2014). Six2 is
largely restricted to the nephron progenitors while Wnt4 expression is absent from nephron
progenitors but activated on progenitor induction in the formation of differentiating renal
vesicles (Self et al., 2006a; Stark et al., 1994). Thus, engagement of the four factors can occur on
target genes for nephron progenitors or genes activated shortly after the onset of nephrogenesis.
Other putative targets of regulatory hotspots include Fgf9 which is expressed by nephron
progenitors and is involved in regulating their maintenance (Barak et al., 2012), and Pax8 which
regulates nephron progenitor differentiation (Narlis et al., 2007) (S1 Table). Tsc22d1 and Mgat5
also represent putative targets and knockouts of these genes are reported to generate kidney
phenotypes (Granovsky et al., 2000; Yu et al., 2009) (S1 Table).
Regulatory information may also converge on a common target through alternative
enhancer usage. To examine this possibility, we intersected the predicted target gene sets for
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each factor and identified 1744 genes sharing Six2, Hoxd11, Osr1, and Wt1 associated peaks
(Fig 6B, S2 Table). The set of genes identified as having all four factors co-associated at one
putative cis-regulatory module or dispersed through multiple interactions sites are predicted to
define a set of genes with a significant role in nephron progenitors or their derivatives; we
termed this group ‘core targets’ (Fig 6C, S2 Table). This set includes genes expressed in nephron
progenitors and implicated in progenitor maintenance and self-renewal including Six2, Pax2,
Sall1, Sox4, and Gas1 (Bouchard et al., 2000; Huang et al., 2013; Kann et al., 2015a;
Nishinakamura et al., 2001; Self et al., 2006a). However, the ‘core targets’ also included genes
normally activated downstream in the induced/developing nephron such as Wnt4, Lhx1, Pax8,
Hes1, and Irx1/2 (Heliot et al., 2013; Kobayashi et al., 2005; Narlis et al., 2007; Piscione et al.,
2004; Stark et al., 1994).
To determine whether interactions amongst these transcription factors exist in vivo, we
performed immunoprecipitations with Six2 antibodies from E16.5 kidney nuclear lysates. Six2
was able to co-immunoprecipitate Hoxd11 and Wt1 (Fig 6E); however, the absence of a working
Osr1 antibody precluded analysis of this factor although recent studies show Six2 and Osr1
complex in vitro (Xu et al., 2014a). Six2 is also purported to complex with Sall1 (Kanda et al.,
2014) though we could not replicate this interaction with available antibodies in our assay. Taken
together, these data provide evidence for endogenous, multi-protein complexes among three of
the four factors.
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Figure 6. Regulatory hotspots in nephron progenitors defined by co-binding of Six2, Hoxd11,
Osr1 and Wt1. (A) Heatmap shows significance of pairwise overlap between transcription factor
binding sites (left, represented by binomial -log10 p-value) or between assigned target genes
(right, represented by ratio). TFBS=transcription factor binding site. (B) Venn diagram shows
the overlap of Six2, Hoxd11, Osr1, and Wt1 binding sites (left) and target genes (right). The 4-
57
way overlapping sites were defined as the ‘regulatory hotspots’. The 4-way overlapping target
genes were defined as ‘core targets’. (C) Barplots show result of gene ontology (GO) analysis on
the ‘regulatory hotspots’ (left). Examples of ‘core targets’ known to have roles in the nephron
progenitors and their differentiation are listed (right). (D) Genome browser view of Six2,
Hoxd11, Osr1, and Wt1 ChIP-seq signals at the ‘regulatory hotspots’ (shadow area) near Six2
and Wnt4. (E) Six2 immunoprecipitation from E16.5 kidney nuclear extracts. Western blot was
probed with antibodies to Six2, Hoxd11, and Wt1 to identify protein complexes.
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59
Figure 7. ChIP-seq reveals Wt1-mediated regulatory programs in the developing kidney. (A)
Venn diagrams show overlap of (left) Wt1-kidney (whole kidney) replicate ChIP-seq peaks,
(right) Wt1-NP (nephron progenitor) replicate peaks. (B) From left to right: the number of peaks
from Wt1-kidney (top) or Wt1-NP (bottom) ChIP-seq, the most enriched motif identified from the
top 1,000 peaks with MEME (using +/- 50 bp window), coverage, p-value, predicted
transcription factor (TF) bound, and histogram showing distribution of motif relative to the peak
center (Gaussian kernel smoothening was applied to reveal the trend, green curve). (C)
Histograms shows distribution of Wt1-NP peaks’ distance to the nearest TSS using both the
‘single nearest gene’ and ‘basal plus extension’ parameters in GREAT. (D) Pie chart shows
distribution of Wt1-NP peaks in the genome. (E) Functional annotation of Wt1-NP peaks using
GREAT. (F) From left to right: Venn diagram shows overlap of Wt1-kidney and Wt1-NP peaks,
Venn diagram shows overlap target genes of Wt1-kidney-unique or shared peaks with Wt1-NP
that are associated with the Gene Ontology term ‘nephron development’, selected genes from the
indicated part of the diagram. (G) Venn diagram show overlap of the CTCF-NP replicate peaks.
(H) Similar as (B), the motif information of the CTCF-NP ChIP-seq dataset.
2.3.4 Transcription factor co-binding is preferentially associated with genes active
in differentiating structures and reveals novel targets
We sought to identify whether Six2, Hoxd11, Osr1, and Wt1 are each involved in
activating or repressing gene expression in nephron progenitors. First, we generated RNA-seq
expression profiles of E16.5 Six2TGC
tg/+
kidney cortex preparations FAC-sorted for
GFP+(Six2+) or GFP-(Six2-) cells. Six2+ cells would represent the nephron progenitor
population (both self-renewing and recently induced) and Six2- cells would largely represent
stromal cells as well as ureteric bud tip cells and endothelial cells. Genes with a TPM
(Transcripts Per Kilobase Million) value >5 and a fold difference >3 between the two cell types
were identified: 246 genes were enriched in the Six2+ fraction and 545 genes were enriched in
the Six2- cortex fraction (Fig 8A, S3 Table). We asked whether ChIP-seq peaks of any of the
transcription factors or the regulatory hotspots are preferentially located adjacent to differentially
expressed genes. The results show that peaks from all ChIP-seq datasets occur significantly more
often around genes enriched in the Six2+ cells (Fig 8C) consistent with a specific role in
regulating the nephron progenitor cell versus other cell types of the kidney cortex. However,
60
regulatory hotspots near Foxd1, a marker of self-renewing stromal progenitors (Kobayashi et al.,
2014), and Wnt11, a ureteric tip marker required for normal kidney development (Majumdar et
al., 2003) (S1 Table), raises the possibility that the four factors may also work together to repress
these genes within nephron progenitors.
Nephron progenitor cells can be divided into Cited1+/Six2+ self-renewing progenitors
and Cited1-/Six2+ differentiating progenitor cells (Brown et al., 2013). To address the
relationship between regulatory hotspots and programs of progenitor maintenance or
commitment, we performed RNA-seq analysis to identify progenitor-specific and early induction
gene sets. For the former, a transcriptional profile was generated for E16.5 Cited1+; RFP+ cells
from Cited1-nuc-TagRFP-T
tg/+
kidneys while Six2+; GFP+ cells from Six2TGC
tg/+
P2 kidneys
were used to generate the latter dataset (S4 Table, (Rumballe et al., 2011)). As expected, Cited1
levels were appreciably lower in the P2 Six2+ cells (200.9 TPM in E16.5 Cited1+ cells vs. 3.8
TPM in P2 Six2+ cells) while Wnt4 transcripts were markedly increased (9.0 TPM in E16.5
Cited1+ cells vs. 219.1 TPM in P2 Six2+ cells) supporting our classification of these datasets (S4
Table).
As expected, a comparison of the genes with a TPM >5 and a fold difference >3 between
the two cell types showed self-renewing nephron progenitor-specific genes such as Cited1 and
Osr1 enriched in the E16.5 Cited1+ cell dataset whereas genes involved in progenitor
differentiation such as Pax8 and Wnt4 were enriched in the P2 Six2+ cell dataset (Fig 8B, S4
Table). Six2 and Hoxd11 displayed similar enrichment near genes up-regulated in either self-
renewing nephron progenitors or in differentiating progenitors (1.4-1.5 fold; Fig 8C) consistent
with roles in promoting the progenitor state, and either preventing or priming nephron forming
programs. Osr1 and Wt1 interactions were slightly enriched near genes associated with self-
renewing nephron progenitors (1.4-fold vs 1.0-fold for Osr1, 1.2-fold vs 1.0-fold for Wt1; Fig
8C). Interestingly, the regulatory hotspot associated gene lists showed a higher enrichment
around genes upregulated in differentiating cells versus self-renewing progenitors (1.6-fold vs.
1.3-fold; Fig 8C).
Next, for each single factor or combination of factors we compared the percent of target
genes in distinct transcriptional categories: nephron progenitor enriched (E16.5 Six2+ cells),
self-renewing nephron progenitor enriched (E16.5 Cited1+ cells), or differentiating nephron
progenitor enriched (P2 Six2+ cells) relative to the whole transcriptome. Target genes unique to
any single factor were not enriched in any of these categories (<1.6% for each) compared to the
61
whole transcriptome (<1.6% for each) suggesting that single factor input has no particular
relevance to nephron progenitor function. Similar observations hold when Hoxd11 co-targeting
is examined with Osr1 and Wt1, (<1.8%). but not with a Six2 binary combination (>2.5%)
suggesting that Hoxd11 has a strong preference for co-regulation of target genes with Six2 (Fig
8D). Generally, the greatest enrichments are observed when all four factors are bound near the
target gene in any category (2.4-7.2%) consistent with co-regulatory input by multiple factors
impacting target gene regulation to the greatest extent. In agreement with our earlier analyses,
the four-factor overlap has a preference for genes expressed upon differentiation rather than in
self-renewing progenitors (5.9% compared to 2.4%; Fig 8D).
While we have described target genes with known functions in kidney development, we
wanted to identify potentially novel candidate genes which are targets of co-regulation either by
one cis-regulatory module or dispersed through multiple interactions sites. Target genes of
interest include Shisa2 and Shisa3 which are enriched in self-renewing nephron progenitors (S2
Table). Shisa2 is a modulator of Wnt and Fgf signaling, specifically attenuating such signals.
The majority of mutant mice exhibit dwarfism and half die postnatally. Shisa3 is a related family
member although no overt phenotype was observed for the null allele (Furushima et al., 2007).
Pdgfc and Pdgfa are enriched in the self-renewing and differentiating progenitors, respectively
(S2 Table). Their conserved expression in these cell populations of developing mouse and human
kidneys have been reported (Alpers et al., 1995; Eitner et al., 2003; Li et al., 2000). Pdgfa and
Pdgfc double mutants have a reported deficiency in cortical renal mesenchyme, however, the
mutant kidney phenotype was not analyzed in detail (Ding et al., 2004). Ccnd1 (cyclin D1) is a
putative target that shows a nearly 7-fold increase in expression in P2 Six2+ cells versus E16.5
Cited1+ cells (S2 Table). In situ hybridization confirms strong Ccnd1 in E15.5 pretubular
aggregates and early differentiating nephrons (www.gudmap.org, (Harding et al., 2011;
McMahon et al., 2008)). This suggests that the regulatory networks may directly modulate cell
cycle dynamics and balance progenitor proliferation or alternatively may prime putative
enhancers of Ccnd1 for rapid activation upon nephron progenitor induction. Sema5a and Epha4
are predicted targets with a similar ~6-7-fold increase in expression in differentiating progenitors
confirmed by in situ studies (S2 Table; www.gudmap.org, (Harding et al., 2011; McMahon et al.,
2008)) suggesting factor regulation of targets genes controlling local cell interactions.
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63
Figure 8. Six2, Hoxd11, Osr1, and Wt1 binding sites are enriched near nephron progenitor
specific genes and those associated with differentiation programs. (A) Scatter plots show gene
expression profiles and correlation of the Six2GFP+ versus the Six2GFP- RNA-seq from E16.5
mouse kidney cortex. Specific genes for each category are highlighted in orange (Six2+) or blue
(Six2-). TPM= Transcripts Per Kilobase Million. (B) Scatter plots show gene expression profiles
and correlation of RNA-seq from E16.5 Cited1RFP+ cells versus P2 Six2GFP+ cells. Genes
specific to each population are highlighted in red (Cited1RFP+) or green (Six2GFP+).
Examples of specific genes are listed and highlighted on the plot. (C) Barplots show p-values
indicating enrichment of Six2, Hoxd11, Osr1, and Wt1 binding sites, as well as regulatory
hotspots (Six2/Hoxd11/Osr1/Wt1 overlapping sites) in genes that are specific to the Six2+ cortex
fraction, specific to the Six2- cortex, enriched in self-renewing nephron progenitors, or enriched
in differentiating nephron progenitors, respectively. The regulatory domain was defined as +/-
500 kb from transcription start site. TFBS=transcription factor binding site. ‘Obs.’, number of
peaks associated with genes annotated with corresponding term; ‘Exp.’, number of peaks
expected to be associated with genes annotated with corresponding term by chance. Fold
represents the fold enrichment or expected values. (D) Bar plots showing the percentage of total
genes for each condition (x-axis) that falls into each category of 1) nephron progenitor (NP)
enriched, 2) enriched in self-renewing nephron progenitors, or 3) enriched in differentiating
progenitors.
2.3.5 Deletion of the Six2 and Wnt4 distal enhancers reveals their roles in
modulating target gene expression
To examine the functional significance of ‘regulatory hotspots”, we focused on Six2-DE
(chr17: 85747271-85749534; Fig 9A) and Wnt-4 DE (chr4:137216986-137217756; Fig 31A)
elements previously verified in transgenic reporter assays (Park et al., 2012). To examine the
requirement for each enhancer, we used gRNA/Cas9 gene editing technology to delete each
enhancer in B6SJLF1/J mice. The Six2-DE deletion and Wnt4-DE deletion were confirmed in
founder lines by PCR and Sanger sequencing of products (Six2
DE
: chr17:85747284-85749542;
Wnt4
DE
: chr4:137216991-137217771). For the Six2-DE knockout, we examined kidneys at
E16.5 and observed no obvious difference in the size of wildtype, Six2
∆DE/+
and Six2
∆DE/∆DE
kidneys (Fig 9B). Six2+ and Wt1+ nephron progenitors were present in Six2
∆DE/∆DE
kidneys
64
though Six2 levels appear reduced relative to wild-type embryos (Fig 9C, D). Nephron structures
were formed as reflected by the presence of podocytes and proximal tubules, labeled by Wt1 and
LTL (Lotus tetragonolobus lectin), respectively (Fig 9C). The Six2
∆DE/∆DE
mice were viable; no
phenotype was observed.
To more accurately assess the effect of the distal enhancer deletion on Six2 expression,
we used qPCR to measure relative Six2 levels in nephron progenitors of E16.5 kidneys. A 40%
reduction of Six2 mRNA was measured in Six2
∆DE/∆DE
nephron progenitors compared to wildtype
(Fig 9E, p-value = 0.006); higher levels than in mice heterozygous for a Six2 null allele
(Six2
CE/+
, (Kobayashi et al., 2008)) where Six2 transcripts were reduced approximately 50%
relative to wild-type as expected (Fig 9E). The levels of Pax2 mRNA, which is not dependent on
Six2 (Self et al., 2006a), were relatively similar across all genotypes showing a Six2-specificity
for the Six2-DE deletion. Strikingly, when Six2 levels were further reduced by combining a
Six2
∆DE
allele with a Six2 null allele (either Six2
CE/+
or Six2
GCE/+
(Kobayashi et al., 2008)), the
resultant Six2
DE/CE
embryos exhibited severely hypoplastic kidneys at E16.5, with a complete
absence of Six2+ nephron progenitors, mirroring the phenotype of complete removal of Six2
activity (Fig 9B,D, Self et al., 2006a) where only a few glomeruli (Wt1+) and tubules (LTL+)
have formed by E18.5 (Fig 10). As early as E11.5, at the outset of active kidney morphogenesis,
Six2
DE/GCE
kidneys were devoid of Six2+ nephron progenitor cells but filled with Pax8+
differentiating nephron progenitors as in Six2 protein null mutant kidneys (Fig 9G, (Self et al.,
2006a)). Taken together these results demonstrate that Six2-DE accounted for approximately
40% of Six2 expression and by combining one Six2-DE allele with a Six2 null allele, the
remaining Six2 mRNA levels (predicted to be 30% of wild-type levels) were insufficient for
Six2-mediated maintenance of the nephron progenitor state.
Next, we investigated a ‘regulatory hotspot’ predicted to function in progenitor
differentiation. Deletion of the Wnt4 distal enhancer resulted in mutant kidneys that are ~25%
smaller than those from wildtype animals (p = 0.2e-4; Fig 31B-C). Nephrons developed in
Wnt4
∆DE/∆DE
kidneys as reflected by presence of both LTL+ proximal tubules and Wt1+
podocytes (Fig 11D) and Wnt4
∆DE/∆DE
mice are viable. Interestingly, in situ hybridization
revealed that expression of Wnt4 is significantly reduced in renal vesicles but remained largely
unchanged in the renal medulla of Wnt4
∆DE/∆DE
kidneys consistent with an overall reduction of
Wnt4 mRNA levels in Wnt4
∆DE/∆DE
kidneys measured by qPCR (Fig 11F). Thus, the Wnt4-DE
plays a functional role in regulating Wnt4 mRNA levels in forming nephrons (Fig 11E). The
65
Wnt4
∆DE/∆DE
phenotype was less severe than Wnt4 protein null mutants where the severely
hypoplastic kidney lacks nephron tubules and glomeruli (Stark et al., 1994); indeed, low levels of
Wnt4 RNA were detected in Wnt4
∆DE/∆DE
kidneys (Fig 11D; arrows in Fig 11E). When the
Wnt4
DE
allele was combined with a Wnt4
GCE
protein null allele (Kobayashi et al., 2008),
Wnt4
DE/GCE
kidney size and nephron structures were further reduced, though kidneys were still
larger than Wnt4 null kidneys (Fig 11B-D) and Wnt4 mRNA levels were markedly reduced in
whole kidney PCR (Fig 11E, F). Taken together these results indicate a dose-dependent
reduction in kidney size through reduced nephrogenesis upon decreasing Wnt4 activity. Further,
residual levels of Wnt4 activity in Wnt4
DE/GCE
kidneys were sufficient to drive low levels of
nephrogenesis. Clearly, the Wnt4-DE plays a role in maintaining appropriate levels of Wnt4
transcripts in the nephrogenic program to ensure a normal program of kidney development.
66
67
Figure 9. Deletion of the Six2 distal enhancer leads to reduction in Six2 levels and
concomitant loss of a Six2 allele results in severe renal hypoplasia. (A) Schematic of the Six2
locus showing the location of the proximal (PE) and distal (DE) enhancer elements. The DE was
targeted for deletion using CRISPR/Cas9 and the resulting Cas9-mediated deletion of the Six2-
DE is shown. (B) Brightfield images of whole urogenital systems from E16.5 embryos resulting
from Six2
DE/+
matings or Six2
CE/+
x Six2
DE/+
crosses. (C) Immunostaining for Wt1 to identify
nephron progenitors and podocytes, LTL (Lotus tetragonolobus lectin) to mark proximal tubules,
and Cdh1 to show the collecting duct network of kidneys associated with (B). (D)
Immunostaining for Six2 to identify nephron progenitors in kidneys associated with (B). (E) Box
plots showing results of qPCR for Six2 and Pax2 (normalized to GAPDH) from nephron
progenitors (NP) and nephron progenitor-depleted cortex. Genotypes and number of samples
analyzed are shown. (F) Samples from Six2
GCE/GCE
were compared to Six2
DE/GCE
collected at
early stages of kidney development and immunostained with Six2 to mark the nephron
progenitors, Pax8 to identify differentiating structures (Pax8 antibody appears to cross react
with Pax2 as seen by expression in Ecad+ collecting duct), and Ecad to mark epithelial
structures.
Figure 10. E18.5 phenotypes of Six2
DE/GCE
compared to Six2
GCE/GCE
mutants. (A) Brightfield
images of E18.5 kidneys from Six2
GCE/GCE
mutants and compound heterozygous Six2
DE/GCE
68
compared to wildtype and single heterozygous littermates. (B) Samples from Six2
GCE/GCE
were
compared to Six2
DE/GCE
collected at E18.5 and stained for Wt1, LTL, and cytokeratin (CK).
Figure 11. Deletion of the distal enhancer for Wnt4 results in reduced expression of Wnt4
specifically in renal vesicles and smaller kidneys. (A) Schematic of the Wnt4 locus showing the
location of the distal enhancer (DE) element. The DE was targeted for deletion using
69
CRISPR/Cas9 and the resulting Cas9-mediated deletion of the Wnt4-DE is shown. (B)
Brightfield images of whole urogenital systems from E15.5 embryos. (C) Measurements of the
anterior to posterior axis and medial to lateral axis of kidneys representing the genotypes shown
in (B). (D) Section in situ hybridization for Wnt4 on kidneys from Wnt4
DE/+
, Wnt4
DE/DE
and
Wnt4
GCE/+
embryos. (E) Box plots showing results of qPCR for Wnt4 (normalized to GAPDH)
from whole kidneys at E15.5. Genotypes and number of samples analyzed are shown. (F)
Immunostaining for Wt1 to identify nephron progenitors and podocytes, LTL to mark proximal
tubules, and Cdh1 to show the collecting duct network of kidneys associated with (B).
2.3.6 The Br mouse is the result of an inversion altering the Six2 regulatory
landscape
Next generation sequencing and sequence alignment identified the underlying sequence
change in the Br mutant (S1 Supplemental Material and Methods). The main feature was a large
inversion of 324,596bp including both the Six2 and Six3 loci. The inversion moves Six3 ~206kb
from the Six2-DE, actually further than in the wild-type organization, but importantly the
inversion removes the intervening TAD boundary (Fig 12B). In contrast, Six2 is repositioned on
the other side of this boundary element within Six3’s unchartered regulatory territory (Fig 12B).
In addition to the inversion, two small deletions were detected: a 4,630bp deletion
(chr17:85414584-85419213) 5’ to the Six3 TSS in the intron of Camkmt, and a 5bp deletion
between the Six2-PE and Six2-DE (chr17:85743809-85743813, Fig 12A,B). The results from the
sequencing and computational analysis were confirmed by allele-specific diagnostic PCR assays
(Fig 13C,D). The inversion also separates the last 4 exons of Camkmt from the rest of the
transcription unit. However, Camkmt has a TPM of only 2.26 in the E16.5 Cited1+ nephron
progenitor cells and homozygous mutant mice are viable (International Mouse Phenotyping
Consortium, http://www.mousephenotype.org/, Release 5.0 (Skarnes et al., 2011)), so Camkmt is
unlikely to contribute to the kidney phenotype. The rearrangement predicts: i) ectopic Six2
expression in Six3’s normal expression domain, the lens, as Six3 enhancers can now target Six2,
and ii) an abnormal interaction between the Six2-DE and Six3 promoter resulting in ectopic Six3
expression in nephron progenitors.
To directly examine interaction of Six2-DE with Six2 and Six3 promoters, we performed
4C-seq (van de Werken et al., 2012) using Six2-DE as the view point. As expected, in wildtype
70
kidney Six2-DE interacts with a broad region that includes Six2 transcription start site (TSS) (Fig
12B), with the local maxima 7.2 kb upstream of Six2 TSS. Noticeably, Six2-DE interaction was
restricted by the TAD boundary between Six2 and Six3 (Fig 12B and Fig 13B) and no interaction
was observed around the Six3 TSS. In kidneys from Br/+ embryos, strong Six2-DE contacts were
now observed in the segment of the inverted region that was repositioned between the Six2-DE
and CTCF-bound TAD boundary element (Fig 12B and Fig 13B). As expected, with the loss of
one wildtype allele in Br/+ embryos, Six2-DE interaction with Six2 TSS, and in general with the
region on Six2 side of the TAD boundary, were significantly reduced. A relatively strong, de
novo interaction of the Six2-DE was observed ~15 kb upstream of Six3 TSS (Fig 12B and Fig
13B) consistent with the model of Six3 expression driven, at least in part, by the Six2-DE in the
Br allele. Importantly, the predicted Six2-DE/Six3 upstream contact in the Br allele occurs over a
distance of 200 kb from the Six2-DE to the Six3 TSS, a longer interval than the ~130 kb that
separates these non-interacting elements in the wild-type allele (Fig 13B). Therefore, the
differential interaction of Six2-DE with Six2 TSS and Six3 TSS between wildtype and Br/+
cannot be attributed to shortened distance from Six2-DE to Six3 TSS. Rather, this data supports
specific regulation by Six2-DE to Six2 and Six3 that is defined by the TAD boundary.
As a result of the altered chromatin architecture introduce by the genomic inversion,
ectopic Six2 expression has been reported in the lens of Br/Br mutants (Fogelgren et al., 2009),
and Six3 expression was reduced in this structure (Fig 13E). Quantitative PCR detected Six3
expression in the kidneys of Br/+ mice at E13.5 (Fig 12C) and Six3 protein was detected in
Six2+ nephron progenitors (Fig 12D). Br/Br mutants resemble Six2 null mutants and have no
nephron progenitors at this stage (Self et al., 2006a) (Figs 12C and D). When Br/Br mutants were
examined at E11.5, they showed a similar loss and premature differentiation of nephron
progenitors as in Six2 null mutants but interestingly low-levels of Six3 were detected in
differentiating progenitors (Fig 12E). Ectopic Six3 was also observed in the cranial base of Br
mutants at E14.5 concomitant with decreased Six2 levels (Fig 13F). Taken together these data
lend additional weight to the importance of the Six2-DE in directing Six2 expression and reveal
higher order principles of topological organization acting in conjunction with this enhancer to
provide target gene specificity to the regulatory landscape.
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72
Figure 12. The Six2 regulatory landscape is altered in the Br mouse leading to reduced Six2
expression and ectopic Six3 expression in the kidney. (A) Schematic showing the X-irradiation
induced breakpoints and subsequent deletion with inversion that resulted in the Br allele.
LE=Lens enhancer (putative), PE=proximal enhancer, DE=distal enhancer. Black box between
the Six2 and Six3 loci represents the predicted boundary. (B) Interaction matrix (top) generated
by Hi-C data (Hardison lab hESC Hi-C data, http://promoter.bx.psu.edu/hi-c/view.php).
Genomic view showing Six2 ChIP-seq, CTCF-NP ChIP-seq, and 4C-seq data (bottom). The
region inverted in the Br allele is highlighted. Dashed square indicates a predicted TAD
boundary element that lies between Six3 and Six2 loci (Dixon et al., 2012). (C) qPCR showing
the relative expression levels of Six2 and Six3 in E13.5 kidneys of the indicated genotype. (D)
E13.5 kidneys of the indicated genotype were sectioned and immunostained for Six2 and Six3.
Arrow points to the low level Six3 expression in nephron progenitors. (E) Immunostaining for
Six2, Six3, cytokeratin (CK), and DAPI in E11.5 kidneys of the indicated genotype.
73
74
Figure 13. Localization of the predicted topologically associating domains around Six2 and
Six3 and further characterization of the Br allele. (A) Hi-C heatmap from Dixon et al. showing
the chromatin interactions and predicted topologically associating domains (TADs) surrounding
the Six2 and Six3 loci, which are boxed in (Dixon et al., 2012). (B) Genomic view of the inverted
region in Br allele, with Six2 ChIP-seq, CTCF-NP ChIP-seq, and 4C-seq data tracks. Pink
highlighted region is the inverted region. The dashed square indicates predicted TAD boundary
from Dixon et al. (Dixon et al., 2012). Green arrows point to the 4C-seq peak near the Six2 TSS
which is reduced in the Br/+ kidneys and the 4C-seq peak near the Six3 TSS which is gained in
Br/+ kidneys. Gray boxes represent the zoomed views of these regions to the right. (C)
Schematic of the wildtype (WT) and Br alleles with red boxes to indicate primer loci used for
PCR. (D) PCR results confirming the appropriate wildtype and mutant Br products for each of 3
wildtype and 2 Br/Br samples. (-) = No DNA control. (E) Wholemount in situ hybridization for
Six3 on E11.5 embryos of the indicated Br genotypes. Arrows highlight the lens expression of
Six3 and its loss in the Br mutant. (F) Immunostaining for Six2 and Six3 in the E14.5 cranial
base of wild type and Br mutants. DAPI (nuclei) is shown in red.
2.4 Discussion
In this study, we utilized novel transgenic mouse strains to map the nephron progenitor-
specific interactions of Six2, Hoxd11, and Osr1, and incorporated nephron progenitor-specific
ChIP-seq profiling of Wt1, to identify the regulatory genome controlled by these four factors in
the developing mouse kidney. Our data identifies a subset of binding sites, or ‘regulatory
hotspots’ where the engagement of all four factors occurs in close proximity. The putative target
genes of their combinatorial action are largely associated with kidney function. Deletion of two
of these hotspots for Six2 and Wnt4 highlight their roles in target gene regulation and their
significance to kidney development. These data suggest that ‘hotspots’ with multi-factor input
play significant roles in target gene regulation. Our analysis on the Br mutant demonstrated that
re-arrangement of the regulatory scenario of Six2 and Six3 genes can causes dramatic,
predictable effects on their expression and the resulting developmental phenotypes highlighting
the importance of appropriate regulatory context to proper gene regulation and biological
function.
75
2.4.1 Transcriptional hierarchy of nephron progenitors
Our ChIP studies reveal a complex regulatory architecture of the nephron progenitors.
Examining co-binding of the four factors suggests each of these genes is itself a target of their
combined actions through auto and cross-regulatory inputs, as are a number of other
transcriptional regulatory components important for kidney development and nephron progenitor
maintenance such as Sall1 and Pax2 (S2 Table). By combining our data with insight from
previous studies, a hierarchical network starts to emerge. For example, mutational analyses have
demonstrated a requirement for the Hox11 paralogues to activate Six2 expression in metanephric
mesenchyme (Wellik et al., 2002). Hox11 members complex with Pax2 and Eya1 binding to an
enhancer that lies within ~1kb of the Six2 TSS, in the Six2-PE (Gong et al., 2007). Hox11 acts as
an activator of Six2 activity and mutations in Hox motifs results in loss of reporter activity in
transgenic assays (Yallowitz et al., 2009). We have also shown that the Hox motif within the
Six2-DE is required for reporter activity (Park et al., 2012). Consistent with this data, Hoxd11 is
bound at the Six2-DE (Fig 6D). However, we did not observe a significant Hoxd11 association to
the Six2-PE as reported (Yallowitz et al., 2009). This discrepancy may result from preferential
enhancer usage at different developmental stages. Two previous studies assayed reporter activity
of the ~1kb Six2-PE at E11.5 (Gong et al., 2007; Yallowitz et al., 2009) while our studies
assayed Six2-DE activity at E15.5 (Park et al., 2012). Hox11 may be required at the Six2-PE to
help initiate Six2 expression, but maintenance of expression may then rely, at least partially, with
the Six2-DE where Hoxd11 is engaged at E15.5. Additionally, Osr1 and Wt1 are enriched at the
Six2-DE compared to the PE (Fig 6D), as is Sall1 (Kanda et al., 2014), supporting multifactor
input at the DE as an important mechanism of Six2 regulation. However, Six2 is bound at both
the PE and DE, though PE association is weaker (Fig 6D), suggesting both may contribute at
some level to the maintenance and autoregulation by Six2 itself. Unfortunately, technical
difficulties preclude detailed temporal analysis of engagement in the small numbers of cells that
are the foundation of the nephron progenitor pool.
When assessing the targets unique to any transcription factor combination, the greatest
enrichment for genes with expression within the nephron progenitors, either in self-renewing or
differentiating cells, generally occurred when they were complexed with Six2 (Fig 8D). Hoxd11
showed the lowest levels of enrichment for these targets when engaged with Osr1 or Wt1 in the
absence of Six2, suggesting that its primary regulatory functions rely on engaging with Six2.
76
Taken together, these data suggest Six2 acts as a master regulator: co-engagement with Six2
predicts a higher probability of regulatory functions within nephron progenitors.
2.4.2 Transcriptional factors: activator, repressor and interactions
Osr1 has been described as a transcriptional repressor in vertebrate kidney development
(Tena et al., 2007). Xu et al. showed that Osr1 works with the Groucho family members and
represses activation of a Wnt4 enhancer specifically in Six2+ nephron progenitors (Xu et al.,
2014a). Consistent with this result, Osr1 associates with the Wnt4 enhancer in our ChIP assay
(Fig 6D). Additionally, other genes that are not present in the nephron progenitors but rather in
differentiating structures such as Pax8 and Lhx1 are also bound by Osr1 suggestive of a
repressive role (Fig 8C, S2 Table, (Kobayashi et al., 2005; Narlis et al., 2007)). However, Osr1 is
also bound near genes actively expressed in nephron progenitors such as Six2 and Osr1 itself
(Fig 8C, S2 Table, (Kobayashi et al., 2008; Mugford et al., 2008b; Self et al., 2006a)). Therefore,
our data suggest a more complex relationship than Osr1 simply repressing transcription at all
engaged targets. Further, our previous ChIP studies supported dual roles for Six2 in activating
transcription within nephron progenitors but also engaging at targets silent in progenitors but
activated as progenitors differentiate towards nephrons (O'Brien et al., 2016; Park et al., 2012).
Similarly, Hox11 has been characterized as an activator, specifically of Six2 expression
(Yallowitz et al., 2009). Consistent with this view, Hoxd11, is bound near nephron progenitor-
specific genes but like Six2 binding is also prominent around differentiation targets (Fig 8C, S3
Table). Similarly, these observations extend to Wt1 nephron progenitor targets. Engagement
most likely reflects dual activator and repressor actions of these complexes and which activity
could be dependent on currently unidentified co-bound factors. Conversely, factor engagement at
differentiation-specific gene targets may facilitate or enable subsequent activation of enhancers
for differentiation-associated genes following the induction of nephron progenitors. In this
scenario, multi-factor engagement may be necessary but not sufficient for target activation for
differentiation associated genes. Additional factors or modification of existing transcriptional
components following progenitor commitment may modify the action of these regulatory
complexes.
77
2.4.3 Genomic co-localization of transcription factors in nephron progenitor cells
We observed a highly significant overlap of transcription factor binding in nephron
progenitors. Motif analysis of each ChIP dataset showed de novo, factor-specific motifs were the
most enriched (Fig 3D), supporting direct protein-DNA binding. Additionally, our EMSA assays
confirmed factor binding to each motif (S2 Fig). On the other hand, previous studies have shown
that Six2 can complex with a number of transcription factors, including Hoxa11 (Park et al.,
2012) and Osr1 (Xu et al., 2014a) in vitro, and Eya1 and Sall1 both in vitro and in vivo (Buller et
al., 2001; Kanda et al., 2014; Xu et al., 2014b). Osr1 has also been shown to interact with Wt1 in
vitro (Xu et al., 2016). These studies support protein-protein interactions amongst these factors
and may account, in part, for the multi-factor co-localization on specific genomic targets.
Additionally, our kidney immunoprecipitation data suggests that Six2 can interact with Wt1 and
Hoxd11 (Fig 6E), confirming such complexes exist in vivo. However, without confirming the co-
association of these factors on any genomic loci at the same time and in the same cell, we can
only suggest their combined function. The association of each factor with its own DNA target
and co-association with each other adds to the difficulty of predicting the actions of the
regulatory circuit. Further, it is likely that there are significant components yet to be discovered.
For example, all of the ChIP datasets recovered a bHLH motif amongst the most-significantly
enriched motifs (Fig 4F). Whereas Myc is a bHLH transcription factor that has been shown to
complex with Eya1 and Six2 in the kidney (Xu et al., 2014b), and loss of function Myc mutants
argue for a role in kidney development (Couillard and Trudel, 2009), the recovered motif is
distinct from the conventional Myc-Max target site (Badis et al., 2009), suggesting a role for
another, unidentified family member.
2.4.4 Target gene functions in nephron progenitors
In addition to identifying target genes with known function during kidney development,
we also uncovered novel putative targets of the four factors (see S2 Table for list of all target
genes). Bmper is a secreted protein that interacts with Bmp proteins and inhibits their function
(Moser et al., 2003). Inactivation of Bmper in the kidney leads to mild hypoplasia (Ikeya et al.,
2006); Bmp signaling plays important roles in the progenitor self-renewal and differentiation
(Nishinakamura and Sakaguchi, 2014). Six2 and the other factors may help fine-tune the level of
Bmp signaling through activation of Bmper. Rspo1 is a secreted protein that binds to G protein-
78
coupled receptors that activate Wnt signaling and its function has been implicated in multiple
developmental systems (Tomizuka et al., 2008). Rspo1 could have a role in modulating Wnt
signaling in the nephron progenitor niche although Rspo1 mutants have no obvious kidney
phenotype, these mutants have not been analyzed in depth (Tomizuka et al., 2008). We also
identified other modulators of Wnt signaling within our data. Shisa2 is reported to attenuate Wnt
and Fgf signaling during development (Furushima et al., 2007). Shisa2 is expressed in the
nephron progenitors along with its related family member Shisa3 (S2 Table). Other targets like
Tsc22d1 and Mgat5 are reported to display kidney phenotypes. Mgat5 is expressed in
differentiating structures including podocytes (S2 Table, Eurexpress, www.eurexpress.org,
(Diez-Roux et al., 2011)) and shows a glomerular phenotype (Granovsky et al., 2000). Tsc22d1
is expressed in the nephron progenitors (S2 Table) and mutants have small kidneys (Yu et al.,
2009). Given the current associations of known targets with kidney development and disease, it
is likely that functional analysis of new targets predicted here will identify additional regulators
of mammalian kidney development.
From our analyses, the majority of significant targets fall under the control of all four
factors. These genes fall into multiple functional categories from transcriptional regulators like
Six2, Sall1, and Pax2 to signaling factors like Fgf9 and Wnt4 to cell cycle regulators such as
Ccnd1 and matrix proteins such as Lamb1 (S2 Table). This suggests that these transcription
factors control many different aspects of progenitor cell biology. Fewer targets with known
kidney functions emerge from the interaction maps where one of more the factors was not bound
at the putative regulatory region (S5 Table). However, Eya1, Wt1, and Bcam lacked an Osr1
association in combined factor interaction analysis (S6 Table) but are well known for their early
roles in the kidney program (Kreidberg et al., 1993; Xu et al., 1999). Bcam, encodes a surface
receptor which binds laminin and is expressed at increasing levels in differentiating progenitors
(S6 Table). Knockouts display glomerular abnormalities suggesting important functions in the
kidney (Rahuel et al., 2008). Phgdh, a Six2-independent target with highest expression in
nephron progenitors (S6 Table) participates in L-serine synthesis and knockouts are embryonic
lethal (Yoshida et al., 2004).
2.4.5 Deletion of regulatory hotspots
Enhancers directing Six2-like and Wnt4-like reporter gene expression (Park et al., 2012),
identified as ‘regulatory hotspots’ co-bound by Six2, Hoxd11, Osr1, and Wt1 in the data here,
79
were shown to play roles in regulating activity of both gene targets. Kidney phenotypes were
observed in embryos homozygous for the enhancer deletion (Wnt4-DE) or when combined with
protein null mutations (Six2-DE and Wnt4-DE). While the study identified functional enhancer
regions, neither works alone in regulating normal transcript levels in the target cell type. An
alternative proximal enhancer has been documented for Six2 (Brodbeck et al., 2004; Yallowitz et
al., 2009). This proximal enhancer lies a few hundred base pairs upstream of Six2’s
transcriptional start site and strongly binds Six2, but not the other regulatory factors analyzed
here. Alternative enhancers have not been functionally demonstrated for Wnt4. In summary, our
studies provide evidence to support a focus on multifactor input to prioritize functional analysis
of large datasets emerging from ChIP-seq studies. CRISPR/Cas9 deletion of an enhancer
region >100kb from the TSS for Sox2 that is co-bound by multiple transcription factors
regulating pluripotency (Oct4, Sox2, Nanog, and Klf4 (Li et al., 2014; Zhou et al., 2014))
provides another example of this strategy to identify strong, bone-fide components of the
regulatory genome.
2.4.6 Topological rearrangements in the Br mutant and cis regulation of Six2
Individual enhancer action depends on the larger context of the chromosomal landscape.
Our demonstration that the inversion in Br mutant strain, repositions Six2 and Six3 in a new
regulatory landscape modifying enhancer interactions that likely contribute to altered features of
each gene’s regulation. Each gene exists in a distinct TAD that is likely enforced by the action
of a CTCF-dependent boundary element between the two genes. In Br heterozygous and mutant
alleles, Six3 is ectopically expressed in nephron progenitors: the boundary element no longer
separates the Six3 promoter from the Six2-DE. We hypothesize that this enhancer, and
potentially undefined regulatory information 5’ to this enhancer, dominate over other regulatory
information that might be present within the Six3 flanking region. As a result, the Six2-DE drives
Six3 expression in nephron progenitor cells while Six3 expression is lost from its normal lens
expression domain. Even though Six3 was detected in nephron progenitors in Br/+ mutants, Six3
is a member of a functionally divergent sub-group of Six factors (Kumar, 2009). Consequently,
Six3 activity failed to compensate for loss of Six2 and Br/Br mutants resemble Six2 null mutants
(Fogelgren et al., 2008).
Interestingly, even though there is no alteration in Six2-PE position relative the Six2
gene, the Six2-PE is not sufficient to drive levels of Six2 which maintain nephron progenitor
80
development in the context of the inversion. Thus, if the Six2-PE were capable of sustaining
normal Six2 levels, the inversion may prevent Six2-PE engagement with regulatory factors
necessary for its activation. Alternatively, there may be distinct enhancers other than the Six2-PE
that are required for Six2’s expression. Six2-bound putative regulatory regions lie upstream of
Six2-DE (S8 Table, Fig 32B) and these would be predicted to disengage from Six2 regulation in
the Br inversion.
Topological domains are highly conserved between cell types and across mammalian
species (Dixon et al., 2012). Recent studies have shown that alterations in TADs and CTCF site
orientation can affect chromosome architecture and result in altered gene expression (Guo et al.,
2015; Lupianez et al., 2015). Specifically, several limb malformations in the human were
attributed to the rearrangement of TADs and disrupted boundaries. When genetically modeled in
the mouse, altered gene expression suggests a mechanism for driving the limb malformations
(Lupianez et al., 2015). The type of topological rearrangements described here could play a role
in a subset of the congenital anomalies of the kidney and urinary tract (CAKUT) syndrome.
Importantly, these micro-rearrangements would not be detected in traditional exome screens.
Even whole genome sequencing approaches required tailored alignment algorithms to uncover
the junction fragments for the rearrangements as performed here. Together, these studies
highlight the importance of non-coding DNA and chromatin architecture to the appropriate
regulation of gene expression and the resulting phenotypic consequences incurred by rearranging
the regulatory landscape.
2.5 Materials and methods
2.5.1 Mouse strains
All surgical procedures, mouse handling, and husbandry were performed according to guidelines
issued by the Institutional Animal Care and Use Committees (IACUC) at the University of
Southern California and after approval from the institutional IACUC committee. The transgenic
construct utilized by Park et al. (Park et al., 2012) to test Six2-DE enhancer activity was modified
to insert PacI and SwaI sites for cloning downstream of the Hsp68 minimal promoter followed
by an IRES-NLS-GFP-BirA cassette. However, the IRES-NLS-GFP-BirA cassette was not
81
included in the generation of the Six2-BF line. Each transcription factor of interest (Six2,
Hoxd11, and Osr1) was amplified from E15.5 kidney cDNA with a BioTag-3XFLAG sequence
on the 3’ C-terminus and inserted into the transgenic vector using PacI and SwaI sites.
Transgenes were purified and injected as previously described (Park et al., 2012). F0 animals
were genotyped and transgenic animals bred to confirm germline transmission. Embryonic
offspring were also analyzed for correct expression patterns of the transgene in the developing
kidney.
Cas9-mediated removal of the Six2-DE (chr17:85747271-85749534) was performed by
identifying optimal gRNAs flanking the enhancer utilizing the CRISPR Design Tool
(crispr.mit.edu; 5’ gRNA: gttaccatctacggtgatgc, chr17: 85747271-85747290; 3’ gRNA:
gatatgattctcccgagctt, chr17: 85749515-85749537). The gRNAs were cloned into the pX330-U6-
Chimeric_BB-CBh-hSpCas9 plasmid (Addgene) as described (‘One-page Protocol for Cloning
Using CRISPR Cas9 Backbone Plasmids’ found at http://www.genome-engineering.org/crispr/;
(Cong et al., 2013)). Pronuclear injection of 2.5ng/l of each construct into B6SJLF1/J x
B6SJLF1/J zygotes (The Jackson Laboratory) with transfer to Swiss Webster (The Jackson
Laboratory) pseudopregnant females was performed in house. A similar strategy was used to
create the Wnt4-DE mouse (chr4:137216986-137217756). The CRISPR Design Tool
(crispr.mit.edu) was utilized to identify optimal gRNAs flanking the enhancer (5’ gRNA:
aggctgacaagcgaagttac, chr4:137216986-137217008; 3’ gRNA: atgtcggttgattaataatc, chr4:
137217756-137217778). The following primers were used to generate complexes for in vitro
transcription of the gRNAs using the MEGAshortscript T7 Transcription Kit (Ambion):
Six2-DE 5’ gRNA F:
AATAATACGACTCACTATAAGGCTGACAAGCGAAGTTACGTTTTAGAGCTAGAAAT
AGC,
82
Six2-DE 3’ gRNA F:
AATAATACGACTCACTATAATGTCGGTTGATTAATAATCGTTTTAGAGCTAGAAATA
GC,
Wnt4-DE 5’ gRNA F:
AATAATACGACTCACTATAGAGGCTGACAAGCGAAGTTACGTTTTAGAGCTAGAAA
TAGC,
Wnt4-DE 3’ gRNA F:
AATAATACGACTCACTATAGATGTCGGTTGATTAATAATCGTTTTAGAGCTAGAAAT
AGC,
Common R:
AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAA
CTTGCTATTTCTAGCTCTAAAAC.
Cytoplasmic injection of 100ng of each gRNA and 50ng of Cas9 mRNA (TriLink Biotech) into
B6SJLF1/J x B6SJLF1/J zygotes with transfer to Swiss Webster pseudopregnant females was
performed in house. To establish lines, F0 animals were born and genotyped to confirm presence
of the deletion. One animal carrying a deletion from chr17:85747284-85749542 for the Six2-DE
and a deletion of chr4:137216991-137217771 for the Wnt4-DE (each confirmed by Sanger
sequencing of PCR product) was mated to C57BL/6J (The Jackson Laboratories) to establish the
line. Genotyping primers:
Six2-DE flank F: GCAGAATGAGATTCTGACAGCCCAG
Six2-DE flank R: CAAGGATGTCTTGTTTGGTCCTTGAGTGAG
Six2-DE internal F: GAGGCCCATAAATAAAGCTGGGACG
Six2-DE internal R: CTCCAGTGACAGATACCACTCTTACTG
Wnt4-DE flank F: AAGCCATGAGGAAAAGAGGGTT
83
Wnt4-DE flank R: TTCTCAACCCCAAACCCCACC
Wnt4-DE internal F: AGTGTGAGGCACTGTGTAGC
Wnt4-DE internal R: GTGGATGCTGCCTTATGGGT
Six2TGC
tg
or Cited1-nuc-TagRFP-T
tg
lines utilized for FACS were previously described
(Kobayashi et al., 2008) (http://www.gudmap.org/index.html). Six2GCE, Six2CE, and Wnt4GCE
mice were previously described (Kobayashi et al., 2008). The Br mutant mouse and mapping are
previously described (Ma and Lozanoff, 1993) and experimental protocols were approved by the
University of Hawaii Institutional Animal Care and Use Committee.
2.5.2 ChIP-seq
Wildtype or transgenic kidneys were utilized for ChIP. Hoxd11-BF
tg/+
and Osr1-BF
tg/+
kidneys
were sorted out prior to ChIP by visualizing GFP+ kidneys. ChIP from E16.5 whole kidneys was
carried out as previously described (O'Brien et al., 2016). Nephron progenitor purification by
MACS prior to ChIP was carried out is described in Brown et al., 2015 (Brown et al., 2015).
2.5.3 4C-seq
Briefly, E16.5 mice kidney cortex cells were dissociated with collagenase/pancreatin as
described in Brown et al., 2015 (Brown et al., 2015), fixed with 1% formaldehyde for 10 min in
room temperature in AutoMACS running buffer. The fixed cells were then processed following
published protocols (van de Werken et al., 2012) to generate 4C libraries. Dpn II and NlaIII were
used in the first and second restriction enzyme digestions, respectively. In order to create a view
point from Six2-DE, the primers used in 4C-PCR:
Six2DE DpnII reading F: tccctacacgacgctcttccgatctGTTCTGAAAGAGCCGTGTAGGGATC
84
Six2DE NlaIII noReading R:
gtgactggagttcagacgtgtgctcttccgatcGGGGCCCATAAATCGTGATTCAAC
The capitalized letters indicate the complimentary sequences to the genomic view point and the
remainder Illumina adaptor sequences. The 4C-libraries were then indexed by PCR and
sequenced by NextSeq500. 4C-seq data were analyzed following the workflow provided by 4C-
ker (Raviram et al., 2016). Briefly, the data was mapped to a reduced genome containing 25 bp
regions from DpnII sites genome-wide and were subsequently quantified in 3 kb windows to
show enrichment. The 4C-seq data is deposited on GEO (GSE90017).
2.5.4 ChIP-seq data analysis
All ChIP-seq sequences were mapped to the mouse reference genome (mm10) using Novoalign
software (Novocraft; parameters: single-end reads trimming 10 bp, polyclonal read filter: 7,10
0.4,2, maximum alignment score acceptable: 120). Mapped ChIP-seq and input data were
analyzed using QuEST 2.4 software (Valouev et al., 2008) using a “transcription factor” setting.
The false discovery rate (FDR) for detecting the bound regions was evaluated by allocating the
same number of mapped reads from a separate mouse input library and performing QuEST
analysis using the same parameters. We generated multiple replicates for each ChIP-seq
experiment (except Hoxd11 due to technical issues), and used the replicate containing the most
peaks using the same peak calling parameters for downstream analyses (see Fig 4G and 7A, G
for peak overlap between replicates. The smaller replicates all had >50% overlap with the larger
replicate). To account for the innate differences between transcription factors in binding to the
genome, we used different parameters in calling peaks of different transcription factors. We used
high ratio of peaks with motif and low variability of motif-peak distances as our standard in
85
determining the validity of a data set. See S7 Table for ChIP-seq data information and
parameters in peak calling.
In this paper, overlapping sites are defined as those with ChIP-seq peak center distance <150 bp
from each other unless otherwise specified. To evaluate the statistical significance of two sets of
peaks overlapping each other (Fig 6A), we performed binomial test with the null hypothesis that
peaks fall randomly into open chromatin regions in nephron progenitors. To determine the open
chromatin regions in nephron progenitor, we performed ATAC-seq (Buenrostro et al., 2013) in
MACS-purified nephron progenitors (Brown et al., 2015). We called ATAC-seq peaks with
QuEST using the ‘transcription factor’ setting following threshold of fold enrichment > 10. Then
we extended the ATAC-seq peak coordinates by 150 bp to both sides, resulting in a total size of
accessible chromatin as 21856500 bp. This process is modeled as following:
",$
~ (
"
,
$
)
$
=
300∙
$
677899:;<8 =8>?@8
where
",$
is the number of peaks in set A that overlap with set B, and
$
is the probability of a
randomly located peak overlapping with set B. Information on all ChIP-seq samples presented in
the paper can be found in S7 Table. The ChIP-seq and ATAC-seq data is accessible from GEO
(GSE90017).
2.5.5 DNA sequence motif analysis
All de novo motif discovery work was carried out using MEME (Multiple Em for Motif
Elicitation, (Bailey et al., 2006)). To find the most enriched motifs for each transcription factor,
MEME was run on a pool of 100 bp sequences around the predicted peak center for the top 1000
ChIP-seq peaks called from each data set (or all peaks if number of the total peaks is less than
86
1000). The locations of motif within 300 bp of peaks are found by FIMO (Find Individual Motif
Occurrences, (Grant et al., 2011)) with data set-specific p-value threshold setting (Six2 motif: 2e-
4; Hoxd11 motif: 2e-4; Osr1 motif: 2e-4; Wt1 motif: 2e-5; bHLH motif: 2e-5). We model the
appearance of a motif near a set of peaks as following:
",@
~ (
"
,
@
)
@
=
@
300∙
"
where
",@
is the number of motif m found in +/-150 bp of the peak set A;
@
is the probability
of finding such motif near a matched random set with the same number of peaks in A. Since
promoter regions of genes are GC-rich, resulting higher rate of discovering GC-rich motif (Wt1,
Osr1 and bHLH) than TA-rich motif (Six2, Hoxd11) in promoter regions. To address this bias,
we created the matched random set of peaks by picking genomic coordinates with the same
distances to the nearest TSS as the observed peaks set, but with permutated nearest genes. We
then screen the matched random peaks for the same motif to obtain
@
.
2.5.6 Genomic Regions Enrichment of Annotations Tool (GREAT) Analysis
GREAT GO analysis was performed utilizing the online GREAT program, version 2.0 (McLean
et al., 2010). Gene regulatory domains utilized for region annotation were defined as minimum
5.0 kb upstream and 1.0 kb downstream of the TSS, and extended up to 500.0 kb to the nearest
gene’s minimal regulatory domain (‘single nearest gene’ option). GO Biological Processes
annotations were assessed for each peak category.
87
2.5.7 Region-based enrichment analysis
To infer the statistical significance of a set of ChIP-seq peaks found near a set of genes (Fig 8),
we performed the following analysis. We assigned +/-500 kb from TSS of a gene as its
‘regulatory domain’. We then calculated the probability of a selected set of ChIP-seq peaks
falling into the merged regulatory domains of a list of selected genes. This is modeled by a
binomial process with the null hypothesis that each peak falls uniformly throughout the genome.
",A
~ (
"
,
A
)
A
=
1
D (
G
)
H
:∈{K
L
}
",A
is the number of peaks in set A that fall into the regulatory domains defined by the gene list
A
.
A
is the probability of a peak falling into regulatory domains defined by the gene list
A
,
assuming the peak randomly falling on any position in the genome. (
:
) is size of a
regulatory region after resolving the overlap with any nearby regulatory regions.
We found that each observed set of peaks fall into any random sets of regulatory domains more
often than expected, which is not observed when doing the same experiment using random sets
of genomic coordinates. To control this background over-representation, we obtained a
background enrichment ratio over random sets of regulatory domains by
",A
=
1
D
",AO
P
>
:QR
/(
"
AO
P
)
where
",A
is the normalizing factor for a specific set of peaks A.
",O
P
is the number of peaks
from set A that fall into a the regulatory domain defined by a random list of genes
AO
P
which
contains the same number of genes as
A
. Therefore, the final binomial model we used in the
analysis is
",A
~ (
"
,
A
",A
)
88
2.5.8 Fluorescence-Activated Cell Sorting
Cortical tissues of E16.5 or P2 kidneys from Six2TGC
tg/+
or E16.5 Cited1-nuc-TagRFP-T
tg/+
embryos were dissociated as described in Brown et al., 2015 (Brown et al., 2015). The
dissociated cells were resuspended in autoMACS buffer (Miltenyi Biotec) and passed through a
40 µm nylon filter to obtain single cells. The respective GFP+, GFP-, or RFP+ cells were then
isolated with the BD FACSAria II.
2.5.9 RNA-seq analysis
RNA was isolated from FACS isolated cells using the QIAGEN RNeasy Micro Kit. RNA was
submitted to the USC Epigenome Center for library preparation and sequencing on the Illumina
HiSeq 2000. All RNA-seq reads were aligned to the mouse reference genome (mm10) using the
TopHat2 (Trapnell et al., 2009). Sequences have been deposited in GEO, accession number
GSE90017. Quantification of RNA-seq reads to generate RPKM was performed by Partek
Genomics Suite software, version 6.6 (St. Louis, MO, USA). TPM was calculated by dividing
the RPKM by the mapping ratio of the library to exon regions of the genome. To identify genes
differentially expressed in a cell type, we select those with a fold difference > 3, TPM > 5 and p-
value < 0.05. Sample information can be found in S7 Table. A complete list of all annotated
genes and their coordinating RNA-seq data can be found in S3 and S4 Table, and coordinating
ChIP-seq data can be found in S8 Table. Gene ontology analysis of gene lists was carried out by
PANTHER (Mi et al., 2017). For the gene set analysis in Fig 8D, we selected the enriched gene
lists using the following metrics: nephron progenitor-enriched (TPM > 10 in E16.5 Six2GFP+
cells and fold change > 2 in E16.5 Six2GFP+ vs. Six2GFP- cells), self-renewing nephron
progenitor-enriched (TPM > 10 in E16.5 Cited1RFP+ cells and fold change > 2 in E16.5
89
Cited1RFP+ vs. P2 Six2GFP+ cells) and differentiating nephron progenitor-enriched (TPM > 10
in P2 Six2GFP+ cells and fold change > 2 in P2 Six2GFP+ vs. E16.5 Cited1RFP+ cells). S5-6
Tables are pre-filtered to show the nephron progenitor-enriched genes only, but entire lists can
be viewed by releasing the filter.
2.5.10 qPCR
qPCR reaction was performed with Luna Universal qPCR Master Mix Protocol (New England
Biolabs) on a Roche LightCycler 96 System. The primers used in this paper includes:
GAPDH F: AGGTCGGTGTGAACGGATTTG
GAPDH R: TGTAGACCATGTAGTTGAGGTCA
Six2 F: CACCTCCACAAGAATGAAAGCG
Six2 R: CTCCGCCTCGATGTAGTGC
Pax2 F: AAGCCCGGAGTGATTGGTG
Pax2 R: CAGGCGAACATAGTCGGGTT
Wnt4 F: AGACGTGCGAGAAACTCAAAG
Wnt4 R: GGAACTGGTATTGGCACTCCT
2.5.11 In situ hybridization
In situ hybridizations were performed on frozen sections as previously described
(https://www.gudmap.org/Research/Protocols/McMahon.html). The primer sequences used to
generate Wnt4 probe template are:
F: GAGAAACTCAAAGGCCTGATCCA
R: TAATACGACTCACTATAGGGGGCTTTAGATGTCTTGTTGCACG
90
2.5.12 Electrophoretic mobility shift assay (EMSA)
EMSA was carried out using Glutathione S-transferase (GST)-tagged recombinant proteins
purified from bacterial lysates. To produce the protein, bacterial expression constructs
(pDEST15 backbone) were prepared using the Gateway system. BL21-AI One Shot (Life
Technology) chemically competent cells were transformed and grown to OD600 = 0.6 before
induction with 0.2% L-(+)-arabinose (Sigma) for 3 hrs at 37 °C. The bacteria pellets were
resuspended in lysis buffer (20 mM Tris-HCl, 150 mM NaCl, 1% TritonX-100, 1x protease
inhibitor, 5 mM DTT) and incubated with 1 mg/mL lysozyme (Sigma). The lysates were
sonicated with Branson digital sonifier at 50% amplitude for 90 s. After sonication, supernatant
of the lysates were incubated with 5mL glutathione-agarose beads (Sigma) per 1 L bacteria
culture for 1 hr at 4°C. The beads were washed with 1% TritonX-100/PBS and eluted with
elution buffer (20 mM Tris-HCl, 150 mM NaCl, 15 mg/mL (50 mM) reduced glutathione, 1x
protease inhibitor). The eluted protein was concentrated to at least 10 mg/mL using Amicon
Centrifugal Filter Unit with the right filter size. The concentrated protein was diluted with PBS
and concentrated again to exchange buffer. To perform the EMSA experiments, the recombinant
protein was incubated with biotinylated DNA probe for 30 min at room temperature, then the
mixture was run through a native TBE gel. The gel was transferred to a nitrocellulose membrane,
which was then illuminated using the LightShift Chemiluminescent EMSA Kit (Pierce). The
sequences of DNA probes can be found in Fig 5.
2.5.13 Immunoprecipitation
Nuclear lysates were prepared from E16.5 whole kidneys using the Nuclear Complex Co-IP Kit
(Active Motif). Normal rabbit IgG or Six2 (Proteintech, 11562-1-AP) antibodies were
crosslinked with dimethyl pimelimidate to Dynabeads Protein G (Thermo Fisher Scientific)
91
using the Protein A/G SpinTrap Buffer Kit (GE Healthcare). Nuclear extracts were incubated
overnight with beads at 4°C. Samples were washed 5x with TBS+0.1% Triton X-100, and
proteins subsequently eluted with 0.1M Glycine-HCl, pH 2.9. Samples were run on a 10% SDS-
PAGE gel, transferred to nitrocellulose, and subjected to standard Western blotting protocols
using Six2 (Proteintech), Hoxd11 (Abcam, ab60715), or Wt1 (Santa Cruz, sc-192) antibodies.
2.5.14 Immunofluorescence
Kidneys were isolated at the appropriate stage and fixed in 4% PFA for 1 hour. Cryosections
were immunostained as previously described (Park et al., 2012). Antibodies used include Six2
(Proteintech, 11562-1-AP), FLAG (Sigma, F1804), Wt1 (Abcam, ab89901), pan-cytokeratin
(Sigma, C2931), Pax8 (Abcam, ab13611), Ecad (Sigma, U3254), Six3 (Rockland, 200-201-
A26S), and LTL-FITC (Vector Labs, FL-1321). Images were acquired on a Nikon Eclipse 90i
epi-fluorescent microscope or Zeiss LSM 780 inverted confocal microscope.
2.5.15 Br sequencing and mapping
Purified genomic DNA from one wildtype and two Br/Br animals was sequenced on the Illumina
Hi-seq 2000 and mapped to the mm10 genome using Bowtie2. Specific details of the mapping
and allele characterization are described further in S1 Supporting Information. Sequences have
been deposited in GEO, accession number GSE90017.
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Chapter 3 A β-catenin driven switch in Tcf/Lef transcription factor
binding to DNA targets sites promotes commitment of mammalian
nephron progenitor cells
Disclaimer:
Data in this chapter is unpublished. A paper submission is planned in the next three
months with the addition of a final section with functional data from genetic modulation of Wnt
pathway components in NPC cultures (my final collaboration with an incoming graduate student
Helena Bugacov who will take over this study). I will be first author on the planned publication.
In terms of contribution, I designed this study with Dr. McMahon and carried out most
experiments (RNA-Seq, ATAC-Seq, ChIP-Seq, Western blots and RT-qPCR) and bioinformatics
analysis (all data except for part of the Hi-C data analysis). Dr. Albert Kim assisted in initial
experimental design and acquisition of immunofluorescence data. Dr. Andrew Ransick generated
the single-cell RNA-seq data. Bin Li from UCSD assisted in processing of Hi-C data. Arima
Genomics generated Hi-C data with us providing the material. Jill McMahon facilitated
management of related mice strains. Dr. Aaron Brown, Dr. Leif Oxburgh, Dr. Nils Lindstrom,
and Xi Chen provided reagents and/or technical advice.
3.1 Abstract
Wnt ligand interactions and activity of the Wnt pathway transcriptional co-activator β-
catenin are required for both self-renewal and differentiation of nephron progenitor cells (NPCs).
To investigate the molecular mechanism underlying Wnt/β-catenin-driven NPC self-renewal and
differentiation, we modeled these processes in nephron progenitor expansion medium (NPEM)
culture (Brown et al., 2015) supplemented with low or high level of CHIR99021 (CHIR), a small
molecule GSK3 inhibitor. Gene expression profiling detected a downregulation of transcriptional
repressor Tcf7l1 and Tcf7l2, and a dramatic up-regulation of transcriptional activators Tcf7 and
Lef1 in differentiated NPCs. As a result of changing β-catenin levels, Tcf7/Lef1 factor binding
replaced Tcf7l1/Tcf7l2 interactions at enhancers regulating expression of key differentiation
promoting genes, correlating with the activation of these genes. Notably, Tcf7/Lef1/β-catenin
utilizes both conserved and CHIR-induced enhancer-promoter loops to activate gene expression.
Together these data suggest NPCs are in a primed state ready to undergo a rapid commitment to
the nephrogenic program. Repressive interactions mediated by Tcf7l1/Tcf7l2 silence
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differentiation promoting enhancers. A β-catenin driven replacement of these factors by
activating Lef1/Tcf7/β-catenin complexes promotes the differentiation of NPCs.
3.2 Introduction
The classical model of canonical Wnt signaling operates in a two-state framework. In the
absence of Wnt ligand, HMG box family Tcf transcription factors bind enhancers of Wnt target
genes recruiting co-repressors (Tle, Ctbp, etc.) to silence target gene expression. In the
cytoplasm, the transcriptional co-activator β-catenin is phosphorylated by an axin/GSK3ß-
dependent β-catenin destruction complex, resulting in ubiquitin-mediated proteosomal
degradation. Upon Wnt ligand binding to Fzd receptor/Lrp co-receptors on the cell surface, the
β-catenin destruction complex is sequestered to the activated receptor protein complex through
axin interactions, removing β-catenin from GSK3ß-directed, phosphorylation-mediated
degradation. As a result of increasing β-catenin levels, β-catenin is free to associate with Lef/Tcf
DNA binding partners, activating Wnt target gene transcription (Mosimann et al., 2009). While
evidence suggests all four mammalian Tcf family members are able to functionally interact with
both Tle family co-repressors and β-catenin (Brantjes et al., 2001), a variety of studies on a range
of biological systems indicate that Tcf7l1 predominantly acts as a repressor, Tcf7l2 as a context-
dependent activator or repressor, and Tcf7 and Lef1 as transcriptional activators in the regulation
of Wnt target gene expression (Lien and Fuchs, 2014).
The mammalian kidney arises from distinct cell population within the intermediate
mesoderm. The metanephric mesenchyme harbors progenitors for the nephron and interstitial
cell types, while the epithelial ureteric bud, which invades the metanephric mesenchyme to
activate kidney morphogenesis, harbors all progenitors for the ureteric epithelium of the
collecting system network. Signaling between these populations drives assembly of the
mammalian kidney.
All nephrons - 14,000 in the mouse and 1,000,000 in humans - arise from the nephron
progenitor cell (NPC) pool established at the onset of kidney development (McMahon, 2016).
Consequently, balancing the maintenance, expansion and commitment of NPCs is critical to
nephron endowment and organ function. Maintenance of NPCs is supported by activities of a
number of signaling pathways in the progenitor niche, including Wnt9b secreted by cells of the
ureteric epithelium and β-catenin acting within NPCs. Loss of Wnt9b or β-catenin results in the
down-regulation of a number of genes expressed in NPCs and an early depletion of the nephron
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progenitor pool (Karner et al., 2011). Wnt9b and β-catenin are also required for commitment of
NPCs to the nephrogenic program. Wnt9b mutants fail to undergo NPC differentiation to the
epithelial nephron precursor, the renal vesicle, while activation of a β-catenin form insensitive to
phosphorylation-mediated degradation in NPCs results in a Wnt9b-independent induction of
differentiation promoting gene targets (Park et al., 2007).
These genetic outcomes in the kidney can be modelled in in vitro NPC cultures. Applying
insights from genetic and biochemical studies, Brown et al. (2015) developed a defined nephron
progenitor expansion medium (NPEM) in which the GSK3b inhibitor CHIR99021 (Cohen and
Goedert, 2004; abbreviated to ‘CHIR’ in the following text) is a key component. Low CHIR
levels are essential to maintenance and expansion of NPCs in vitro. In contrast, elevating CHIR
levels, or adding an alternative GSK3b inhibitor (BIO) induced the differentiation of NPCs (Park
et al., 2012). Genomic analysis of BIO induced NPC cultures showed β-catenin engagement at
Tcf/Lef recognition motifs within enhancers linked to genes driving NPC differentiation (Park et
al., 2012). Transgenic studies confirmed Tcf/Lef-dependent target enhancer activities (Park et al.,
2012), indicating that NPC differentiation follows a canonical Wnt/β-catenin/Tcf regulatory axis
(Mosimann et al., 2009).
In this study, we used the in vitro NPEM model and varying CHIR levels to examine the
regulatory actions underlying the differential effects of β-catenin levels in maintenance and
commitment of NPCs. The data provides a comprehensive molecular insight into a key
regulatory switch in progenitor programs directing mammalian nephrogenesis.
3.3 Results
3.3.1 NPEM supplemented with higher level of CHIR manifested signs of early
differentiation of mouse nephron progenitor cells within a day
A low level of CHIR (1.25 µM) is an essential component in NPEM medium supporting
the expansion of NPCs while maintaining nephron-forming competence (Brown et al., 2015).
Within 3 days of elevating CHIR levels (3 µM), aggregate NPC cultures show a robust signature
of nephron differentiation (Brown et al., 2015). To develop this system further for detailed
molecular characterization of CHIR/β-catenin-directed transcriptional events, we collected NPCs
from E16.5 embryonic kidneys by magnetic-activated cell sorting (MACS), and cultured NPCs
in NPEM supplemented with maintenance CHIR levels (1.25 µM) and titrated CHIR to
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determine an effective concentration for activation of early target genes of NPC commitment
with 24 hours. As expected, 1.25 µM CHIR (‘low CHIR’ throughout) maintained Six2
expression, a key determinant of the NPC state, but did not lead to induction of Jag1 (B-C, Fig
17B), a Notch pathway ligand activated early on NPC commitment (Georgas et al., 2009). A
significant increase of cellular and nuclear β-catenin level (Fig 14A, B and Fig 19B) was
observed in 5 µM CHIR (‘high CHIR’ throughout), along with strong inductive response: Six2
persists but there was a robust induction of NPC differentiation markers Wnt4, Jag1 and
Lef1(Fig 14B-C, Fig 15B, Fig 17B), mirroring early inductive events in the pretubular aggregate
and renal vesicle in vivo (Mugford et al., 2009; Georgas et al., 2009; Xu et al., 2014a).
Interestingly, immunofluorescence staining suggests an increased presence of nuclear β-catenin
consistent with a β-catenin co-activator driven transcriptional response (Fig 17B). We adopted
this induction condition throughout the study.
Next, we sought to systematically characterize gene expression profiles of NPCs in low
and high CHIR conditions by mRNA-seq. Additionally, to explore the effect of the low CHIR
cells to NPC, we also generated data from a 0 uM CHIR (‘No CHIR’ throughout) culture
condition (Fig 15A), and from freshly isolated NPCs prior to culture. Low CHIR maintains
expression of transcriptional regulators required for NPC specification and/or maintenance,
including Pax2, Wt1, Hoxa/d11, Sall1 (McMahon, 2016) (Fig 14C). In contrast, high CHIR led
to a downregulation of specific markers of self-renewing NPCs, including Six2 (Self et al.,
2006b), Cited1 (Mugford et al., 2009), Osr1 (Xu et al., 2014a) and Eya1 (Xu et al., 2014b) and a
concomitant increase in expression of genes associated with induction of nephrogenesis, such as
Wnt4, Jag1, Lhx1, Pax8 and Fgf8 (Fig 14; Park et al., 2007). These trends in gene expression
were confirmed by RT-qPCR analysis (Fig 15B).
To understand high-level changes in biological processes within NPC, we performed
gene ontology (GO) enrichment analysis of differentially expressed genes (differential
expression analysis described in Methods) with DAIVD (Huang da et al., 2009). Comparing
input NPCs freshly isolated from the kidney with NPC-free cortical (‘NFC’ throughout) kidney
cell populations, a strong enrichment was observed in NPC-relevant terms consistent with the
fact that most cells are Six2+ indicative of strong enrichment of NPCs (Fig 14D, top panel).
Comparing high with low CHIR conditions, a strong enrichment was observed in terms
associated with progressive development (Fig 14D, middle and bottom panels). However,
transcriptome-wide comparison of all samples, clustered non-cultured NPCs into a distinct group
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from NPCs cultured in either low and high CHIR (Fig 14F). This is explained by a pronounced
metabolic shift in culture where there is a strong enrichment in GO analysis in metabolic
processes such as sterol biosynthesis (Fig 15D). In addition, freshly-isolated NPCs showed a low
level inductive signature that likely reflects the contribution of small numbers of early induced
cell types (expression of Fgf8, Wnt4, Lhx1, Heyl, Bmp4, Mafb, Podxl). Importantly, the
differentiation signature was markedly reduced in low CHIR culture while genes strongly
associated with undifferentiated NPCs were significantly upregulated over the 24 hr period of
isolated NPC culture (Fig 15D). Thus, in vitro pre-culture establishes a more rigorous model for
distinguishing uninduced versus induced NPC responses than was possible with directly isolated
NPC populations.
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Figure 14. NPEM supplemented with differential levels of CHIR99021 models nephron
progenitor cell maintenance or differentiation in a plate. (A) Immuno-fluorescence (IF)
staining showing expression level of Six2, non-phospho (NP) β-catenin and Jag1 in NPC
cultured in NPEM supplemented with various CHIR dosages. (B) Relative intensity of IF signals
from individual cells in experiment associated with A. (C) Heatmap/Hierachical cluster of
expression levels of NPC signature, self-renewal and differentiation marker genes. (D) Top 5
enriched GO terms of indicated differentially expressed gene lists, analyzed by DAVID.
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Figure 15. Supplementary RNA-Seq data analysis. (A) Overview of experiment design and data
available (grey). (B) Barplots show RT-qPCR measurement of relative expression of the
indicated genes, as verification of results in Fig 14C. (C) Hierachical cluster of R-square values
between transcriptome-wide TPM of the indicated pair of replicate RNA-Seq data sets. (D) Top 5
enriched GO terms of genes differentially expressed between low CHIR condition and uncultured
NPC. (E) Top 5 enriched GO terms of genes differentially expressed between low CHIR and no
CHIR conditions.
3.3.2 CHIR-mediated induction modifies the epigenomic profile of NPCs
To begin to understand the chromatin landscape regulating NPCs, we integrated
chromatin accessibility through ATAC-Seq analysis (Buenrostro et al., 2013) with chromatin
immunoprecipitation studies examining active (H3K27ac ChIP-seq) and repressive (H3K27me3
ChIP-seq) chromatin features, RNA Pol II recruitment (RNA Pol II Ser5P ChIP-Seq) and RNA-
seq expression profiling (Fig 15A). Initially, we evaluated enhancers previously validated in
transgenic studies (Park et al., 2012) that are associated with Six2 expression in uncommitted
NPCs (Six2 distal enhancer; Six2DE) and Wnt4 activation on NPC differentiation (Wnt4 distal
enhancer; Wnt4DE). In low CHIR conditions, the Six2DE shows an open and active
configuration: an ATAC-seq peak, flanked by H3K27ac peaks with Pol II engagement at the
enhancer and within the gene body. In high CHIR, ATAC-Seq, H3K27ac and Pol II ChIP-Seq
signals were reduced correlating with the down-regulation of gene expression (Fig 16A). As
predicted, the Wnt4DE displayed an opposite trend in the shift from low to high CHIR: the
ATAC and H3K27ac ChIP signals increased together with increased Pol II engagement in the
gene body. Surprisingly, a marked enhancer specific Pol II ChIP-seq signature was visible in
NPC conditions and reduced on initiation of active transcription in high CHIR (Fig 16B).
Having validated the datasets at target loci, we examined the datasets more systematically
for broad features of epigenetic regulation. Differentially accessible (DA) regions enriched in
uncultured NPCs relative to kidney cortex cells depleted of NFC were compared. The NPC-
specific DA regions were significantly enriched in transcription factor binding sites for Six, Pax,
Wt1 and Hox factors consistent with the critical roles of Six1/2, Pax2, Wt1 and Hox11
paralogues in NPC programs (Fig 16D; Kreidberg et al., 1993; Naiman et al., 2017; Self et al.,
2006b; Wellik et al., 2002)). Functionally, GO-GREAT analysis (McLean et al., 2010) predicted
these regions were enriched near genes linked to kidney development (Fig 16C).
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Hierarchical clustering of ATAC-Seq data was used to examine the relationship between
CHIR dosage and the open chromatin landscape identifying differentially accessible (DA)
regions in NPCs cultured in low and high CHIR conditions (Fig 17A). Most of the DA regions
are distal to TSS of genes, indicative of enhancer elements (Fig 17B). Interestingly, the top motif
identified in high CHIR-specific DA regions is the Lef/Tcf motif supporting a β-catenin driven
increase in accessibility through engagement with Lef/Tcf factors (Fig 16D).
Figure 16. High dosage of CHIR99021 triggered change of NPC epigenome. (A and B)
Genome browser view of RNA-Seq, ATAC-Seq, as well as Six2, H3K27ac, H3K27me3 and Ser5P
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ChIP-Seq data near Six2 (A) and Wnt4 (B) in low CHIR (left) and high CHIR (right) conditions.
Black Arrow indicates Six2DE, Wnt4DE, respectively. (C) Top 5 most significant gene ontology
terms associated with the differentially accessible (DA) regions extracted from the indicated
comparisons. (D) Top 5 most enriched motifs discovered de novo in the indicated DA regions.
Figure 17. Supplementary ATAC-Seq data analysis. (A) Hierarchical cluster of R-square
values between normalized ATAC-Seq reads within merged peaks from all samples. (B)
Histograms of distances from differentially accessible (DA) chromatin regions to TSS implicate a
predominant enhancer feature. (C) Top 5 most significant GO terms associated with the NFC-
specific DA regions. (D) Top 5 most enriched motifs discovered de novo in the NFC-specific DA
regions.
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3.3.3 Differential expression and DNA binding of Tcf family members in the
regulation of NPC programs
To examine the role of Lef/Tcf factors directly, we characterized expression of each of
the four members (Lef1, Tcf7 Tcf7l1 and Tcf7l2). All four genes were expressed in NPCs;
however, Tcf7l1 and Tcf7l2 were markedly down-regulated on CHIR-mediated induction of
NPCs, while Tcf7 and Lef1 were dramatically up-regulated (Fig. 18A, 19A). The same trend was
observed at the nuclear protein level through quantitative immunofluorescence (Fig 18B-C) and
Western blot (Fig 19B) analyses. To compare in vitro findings with NPCs in vivo, single cell
RNA-seq transcriptional profiles were examined in cells isolated from the early postnatal (P0
day of birth) kidney cortex. In agreement with in vitro data, Tcf7l1 transcripts were enriched in
self-renewing NPCs (Fig 19 E-F) while Lef1 levels were elevated in differentiated NPCs (Fig 19
E-F), However, transcripts of Tcf7l2 and Tcf7 are relatively low (evidence: TPM) and not
significantly different between NPC and early differentiated cells (Fig 19C-F). These results
were confirmed with RNA scope on E16.5 kidney section (Fig 18D), suggesting in vivo Tcf7l1
and Lef1 play dominant roles in self-renewing and differentiated NPC, respectively.
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Figure 18. Differential expression of Tcf family transcription factors in NPC in response to
distinct level of CHIR. (A) Barplots showing RNA-Seq measured expression levels of Tcf/Lef
family factors in NPC cultured in NPEM culture supplemented with various concentration of
CHIR. (B) Immuno-fluorescence (IF) staining of Tcf/Lef family factors in NPEM cultured with
conditions indicated. (C) Relative intensity of IF signals from individual cells in experiment
associated with B.
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Figure 19. Supplementary evidence for differential expression of Tcf/Lef factors. (A) Barplots
show RT-qPCR measurement of relative expression of Tcf/Lef family factors, as verification for
results in Fig 18A. (B) Immunoblots of Tcf/Lef family factors in NPC cultured in NPEM with the
indicated treatment. (C) tSNE plot displaying unbiased cluster of nephron lineage cells profiled
by single-cell RNA-Seq. (D) Feature plots displaying distribution of self-renewal (red) and
differentiation (green) marker genes transcripts on the tSNE plot. (E) Feature plots showing
distribution of Tcf/Lef factors transcripts on the tSNE plot. (F) Dotplots showing accumulated
expression level of marker genes as well as Tcf/Lef factor in selected clusters of cells.
To directly address Lef/Tcf target interactions and β-catenin-mediated regulation of
NPCs, we generated Lef1, Tcf7, Tcf7l1, Tcf7l2 and β-catenin ChIP-Seq data sets from freshly
isolated, uncultured NPCs, and NPCs cultured in low and high CHIR (Fig 15A). Further, given
the key role for Six2 in NPC maintenance and evidence supporting Six2 engagement in Tcf7l2
and β-catenin containing complexes (Park et al., 2012), we collected similar Six2 ChIP-seq
datasets.
Individually, motif discovery of Lef/Tcf/β-catenin ChIP-seq binding sites showing
maximum DNA enrichment predicted a Lef/Tcf binding site as the most enriched regulatory
motif indicating a high specificity to the data sets and supporting direct Lef/Tcf/β-catenin target
interactions (Fig 21C). In addition, a Hox motif is highly enriched in Tcf/Lef binding sites in
both low CHIR and high CHIR conditions consistent with Hox11 paralog action (Wellick et al.,
2005; O’Brien et al., 2018). Additionally, a Runx motif is enriched in Tcf/Lef biding sites in high
CHIR and Runx1 was up-regulated in high CHIR conditions but the significance of this finding
is not clear.
Consistent with the different levels of each protein in low and high CHIR conditions,
Tcf7l1 and Tcf7l2 have fewer, while Tcf7, Lef1 and β-catenin have many more binding sites in
high CHIR than in low CHIR conditions (Fig 21A). In both low and high CHIR conditions β-
catenin association overlapped extensively with binding of the cognate Lef/Tcf factors
specifically enriched in each condition (Fig 21B). Significant difference was observed in Tcf7l2
interactions between low and high CHIR conditions. Moreover, hierarchical cluster result
suggests in general Tcf/Lef factor binding in low CHIR and high CHIR are different, while in the
same condition different Tcf/Lef factors, as long as expressed, target to common genomic
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regions (Fig 21D). The data indicates somewhat distinct factors driving Tcf/Lef binding in low
and high CHIR conditions, respectively.
In vivo, Tcf7l1 and Lef1 showed the most restricted activity to NPCs and their early
differentiating derivatives, respectively (Fig. 19E-F). Moreover, analysis of embryonic stem cell
cultures and hair follicle stem cell differentiation suggests a transcriptional repressor role of
Tcf7l1, and transcriptional activator roles of Lef1 (Yi et al., 2011). Therefore, we focused on
Tcf7l1 and Lef1 binding for downstream analysis. When sites bound by Tcf7l1 in low CHIR are
compared with those bound by Lef1 in high CHIR, we observed a significant overlap (p value =
1e-569), though over half were unique between each dataset (Fig 20A). We assigned the
Tcf7l1/low CHIR-specific sites as set 1, the overlapping sites as set 2 and the Lef1/high CHIR-
specific sites as set 3. Interestingly, distinct from their counterparts from set 2 and set 3, sites
from set1 did not show strong enrichment of the Tcf/Lef1 motif, indicating that the Tcf7l1/low
CHIR-bound sites missing in high CHIR are indirect Tcf7l1/DNA binding mediated by some
unknown factors. Consistently, the majority (82%) of Tcf7l1 sites in low CHIR with Tcf/Lef
motif overlap with Lef1 sites in high CHIR, while only a relatively small proportion (33%) of
Tcf7l1 sites without Tcf/Lef motif overlap with Lef1 binding sites (Fig 22A).
Comparing with the ones from set 3, the sites from set 2 displayed stronger binding of
Tcf7l2, Tcf7, Lef1 and β-catenin in high CHIR condition, indicating that the sites occupied by
Tcf7l1 in low CHIR are poised for stronger binding (and therefore activation) by Tcf activators
in high CHIR condition. Gene Ontology analysis through GREAT concluded all 3 sets of
Tcf/Lef binding sites are enriched near genes involved in ‘metanephric nephron morphogenesis’
or ‘renal vesicle morphogenesis’ (Fig 20C), albeit set 3 showed lower enrichment (2E-14 vs. 8E-
18 and 9E-19 in ‘renal vesicle morphogenesis’). Despite of the similarity in gene ontology
analysis results, predicted target genes of set 3 sites overlap (n = 987) with those of set 2 sets but
also include many unique genes (n = 3,738) (Fig 20E).
Notably, we observed stronger enrichment of active enhancer markers H3K27ac and
RNA Pol II in low CHIR on set 1 sites than on set 2 sites (Fig 20A). In line with it, the Tcf7l1
sites without the Tcf/Lef motif showed stronger enrichment of ATAC-Seq, H3K27ac and RNA
Pol II loading than their counterparts with the Tcf/Lef motif. These data indicate indirect Tcf7l1
are more likely to engage on relatively active enhancers in low CHIR, while direct binding of
Tcf7l1/Tcf7l2 on set 2 sites are likely to be present on repressed enhancers. Indeed, predicted
target genes of set 1 sites are differential enrichment of gene expression analysis indicates target
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genes of set 1 sites showed higher enrichment for genes specific to low CHIR condition, while
those of set 2 and set 3 are enriched for genes specific to high CHIR condition (Fig 20D). This
suggests that low CHIR-specific interaction sites of Tcf7l1 were association was indirect (no
enrichment of a Lef/Tcf motif) were more likely to be associated with active transcription in
NPCs. In contrast, those sites where Tcf7l1 bound directly through its DNA binding motif
(strong enrichment of a Lef/Tcf motif) in NPCs were more likely to be silent in NPCs, then
activated on NPC differentiation in high CHIR, with an associated shift to Lef1 engagement.
We observed strong enrichment of Tcf/Lef motif in DA regions specific to high CHIR
(Fig 16D). This likely indicates the increment of β-catenin, coupled with switch-on of Tcf
activators Tcf7/Lef1, contributes to establish or elevated openness of Tcf/Lef1-dependent
enhancers. The ChIP-Seq data sets enabled us to directly examine the change of chromatin
marker enrichment level on β-catenin binding sites. Indeed, at β-catenin binding sites in high
CHIR, we observed elevated ATAC-Seq signal correlated with the increment of CHIR (Fig 20F).
We also observed elevated level of H3K27ac as well as loading of RNA Pol II (Ser5P) on the
same loci (Fig 20F), supporting activation of β-catenin-bound enhancers with elevated level of
CHIR.
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Figure 20. Increased CHIR dosage induces a switch of Tcf/Lef factors binding to the genome.
(A) Histograms showing binding intensity of Tcf/Lef factors and chromatin markers on the 3 sets
of Tcf/Lef binding sites assigned by overlap of low CHIR Tcf7l1 and high CHIR Lef1 binding
sites. (B) Result from de novo motif discovery of the 3 sets of Tcf/Lef binding sites described in A.
(C) Top Gene Ontology terms associated with the corresponding sets of Tcf/Lef binding sites
shown in B. (D) Percentage of different sets of Tcf/Lef target genes in differential expressed
genes specific to low CHIR or high CHIR conditions as described in Fig 14. (E) Histograms
showing quantification of reads from the indicated data sets in +/-2kb of β-catenin binding sites
in high CHIR condition.
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Figure 21. Supplementary ChIP-Seq data analysis. (A) Numbers of and overlap between
binding sites of the same factors between different conditions. (B) Numbers of and overlap
between binding sites of different factors in the same conditions. (C) Most enriched motifs by de
novo discovery (Homer) from binding sites of the data sets indicated. (D) Hierachical clustering
of normalized read counts of ChIP-Seq data sets on merged binding sites.
Figure 22. Analysis of Tcf7l1 binding in low CHIR. (A) Overlap of direct and indirect Tcf7l1
binding in low CHIR with Lef1 binding in high CHIR (left) and enrichment of ChIP-Seq reads on
the two types of binding events. (B) Distribution of ATAC-Seq, H3K27ac ChIP-Seq and Ser5P
ChIP-Seq signals on Tcf7l1 binding sites in low CHIR with or without Tcf/Lef motifs. (C) Gene
Ontology analysis by GREAT on Tcf7l1 binding sites in low CHIR with or without Tcf/Lef motifs.
(D) Overlap of predicted target genes between Tcf7l1 binding sites in low CHIR with or without
Tcf/Lef motifs.
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3.3.4 β-catenin uses both pre-established and de novo enhancer-promoter loops to
drive NPC differentiation program
To examine organization of the genome at a higher level before and after NPC induction,
we performed HiC analysis to examine global chromatin-chromatin interactions in NPCs on low
and high conditions. Analysis revealed 19,494 and 20,729 reproducible chromatin-chromatin
interaction loops in low CHIR and high CHIR conditions, respectively. Almost half (40% and
44%) of the loops in each condition are unique, and somewhat similar proportion (35% and 49%)
of loops that connect to TSS are also unique (Fig 23 B), suggesting dynamic chromatin looping
between low CHIR and high CHIR coditions. Since Ctcf is known to mediate chromatin
interaction and its appears to be stable across cell types, we integrated a previously published
Ctcf ChIP-seq dataset generated from uncultured NPCs as a reference (O'Brien et al., 2018).
Next, we explored the chromatin loops that connect β-catenin-associated enhancers to target
promoters. Of the 5,530 β-catenin associated sites in high CHIR condition, 28% (1,573) were
located in one anchor of a loop, of which 41% (647) were connected to a transcriptional start site
(TSS). Among the loops that connect β-catenin binding sites to a TSS, 57% (371) are
‘conserved’ loops that are shared between low CHIR and high CHIR, the remaining 43% (276)
can be considered established de novo under high CHIR conditions (Fig. 23A).
To understand the biological consequences of these regulatory events, we identified
promoters connected to β-catenin-bound enhancers. Among those connected by conserved loops,
we found 59 genes that were highly expressed in high CHIR conditions, including Wnt4, Lhx1,
Emx2, Bmp7 and Cxcr4. These genes are required for NPC differentiation in vivo. In low CHIR
conditions, Six2, Tcf7l1, Tcf7l2 and β-catenin bind to the Wnt4 distal enhancer (Wnt4DE) and
loop to proximity of Wnt4 TSS; in high CHIR, Tcf7l1 and Tcf7l2 was replaced by Tcf7 and Lef1
on the same locus, concomitant with decreased Six2 and increased β-catenin binding (Fig 23C).
There are 2 additional loops further upstream, notably with Ctcf binding on the other anchor, that
also loop to Wnt4TSS (Fig 23C). All of these 3 loops are stable between low CHIR and high
CHIR conditions, and our data suggests one of them is mediated by Tcf/β-catenin while the other
two by Ctcf. It is likely that the stable loops, which are formed before activation of target genes,
facilitate activation of transcription (Fig 23D).
The de novo loops are connected to 50 genes highly expressed in high CHIR condition;
Lef1 and Jag1 are among the targets of these loops. In the Lef1 locus, no loop connected to Lef1
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TSS was observed in low CHIR; in high CHIR condition, there are 4 such loops formed, one of
which are mediated by Lef1/β-catenin and two others by Ctcf (Fig 23E). Concomitantly, in low
CHIR, Lef1/Tcf7/β-catenin binding was not detected, and the upstream intergenic region of Lef1
is not marked with ATAC-Seq nor high level of H3K27ac signal, suggesting silenced chromatin.
In high CHIR, a string of Lef1/Tcf7/β-catenin binding events accompanied enhanced
accessibility (ATAC-seq) and the appearance of an active chromatin signature (H3K27ac) (Fig
23E). These data suggest that the de novo loop formation is made possible by de novo chromatin
binding events associated with Lef1/Tcf7/β-catenin binding (Fig 23F). Given the direct actions
of Lef1 in NPC induction, this regulatory interaction may act as a feed-forward loop driving
NPC commitment (see Discussion).
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Figure 23. β-catenin activates gene expression through both stable and de novo enhancer-
promoter loops. (A) β-catenin binding sites that overlap with an anchor of chromatin loop in
high CHIR, the proportion that connects to a TSS (grey in the pie chart) and segregation
between 2 types of loops defined in B. (B) Overlap of chromatin loops (left) and loops that
connect to a TSS between low CHIR and high CHIR conditions. (C and D) Examples of β-
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catenin utilizing low/high CHIR-shared enhancer-promoter loops to activate Wnt4 (C) or high
CHIR-specific loops to activate Lef1 (D). Black arrow at the bottom indicates the β-catenin
binding sites involved in the loops.
3.4 Discussion
3.4.1 Summary of major findings
Analysis of genetic mouse models point to a requirement for β-catenin in both the
maintenance (Karner et al., 2011) and differentiation (Park et al., 2007) of NPCs. Previous
studies directly examining β-catenin association in differentiating NPCs showed direct
engagement at enhancers regulating expression of differentiation promoting genes such as Wnt4
(Park et al., 2012). Data here confirmed these earlier findings and extended our understanding
through a comprehensive analysis of Wnt-directed transcriptional engagement and epigenetic
organization, in a simple in vitro model of mammalian NPC programs.
The key findings are as follows:
1. Elevated β-catenin level leads to decreased expression of Tcf repressor Tcf7l1 and
Tcf7l2, and dramatic activation of Tcf activators Tcf7 and Lef1; Tcf7/Lef1 replaces
Tcf7l1/Tcf7l2 binding on enhancers involved in activation of NPC differentiation programs,
which might be important in switching the related genes from repression to activation.
2. Elevated β-catenin level results in activation of Tcf/Lef bound enhancers and
significant change of NPC chromatin landscape.
3. In activating target gene expression, β-catenin uses both enhancer-promoter loops that
are pre-established in uncommitted NPC and those established de novo upon differentiation,
suggesting an innate mechanism by NPC to be pre-configured for differentiation.
3.4.2 Function and regulation of distinct Tcf/Lef factors in NPC and other stem cell
systems
Tcf/Lef factors are the transcription factors that ultimately mediate the transcriptional
response to Wnt signaling. We provide concrete evidence of an association between CHIR-
mediated elevation of β-catenin levels (Fig 14A and 19B) and decreased expression of Tcf7l1
and Tcf7l2, a reduction in their DNA target interactions, and induction of Tcf7 and Lef1, and
117
their replacement of Tcf7l1 and Tcf7l2, on enhancers of differentiation promoting genes (Fig 18
and 20). Through gene expression and ChIP-Seq analysis, we identified 3 modes of actions by
Tcf/Lef factors (Fig 24). In the first mode, Tcf7l1, indirectly engaged to enhancers through other
factors, is involved in activating or maintaining gene expression in low CHIR/maintenance state.
The second mode is applied to the classical set of genes required for NPC differentiation. where
Lef1 replaces Tcf7l1 on the same set of enhancers, resulting in a shift from transcriptional
repression to activation. The third mode relies on de novo opening and activation of enhancers by
Lef1/β-catenin, where new Tcf/Lef target genes are activated.
Consistent with what we have found in this study, distinct Tcf/Lef factors displayed
distinguishable functions in various stem cell systems. In mouse embryonic stem cell (mESC)
culture, canonical Wnt signaling, induced by CHIR22091, supports long-term self-renewal (Ying
et al., 2008). Tcf7l1 has been shown to repress expression of genes involved in this process,
while Tcf7 and Lef1 activates it (Yi et al., 2011). In hair follicle stem cells (HFSC), Wnt induces
a transition of the stem cells from the quiescent to the proliferative state. Tcf7l1 and Tcf7l2 are
preferentially expressed in HFSC, while Lef1 and Tcf7 are preferentially expressed in the
differentiated HFSC, i.e., hair germ (HG) cells (Lien et al., 2014; Merrill et al., 2001). Loss of
Tcf7l1 is required for β-catenin (Huelsken et al., 2001) and Lef1 (Genderen et al., 1994) are
required for HFSC differentiation. Tcf7l1/Tcf7l2 repress genes involved in HFSC differentiation,
which are activated by Tcf7/Lef1, correlating of replacement of Tcf7l1/Tcf7l2 by Tcf7/Lef1 on
relevant enhancers. Notably, Tcf7l1/Tcf7l2 also maintains expression of a set of HFSC-specific
genes, suggesting their dual functions (Adam et al., 2018). Overall, the role of Tcf7l1 as a
transcriptional repressor has been described in both mESC and HFSC, and Tcf7 and Lef1 as
transcriptional activators in mESC and HFSC, respectively. Replacement of Tcf/Lef repressors
by activators was also observed in HFSC differentiation consistet with canonical Wnt signaling
pathway regulation.
3.4.3 Elevated level of β-catenin leads to activation of Tcf/Lef-bound enhancers
In the Wnt-off state, Tcf factors are known to be able to recruit Groucho family co-
repressors (Cavallo et al., 1998) and histone deacetylase (Billin et al., 2000) to repress Wnt target
gene expression. Upon Wnt ligand stimulation, Groucho is replaced by β-catenin for activation
(Brantjes et al., 2001; Daniels and Weis, 2005). From evidence in vitro, β-catenin has been
shown to be able to recruit various chromatin modulator, including histone acetyl transferase
118
(HAT) (Hecht et al., 2000), histone methyl transferase (Sierra et al., 2006), and chromatin
remodeler (Barker et al., 2001). Furthermore, through interaction with Pygo and Bcl9 (Kramps et
al., 2002), as well as direct interaction (Kim et al., 2006), β-catenin can form a complex with the
Mediator complex , which bridges the Tcf-bound enhancer to RNA Pol II complex at the target
gene promoter (Jeronimo and Robert, 2017). With all these clues, no study so far has examined
genome-wide the change of chromatin state as a result of Wnt/β-catenin activation, and it is not
clear whether β-catenin is capable of open new chromatin sites, rather than simply carry out an
activation function on loci already primed. Our data serves as direct evidence that high level of
β-catenin, possibly through activating Tcf7 and Lef1, is sufficient to open chromatin with
Tcf/Lef motif and induce histone acetylation which culminates in enhancer activation.
3.4.4 Pre-establishment of enhancer-promoter loops contributes to potency of stem
cells to destined fates
Enhancers, bound by transcription factors and co-activators, can recruit RNA Pol II and
bring it close to target gene promoters through enhancer-promoter loops (Yu and Ren, 2017). As
a co-activator, β-catenin has been shown to be able to interact with Mediator complex (Jeronimo
and Robert, 2017) which bridges enhancers and promoters. Some previous studies in Drosophila
indicated that during development, certain enhancer-promoter loops are stable (Ghavi-Helm et
al., 2014), i.e. the enhancer-promoter loop is established before the target gene being activated.
This was implicated as a mechanism in priming developmentally potent cells to differentiate into
their destined fate. Since NPCs are pre-disposed to nephrogenic differentiation, we hypothesized
that some of the enhancers to activate gene expression involved in differentiation might also be
in contact with their target promoter before transcription is fired. Indeed, there are about 56%
loops in high CHIR are established in low CHIR condition, of which 70% are connected to a
TSS (Fig 23B). Example of such ‘conserved’ loops that connect β-catenin binding events to TSS
include Wnt4, Lhx1, Emx2, Bmp7 and Cxcr4. These data are consistent with the concept that at
least part of the differentiated program in NPC is primed through enhancer-TSS loop
establishment prior to gene expression. Clearly, activating some other genes involved in
differentiation requires de novo activation by β-catenin, the presence of conserved loops is likely
to lower the threshold for cell fate transition and be a unique feature of NPC.
119
Figure 24. Proposed model of Wnt/β-catenin-dependent gene regulatory mechanism during
NPC maintenance and differentiation
3.4.5 Future Studies
Taking the data here to our understanding of nephron programs in vivo (Figure 24), there
are clearly several key questions that remain to be resolved, several of which will benefit from
continued development of the in vitro NPC model. To this end, recent studies have focused on
RNA-mediated transfection to ectopically express factors of interest and to perform gene-editing
120
to modify the activity of specific genes. Preliminary data are encouraging, these approaches will
likely play key roles in addressing the outstanding questions:
1. The current model presumes a decreasing gradient of Wnt9b expression level from
ureteric stalk to tips. This is supported by data from early developing kidneys (Carroll et al.,
2005), but no data has been collected on later stages, including the E16.5 NPCs collected in this
study. Also, if the differential level of β-catenin between uncommitted and committed NPC is a
direct result of distinguishable accessibility to Wnt9b, what is the threshold to make such a
difference, and can we model this in the dish?
2. Genetic studies has suggested multiple signaling pathways (Bmp, Notch, Fat, Fgf8)
involved in NPC differentiation, and some evidence suggested interaction between these
pathways (see Chapter 1). We wonder how these other signaling pathways interact with Wnt/β-
catenin at the transcriptional level, and what is the consequence of such interaction. The in vitro
culture seems to be a reasonable system to test these ideas.
3. What is the mechanism underlying β-catenin-mediated NPC maintenance? Our
preliminary data showed removal of CHIR likely affects proliferation of NPC in culture (Fig
15E), consistent with the phenotype of loss of β-catenin in NPC in vivo (Karner et al., 2011).
However, we did not observe β-catenin engagement near the genes that are differentially
expressed between the low CHIR and no CHIR conditions (Fig 15F). We suspect that the pro-
proliferation role of β-catenin might be implemented through secondary role of another
transcriptional regulator or a cytoplasmic mechanism.
4. Does Tcf7l1/Tcf7l2 silence targets? If so, through what mechanism? The obvious
candidates are the ones through Groucho or CtBP. Are there differential effects on different
targets through direct and indirect engagement to DNA, as we predicted (Fig 20 and 22)?
Functionally, is removal of Tcf7l1 and Tcf7l2 sufficient to activate differentiation program?
Does Tcf7l2 have a role in preparing Tcf/Lef binding sites for engagement by activators? If so,
does removal of Tcf7l2 reduced the sensitivity of NPC to CHIR-mediated induction?
5. Do Lef1/Tcf7 actively promote differentiation target gene transcription? It won’t
surprise us if they do. However, whether they are able to activate gene expression in low CHIR
is less certain.
6. How does elevated β-catenin level triggers expression of Lef1 and Tcf7, while
repressing Tcf7l1 and Tcf7l2? Our data showed engagement β-catenin and Tcf7/Lef1 at
promoter and possible enhancers of Lef1 (Fig 22E) and Tcf7 (data not shown) in high CHIR,
121
indicative of a feed-forward loop. The question lies in how the initial fire of Lef1/Tcf7
transcription was attained in the state where there is no or low level of the activators. We think
there are two possibilities: 1) assuming Tcf7l2 being an inefficient activator rather than an
absolute repressor (Brantjes et al., 2001), high level of β-catenin can trigger low level of
expression of Lef1/Tcf7 through binding to Tcf7l2, which would be sufficient to ; 2) low level of
Tcf7 is likely to be present in low CHIR condition, which might mediate the initial
transcriptional activation upon increment of β-catenin in the cells.
7. What prefigures the chromatin landscape to ensure the specific output? An intuitive
clue would be the transcription factors required for NPC maintenance and self-renewal, e.g.,
Six2, Osr1, Hox11, Sall1, Pax2. It won’t surprise us if Tcf/β-catenin binding highly overlaps
with the binding of these factors. To functionally validate it, we might be able to inactivate
individual factors, then perform chromatin profiling to see if significant change can be observed
in any of the cases.
3.5 Materials and Methods
3.5.1 mRNA-Seq and data analysis
50,000 – 100,000 cells were collected for each RNA experiment. RNA was isolated with
RNeasy micro kit (Qiagen, #74004). mRNA-Seq libraries were prepared with KAPA Stranded
mRNA-Seq Kit (Kapa Biosystems, #KK8420). The libraries were subsequently sequenced with
Illumina NextSeq500 model with pair-end 75 bp setting.
mRNA-Seq reads were aligned with STAR (Dobin et al., 2013) to mm10 assembly and
quantified with Partek E/M to generate a count table, and finally converted to TPM for
representation. All the steps above were implemented in the Partekflow web platform (St. Louis,
MO, USA) sponsored bu USC Norris Medical Library.
To identify differentially expressed genes, count tables of the two groups of data being
compared were processed through DESeq2 (Love et al., 2014) to obtain the negative binomial p
values which evaluates the significance of difference by read counts. The differentially expressed
genes were defined with the following threshold: TPM > 5, fold change >3, negative binomial p
value < 0.05, unless otherwise specified.
Gene Ontology enrichment analysis was performed with DAVID (Huang da et al., 2009).
122
3.5.2. ChIP-Seq
1. Fixation. Freshly isolated nephron progenitor cells were fixed in 1 mL AutoMACS
running buffers (for each 3-5 million cells). Cultured nephron progenitor cells were fixed in
NPEM medium before scraping. In both cases, cells were fixed with final 1% formaldehyde
(Thermo Fisher Scientific, #28908) for 20 min in room temperature.
2. Chromatin preparation. 3-5 million cells were processed for each chromatin
preparation. Chromatin preparation includes cell lysis and nuclei lysis, which were done with
SimpleChIP® Sonication Cell and Nuclear Lysis Buffers (Cell Signaling Technology #81804)
following manufacturer’s instruction.
3. Chromatin fragmentation. For chromatin fragmentation, lysed nuclei were sonicated
with Branson Ultrasonics Sonifier S-450, using a double-step microtip. Each sample was re-
suspended in 1 mL nuclear lysis buffer in a 15 mL conical tube, embedded in water-filled ice.
Sonication was performed at 20% amplitude for 4 min, with 3 secs of interval after each 1 sec of
duty time.
4. Immuno-precipitation. 1 million-equivalent fragmented chromatin was used for each
immunoprecipitation experiment. Immuno-precipitation was done with SimpleChIP® Chromatin
IP Buffers (Cell Signaling Technology #14231) following the manufacturer’s instruction, with
the following details. The amounts of antibody used were case-dependent. In general, 2 μg or
1:50 to 1:100 antibody was used for each precipitation. Chromatin with antibody were rotated
overnight at 4 °C before 1:40 protein A/G agarose beads (Thermo Fisher Scientific, #20423)
were added and incubated for another 6 hrs to overnight. After washing and elution, antibody-
precipitated input DNA were purified with minElute reaction cleanup kit (Qiagen, #28204),
reconstituting to 35 uL EB buffer.
5. ChIP-qPCR. qPCR was performed with Luna® Universal qPCR Master Mix Protocol
(New England Biolab #M3003) on a Roche LightCycler 96 System. For each reaction, 0.5 out of
35 uL ChIP or input DNA was used. The qPCR primers used are listed below:
Six2-DE:
F: ggcccgggatgatacatta
R: cgggtttccaatcaccatag
Wnt4-DE:
F: GACCCATAAGGCAGCATCCA
R: CTTGCTGGGCAGAGATGAA
123
Non-ChIP:
F: tctgtgtcccatgacgaaaa
R: ggaagtcatgtttggctggt
6. Sequencing. ChIP-Seq libraries were prepared with Thruplex DNA library prep kit
(Clontech, # R400523). The libraries were sequenced with Illumina NextSeq500 model using
single-end 75 bp setting.
3.5.3 ChIP-Seq data analysis
ChIP-Seq reads were aligned with bowtie2. The alignment files are filtered to remove
duplicate reads with Picard (http://broadinstitute.github.io/picard/index.html). Peak calling was
performed with MACS2 (Feng et al., 2012) with combined replicate data sets of the
ChIP/condition being considered, and using combined replicate input from the same condition as
control. To obtain relatively strong peaks, the peaks were first filtered for q-value < 1e-4.
Afterwards, the counts of normalized reads were generated within +/- 250 bp windows of the
filtered peaks. To obtain consistent peaks in both replicates, we filtered the ones with > 10 fold
enrichment in the +/- 250 bp window in both replicates for downstream analysis. For data shown
in Fig. 20 and Fig. 21, the peals were further filtered for those with fold enrichment > 20 in order
to focus on strong peaks. Overlap peaks were defined as those within 150 bp from each other’s
center.
For visualization, wiggle tracks were generated with QuEST (Valouev et al., 2008). The
intensity of peaks is measured as fold enrichment, which is calculated by the number of reads
within the +/- 250 bp window divided by the total mapped reads in the library, normalized to the
size of genome. De novo Motif discovery and motif scan was performed with Homer (Heinz et
al., 2010). Gene Ontology analysis was performed with GREAT (McLean et al., 2010). To
determine the overlap of ChIP-Seq peaks, peak centers from the two compared data sets were
overlapped, and centers beyond 150 bp from each other were considered as unique sites (Fig.
S4A-B).
124
3.5.4 ATAC-Seq and data analysis
Each ATAC-Seq experiment was performed with 50,000 cells, following the published
protocol (Buenrostro et al., 2013). ATAC-Seq libraries were sequenced with Illumina
NextSeq500 model using single-end 75 bp setting.
ATAC-Seq reads were aligned with bowtie2. Peak calling was performed with MACS2
(Feng et al., 2012) without control data. Subsequently, reads from each replicate were counted
with Homer within +/- 250 bp of peak center. Peak intensity was represented as fold enrichment
as described in ChIP-Seq data analysis. To obtain consistent peaks, only the ones passing
threshold (fold enrichment > 3) in all 3 replicates were retained for downstream analysis.
To identify enriched regions in condition A over condition B, ATAC-Seq peak
coordinates from condition A were used to count reads within a +/- 250 bp windows, from both
condition A and condition B. Fold enrichment in each replicate was calculated and went through
DESeq2 procedure in order to identify statistically significantly differentiated accessible (DA)
regions. The threshold for identifying DA regions is fold enrichment > 5, fold change > 2 and
negative binomial p-value < 0.05.
To perform genome-wide hierarchical clustering, peaks from all replicate data sets in
comparison were merged (peaks < 150 bp from each other are combined into one peak taking the
mid-point as the new coordinate). Subsequently, ATAC-Seq reads from all samples concerned
were counted in +/- 250 bp bins centering on the merged peaks, generating a count table.
Hierarchical clustering was generated based on fold enrichment calculated from the count table.
De novo Motif discovery was performed with Homer (Heinz et al., 2010). Gene Ontology
analysis was performed with GREAT (McLean et al., 2010).
3.5.5 Hi-C data generation and analysis
We generated about 700 million raw reads for each sample. The reads were aligned by
bwa (Li and Durbin, 2009), then duplicates were removed with Picard. The hic files were created
and loop calling was done with the Juicer Tools (Durand et al., 2016).
To identify loops that are consistently present in both replicates, we extracted loops
whose coordinates of anchors are within 10 kb between replicates.
Condition replicate1 replicate2 overlap
NPC-1.25 35854 39693 12181
125
NPC-5 36731 40595 12231
To find TSS of genes and peaks connected by loops, we look for peaks that are within 5
kb from center of one of the loop anchors and TSS that are within 15 kb from center of the other
loop anchor.
3.5.6 Single-cell RNA-Seq and data analysis
Single-cell RNA-Seq library was synthesized and reads were mapped in the same fashion
as described in (Lindstrom et al., 2018). Clusters of single cells, feature plots and dot plots were
generated with Seurat (Satija et al., 2015).
3.5.7 Reverse transcription followed by qPCR (RT-qPCR)
Total RNA was reverse-transcribed with SuperScript™ IV VILO™ Master Mix with
ezDNase™ Enzyme
(cat #: 11766050). qPCR was performed with Luna® Universal qPCR Master Mix
Protocol (New England Biolab #M3003) on a Roche LightCycler 96 System. p-values were
obtained by performing t-test between replicates of samples indicated. Primers used in RT-qPCR
are listed as following:
Six2:
F: CACCTCCACAAGAATGAAAGCG
R: CTCCGCCTCGATGTAGTGC
Cited1:
F: AACCTTGGAGTGAAGGATCGC
R: GTAGGAGAGCCTATTGGAGATGT
Wnt4:
F: AGACGTGCGAGAAACTCAAAG
R: GGAACTGGTATTGGCACTCCT
Jag1:
F: CCTCGGGTCAGTTTGAGCTG
R: CCTTGAGGCACACTTTGAAGTA
Fgf8:
F: CCGAGGAGGGATCTAAGGAAC
126
R: CTTCCAAAAGTATCGGTCTCCAC
Lhx1:
F: CCCATCCTGGACCGTTTCC
R: CGCTTGGAGAGATGCCCTG
Pax8:
F: ATGCCTCACAACTCGATCAGA
R: ATGCGTTGACGTACAACTTCT
Tcf7l1:
F: CCCGCTGACACCTCTCATC
R: ACAGTGGGTAATACGGTGACAG
Tcf7l2:
F: AACGAACACAGCGAATGTTTCC
R: CACCTTGTATGTAGCGAACGC
Tcf7:
F: AACTGGCCCGCAAGGAAAG
R: CTCCGGGTAAGTACCGAATGC
Lef1:
F: TGTTTATCCCATCACGGGTGG
R: CATGGAAGTGTCGCCTGACAG
3.5.8 Immunofluorescence staining
To perform immunofluorescence staining, cell cultures were fixed with 4% PFA in PBS
for 10 min, then wash with PBS twice before blocking in 1.5% SEA block (ThermoFisher,
107452659) in TBST (0.1% Tween-20 in TBS). After minimally 30 min in room temperature,
switch to primary antibody (diluted in blocking reagent) incubation in 4 degree overnight. After
washing 3 times with TBST, switch to secondary antibody (diluted in blocking reagent)
incubation for minimally 45 min in room temperature, blocking light. This was followed by 3
wash with TBST, then the cells were kept in PBS for confocal imaging.
127
3.5.9 Immunoblots
To separate, protein samples were boiled with β-mercaptol and ran in SDS-PAGE gels
casted from 30% Acrylamide/Bis solution 29:1 (Bio-rad, 1610156) using the Mini-PROTEAN®
system (Bio-rad). Afterwards, the gel was transferred in Mini Trans-Blot® Cell (Bio-rad) system
to PVDF membranes (Immobilon-P, EMD millipore, IPVH08100). The membrane with protein
was blocked with I-block (Applied Biosystem, T2015) in TBST (0.1% TritonX-100 in TBS) in
room temperature for 45 min before switching to primary antibody (diluted in blocking reagent)
incubation in 4 degree overnight. Subsequently, the membrane was washed 3 times and switched
to secondary antibody incubation (diluted in blocking reagent) for 45 min in room temperature.
This was followed by 3 washes with TBST before drying the membrane and adding HRP
substrate (Pierce™ ECL Plus Western Blotting Substrate, Thermo, 32132). Finally, the
membrane was used on Autoradiography Film (5x7, Blue Devil, Premium, 100 Sheets/Unit,
Genesee Scientific/Amazon) to visualize location of protein.
128
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Appendices
Publications and contributions
Lindström NO, Guo J, Kim AD, Tran T, Guo Q, Brandine GD, Ransick A, Parvez RK, Thornton
ME, Basking L, Grubbs B. Conserved and divergent features of mesenchymal progenitor cell
types within the cortical nephrogenic niche of the human and mouse kidney. Journal of the
American Society of Nephrology. 2018 Mar 1;29(3):806-24.
I performed bioinformatics analysis of RNA-Seq data, as well the experiments in generating
RNA-Seq libraries.
Lindström NO, McMahon JA, Guo J, Tran T, Guo Q, Rutledge E, Parvez RK, Saribekyan G,
Schuler RE, Liao C, Kim AD. Conserved and divergent features of human and mouse kidney
organogenesis. Journal of the American Society of Nephrology. 2018 Mar 1;29(3):785-805.
I performed bioinformatics analysis of RNA-Seq data.
O’Brien LL, Guo Q, Bahrami-Samani E, Park JS, Hasso SM, Lee YJ, Fang A, Kim AD, Guo J,
Hong TM, Peterson KA. Transcriptional regulatory control of mammalian nephron progenitors
revealed by multi-factor cistromic analysis and genetic studies. PLoS genetics. 2018 Jan
29;14(1):e1007181.
See in Chapter 2.
O'Brien LL, Guo Q, Lee Y, Tran T, Benazet JD, Whitney PH, Valouev A, McMahon AP.
Differential regulation of mouse and human nephron progenitors by the Six family of
transcriptional regulators. Development. 2016 Feb 15;143(4):595-608.
I performed bioinformatics analysis of ChIP-Seq data and RNA-Seq data, as well as the
experiments in verifying specific of antibodies against SIX1 and SIX2, respectively.
Abstract (if available)
Abstract
Chapter 1. As in other stem/progenitor systems, in NPCs environmental signals regulate a collection of transcription factors leading to transcriptional programs that determine NPC states. In this review, I discuss the role of signaling pathways and transcription factors in the specification and maintenance of NPCs focusing on mammalian genetic studies. Insights here have led to protocols that support the culture and expansion of NPCs in vitro, and the de novo formation of NPCs and differentiated NPC derivatives from pluripotent stem cells. I review the rationale for selecting key components that underlie the effectiveness of these procedures. Wnt signaling is particularly important and the central pathway of focus in my thesis. Given Wnt-directed mechanisms are a major theme, I expand beyond the kidney to consider the function of Wnt signaling in some well-studied mammalian stem cell systems. ❧ Chapter 2. Nephron progenitor number determines nephron endowment
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Guo, Qiuyu
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Transcriptional regulation in nephron progenitor cells
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Keck School of Medicine
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Development, Stem Cells and Regenerative Medicine
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04/29/2019
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