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Linking air pollution to integrative gene and metabolites networks in young adult with asthma
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Linking air pollution to integrative gene and metabolites networks in young adult with asthma
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Content
Linking Air Pollution to Integrative Gene and Metabolites Networks
in Young Adult with Asthma
By
Xin Li
A Thesis Presented to the
FACULTY OF THE USC Keck School of Medicine
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
BIOSTATISTICS
May 2020
Copyright 2020 Xin Li
ii
ACKNOWLEDGMENTS
I would like to thank my advisor Dr. Zhenghua Chen for being a great mentor, allowing me to
join her research, and for being incredibly supportive and helpful. In addition, I would like to
thank my committee members Dr. Frank D. Gilliland and Dr. Duncan Campbell Thomas for
their expertise and guidance.
I would like to thank Dr. Meredith Franklin for helping me to switch to the Master of
Biostatistics program at USC.
Lastly, I would like to thank the Biostatistics program at USC showing me how useful and fun
biostatistics can be.
iii
TABLE OF CONTENTS
Acknowledgements ......................................................................................................................... ii
List of Tables & Figures ................................................................................................................ iv
Abstract ........................................................................................................................................... v
Introduction ..................................................................................................................................... 1
Methods........................................................................................................................................... 3
Study Population ......................................................................................................................... 3
Air pollution ................................................................................................................................ 3
Gene Profiling ............................................................................................................................. 4
Metabolomic Profiling ................................................................................................................ 4
Statistical Analysis ...................................................................................................................... 5
Results ............................................................................................................................................. 7
Baseline Characteristics .............................................................................................................. 7
Integrative Gene-metabolite-exposure Networks ....................................................................... 9
Discussion ..................................................................................................................................... 11
References ..................................................................................................................................... 27
iv
List of Tables & Figures
Table 1. Characteristics of 102 adults in the IGERA cohort who have complete gene expression,
metabolite profiling data ................................................................................................................. 8
Table 2.Air Pollution and Traffic-related Pollution Exposure Level ............................................. 9
Table 3: Omics and Pathway Enrichment in Cluster 1 (Long-term) ............................................ 16
Table 4: Omics and Pathway Enrichment in Cluster 2 (Long-term) ............................................ 17
Table 5: Omics and Pathway Enrichment in Cluster 3 (Long-term) ............................................ 19
Table 6: Omics and Pathway Enrichment in Cluster 4 (Long-term) ............................................ 20
Table 7: Omics and Pathway Enrichment in Cluster 5 (Long-term) ............................................ 20
Table 8: Omics and Pathway Enrichment in Cluster 1 (Short-term) ............................................ 20
Table 9: Omics and Pathway Enrichment in Cluster 2 (Short-term) ............................................ 21
Table 10: Omics and Pathway Enrichment in Cluster 3 (Short-term) .......................................... 22
Table 11: Omics and Pathway Enrichment in Cluster 4 (Short-term) .......................................... 22
Table 12: Omics and Pathway Enrichment in Cluster 5 (Short-term) .......................................... 25
Figure 1: Long-term air pollution exposures, Metabolites and Genes Integration Result ............ 14
Figure 2: Short-term air pollution exposures, Metabolites and Genes Integration Result............ 15
v
Abstract
Background: Previous studies have shown substantial adverse effects of both regional and
traffic-related air pollution exposures on lung function in children and adults. Besides, a growing
number of studies suggested that sphingolipid, lipid, and fatty acid metabolism are all associated
with lung function in asthmatic patients (14). Air pollution exposure specifically ozone (O3),
organic acids, nitrogen oxides and particular matter are known to affect respiratory system
through hypoxia response, oxidative stress, immunity, inflammation, lipid metabolism and the
tricarboxylic acid cycle (15). However, how air pollution exposure influences gene expression
profile and leads to dysregulated metabolism in asthmatic adults is unclear.
Objective: To investigate the joint effects of regional and traffic-related air pollution exposures
on the regulatory network of gene expression and metabolites network in young adults with
asthma history.
Methods: A total of 102 adults (mean±SD age=26.2±2.1 yrs) originally enrolled in the southern
California Children’s Health Study (CHS) were followed for gene expression and metabolomics
profiling in year 2010-2011. All participants had previously diagnosed asthma during the CHS
follow-up in year 1995-2003. Individual exposures to regional air pollutants, including ozone
(O3), nitrogen dioxide (NO2), nitric oxide (NO), total nitrogen oxides (total NOx), organic acid,
carbon monoxide (CO), particular matter less than 10 microns in diameter (PM10) and 2.5
microns in diameter (PM2.5) were estimated using central monitor data near residential addresses.
Traffic-related air pollution levels were estimated as freeway and nonfreeway NOx exposure
using CALINE dispersion models (37). We mainly defined these air pollution variables into two
vi
categories, long-term exposure level (1-year averaged air pollution exposure level before the
2010-2011 study visit and average exposure level during childhood from 8-18 years old) and
short-term exposure level (1-month average air pollution exposure level before the 2010-2011
study visit). The gene expression data were generated with 20,869 probes from the Illumina
HumanHT-12 v4 Expression BeadChip (Illumina, Inc., San Diego, CA). In addition, untargeted
metabolomics was measured from archived serum samples using liquid chromatography–mass
spectrometry (LC-MS) analysis. After quality control of gene expression data and metabolomics
data, we used integrated network analysis to investigate the association among air pollution
exposure, genes, and metabolites (10). Based on the network results connecting genes and
metabolites with specific air pollution exposures, we further used pathway enrichment analysis
to examine gene-metabolite pathways related to different air pollution exposures.
Results: By integrating air pollution exposures, genes and metabolites, a total of 5 sub-networks
were found. In one subnetwork involving childhood average exposure to O3, metabolites
including oxoglutaric acid, L-Glutamic acid Choline, and glycerophospholipid were also
included, which indicated metabolic pathways related to oxidative stress (arginine biosynthesis,
alanine, aspartate and glutamate metabolism, histidine metabolism, D-Glutamine and D-
glutamate metabolism). In additional, genes involved in signaling pathways regulating
pluripotency of stem cells were also connected to O3-related sub-netowrk. Besides, we also
observed that perturbations in glycerophospholipid was linked to long-term exposures to PM10
and PM2.5. The subnetwork including long-term exposures PM10, PM2.5 were also connected to
genes in several genetic pathways including cancer related pathways (small-cell and non-small
cell lung cell pathways, etc.), signaling pathways (mTOR signaling pathway, T cell receptor
vii
signaling pathway, B cell receptor signaling pathway, etc.), and inflammation pathways
(pathogenic Escherichia coli infection, human immunodeficiency virus 1 infection, etc.).
Furthermore, childhood average exposure to freeway NOx could induce perturbations of some
amino acid metabolism such as biosynthesis of unsaturated fatty acids. By contrast, short-term
exposures to total NOx and NO2 were mainly associated with specific gene pathways, including
signaling pathways (mTOR signaling pathway, phosphatidylinositol signaling system,
sphingolipid signaling pathway, chemokine signaling pathway, etc.), and certain inflammation
pathways (yersinia infection, human cytomegalovirus infection, etc.). In addition, short-term
exposures were shown to be associated with dysregulated fatty acid metabolism, such as linoleic
acid, gamma-linolenic acid, palmitic acid, stearic acid, and arachidic acid. Short-term exposures
to PM10 and PM2.5 were connected with a group of genes (TSC2, MAP2K1, SLC38A9, TTI1,
etc.) and two metabolites (mannose and cysteinyl glycine) in one subnetwork. These gene and
metabolite findings suggested short-term exposures to PM10 and PM2.5 were associated with
altered mannose, sugar and glutathione metabolisms.
Conclusions: Both long-term and short-term exposures to regional and traffic-related air
pollutants are associated with a broad spectrum of alterations in amino acid and fatty acid
metabolism, inflammation and oxidative stress pathways. Specifically, childhood exposure of
long-term air pollution exposures (O3, nonfreeway NOx, PM10, PM2.5 and total acids) were
associated with dysregulation of glycerophospholipid metabolism. mTOR signaling pathway was
affected by both long-term and short-term PM10 and PM2.5 exposure with the disturbance of
genes including TTI1, SLC38A9, RRAGB and MAP2K1. Furthermore, oxidative
viii
phosphorylation was associated with short-term exposures to total NOx and NO2, which was
consistent with previous studies suggesting associations between NO2 and oxidative stress.
1
Introduction
Regional air pollution and traffic-related air pollution are associated with airway diseases and
asthma exacerbations in children and adults (8,12,13,16,17,20-22,28,29,31-33,37). Previous
studies have indicated that outdoor NO2, residential proximity to a freeway, freeway NOx and
NO2 are associated with lower forced vital capacity (FVC) and forced expiratory volume
(FEV1), which suggests adverse effect of air pollution exposures on childhood lung function (9).
Additionally, significant associations of air pollution exposures to NO2, PM2.5, CO and O3, with
asthma exacerbations in adults were reported in a meta-analysis (32). Specifically, A study
indicated that 9–23 million and 5–10 million annual asthma emergency room visits around the
world in 2015 could be attributable to O3 and PM2.5 exposures, respectively, representing 8%–
20% and 4%–9% of the annual number of global emergency room visits, respectively (34).
Besides, O3 exposure was also found to be associated with elevated asthma risk in children and
adults (18). Findings of previous studies have all indicated that regional and traffic-related air
pollution exposures may contribute to elevated airway inflammation, increased risk of both
chronic and acute respiratory symptoms and asthma prevalence in adults.
In addition, the exposure mixture of air pollutants is highly heterogeneous, including various
organic and inorganic chemicals. Little is known about the effect and disease mechanism of
individual air pollutant among the exposure mixture on asthma and asthma-related phenotypes.
Furthermore, the etiology of asthma is complicated. There are many subtypes of asthma,
differentiated by obesity status, allergy and severity (36). These factors affect the onset and
treatment effect of asthma (35). Therefore, investigating biological mechanisms related with
2
asthma subtypes and the effect of air pollution exposures on asthma-related mechanisms is
important.
Findings of several recent studies have suggested that the association between air pollution
exposure and asthma development could be driven by altering key gene and metabolic pathways
(5, 19, 23-27). Huang et al. identified seven metabolites significantly correlating to PM2.5
exposure, which were involved in purine and amino acid metabolism as well as glycolysis (23).
Also, air pollution exposure could induce alterations in gene expression in gene pathways. For
instance, exposure to ultrafine PM significantly disturbs the NF-κβ mediated signaling cascade.
The aberrant NF-κβ expression dysregulates proinflammatory cytokine production, leukocyte
recruitment, or cell survival, finally affects the magnitude and duration of the inflammatory
response (29,43).
Thus, findings of previous studies suggest that air pollution exposures could alter multiple
genetic and metabolic pathways. Since genetic and metabolic pathways are intertwined, whether
air pollution exposure can alter the joint gene-metabolite network and play a role in asthma
etiology is unknown and needs more studies to investigate. To fill this gap, we leveraged existing
data of whole-genome gene expression profiles, untargeted metabolomics and air pollution
exposures to investigate the impact of regional and traffic-related air pollution exposure on
altering gene-metabolite network in young asthmatic adults. Findings of this study will help to
identify key molecular pathways that can classify asthma subtypes in future studies.
3
Methods
Study Population
This integrative omics study was nested within the IGERA study, in which 103 adults (mean±SD
age=26.2±2.1 yrs) were enrolled between 2010 to 2011. All IGERA participants were originally
derived from the southern California Children’s Health Study (CHS). These 103 adults were
recruited from schools across Southern California communities and followed from kindergarten
or first grade (starting in year 2002) to the high school graduation in CHS. All subjects of the
IGERA study had diagnosed asthma in childhood. In this study, we used archived fasting serum
samples collected from all participants at the IGERA study visit for high-resolution untargeted
metabolomics analysis performed at the Clinical Biomarkers Laboratory at Emory University.
Whereas, one individual was excluded because of the incompleteness of gene expression data.
Therefore, a total 102 individuals with complete gene expression, metabolomics profiles, air
pollution exposures and covariates data were remained in the final analysis.
Air pollution
Individual exposures to regional air pollutants , including ozone (O3), organic acid, PM10, PM2.5,
Nitric oxide (NO), nitrogen dioxide (NO2), and carbon monoxide (CO) were estimated based on
individual residential addresses by exposure data measured continuously at central monitoring
stations within each community in Southern California (1). Traffic-related air pollution level was
estimated as freeway and nonfreeway NOx exposure using CALINE dispersion models (37).
Regional and traffic-related air pollution exposures were further classified into long-term
exposure and short-term exposure. The long-term exposure includes two exposure metrices: 1-
4
year average exposure level before the IGERA study visit and the average exposure level of
patients during childhood (from 8-18 years). On the other hand, the short-term air pollution
exposure was calculated as 1-month averaged exposure level before the IGERA study visit.
Before conducting statistical analysis, we pre-processed all exposure variables by standardizing
all exposure variables by two standard deviations of the exposure distribution of each air
pollutant.
Gene Profiling
The gene expression data were generated with 20,869 probes from the Illumina HumanHT-12 v4
Expression BeadChip (Illumina, Inc., San Diego, CA) passing quality controls (QC).
Expression data were then log2 transformed and quantile normalized as a single batch using the
“lumiT” and “lumiN” functions, respectively, from the R package lumi (version 2.10). Prior to
downstream statistical analyses, we filtered the expression data using the “nsFilter” function
from the R package “genefilter” (version 1.48) to screen out probes with inter-quartile ranges
(IQR) of expression variance below the 50th percentile to only keep the most informative probes
were used for analysis.
Metabolomic Profiling
937 metabolites were measured by high resolution metabolomics (HRM) from subjects’ serum
samples collected at fasting status. All serum samples were stored in -80 0C freezers. Archived
serum samples were shipped to Metabolon, Inc. on dry ice. Next, we added three types of
controls, including a small volume of each experimental sample served as a technical replicate,
water samples as blank controls, and a carefully selected cocktail to assess instrument
5
performance and assist on chromatographic alignment, for quality control purposes. At
Metabolon laboratory, serum sample extracts were analyzed for untargeted metabolomic profiles
using Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo
Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated
electrospray ionization (HESI-II) source and Orbitrap mass analyzer (11). Then, compounds
were identified by comparison to library entries of purified standards or recurrent unknown
entities. Prior to data analysis, peaks were quantified using AUC (area-under-the-curve) and data
were normalized to correct variations due to instrument inter-day tuning differences.
Statistical Analysis
We used integrated network analysis built in R package “xMWAS” (10) to assess the associations
of air pollution exposures with gene expression and metabolites. The statistical theory of the
integrated network analysis includes the first step of pairwise partial least squares (PLS)
regressions (2,10). We denote the data matrices X(n), Y(n) and Z(n), where X, Y and Z are three
different sets of variables (i.e. metabolomic profiling, gene profiling and air pollution exposures
respectively in our study) measured on the same n samples (2). Firstly, PLS searched for the
largest covariance between orthogonal components which were linear combinations of the X and
Y variables (3). Then, the association score matrix between variables Xi and Yi was generated as
an approximation of their correlation coefficients determined by the PLS components and
regression coefficients (3). This matrix was also called an edge list matrix Li. With a set of nodes
(Vi), they composed a graph, defined as Gi=(Vi, Li). We first set a high correlation threshold
(r=0.7) for integration of gene expression and metabolomic profiles to assure the selected genes
have high correlations with metabolites considering the fact that the dimension of gene
6
expression data is 20 times of metabolites. Sparse communities which had few units (<10 nodes)
in it were further screened out from the entire gene-metabolite network.
Next, the variable set of air pollutants (Z) were added into the model, the same process was
repeated for generating edge list matrices from all pairwise association analyses in the datasets,
e.g. Lj=cor(Y,Z), and Lk=cor(X,Z) (4). In the three-way integration, we selected a series of
correlations thresholds from 0.2-0.7 to compare the network results by balancing the separation
among subnetworks, while enough genes and metabolites (>10 for each omics signatures) can be
detected in each subnetwork. In result, we chose the threshold of 0.7 among genes, metabolites
and air pollution exposures for further network analysis, which provides the best separation of
subnetworks.
Furthermore, multilevel community detection algorithm was used to unfold hierarchical
communities in metabolite-gene-exposure 3-dimentional networks (5). Two phases were iterated
to achieve this algorithm. The first phase was to assign a different community to each node of the
network. Then, we evaluated the gain of modularity by removing the nodes from their original
communities. Taking node i for example, we assessed the improvements of modularity by
removing i and placing it in each of its n neighboring communities. We would place it in the
community for which the gain is maximized if it’s positive. If no positive gain could be obtained,
node i would keep in its original community. This phase would be repeated for all nodes until no
further optimization could be reached (5). Secondly, the algorithm set up a new network in
which the weights of links between new communities were the sum of weights of links between
nodes in their corresponding original communities. Throughout this whole process, modularity
over possible divisions of a network is the parameter used to examine the accuracy and
7
computing time of detecting community structure (6). Finally, the community network graph was
generated by igraph package in R (7).
In conclusion, after data integration and community detection, we could get both a table
containing all the statistically significant communities clustered by specific nodes (i.e.
metabolisms, genes or pollutants) and their network graph. Each subnetwork included various
nodes of gene expression, metabolites and air pollution exposures, indicating these nodes
clustered in one subnetwork are correlated.
Pathway enrichment Analysis
We did metabolite-pathway, gene-pathway and integrated pathway enrichment analysis at
MetaboAnalyst platform (38) based on the genes and metabolites found from previously
identified subnetworks that are related to different air pollution exposures. Metabolites and gene
signatures identified in each subnetwork was compared to the reference Kyoto Encyclopedia of
Genes and Genomes (KEGG) (39-41) and perform pathway enrichment analysis (38). The goal
of this step is to identify significant metabolite and gene pathways associated with various air
pollution exposures.
Results
Baseline Characteristics
Of 102 young adults with asthma from the IGERA cohort, 64 had atopic asthma, 38 had non-
atopic asthma. 66% of them were Hispanic White, 60 (59.2%) were females, the mean age was
26.2 years (SD: 2.1yrs), and 62.8% were reported obese or overweight. Long-term air pollution
exposures included average exposure levels of organic acids, total acids, O3, NO2, NO, PM10,
8
PM2.5, CO, freeway NOx, nonfreeway NOx, total NOx of participants during their childhood
(from age 8 to 18 years old), and annual average exposure levels of O3, NO2, PM10, PM2.5,
freeway NOx, nonfreeway NOx, total NOx before the IGERA study visit. Short-term air pollution
exposures included one-month average exposure levels of O3, NO2, PM10, PM2.5, freeway NOx,
nonfreeway NOx and total NOx before the IGERA study visit. Means and standard deviations of
long-term and short-term air pollution exposures are presented in Table 2 Additionally, 20869
genes and 937 metabolites were included in the integrated network analysis.
Table 1. Characteristics of 102 adults in the IGERA cohort who have complete gene expression, metabolite profiling data.
Characteristics N(%)
Male
Ethnicity
Hispanic White
Non-Hispanic White
Weight
Normal Weight
Overweight
Obese
Ever Smoking
Yes
No
41(40.2)
67(65.7)
35(34.4)
37(36.3)
37(36.3)
27(27.4)
43(42.2)
59(57.8)
Age
BMI
Mean(SD)
26.2(2.1)
27.5(5.9)
9
Table 2.Air Pollution and Traffic-related Pollution Exposure Level
Integrative Gene-metabolite-exposure Networks
Long-term air pollution exposures
In the integrative long-term air pollution exposure-gene-metabolite network, 5 clusters were
classified, including 107 metabolites, 1461genes, and 17 pollutants (Figure1). Table 3-7 provide
details for the subnetworks.
In one subnetwork, childhood average exposure level of O3 in asthmatic adults was connected
with 472 genes and 36 metabolites, among which oxoglutaric acid, L-Glutamic acid Choline, and
Exposures Mean(SD)
Average exposure
level during
childhood (8-18
years)
organic acid 1.4(0.5)
organic and inorganic acid 2.6(0.5)
O3 2.4(0.5)
NO2 1.6(0.5)
NO 0.5(0.5)
CO 1.0(0.5)
PM10 1.3(0.5)
PM2.5 0.65(0.5)
freeway NOx 0.4(0.5)
nonfreeway NOx 0.9(0.5)
total freeway NOx 1.3(0.5)
1-year average
exposure level
before the IGERA
study visit
O3 3.1(0.5)
NO2 1.4(0.5)
PM10 2.0(0.5)
PM2.5 1.9(0.5)
freeway NOx 0.5(0.5)
nonfreeway NOx 0.5(0.5)
total freeway NOx 1.0(0.5)
1-month average
exposure level
before the IGERA
study visit
O3 1.9(0.5)
NO2 1.1(0.5)
PM10 1.5(0.5)
PM2.5 1.6(0.5)
freeway NOx 0.5(0.5)
nonfreeway NOx 0.5(0.5)
total NOx 1.0(0.5)
10
glycerophospholipid suggested oxidative stress pathways, including D-Glutamine and D-
glutamate metabolism, glycerophospholipid metabolism and glutamate metabolism.
Additionally, 1-year average exposure levels of PM10 and PM2.5 before the IGERA study visit
and the childhood average exposure levels of total acids, PM10 and PM2.5 were linked to
metabolites including gamma-glutamyl glutamine, linoleylcarnitine, sebacic acid and gene
signatures including Caspase 9, Zinc Finger And BTB Domain Containing 17, Cyclin-dependent
kinase inhibitor 1B (p27Kip1) and Retinoid X receptor alpha (RXR-alpha). These metabolites
and genes indicated long-term exposures of PM10 and PM2.5 were associated with
glycerophospholipid metabolism and gene pathways including cancer related pathways (small-
cell and non-small cell lung cell pathways, etc.), signaling pathways (mTOR signaling pathway,
T cell receptor signaling pathway, B cell receptor signaling pathway, etc.), and inflammation
pathways (pathogenic Escherichia coli infection, human immunodeficiency virus 1 infection,
etc.).
Apart from this, childhood average exposure level of freeway NOx was correlated with
biosynthesis of unsaturated fatty acids, such as linoleic acid, palmitic acid, stearic acid, and
arachidic acid.
Besides, 1-year average exposure level of O3 before the IGERA study visit was shown to be
associated with Ribosomal protein S6 kinase beta-1, TBC1 domain family member 4, Cullin 5,
and Ubiquitin-protein ligase E3A, which were linked to gene pathways including insulin
resistance and proteoglycans in cancer.
Short-term air pollution exposures
11
Among the integrative short-term air pollution exposure-gene-metabolite network, 5
subnetworks were generated, including 110 metabolites, 1542 genes, and 7 short-term pollution
exposures (Figure2). Table 8-12 provide details of the identified 5 subnetworks.
First of all, 1-month average exposure level of O3 before the IGERA study visit was associated
with oxidative stress related to fumaric acid, L-aspartic acid, oxoglutaric acid and L-glutamic
acid.
In addition, we observed that short-term exposure of NO2, freeway NOx, nonfreeway NOx and
total NOx , were correlated with certain inflammation pathways (yersinia infection, human
cytomegalovirus infection, etc.) and signaling pathways (mTOR signaling pathway,
phosphatidylinositol signaling system, sphingolipid signaling pathway, chemokine signaling
pathway, etc.). Furthermore, short-term exposures to NO2 and total NOx were shown to be
associated with dysregulated amino acid metabolism, with perturbations of linoleic acid, gamma-
linolenic acid, palmitic acid, stearic acid, and arachidic acid.
Finally, short-term exposures of PM10 and PM2.5 were connected with a series of genes
(Tuberous Sclerosis Complex 2, Mitogen-Activated Protein Kinase Kinase 1, Solute Carrier
Family 38 Member 9, TELO2 Interacting Protein 1, etc.) and two metabolites (mannose and
cysteinyl glycine) in one subnetwork. These genes and metabolites suggested short-term
exposures to PM10 and PM2.5 were associated with mTOR signaling pathway and altered
mannose, sugar and glutathione metabolisms.
Discussion
By integrating information of multiple air pollution exposures, whole blood gene expressions
and untargeted metabolomic profiles from 103 young asthmatic adults, we detected various air
pollution exposures could be related with different gene-metabolic pathways.
12
Among all subnetworks, we observed that various air pollution exposures were associated with
oxidative stress pathways. For instance, childhood exposure level of O3 were linked to arginine
biosynthesis, alanine, aspartate and glutamate, histidine, D-glutamine and D-glutamate, and
glycerophospholipid metabolisms, which is consistent with a previous study demonstrating that
O3 inhalation could induce oxidative stress. Short-term exposure of O3 was also correlated to
fumaric acid, L-aspartic acid, oxoglutaric acid and L-glutamic. Additionally, long-term
exposures to elevated particular matter and total acids exposure were also associated with
perturbations in glycerophospholipid.
Furthermore, we also found that short-term exposures to PM10, PM2.5, NO2 and total NOx were all
correlated with inflammation pathways. In details, short-term exposures of NO2 and total NOx
were linked to yersinia infection, human cytomegalovirus infection, pathogenic Escherichia coli
infection, human immunodeficiency virus 1 infection and human papillomavirus infection.
Short-term exposures of PM10 and PM2.5 were also associated with yersinia infection.
In addition, previous studies demonstrated that there were consistent and robust associations
between metabolomic features related with fatty acid metabolism and NOx and particular matter
(14). Findings of this study supported our observations that biosynthesis of unsaturated fatty
acids was associated with childhood exposure of freeway NOx, and short-term exposure of O3
altered fatty acid metabolism in the adults.
There are some limitations of our study that may inform further air pollution gene and
metabolomic studies. Firstly, the population size in our study, 102 asthmatic adults, is relatively
small resulting in a limited statistical power. Besides, the dimensions of gene expression data and
metabolites were unbalanced, as the dimension of gene expression data is 20 times of
metabolites. Thus, our findings did not represent the entire dysregulated genetic pathways in
13
terms of the effect of air pollution exposures. Additionally, the main limitation of untargeted
metabolomics is that it only provides relative abundances of metabolomic features rather than
absolute concentrations of metabolites. Also, the chemical annotations of metabolomic features
is challenging. However, the metabolomics profiles analyzed in this project have been focused
on known chemical annotations integrated in Kyoto Encyclopedia of Genes and Genomes
(KEGG), PubChem and Human Metabolome Database (HMDB), which strengthens the
biological interpretations of our pathway findings. Furthermore, in our study design, we only
focused on whole blood gene expression, more replication analyses, including CD4+ and CD8+
lymphocyte expression profiles were also required to be assessed in the future. These cell-
specific gene expression profiles would be more related with air pollution exposures and asthma,
since they play a more important role in inflammatory pathways.
In summary, the observed metabolic and genetic perturbations among different genes and
metabolites pointed to increased risk of inflammation, oxidative stress, cancer and the disorder of
signaling pathways for asthma patients exposed to elevated air pollution and traffic-related air
pollution exposures. Future studies are warranted to investigate whether our identified gene-
metabolic pathways could influence different asthma subtypes.
14
Figure 1: Long-term air pollution exposures, Metabolites and Genes Integration Result
(Edges) Red: positive correlation; Blue: negative correlation
(Nodes) Circle: metabolites; Triangle: gene expression; Rectangular: air pollution and traffic-related pollution exposures
Each community is represented by a different color. Orange community represented childhood average exposure levels of O3 and
total NOx from 8 to 18 years old for the adult asthma patients, and the genes and metabolites correlated with childhood average
exposure level of O3 and total NOx; Light blue community represented childhood average exposure levels of organic acid, PM10,
PM2.5 from 8 to 18 years old for the adult asthma patients, 1-year average exposure level of PM10, PM2.5, nonfreeway NOx and
total NOx before the IREGA study visit, and the genes and metabolites correlated with childhood average exposure level of
organic acid, PM10, PM2.5,1-year average exposure level of PM10, PM2.5, nonfreeway NOx and total NOx; Green community
represented childhood average exposure levels of CO, total NOx and freeway NOx from 8 to 18 years old for the adult asthma
patients, and the genes and metabolites correlated with childhood average exposure level of CO, total NOx and freeway NOx;
Green community represented 1-year averaged exposure level of O3 before the IGERA study visit, and the genes and metabolites
correlated with 1-year averaged exposure level of O3; Dark blue community represented childhood average exposure levels of
NO2 and CO from 8 to 18 years old for the adult asthma patients, 1-year averaged exposure level of NO2 before the IGERA study
visit, and the genes and metabolites correlated with childhood average exposure levels of NO2, CO, and 1-year averaged
exposure level of O3.
15
Figure 2: Short-term air pollution exposures, Metabolites and Genes Integration Result
(Edges) Red: positive correlation; Blue: negative correlation
(Nodes) Circle: metabolites; Triangle: gene expression; Rectangular: air pollution and traffic-related pollution exposures
Each community is represented by a different color.
Light blue community represented 1-month average exposure level of O3 before the IREGA study visit, and the genes and
metabolites correlated with 1-month average exposure level of O3; Yellow community represented 1-month average exposure
level of NO2, total NOx, freeway NOx and nonfreeway NOx before the IREGA study visit, and the genes and metabolites
correlated with 1-month average exposure level of NO2, total NOx, freeway NOx and nonfreeway NOx; Dark blue community
represented 1-month average exposure level of PM10 and PM2.5 before the IREGA study visit, and the genes and metabolites
correlated with 1-month average exposure level of PM10 and PM2.5; Each of orange and green communities represented
metabolites and genes that’re correlated, respectively.
16
Table 3: Omics and Pathway Enrichment in Cluster 1 (Long-term)
Pollutants
Exposure
Average exposure level of total NOx during the patients’ childhood;
Average exposure level of O3 during the patients’ childhood
Genes
UBLCP1, TM2D1, ETFRF1, SDHAF2, SEPTIN11, AMD1, TCF12, SCAMP2, AARSD1, RFXANK,
NDFIP1, PKD2, NDUFS7, USO1, PRKCZ, ARL8B, ZNF142, UBA1, MTMR6, TSSC4, ZNF627, MORC3,
CAND1, DHX36, ZNF230, RASAL3, SLC35A4, CAAP1, SLC36A4, YIPF5, TSC2, MTX2, SPAST,
TMEM165, VPS50, BMPR2, TOR1AIP2, ZNF404, REEP5, TBL1XR1, GON7, USP25, TAPT1, FAM133B,
N4BP2L1, ZNF189, SYNJ1, OXR1, MED26, JADE2, LSM8, RAP2A, CHM, NDUFV1, TBK1, GMPPA,
RNF31, SNX16, SCAF4, MBNL2, PARPBP, FBXL18, TRAPPC6B, DDX24, RRM2B, PDCD4-AS1,
CLDN12, METTL17, COPE, KLHL2, SLF1, PMPCA, VPS37A, ZNF680, RYK, TATDN1, CPOX,
TMED10P1, OMA1, ACD, INAFM1, TMEM144, PHRF1, HSDL2, FGFR1OP2, TBC1D9, MICU2, GOPC,
SCML1, ARHGAP30, MIS18BP1, FXYD5, CPNE8, DCPS, TAB1, SNX13, EPS15, SCYL2, BET1, PLCB1,
NETO2, FNDC3A, RIF1, NR3C1, SERP1, RPGR, RABL6, TMX3, C4orf33, STUB1, NME6, TNK2,
SMARCA5, RMI1, GPS1, PSMB10, INTS2, PIGS, UBE2I, MRPL50, MNAT1, PDE8A, ALCAM, GOLT1B,
VAMP4, NOL8, DMAC2L, POM121C, NAPEPLD, PSMA2, ITGA6, DPY19L4, HCST, SNRNP27,
GOLPH3, SLC25A13, IFRD1, SENP7, GLCE, UBXN8, CENPQ, ZNF184, KPNA5, DPM1, ARRDC5,
ZNF614, NFE2L2, THAP7, SLC9A3R1, MED7, IKBKE, PLEKHF1, FBH1, AKT1, SETD1A, TRIAP1,
ARL5A, CSTF2T, IDI1, GANAB, GPBP1L1, DCUN1D5, EVI2A, SERINC1, HIF1A, NCOA5, TATDN2,
VPS39, COG4, CD69, PMAIP1, ANKRD46, STK38L, INTS4, AKNA, NUDT16L1, G2E3, TMTC4, TAF5,
PCSK7, GOLGA4, AGL, LTN1, PTGER4, TRAPPC10, RNPC3, ZNF296, MINDY3, FAM149B1, KCTD18,
MAPKAPK5, PTPN12, SCAP, CD2AP, EXOC8, IFT74, MED13L, PCMTD1, TTC33, TMEM263, SMC2,
CAD, SNX10, CGGBP1, RECK, CXXC1, PSMA4, MYNN, ZGPAT, ANXA4, C10orf88, DNAJC4,
CSNK2B, GASK1B, OCEL1, JAZF1, ZNF480, LAT, DENND1C, SF3B6, GTF2E1, EMP3, EFHC2,
TADA1, ZRANB2, MAZ, BCLAF1, CLIC4, DPF2, ETNK1, TRIM28, DNM1L, NUMA1, SF3B2, ZNF12,
AP5S1, JAK2, TAF1B, SBF1, PPP1CA, RABGGTB, PPP1R12A, SLC38A6, BAP1, SCOC, DNAJC25, PTS,
DIPK2A, OSTM1, KMT2B, APBA3, ZNF558, ATAD3A, THAP1, ZNF684, RNF4, MRPL13, MTMR14,
P4HA1, CCNC, SLA2, BTBD10, TPK1, ERCC2, SNX14, SEC24C, RNF14, TMC6, LTBP4, TXNDC9,
HNRNPH3, LAMTOR3, SND1, MRPL32, CCDC88A, XRCC1, ACVR2A, ISOC1, TIPRL, LYPLAL1,
EXOC3, RPS6KA1, NEK1, CEP290, STX7, ABCB10, TTI1, NUP54, ZNF776, ZFAND6, ITPKB, GMNN,
PGM2, SNRPB, SLC25A46, TOGARAM1, MPC1, DR1, GPS2, FOXJ2, MRTFB, PUF60, PNPLA7,
LONRF1, ING1, DHX29, ACSS1, DEGS1, GMFB, CCP110, ST3GAL6, PPP2R3C, TUBD1, VPS29,
HSDL1, MIR22HG, KLHL22, ADAM10, SAR1B, GPR137, MRPS12, ATAD1, STRBP, KRCC1, CCNG2,
ZMPSTE24, GPR108, AZI2, DMAP1, OTUD6B, RIPK1, IVNS1ABP, TIAL1, SKIV2L, TXNDC17,
C6orf120, ERLIN2, SRM, CMTR2, RWDD4, OSBPL11, ERO1B, PPM1D, JKAMP, UBL4A, ORAI1, IGIP,
PDIK1L, UBR4, FCHO2, PRPF18, CEPT1, L3MBTL2, ITGB3BP, SNORA32, CD58, DCLRE1A, SREK1,
LARP1B, SMC4, DRG2, CBX3P9, SLC39A10, PMS1, PIP4P2, ARID3B, AATF, TRIM26, NNT, SDHAF3,
FBXO28, ZFR, PRPF8, VPS54, BORCS7, CYFIP2, ABCC4, NBN, FAM76B, CPNE1, PIGK, NLK, SOAT1,
IDH3G, PCMT1, ANKRD39, SLC35B3, LZTFL1, AP2A2, ZNF140, SMARCAD1, SPG7, ATP7A, SRPRA,
TOR1AIP1, ZNF672, KBTBD8, ARHGAP45, XRCC6, CNIH4, FAM110A, PDCD10, TCERG1, SETDB1,
NHLRC3, SS18L1, COMMD10, VAMP7, FOPNL, SMNDC1, KLHL9, TMED7, DNM2, BCAT2, PTPN22,
LSM1, RIN2, ERGIC2, CIAO2A, SUPT5H, TOX4, MTFR1, NSMAF, VPS52, ANAPC10, VCPKMT,
ACVR1, PAPOLA, PAQR3, EDC4, USP48, WDR46, SLC38A9, SIRT1, ACSL4, CCDC126, USP38,
PANK4, CUEDC2, CEP83, ARID2, CEP57, CALM2, MAPK6, DCTN3, ZFPM1, SNX4, MCM5, ALDOC,
APIP, WASH5P, MRPS35, ZNF24, UNK, TBX19, PRORSD1P, GPR160, SACS, HIGD1A, NOC3L,
RAVER1, SMAD5, ESS2, RNF40, STAG1, CHCHD1, PRR14, BTBD2, ECHDC1, USP47, WASHC4
Metabolites LysoPC(P-16:0), 12-HETE, 3-Amino-2-piperidone, 4-Hydroxy-2-oxoglutaric acid, 5'-Methylthioadenosine,
Adenine, Oxoglutaric acid, L-Aspartic acid, Carnosine, Choline, Cysteinylglycine, Fumaric acid, L-Glutamic
acid, Glycerol 3-phosphate, Leucyl-Alanine, D-Mannose, N-Acetyl-L-methionine, Undecanedioic acid
Metabolite
Pathways
Arginine biosynthesis
Alanine, aspartate and glutamate metabolism
Histidine metabolism
D-Glutamine and D-glutamate metabolism
Glycerophospholipid metabolism
Butanoate metabolism
Citrate cycle (TCA cycle)
beta-Alanine metabolism
FDR
0.00023888
0.0022878
0.0067829
0.020056
0.047067
0.089686
0.13076
0.13076
Impact
0.46154
0.66667
0.33333
0.6
0.37143
0.14286
0.26316
0.2
Gene
Pathways
Signaling pathways regulating pluripotency of stem cells
Herpes simplex virus 1 infection
SNARE interactions in vesicular transport
0.083016
0.083016
0.16704
0.22449
0.15152
0.23684
17
Enriched
Metabolites
Fumaric acid, L-Aspartic acid, Oxoglutaric acid, L-Glutamic acid, LysoPC(P-16:0), Choline, Glycerol 3-
phosphate, Carnosine
Enriched
Genes
SNRPB, TCERG1, PUF60, SMNDC1, PRPF8, SNRNP27, PRPF18, SMARCAD1, SETDB1, RIF1, TAB1,
IKBKE, TBK1, PPP1CA, TSC2, SKIV2L, DCPS, LSM1, LSM8, EDC4, DHX36, BET1, STX7, VAMP4,
VAMP7
Table 4: Omics and Pathway Enrichment in Cluster 2 (Long-term)
Pollutants
Exposure
Average exposure level of organic acid during the patients’ childhood;
Average exposure level of acids during the patient’s childhood;
Average exposure level of PM10 during the patient’s childhood;
Average exposure level of PM2.5 during the patient’s childhood;
1-year average exposure level of PM10 the year before the IGERA study visit;
1-year average exposure level of PM2.5 the year before the IGERA study visit;
1-year average exposure level of total NOx before the IGERA study visit;
1-year average exposure level of total NOx the year before the IGERA study visit
Genes
SLC9A1, EAPP, SRF, ARPC5, TMEM106B, NUCB2, ZC3H11B, WAS, SEC62, RAC2, SEC24B, SEC11C,
MAP1S, UGP2, TPT1P9, MBNL1, RASA3, SH3BP5L, MMGT1, NIPSNAP2, SH2D3C, EEF1B2P6, CMIP,
CIPC, RPL23, NPTN, FAM120A, HMGB1P37, NAT9, RAP1BL, IKBKG, CLK1, CLN3, UBE4A, SSH3,
GSDMD, HECA, LASP1, PPP1R11, SLC4A7, NACC2, PSMA3-AS1, TMBIM6, RPL21P28, ZNF700,
DPYD, SLK, DHRS7B, ADPRS, ARMC7, PRKRA, EIF4A2P4, SPOUT1, RPS27AP11, LOC642975,
SLC10A3, RTN4, CKS2, RPS24, CHFR, TBRG4, UBE2Q2, RPL5, BIRC2, MGAT2, MKNK2, MIDN,
CFAP36, LIMS1, VPS18, LHFPL6, MICU1, SP4, MMADHC, CDKN1B, MIS12, CDC42EP4, EFHD2,
UQCRHL, HAUS7, RNF41, ATP5F1D, UQCRC1, USP1, WDR83, UBE2L3, RPL26, SP3, TMEM30A,
KLHL21, KDM3A, DLD, SP2, FLII, CNOT10, GNAI2, RBM7, UBR5, LDHB, TOMM70, ELF4, VRK3,
ARF1, MAPKAPK3, OPRL1, UPF1, AKAP11, DCP1A, MTREX, NPLOC4, EXOC7, FBXL3, VPS36,
CD37, GTF3C5, RBIS, TRAFD1, SEC13, ACTR6, SMARCD2, DHPS, SUB1, PGRMC2, MAP3K3,
SNAPC2, RBM4B, SLC38A2, THUMPD1, TINF2, RAB8B, DENND6A, MALT1, ATG101, ASB6, EHD1,
PTGES3, SLC43A2, WTAP, TEX30, TC2N, CSGALNACT2, WWP1, CCNH, DMAC2, ADD3, XRN1,
TM9SF1, MTF1, ATG5, TM9SF3, TUBGCP6, CD48, EIF3A, PREB, UBE2E1, UBA6, TUBB4B, ZNF721,
RBX1, FAM98A, FLI1, SLX4, DBNL, RFX7, RPL31P17, PPP4R3B, CLIP1, TRMT1L, INTS1, RAD21,
COX7B, RPS14P3, CCDC91, CHD1, TYMP, MATR3, TOM1, RAD9A, TSC22D2, FLOT2, STK26,
ACADM, INPPL1, FRG1, SORL1, CEP295, TRA2B, RPS3AP13, ZNF644, TNFRSF1B, STRADA,
HNRNPDL, PPCS, PSMD14, TRMT10C, SACM1L, RNGTT, RPF1, RNF24, NOMO1, MRPL1, MAP4K5,
TARS1, SRP72, KIAA2013, HMGN1P30, RASGRP1, KPNA3, UBA3, RRAGC, KLHDC2, CRY1,
SEMA4A, HMGXB3, RFX1, CXCR2, GET4, PGD, TMEM14B, TMEM245, ABHD2, ALDH3B1, DIPK1A,
IMPDH1, ZNF146, SAR1A, CCNG1, ZNF319, FYTTD1, KATNB1, LRSAM1, DSTN, ARSG, ZNF3,
CHMP2A, RTRAF, PHKG2, SND1-IT1, SHLD2, TNPO3, MVP, PIK3CD, BSDC1, PLEKHM2, HAT1,
SLFN11, TMEM50B, DOCK8, LOC728877, PSMB3, BANP, VPS16, ZNF341, TRIM11, LARP4, RSRC2,
RPL21P134, MTDH, TMEM167A, DUSP18, PCIF1, DEDD2, OSBPL8, PREPL, NMD3, XPR1, CEBPZ,
RHOG, DNAJC5, SUMO1P3, TRAPPC11, DNAJA2, KAT8, SLTM, ZC3H15, SUCLG2, ITGAE,
ARHGEF2, FES, FCHO1, C6orf47, FAM214A, NDUFA4, RTF2, FCGRT, TMUB2, STRAP, NEU1,
ELOVL5, PFDN5, ATP1B3, NSA2P3, ATP2B1, PRKCD, CMTM7, RPS29P17, DNAJC10, SQSTM1,
METAP2, MAPKAPK2, NFKBIA, ADRM1, PPP4C, ACTR1A, CASP9, FLAD1, STK11IP, NUDCD3,
NCLN, RBMX2, NOL12, DEK, ADO, DAAM1, EIF4E2, ATG14, ST13, CASP2, NDUFAF3, DDX41,
DHX15, RPS23P8, OSTC, MEI1, STK19, TMEM185A, AIDA, KHNYN, USP7, METTL16, ELMO2,
AP5M1, TNFRSF1A, SASH3, PSD4, KTN1, RAP2C, UGDH, ATP5F1C, PRRC2B, CYB561D2, MADD,
PNRC2P1, ENTPD3, WDR1, TESK1, NDUFAB1, TCEA2, ZBTB17, RBM42, TSEN54, RRP8, ZCCHC7,
MORF4L1, MED23, RITA1, GALNT1, ACTN1, GRPEL2, LRIG1, DDX23, NIPAL3, HGS, PIP4P1,
UQCRC2, BCKDK, NARS1, OPTN, NXT2, SMARCA2, TMEM115, MON1B, PSIP1, UBE2V2, TWF2,
MTRR, ZDHHC7, RBM34, ADAR, ZNF302, GPBAR1, ZNF212, UBQLN2, EDEM3, GNA15, SLCO3A1,
WIPF1, PSMA3, STARD7, AKTIP, CDV3, DCTN2, CCT6A, KIAA0513, RAB33B, TM9SF2, ACBD3,
UQCRBP1, SH3BGRL, FYB1, LUC7L3, LYRM7, IFT20, FAM13B, MGAT1, RHOQP3, NISCH, DCTN4,
SELENOF, WSB1, RPS6P1, GIMAP2, NR1H2, FCSK, GDI2, PSAP, IBTK, PLAUR, MSRA, APOBR,
YWHAG, TBC1D20, USP16, ATP6V0D1, RPS18P12, DLAT, COPS6, IMPA1, ATP6V1G1, ALG5,
LEPROTL1, SDHDP2, ETS1, HDHD2, BUD23, AP2S1, PTBP3, CCND3, RXRB, SS18, PTPN6,
RETREG1, CMPK1, RPL9, FKBP3, MRPS28, ARHGAP1, CDH23, CYTH4, ARID3A, NAE1, TRIM21,
CAST, PRKCA, METTL23, TRIP12, NAP1L1, MYH9, KLHL24, ARPP19, GNA13, ATP11B, TOLLIP,
RPUSD3, DCUN1D4, YY1, MNT, DEDD, CDC42SE2, RPS15AP17, TLK1, CTSA, ATM, MEX3C,
KIAA2026, IER3IP1, TYK2, HDAC2, COPS8, ACTR3, DDX52, TOMM7, ADGRE5, PRKAA1, RPS13P2,
RXRA, RALGAPA1, PRRC1, AASDHPPT, BZW1P2, RAB40C, FERMT3, BCS1L, RTCA, RAB8A,
18
APH1B, UBN1, SNHG5, POC1B, CUL4A, ITGB2, PRMT6, TTC1, ATRIP, TMEM205, NCSTN, GAK,
HSPD1P4, YIPF4, ZYX, POU2F1, DYNLT3, TMEM127, CNBP, ZMYM4, DDX56, ANKRA2, RESF1,
TRAF3IP2, TCEAL8, RPS9P4, PPP1R18, C9orf72, ARMC1, GPR65, SELENOO, PNKP, TIMP1,
NCKAP1L
Metabolites LysoPC(16:0), PE(18:0/18:2(9Z,12Z)), PE(18:0/18:1(9Z)), LysoPE(18:0/0:0), Gamma-Glutamyl Glutamine,
DG(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)/0:0), DG(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)/0:0), Linoleyl
carnitine, DG(18:1(9Z)/20:4(5Z,8Z,11Z,14Z)/0:0), DG(18:1(9Z)/18:2(9Z,12Z)/0:0),
DG(18:1(9Z)/18:2(9Z,12Z)/0:0), DG(18:1(9Z)/18:1(9Z)/0:0), DG(16:0/20:4(5Z,8Z,11Z,14Z)/0:0), L-
Palmitoylcarnitine, DG(16:0/18:2(9Z,12Z)/0:0), DG(16:0/18:2(9Z,12Z)/0:0), DG(16:0/18:1(9Z)/0:0),
DG(16:0/18:1(9Z)/0:0), Sebacic acid, Stearoylcarnitine
Metabolite
Pathways
Glycerophospholipid metabolism
FDR
0.13014
Impact
0.34286
Gene
Pathways
Pathogenic Escherichia coli infection
Alzheimer disease
Small cell lung cancer
Autophagy - animal
Adipocytokine signaling pathway
Huntington disease
Protein processing in endoplasmic reticulum
VEGF signaling pathway
B cell receptor signaling pathway
Parkinson disease
Human immunodeficiency virus 1 infection
Thermogenesis
Endocytosis
Oxidative phosphorylation
Cell cycle
HIF-1 signaling pathway
Epstein-Barr virus infection
Fc gamma R-mediated phagocytosis
Non-alcoholic fatty liver disease (NAFLD)
Legionellosis
Thyroid hormone signaling pathway
mTOR signaling pathway
Shigellosis
PD-L1 expression and PD-1 checkpoint pathway in cancer
Influenza A
Non-small cell lung cancer
Yersinia infection
Circadian rhythm
Regulation of actin cytoskeleton
NF-kappa B signaling pathway
Kaposi sarcoma-associated herpesvirus infection
T cell receptor signaling pathway
Vibrio cholerae infection
Measles
Endocrine and other factor-regulated calcium reabsorption
Human cytomegalovirus infection
Apoptosis
Tight junction
Insulin signaling pathway
Toxoplasmosis
C-type lectin receptor signaling pathway
Pathways in cancer
Hepatitis C
Insulin resistance
Human T-cell leukemia virus 1 infection
Chemokine signaling pathway
Sphingolipid signaling pathway
Cellular senescence
0.00032633
0.0019939
0.0019939
0.0024819
0.0087562
0.0091524
0.0091524
0.011704
0.011704
0.011704
0.01484
0.017336
0.017336
0.017336
0.017336
0.017336
0.017336
0.017336
0.017336
0.017536
0.020373
0.020373
0.036996
0.036996
0.036996
0.04194
0.047958
0.048996
0.057294
0.060287
0.060773
0.066027
0.067242
0.069157
0.069157
0.069157
0.069157
0.069157
0.069157
0.074507
0.076136
0.08463
0.10811
0.11039
0.1145
0.12136
0.12559
0.12559
0.376
0.074627
0.39216
0.35833
0.46512
0.058824
0.084906
0.33333
0.38
0.089552
0.26866
0.14458
0.058394
0.0072464
0.28182
0.29268
0.23129
0.51786
0.12857
0.10417
0.26415
0.29762
0.33333
0.4625
0.13158
0.25
0.25
0.45
0.46988
0.12883
0.17293
0.25352
0.10714
0.21348
0.125
0.24242
0.27434
0.17778
0.22727
0.27869
0.3
0.21296
0.26804
0.19118
0.24615
0.51562
0.32927
0.23932
19
Epithelial cell signaling in Helicobacter pylori infection
Chronic myeloid leukemia
Natural killer cell mediated cytotoxicity
Chagas disease (American trypanosomiasis)
Choline metabolism in cancer
Th17 cell differentiation
Apoptosis - multiple species
TNF signaling pathway
Amoebiasis
Fluid shear stress and atherosclerosis
Parathyroid hormone synthesis, secretion and action
Leukocyte transendothelial migration
Pancreatic secretion
Autophagy - other
Gastric acid secretion
Transcriptional misregulation in cancer
Longevity regulating pathway - multiple species
Neurotrophin signaling pathway
0.12559
0.12559
0.12559
0.13545
0.13858
0.14738
0.14821
0.14821
0.16157
0.16157
0.16157
0.16157
0.165
0.16805
0.17324
0.17998
0.19522
0.19522
0.16667
0.21818
0.23469
0.19178
0.16981
0.07619
0.33333
0.20619
0.083333
0.18095
0.14634
0.24176
0.033333
0.27778
0.11538
0.0096618
0.26415
0.20732
Enriched
Metabolites
LysoPC(16:0), PE(18:0/18:2(9Z,12Z))
Enriched
Genes
ARHGEF2, GNA13, ARPC5, TNFRSF1A, NFKBIA, PTPN6, CYTH4, ARF1, NCKAP1, CASP9, TMBIM6,
IKBKG, SEC24B, RAB8A, NCSTN, CASP9, NAE1, RBX1, UBA3, BIRC2, WWP1, UBE2L3, UBA6,
UBR5, UBE4A, ZBTB17, CDKN1B, RXRA, CASP9, BIRC2, NFKBIA, PRKCD, ATG101, PRKAA1,
ATG14, ATG5, RAB33B, C9orf72, SQSTM1, NFKBIA, RXRA, TNFRSF1B, TNFRSF1A, DCTN2, CASP9,
SEC24B, NPLOC4, UBQLN2, RBX1, DNAJA2, PREB, MAPKAPK3, PRKCA, MALT1, CASP9, ATM,
HGS, RNF41, VPS36, EHD1, SRF, USP7, MAPKAPK2, NDUFAB1, NDUFA4, COX7B, UQCRHL,
UQCRC2, UQCRC1, ATP5F1C, ATP5F1D, CCNH, RAD21, TIMP1, MKNK2, PSMD14, TYK2, WAS,
CLIP1, CLK1, ITGB2, SLC9A1
Table 5: Omics and Pathway Enrichment in Cluster 3 (Long-term)
Pollutants
Exposure
Average exposure level of total NOx during the patients’ childhood;
Average exposure level of CO during the patients’ childhood;
Average exposure level of freeway NOx during the patients’ childhood
Genes
PDIA6, SLC25A25, GART, DNAJC9, PUS1, NCF2, GSS, AGA, LCK, ZNHIT6, CEP135, EBNA1BP2,
ITPA, SPATA7, TRRAP, HACL1, MAGED1, PCCB, FAM200B, KDM1A, TCEAL3, DOP1A, OARD1,
TFCP2, UPF3B, PTGES2, GTPBP8, RETSAT, CLDN14, DONSON, RNY1, MAP2K1, MYH3, F8A1,
FTSJ1, AFG3L2, REXO4, THUMPD2, FOXRED1, CDK5RAP1, DOLPP1, DNMT1, BAG3, SPTSSA,
S100A12, WASHC5, DRAM2, RING1, THEM6, ARV1, JOSD1, OPA1, FBXO4, BPHL, ZDHHC16,
RUVBL1, ADCK2, HNRNPAB, PIGP, SLC25A26, PHACTR4, RPE, VTA1, TMEM41A, CREBZF, AKIP1,
MTIF2, GFPT1, WDR12, SNU13, ALDH18A1, TRIM68, HINFP, POLR3H, ZNF207, ZFP82, THOC3,
DCTD, SMG8, EEF1AKNMT, SURF6, MCRS1, EXOC2, RFX5, SFMBT1, PPIE, KIF5B, PWP1, LCMT1,
PPHLN1, RCAN1, RRM1, FTO, ZW10, OXNAD1, ZSCAN2, PAAF1, AIFM1, SDR39U1, TIMM8A, ILF3,
MRRF, GORASP2, TUBG2, PTPDC1, PJA1, MAF, GGCT, SHKBP1, VPS37B, SORBS3, PDCL3, NUP37,
DDX10, LIX1L, PTPN2, LARP1, LACTB2, SLC35D2, PIGC, IWS1, ERP29, RRS1, DNAJC19, POLR3E,
ABL1, UTP4, PPIL1, CSNK1G3, BRCC3, TUT4, NSDHL, SUN1, TOB1, CHAMP1, TSPAN3, C1orf109,
PDCD2L, COPS7B, ALKBH3, TIGD7, PCCA, ADARB1, ZC3HC1, FASTKD1, ZNF484, ZNF17, DDX19B,
ARL1, LTO1, SHPRH, ANKMY2, RRP15, ZBTB9, MRPL12, EPRS1, ZNF823, NOA1, EMC4, PDS5B,
FLNB, YEATS2, APPBP2, MCEE, MRPL35, NGRN, PSMD10, GEMIN2, SMIM8, NIPSNAP1, SLC40A1,
MRM3, PAFAH1B1, RXYLT1, GOT1, ILF2, FANCE, WRN, FNTA, RPUSD2, AHI1, MIDEAS, SPECC1L,
NRAS, SELENOS, TBC1D31, CRYZL1, WDCP, PRPF4, CSNK2A2, POGLUT3, ATP5F1A, PFDN1,
MAT2B, TSR2, MAN1C1, TIMM29, GEMIN4, PXN, HNRNPM, EXOSC7, U2SURP, CAPRIN1, ITFG2,
HNRNPR, RADX, RBBP8, TACO1, TRMT11, PROSER1, TEX10, ORC3, ERCC3, GMDS, SLC9A6,
SMARCA4, POLR2D, QDPR, PREP, RRAGB, TCTEX1D2, LCOR, PPRC1, CCT7, SAE1, MSANTD4,
MRPS30, MAP3K4, ILVBL, MLYCD, APOBEC3A, CLUH, TIMM17BP1
Metabolites Nonadeca-10(Z)-enoic acid, Adrenic acid, Arachidic acid, Eicosadienoic acid, Docosadienoate (22:2n6),
Docosapentaenoic acid, Docosatrienoic acid, Eicosenoic acid, Linoleic acid, Gamma-Linolenic acid,
Linoleoyl ethanolamide, Heptadecanoic acid, Myristic acid, Nonadecanoic acid, Palmitoylglycine, N-
Oleoylethanolamine, Palmitic acid, Palmitoleic acid, Palmitoylethanolamide, Pentadecanoic acid, Stearic acid
20
Metabolite
Pathways
Biosynthesis of unsaturated fatty acids
FDR
2.51E-06
Impact
0.14286
Enriched
Metabolites
Linoleic acid, Gamma-Linolenic acid, Palmitic acid, Stearic acid, Arachidic acid
Table 6: Omics and Pathway Enrichment in Cluster 4 (Long-term)
Pollutants
Exposure
1-year averaged exposure level of O3 before the IGERA study visit
Genes
EEF1E1, DMAC1, C12orf29, SRSF1, RPS17, SRP9, ITGB1, SCAF11, RBM15, AHCTF1, TMEM41B,
RALY, UBXN4, PIAS4, THAP12, ANGEL2, RPS6KB1, UBE3A, BTAF1, ARL6IP5, RBM25, CD82,
DERL1, LANCL1, N4BP1, PPP1CC, RBM12, DAXX, ORMDL1, EDRF1, PDS5A, RSL24D1, CRBN,
NGLY1, GTF3C3, DESI2, THEMIS, VCPIP1, TRMT13, SLC30A9, PPP1R2, SERTAD1, TMEM243,
ZBTB33, CCDC14, FAM122B, ZC3H3, GBA, VBP1, ZBED5, SUZ12, NIN, FBXW7, XPO1, CLK4, CUL5,
PPP1CB, NAB1, PAXBP1, RSBN1, SELENOT, ENO1, PIGA, MFSD5, ZNF22, ANKRD12, API5, PTBP2,
UBQLN1, VMA21, TBC1D4
Metabolites LysoPC(18:1(9Z)), LysoPC(16:1(9Z)), LysoPC(18:0)
Gene
Pathways
Insulin resistance
Proteoglycans in cancer
FDR
0.033982
0.11932
Impact
0.044118
0.07
Enriched
Genes
RPS6KB1, TBC1D4, CUL5, UBE3A, RPS6KB1
Table 7: Omics and Pathway Enrichment in Cluster 5 (Long-term)
Pollutants
Exposure
1-year averaged exposure level of NO2 before the IGERA study visit;
Average exposure level of NO2 during the patients’ childhood;
Average exposure level of CO during the patients’ childhood
Genes
COPB2, ELAC2, ENTR1, CSNK2A1, HDAC6, NCOR1, DPP3, TOR3A, TESK2, CBY1, TP53BP1, CKAP5,
ABCC10, TBC1D13, AHCYL2, CTNNBL1, OS9, CEP164, CSRNP2, MRPL4, ATL3, P3H1, PRKAB1,
APEX2, TTLL12, ARF4, PDE6D, MFSD3, PIK3R5, TBL2, UVRAG, THADA, FBXW2, TTYH2,
RHBDD1, MED24, PITPNB, DAG1, ZNF655, PKNOX1, SLC37A1, ZMYM3, GSN, TTPAL, KIF3B,
CASZ1, VCP, CS, GOLGA1, DIDO1, FGGY, PLP1, TBC1D2, ZNF609, GLI4, GTF3C1, SMG9, TBC1D9B,
ENDOG, UGT1A3, FAF1, LRRC28, ARHGAP17, SGPL1, MBD1, SREBF1, CEP131, DOLK, WIPI1,
HVCN1, MPV17, CDAN1, RABGGTA, VPS13D, DXO, CAPN3, CYFIP1, MET, NFE2L1, NUBP1, GLB1,
ABCF3, ACVR1B, KEAP1, POLD3, TTI2, BRMS1, INTS5, CTBP1, FLYWCH2, VAC14, DGUOK,
ZNF213, FAM168B, ACAD9, DDAH2, UBAP2, CCDC22, RBM15B, RANBP3, LRRC42, TUBG1, UBTF,
PHYKPL, HADHA, ZXDC, WDR55, RTL8A, METRNL, ZNF668, HIC2, GATD3A, SMAP1, VIPAS39,
DHX30, PARP3, ZBTB3, SLC38A10, LAMP1, VPS33B, MAP2K5, SMARCAL1, PLD3, GRAMD4,
NAGPA, POFUT2, GBF1, MIA3, PITPNA, XPO5, CLRN1, RNF135, HDGFL2, AIMP2, EDC3, PRPF6,
ILRUN, VPS26C, BCL2L12, RRNAD1, TAF6L
Metabolites MG(18:3(6Z,9Z,12Z)/0:0/0:0), MG(18:1(9Z)/0:0/0:0)
Table 8: Omics and Pathway Enrichment in Cluster 1 (Short-term)
Genes
UBLCP1, TM2D1, SRF, SDHAF2, SEPTIN11, RFXANK, PKD2, NDUFS7, PRKCZ, ARL8B, UGP2,
MTMR6, LOC642513, TSSC4, CAND1, DHX36, RASAL3, SLC35A4, C9ORF82, SLC36A4, YIPF5,
MTX2, SPAST, CCDC132, BMPR2, RAP1BL, C14ORF142, USP25, TAPT1, FAM133B, ZNF189, SYNJ1,
MED26, PHF15, RTN4, RNF31, SFRS15, MBNL2, TRAPPC6B, COG7, RRM2B, LOC282997,
METT11D1, COPE, KLHL2, ANKRD32, PMPCA, VPS37A, ZNF680, TATDN1, CPOX, TMED10P,
OMA1, PHRF1, HSDL2, FGFR1OP2, TBC1D9, ZNF585B, GOPC, MVD, ARHGAP30, FXYD5, CPNE8,
DCPS, SNX13, SCYL2, PLCB1, FNDC3A, RIF1, SUB1, RPGR, TMX3, C4ORF33, STUB1, NME6,
SMARCA5, GPS1, PSMB10, INTS2, UBE2I, MNAT1, PDE8A, GOLT1B, FLI1, VAMP4, NOL8, ATP5S,
NAPEPLD, ITGA6, SLC25A13, UBXN8, NFE2L2, THAP7, IKBKE, FRG1, AKT1, PPCS, RG9MTD1,
GPBP1L1, DCUN1D5, SERINC1, HIF1A, NCOA5, TARS, COG4, PMAIP1, KPNA3, STK38L, INTS4,
SSR3, CRY1, NUDT16L1, KIAA1333, PCSK7, AGL, TMEM1, RNPC3, FAM188A, KCTD18,
MAPKAPK5, ZNF146, PTPN12, EXOC8, IFT74, C10ORF137, KATNB1, MED13L, CHMP2A, TTC33,
C12ORF23, SNX10, RECK, CXXC1, ZGPAT, ANXA4, C10ORF88, DNAJC4, CSNK2B, C4ORF18,
21
SF3B14, EMP3, EFHC2, MAZ, FUT8, CLIC4, ETNK1, DNM1L, NUMA1, ZNF12, C20ORF29, TAF1B,
SBF1, PPP1CA, PPP1R12A, SLC38A6, DNAJC25, SUMO1P3, C3ORF58, APBA3, ARHGEF2, ATAD3A,
THAP1, RNF4, STRAP, MTMR14, ZNF431, P4HA1, SLA2, TPK1, SQSTM1, ERCC2, SEC24C,
SERTAD1, TXNDC9, SND1, CCDC88A, LOC400890, ACVR2A, KHNYN, ISOC1, TIPRL, EXOC3,
NEK1, STX7, NUP54, ZNF776, ZFAND6, GMNN, GMPPB, SNRPB, FAM179B, BRP44L, DR1, FOXJ2,
MKL2, RRN3, SLC5A6, PUF60, ING1, ACSS1, DEGS1, CP110, PPP2R3C, TUBD1, HSDL1, C17ORF91,
KLHL22, GPR137, MRPS12, ATAD1, KRCC1, CCNG2, RBM34, AZI2, DMAP1, RELA, OTUD6B,
RIPK1, TIAL1, SKIV2L, C6ORF120, ERLIN2, OSBPL11, PPM1D, UBL4A, UBR4, FCHO2, PRPF18,
RAB33B, L3MBTL2, SNORA32, CD58, SFRS12, DRG2, SLC39A10, PMS1, TMEM55A, ARID3B,
TRIM26, NNT, ACN9, ZNF354A, WSB1, ABCC4, NBN, CPNE1, NLK, SOAT1, IBTK, IDH3G,
ANKRD39, LZTFL1, AP2A2, ZNF140, SPG7, SRPR, TBC1D20, ZNF672, LOC647081, XRCC6, CNIH4,
FKBP3, TCERG1, TRIM24, NHLRC3, SMNDC1, TMED7, BCAT2, PTPN22, RIN2, ARPP19, ERGIC2,
FAM96A, SUPT5H, TOX4, MTFR1, TLK1, ORC3L, VPS52, IER3IP1, C14ORF138, MFSD5, HDAC2,
ACVR1, EDC4, USP48, WDR46, SIRT1, ACSL4, CCDC126, PANK4, ARID2, CEP57, CUL4A, GNPTG,
DCTN3, SNX4, MCM5, FAM39E, MRPS35, ZC3H5, TBX19, SACS, HIGD1A, TCEAL8, NOC3L, STAG1,
CHCHD1, PRR14, BTBD2, ECHDC1, USP47, KIAA1033
Metabolites LysoPC(P-16:0), 12-HETE, 3-Amino-2-piperidone, 4-Hydroxy-2-oxoglutaric acid, 5'-Methylthioadenosine,
Adenine, Oxoglutaric acid, L-Aspartic acid, Carnosine, Choline, Fumaric acid, Gamma-Glutamyl Glutamine,
L-Glutamic acid, Glycerol 3-phosphate, Leucyl-Alanine, Linoleyl carnitine, N-Acetyl-L-methionine, N-
Acetylserine, L-Palmitoylcarnitine, L-Phenylalanine, Stearoylcarnitine, Undecanedioic acid
Metabolite
Pathways
Arginine biosynthesis
Alanine, aspartate and glutamate metabolism
Histidine metabolism
D-Glutamine and D-glutamate metabolism
Butanoate metabolism
Pantothenate and CoA biosynthesis
Phosphatidylinositol signaling system
beta-Alanine metabolism
Aminoacyl-tRNA biosynthesis
Inositol phosphate metabolism
Phenylalanine, tyrosine and tryptophan biosynthesis
Nitrogen metabolism
FDR
0.00013718
0.0013122
0.003826
0.011125
0.059652
0.072439
0.072439
0.072439
0.092159
0.13794
0.13794
0.18819
Impact
0.34615
0.38333
0.22581
0.55556
0.071429
0.42424
0.26027
0.11628
0.068493
0.13235
0.6
0.22222
Gene
Pathways
Fluid shear stress and atherosclerosis
Central carbon metabolism in cancer
0.01745
0.038098
0.45714
0.10619
Enriched
Genes
NFE2L2, PRKCZ, SQSTM1, ARHGEF2, HIF1A, PPCS
Enriched
Metabolites
Fumaric acid, L-Aspartic acid, Oxoglutaric acid, L-Glutamic acid, Carnosine
Table 9: Omics and Pathway Enrichment in Cluster 2 (Short-term)
Pollutants
Exposure
1-month average exposure level of ozone before the IGERA study visit
Genes
PDIA6, SLC25A25, GART, DNAJC9, PUS1, NCF2, GSS, LCK, ZNHIT6, RASA3, EBNA1BP2, ITPA,
TRRAP, TOR3A, CDK9, ALG9, HACL1, MAGED1, CBY1, PCCB, LOC285550, AOF2, TCEAL3,
C6ORF130, TFCP2, LBH, PTGES2, GTPBP8, RETSAT, CLDN14, RNY1, DDX24, FTSJ1, LOC100130633,
AFG3L2, REXO4, CDK5RAP1, DNMT1, BAG3, CTGLF7, S100A12, NUP85, KIAA0196, RING1,
C8ORF55, JOSD1, BPHL, ZDHHC16, RUVBL1, ADCK2, HNRNPAB, SLC25A26, PHACTR4,
TMEM41A, MTIF2, NHP2L1, ALDH18A1, TRIM68, HINFP, POLR3H, ZNF207, ZFP82, THOC3, DCTD,
C17ORF71, METTL13, SURF6, EXOC2, RFX5, SFMBT1, PPIE, PWP1, UBE1C, PTK2B, LCMT1,
PPHLN1, RRM1, ZW10, MGC15763, PAAF1, C14ORF124, ILF3, MRRF, GORASP2, MPV17, PJA1,
MAF, SORBS3, PDCL3, NUP37, DDX10, PTPN2, LARP1, SLC35D2, PIGC, IWS1, ERP29, RRS1,
POLR3E, CIRH1A, ZCCHC11, UNC84A, TOB1, ZNF828, C1ORF109, NONO, PDCD2L, ALKBH3,
SARS2, ADARB1, ZC3HC1, DDX19B, ORAOV1, ZBTB9, EPRS, NPAL3, C4ORF14, TMEM85, FLNB,
YEATS2, FAM168B, MRPL35, NGRN, PSMD10, CPT2, NIPSNAP1, SLC40A1, RNMTL1, PAFAH1B1,
TMEM5, GOT1, ILF2, FNTA, RPUSD2, HIC2, C14ORF43, LOC284988, CYTSA, SELS, DCTN4, WDR67,
CRYZL1, C2ORF44, PRPF4, CSNK2A2, ATP5A1, PRPSAP1, PFDN1, POGK, MAT2B, TSR2, MAN1C1,
EP400, WBSCR22, ABLIM1, GEMIN4, PXN, HNRPM, EXOSC7, SR140, ACTG1, CAPRIN1, ITFG2,
22
SLC25A5, HNRPR, TACO1, ATP11B, TEX10, ERCC3, SMARCA4, POLR2D, QDPR, ACTR3, PREP,
LOC647834, LCOR, PPRC1, CCT7, SAE1, MRPS30, DSCR3, MAP3K4, ILVBL, APOBEC3A, LOC390298
Metabolites Nonadeca-10(Z)-enoic acid, Adrenic acid, Arachidic acid, Eicosadienoic acid, Docosadienoate (22:2n6),
Docosapentaenoic acid, Docosatrienoic acid, Eicosenoic acid, Linoleic acid, Gamma-Linolenic acid,
Linoleoyl ethanolamide, Heptadecanoic acid, Myristic acid, Nonadecanoic acid, Palmitoylglycine, N-
Oleoylethanolamine, Palmitic acid, Palmitoleic acid, Palmitoylethanolamide, Pentadecanoic acid, Stearic acid
Metabolite
Pathways
Biosynthesis of unsaturated fatty acids
Cysteine and methionine metabolism
Fatty acid biosynthesis
Linoleic acid metabolism
FDR
1.01E-06
0.12792
0.12792
0.15156
Impact
0.1087
0.22857
0.023438
0.75
Gene
Pathways
Leukocyte transendothelial migration
Yersinia infection
0.17202
0.17202
0.17582
0.17708
Enriched
Genes
U2SURP, PPIE, PRPF4, SNU13, POLR2D, POLR3H, POLR3E, NCF2, PXN, PTK2B, ACTR3, LCK
Enriched
Metabolites
Linoleic acid, Gamma-Linolenic acid, Palmitic acid, Stearic acid, Arachidic acid
Table 10: Omics and Pathway Enrichment in Cluster 3 (Short-term)
Genes
COPB2, ELAC2, SDCCAG3, SEC62, CSNK2A1, HDAC6, NCOR1, DPP3, NAT9, TESK2, CLN3,
TP53BP1, CKAP5, ABCC10, ARMC7, TBC1D13, TBRG4, AHCYL2, CTNNBL1, LHFP, CBARA1, OS9,
CEP164, MYH3, CSRNP2, MRPL4, MORG1, ATL3, FOXRED1, LEPRE1, DOLPP1, SP2, PRKAB1,
CNOT10, VRK3, APEX2, TTLL12, PDE6D, MFSD3, PIK3R5, TBL2, UVRAG, THADA, FBXW2, TTYH2,
MAP3K3, RBM4B, RHBDD1, MED24, PITPNB, DAG1, ZNF655, PKNOX1, SLC37A1, ZMYM3, GSN,
TTPAL, KIF3B, CASZ1, VCP, CS, GOLGA1, MCRS1, DIDO1, NOMO1, FLJ10986, TBC1D2, ZNF609,
GLI4, GTF3C1, C19ORF61, TBC1D9B, KIAA0194, FTO, C7ORF20, ENDOG, UGT1A3, ALDH3B1,
FAF1, ZSCAN2, LRRC28, ARHGAP17, ZNF3, AIFM1, PHKG2, SGPL1, MBD1, AZI1, DOLK, WIPI1,
HVCN1, TUBG2, LIX1L, CDAN1, DNAJC5, RABGGTA, VPS13D, C6ORF47, DOM3Z, ABL1, CAPN3,
CYFIP1, MET, NEU1, NFE2L1, NUBP1, FANCG, NSDHL, CASP9, NUDCD3, GLB1, ABCF3, CASP2,
ACVR1B, KEAP1, POLD3, C8ORF41, COPS7B, MEI1, PCCA, BRMS1, INTS5, MADD, ENTPD3, WDR1,
ZBTB17, RRP8, CTBP1, C12ORF52, FLYWCH2, VAC14, ZNF408, DGUOK, MON1B, ACAD9, DDAH2,
UBAP2, CCDC22, RBM15B, ZNF212, RANBP3, LRRC42, AGXT2L2, HADHA, ZXDC, WDR55, FANCE,
FAM127B, METRNL, CIDEB, ZNF668, C21ORF33, LOC90624, SMAP1, C14ORF133, DHX30, PARP3,
FUK, ZBTB3, SLC38A10, MSRA, APOB48R, LAMP1, C19ORF52, VPS33B, MAP2K5, COPS6,
SMARCAL1, PLD3, GRAMD4, NAGPA, POFUT2, GBF1, PRKCA, MIA3, PITPNA, XPO5, CLRN1,
RNF135, GMDS, HDGF2, AIMP2, EDC3, PICK1, BCS1L, PRPF6, ATRIP, C6ORF106, MLYCD,
KIAA0664, TRAF3IP2, BCL2L12, C1ORF66, TAF6L
Metabolites MG(18:3(6Z,9Z,12Z)/0:0/0:0), MG(18:1(9Z)/0:0/0:0), Glycerol 1-hexadecanoate,
PE(18:0/20:4(5Z,8Z,11Z,14Z)), PE(18:0/18:2(9Z,12Z)), PE(18:0/18:1(9Z)), MG(0:0/18:2(9Z,12Z)/0:0),
MG(0:0/18:1(9Z)/0:0), LysoPE(0:0/18:0)
Metabolite
Pathways
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
beta-Alanine metabolism
Propanoate metabolism
FDR
0.18092
0.18092
0.18092
Impact
0.13333
0.18605
0.17021
Enriched
Metabolites
PE(18:0/20:4(5Z,8Z,11Z,14Z)), PE(18:0/18:2(9Z,12Z)), PE(18:0/18:1(9Z))
Table 11: Omics and Pathway Enrichment in Cluster 4 (Short-term)
Pollutants
Exposure
1-month average exposure level of NO2 before the IGERA study visit;
1-month average exposure level of freeway NOx before the IGERA study visit;
1-month average exposure level of nonfreeway NOx before the IGERA study visit;
1-month average exposure level of total NOx before the IGERA study visit
SLC9A1, MOBKL2A, EAPP, ARPC5, AMD1, EEF1E1, SCAMP2, TMEM106B, NUCB2, ZC3H11B, WAS,
C9ORF123, C12ORF29, RAC2, USO1, SEC24B, SEC11C, MAP1S, PSMC4, LOC389787, MBNL1,
SH3BP5L, MMGT1, GBAS, SH2D3C, LOC647030, CMIP, KIAA1737, RPL23, SFRS1, NPTN, FAM120A,
LOC100132863, RPS17, SRP9, REEP5, IKBKG, CLK1, UBE4A, SSH3, GSDMD, HECA, ITGB1, LASP1,
23
Genes
PPP1R11, SLC4A7, NACC2, FLJ31306, TMBIM6, LOC100131205, CORO1C, ZNF700, DPYD, SLK,
DHRS7B, ADPRHL2, PRKRA, LOC286512, LOC728590, SFRS2IP, LOC642975, SLC10A3, CKS2,
RPS24, CHFR, UBE2Q2, RPL5, BIRC2, RBM15, MGAT2, MKNK2, MIDN, CCDC104, LIMS1, FBXL18,
VPS18, SP4, MMADHC, CDKN1B, MIS12, CDC42EP4, EFHD2, UQCRHL, UCHL5IP, RNF41, ATP5D,
UQCRC1, USP1, ZMYND11, UBE2L3, TMEM41B, SP3, TMEM30A, KLHL21, JMJD1A, FLII,
LOC255783, GNAI2, RALY, RBM7, UBR5, LDHB, TOMM70A, EFHA1, ELF4, ARF1, MAPKAPK3,
OPRL1, AKAP11, DCP1A, SKIV2L2, NPLOC4, PIAS4, FBXL3, VPS36, CD37, GTF3C5, C8ORF59,
TRAFD1, ACTR6, DHPS, PGRMC2, SNAPC2, SLC38A2, THUMPD1, TINF2, RAB8B, FAM116A,
MALT1, ASB6, EHD1, PTGES3, SLC43A2, PRKRIR, ANGEL2, C13ORF27, RPS6KB1, WWP1, ATP5SL,
ADD3, XRN1, TM9SF1, MTF1, ATG5, TM9SF3, EIF3A, PREB, UBE2E1, UBA6, BTAF1, TUBB2C,
ARL6IP5, ZNF721, RBX1, FAM98A, BTBD12, DBNL, RFX7, NOP58, LOC653773, SMEK2, RBM25,
CLIP1, C1ORF25, INTS1, RAD21, COX7B, GOLPH3, CCDC91, CD82, CHD1, SENP7, ECGF1, MATR3,
DPM1, TOM1, RAD9A, DERL1, TSC22D2, FLOT2, LANCL1, FBXO18, ACADM, INPPL1, SORL1,
POP7, KIAA1731, SFRS10, TBC1D10B, LOC100129742, ZNF644, TNFRSF1B, STRADA, HNRPDL,
PSMD14, SACM1L, RNGTT, RPF1, RNF24, IDI1, PLP1, LOC649555, MRPL1, N4BP1, LOC730432,
MAP4K5, SRP72, LOC100132992, RASGRP1, PPP1CC, RRAGC, KLHDC2, SEMA4A, RAB5B, RFX1,
IL8RB, PGD, TMEM14B, PTGER4, C9ORF5, ABHD2, ZNF296, FAM69A, DAXX, FAM117B, IMPDH1,
SAR1A, CCNG1, ORMDL1, ZNF319, FYTTD1, LRSAM1, DSTN, PCMTD1, PDS5A, C14ORF166,
C7ORF54, FAM35A, TNPO3, MVP, PIK3CD, BSDC1, CGGBP1, PLEKHM2, HAT1, RSL24D1, PSMA4,
SLFN11, LOC646900, TMEM50B, DOCK8, JAZF1, LOC728877, PSMB3, FLOT1, BANP, CRBN, NGLY1,
GTF3C3, VPS16, ZNF341, TRIM11, LARP4, LOC728782, MTDH, TMEM167A, ZRANB2, PPPDE1,
DUSP18, LOC728973, MAPK3, PCIF1, DPF2, DEDD2, SF3B2, OSBPL8, PREPL, NMD3, XPR1, CEBPZ,
RHOG, C6ORF190, VCPIP1, C4ORF41, DNAJA2, MYST1, SLTM, ZC3H15, MLL4, SUCLG2, ITGAE,
FES, FCHO1, KIAA1370, NDUFA4, C20ORF43, FCGRT, ELOVL5, SLC30A9, PFDN5, ATP1B3,
LOC100132658, ATP2B1, PRKCD, CMTM7, LOC100129379, DNAJC10, CDC2L1, SNX14, METAP2,
MAPKAPK2, ADRM1, PPP4C, ACTR1A, RNF14, FLAD1, STK11IP, C7ORF23, ZBTB33, NCLN,
LOC100132715, HNRPH3, RBMX2, MGC3731, CCDC14, DEK, ADO, DAAM1, LOC100130308, EIF4E2,
KIAA0831, ST13, DDX41, DHX15, LOC653658, OSTC, STK19, TMEM185A, AIDA, USP7, METT10D,
ELMO2, MUDENG, TNFRSF1A, SASH3, PSD4, RPS6KA1, LOC648210, RAP2C, ZC3H3, UGDH, GBA,
ATP5C1, BAT2L, CYB561D2, LOC100131261, LOC647346, NDUFAB1, TCEA2, SLC25A46, RBM42,
ZCCHC7, MORF4L1, MED23, GALNT1, WDFY2, ACTN1, GRPEL2, LRIG1, DDX23, HGS, ZNF213,
TMEM55B, UQCRC2, BCKDK, NARS, GMFB, OPTN, NXT2, SMARCA2, TMEM115, NIN, PSIP1,
UBE2V2, MTRR, ZDHHC7, ADAR, ZNF302, GPBAR1, IVNS1ABP, UBQLN2, TUBG1, EDEM3, GNA15,
XPO1, SLCO3A1, WASPIP, PSMA3, AKTIP, CDV3, REXO1, CCT6A, TM9SF2, ACBD3, LOC442454,
SH3BGRL, FYB, CROP, IFT20, C5ORF5, MGAT1, NISCH, AATF, ZFR, SELENOF, AP3D1, CYFIP2,
GIMAP2, NR1H2, GDI2, PSAP, PLAUR, NAB1, YWHAG, LOC642989, C21ORF66, TOR1AIP1, USP16,
ATP6V0D1, DLAT, IMPA1, ATP6V1G1, ALG5, LEPROTL1, ETS1, HDHD2, AP2S1, ROD1, SELT,
CCND3, RXRB, ENO1, SS18, PTPN6, FAM134B, CMPK1, RPL9, SETDB1, ARHGAP1, CDH23, PSCD4,
ARID3A, NAE1, TRIM21, CAST, LOC124512, TRIP12, LOC651149, NAP1L1, MYH9, KLHL24, GNA13,
TOLLIP, PIGA, RPUSD3, DCUN1D4, BRD3, YY1, MNT, DEDD, CDC42SE2, LOC391656, BNIP3, CTSA,
ATM, OGFR, MEX3C, KIAA2026, TYK2, COPS8, DDX52, CD97, PRKAA1, ZFAND1, LOC729236,
RXRA, USP38, RALGAPA1, VAV1, ANKRD12, PRRC1, AASDHPPT, LOC151579, RAB40C,
LOC100134504, FERMT3, RTCD1, CALM2, RAB8A, APH1B, UBN1, C6ORF160, WDR51B, UBQLN1,
ITGB2, PRMT6, TMEM205, NCSTN, GAK, LOC644745, YIPF4, ZYX, POU2F1, DYNLT3, TMEM127,
LOC728672, CNBP, ZMYM4, DDX56, ANKRA2, C12ORF35, LOC203547, LOC388556, KIAA1949,
ARMC1, GPR65, SELO, GRK6, PNKP, TIMP1, NCKAP1L, TBC1D4
Metabolites LysoPC(18:1(9Z)), LysoPC(16:1(9Z)), LysoPC(16:0), LysoPC(18:0), LysoPE(18:0/0:0),
DG(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)/0:0), DG(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)/0:0), Cer(d18:0/16:0),
DG(18:1(9Z)/20:4(5Z,8Z,11Z,14Z)/0:0), DG(18:1(9Z)/18:2(9Z,12Z)/0:0), DG(18:1(9Z)/18:2(9Z,12Z)/0:0),
DG(18:1(9Z)/18:1(9Z)/0:0), DG(16:0/20:4(5Z,8Z,11Z,14Z)/0:0), DG(16:0/18:2(9Z,12Z)/0:0),
DG(16:0/18:2(9Z,12Z)/0:0), DG(16:0/18:1(9Z)/0:0), DG(16:0/18:1(9Z)/0:0), 5,8-Tetradecadienoic acid
24
Gene
Pathways
Pathogenic Escherichia coli infection
Regulation of actin cytoskeleton
Alzheimer disease
Fc gamma R-mediated phagocytosis
HIF-1 signaling pathway
B cell receptor signaling pathway
Insulin signaling pathway
Protein processing in endoplasmic reticulum
Endocytosis
Yersinia infection
Insulin resistance
mTOR signaling pathway
Shigellosis
Thermogenesis
Autophagy - animal
Small cell lung cancer
Oxidative phosphorylation
Adipocytokine signaling pathway
Non-alcoholic fatty liver disease (NAFLD)
Parkinson disease
Human immunodeficiency virus 1 infection
T cell receptor signaling pathway
Chemokine signaling pathway
Platelet activation
VEGF signaling pathway
Human cytomegalovirus infection
C-type lectin receptor signaling pathway
PD-L1 expression and PD-1 checkpoint pathway in cancer
Thyroid hormone signaling pathway
NF-kappa B signaling pathway
Cellular senescence
Legionellosis
Choline metabolism in cancer
Human papillomavirus infection
Focal adhesion
Pathways in cancer
Influenza A
Toxoplasmosis
Leukocyte transendothelial migration
cAMP signaling pathway
Apelin signaling pathway
Rap1 signaling pathway
Neurotrophin signaling pathway
Adherens junction
Kaposi sarcoma-associated herpesvirus infection
Adrenergic signaling in cardiomyocytes
Non-small cell lung cancer
Bacterial invasion of epithelial cells
Long-term potentiation
Huntington disease
Oocyte meiosis
Sphingolipid signaling pathway
Natural killer cell mediated cytotoxicity
FoxO signaling pathway
Pancreatic cancer
Tight junction
Epstein-Barr virus infection
Fc epsilon RI signaling pathway
Chagas disease (American trypanosomiasis)
Apoptosis
FDR
0.00076148
0.00090054
0.001071
0.0019388
0.0019388
0.0023664
0.0039466
0.004596
0.004596
0.0048497
0.0054048
0.0058459
0.0082549
0.0092982
0.0093261
0.0095185
0.011184
0.011184
0.011571
0.014918
0.015969
0.016667
0.019041
0.019041
0.019813
0.021809
0.021809
0.024465
0.041402
0.043946
0.0485
0.049417
0.049417
0.050528
0.05081
0.054501
0.054501
0.056504
0.056867
0.056867
0.056867
0.066439
0.071925
0.079984
0.079984
0.079984
0.081372
0.084418
0.08507
0.08507
0.08507
0.090158
0.091983
0.093834
0.094534
0.094534
0.094534
0.094534
0.094534
0.096957
Impact
0.32
0.57831
0.044776
0.57143
0.35366
0.44
0.33333
0.066038
0.058394
0.29167
0.22059
0.38095
0.2381
0.19277
0.38333
0.27451
0.0072464
0.4186
0.12857
0.044776
0.28358
0.29577
0.59375
0.3908
0.27273
0.25455
0.30526
0.4625
0.27358
0.067485
0.25641
0.020833
0.22642
0.14375
0.66129
0.19753
0.070175
0.22951
0.32967
0.15574
0.38667
0.23404
0.2561
0.24
0.16541
0.23944
0.19643
0.32558
0.51613
0.014706
0.18824
0.34146
0.33673
0.14286
0.21053
0.11111
0.17007
0.27778
0.16438
0.22124
25
Gene
Pathways
Th17 cell differentiation
TNF signaling pathway
Proteoglycans in cancer
EGFR tyrosine kinase inhibitor resistance
Amoebiasis
Fluid shear stress and atherosclerosis
Parathyroid hormone synthesis, secretion and action
Circadian rhythm
Axon guidance
Pertussis
Vibrio cholerae infection
Gastric acid secretion
ErbB signaling pathway
Phagosome
Endocrine and other factor-regulated calcium reabsorption
Amyotrophic lateral sclerosis (ALS)
Human T-cell leukemia virus 1 infection
MAPK signaling pathway
Hepatitis C
Cell cycle
Thyroid cancer
Longevity regulating pathway - multiple species
Phospholipase D signaling pathway
Phosphatidylinositol signaling system
Longevity regulating pathway
Acute myeloid leukemia
Ras signaling pathway
Long-term depression
RNA transport
Inflammatory mediator regulation of TRP channels
p53 signaling pathway
Renal cell carcinoma
Measles
Aldosterone-regulated sodium reabsorption
Salivary secretion
cGMP-PKG signaling pathway
Platinum drug resistance
AGE-RAGE signaling pathway in diabetic complications
Epithelial cell signaling in Helicobacter pylori infection
Chronic myeloid leukemia
Estrogen signaling pathway
0.10354
0.10583
0.10583
0.10632
0.11228
0.11228
0.11228
0.11228
0.11228
0.11228
0.11748
0.12131
0.12131
0.12131
0.12142
0.12142
0.12184
0.12682
0.12856
0.12856
0.12956
0.13069
0.13069
0.14027
0.14529
0.15264
0.15446
0.15633
0.1573
0.16179
0.16917
0.16917
0.17635
0.18267
0.18267
0.18267
0.18953
0.19063
0.19158
0.19158
0.19796
0.085714
0.18557
0.21
0.13636
0.066667
0.18095
0.13415
0.15
0.18121
0.11765
0.035714
0.13462
0.17391
0.064103
0.089286
0.092593
0.14615
0.29032
0.20619
0.23636
0.12
0.32075
0.3662
0.74026
0.27778
0.20833
0.29167
0.19565
0.15596
0.20225
0.075758
0.12281
0.1573
0.14286
0.057143
0.19512
0.18
0.23529
0.095238
0.16364
0.25974
Enriched
Genes
ITGB1, ARPC5, GNA13, TNFRSF1A, PTPN6, CYTH4, ARF1, NCKAP1, TMBIM6, IKBKG, SEC24B,
SLC9A1, MYH9, NCSTN, NAE1, PRKCD, RPS6KB1, RBX1, ENO1, TIMP1, MKNK2, MALT1, BIRC2,
WWP1, UBE2L3, UBA6, UBR5, TRIP12, INPPL1, FLOT1, NPLOC4, UBQLN2, NGLY1, DNAJA2,
DERL1, PREB
Table 12: Omics and Pathway Enrichment in Cluster 5 (Short-term)
Pollutants
Exposure
1-month average exposure level of PM10 before the IGERA study visit;
1-month average exposure level of PM2.5 before the IGERA study visit
Genes
LYRM5, TCF12, AARSD1, NDFIP1, AGA, ZNF142, UBE1, LOC641710, ZNF627, MORC3, CEP135,
ZNF230, SPATA7, FBRS, TSC2, TMEM165, TOR1AIP2, PKIA, ZNF404, TAOK2, TBL1XR1, ENPP5,
NDUFS4, N4BP2L1, OXR1, DOPEY1, LSM8, ZNF280D, RAP2A, CHM, NDUFV1, TBK1, UPF3B,
GMPPA, SNX16, C12ORF48, DONSON, NR3C2, MAP2K1, PARP11, CLDN12, MKI67IP, RYK, ABCE1,
F8A1, BRIX1, AHCTF1, THUMPD2, DLD, FAM3C, ACD, TMEM144, C14ORF147, SCML1, TMEM77,
TMEM209, BCCIP, C14ORF106, EXOC7, MAP3K7IP1, ARV1, EPS15, OPA1, BET1, FBXO4, SEC13,
NETO2, SMARCD2, NR3C1, SERP1, C12ORF44, C9ORF86, WTAP, PIGP, TC2N, TNK2, RPE, VTA1,
UBE3A, PIGS, MRPL50, ALCAM, POM121C, CREBZF, PSMA2, DPY19L4, TTC35, HCST, SNRNP27,
IFRD1, GFPT1, WDR12, WDR5B, FBXO22, GLCE, CENPQ, ZNF184, ARRDC5, ZNF614, MST4,
SLC9A3R1, MED7, PLEKHF1, STX6, SETD1A, TRIAP1, VRK1, ARL5A, CSTF2T, GANAB, EVI2A,
26
TATDN2, KIF5B, MRPL3, VPS39, HELZ, CD69, KIAA2013, ANKRD46, RBM12, AKNA, TMTC4,
MGC12965, RCAN1, TAF5, GOLGA4, RNF160, FAM149B1, SCAP, CD2AP, EID3, TIMM8A, SMC2,
CAD, MYNN, GFM1, KIAA0261, OCEL1, PTPDC1, ZNF480, LAT, DENND1C, GGCT, GTF2E1, BBS10,
SHKBP1, VPS37B, TADA1L, GNL3, BCLAF1, TRIM28, JAK2, RABGGTB, BAP1, SCOC, PTS, LACTB2,
OSTM1, CCDC76, ZNF558, DNAJC19, AASDH, ZNF684, PPIL1, CSNK1G3, MRPL13, BRCC3, CCNC,
PPP1R2, BTBD10, TMC6, LTBP4, COPS4, MAP2K1IP1, LOC255809, MRPL32, IL10RA, TSPAN3,
TRAPPC2P1, XRCC1, MANEA, FAM122B, TIGD7, KTN1, LYPLAL1, CEP290, C4ORF27, ABCB10,
KIAA0406, FASTKD1, ZNF484, ITPKB, PGM2, ZNF17, TESK1, VBP1, ARL1, SHPRH, TMX1, LIMA1,
ANKMY2, ZBED5, GPS2, RRP15, SUZ12, PNPLA7, LONRF1, DHX29, HSZFP36, PDS5B, ST3GAL6,
APPBP2, VPS29, ADAM10, MCEE, SAR1B, ACYP2, TWF2, AKAP7, STRBP, SIP1, C6ORF162,
ZMPSTE24, SETDB2, GPR108, TXNDC17, FBXW7, SRM, TWISTNB, FTSJD1, HMG20B, RWDD4A,
ERO1LB, C14ORF100, SEPHS1, SPRED1, ORAI1, WRN, C5ORF53, CLK4, PDIK1L, MED16, AHI1,
KIAA0513, CEPT1, ITGB3BP, LARP1B, SMC4, LOC644101, NRAS, C20ORF7, FBXO28, CUL5, PRPF8,
VPS54, C10ORF32, KDELC2, FAM76B, PIGK, PPP1CB, PCMT1, SLC35B3, SMARCAD1, ATP7A, PLS1,
RSBN1, CLECL1, KBTBD8, HMHA1, FAM110A, PREI3, PDCD10, MRPS28, MRS2, NSMCE4A,
SS18L1, COMMD10, VAMP7, C16ORF63, KLHL9, DNM2, CXORF57, RBBP8, LSM1, TRMT11,
CCDC97, RBBP9, C13ORF23, NSMAF, TRDMT1, ANAPC10, SLC9A6, RRAGB, PAPOLA, PAQR3,
SLC38A9, CUEDC2, CCDC53, CCDC41, API5, PTBP2, TCTEX1D2, MAPK6, KIAA1826, ZFPM1,
ALDOC, APIP, ZNF24, GPR160, NRIP1, TMEM126A, RAVER1, SMAD5, DGCR14, RNF40, C9ORF72,
ZNF706, BCAT1
Metabolites Cysteinylglycine, D-Mannose
Metabolite
Pathways
Fructose and mannose metabolism
Amino sugar and nucleotide sugar metabolism
Lysine degradation
Pentose phosphate pathway
Glutathione metabolism
FDR
0.17067
0.17067
0.17067
0.17067
0.17067
Impact
0.33333
0.17949
0.16667
0.43478
0.27273
Gene
Pathways
Herpes simplex virus 1 infection
mTOR signaling pathway
Autophagy - animal
0.040514
0.061947
0.091534
0.11364
0.38095
0.2
Enriched
Metabolites
mannose, cysteinylglycine
Enriched
Genes
TAB1, TBK1, TSC2, MAP2K1, RRAGB, SLC38A9, TTI1, ANAPC10, CUL5, UBE3A, MAP2K1, ATG101,
C9orf72
27
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Abstract (if available)
Abstract
Background: Previous studies have shown substantial adverse effects of both regional and traffic-related air pollution exposures on lung function in children and adults. Besides, a growing number of studies suggested that sphingolipid, lipid, and fatty acid metabolism are all associated with lung function in asthmatic patients (14). Air pollution exposure specifically ozone (O₃), organic acids, nitrogen oxides and particular matter are known to affect respiratory system through hypoxia response, oxidative stress, immunity, inflammation, lipid metabolism and the tricarboxylic acid cycle (15). However, how air pollution exposure influences gene expression profile and leads to dysregulated metabolism in asthmatic adults is unclear. ❧ Objective: To investigate the joint effects of regional and traffic-related air pollution exposures on the regulatory network of gene expression and metabolites network in young adults with asthma history. ❧ Methods: A total of 102 adults (mean±SD age=26.2±2.1 yrs) originally enrolled in the southern California Children’s Health Study (CHS) were followed for gene expression and metabolomics profiling in year 2010-2011. All participants had previously diagnosed asthma during the CHS follow-up in year 1995-2003. Individual exposures to regional air pollutants, including ozone (O₃), nitrogen dioxide (NO₂), nitric oxide (NO), total nitrogen oxides (total NOₓ), organic acid, carbon monoxide (CO), particular matter less than 10 microns in diameter (PM₁₀) and 2.5 microns in diameter (PM₂.₅) were estimated using central monitor data near residential addresses. Traffic-related air pollution levels were estimated as freeway and nonfreeway NOₓ exposure using CALINE dispersion models (37). We mainly defined these air pollution variables into two categories, long-term exposure level (1-year averaged air pollution exposure level before the 2010-2011 study visit and average exposure level during childhood from 8-18 years old) and short-term exposure level (1-month average air pollution exposure level before the 2010-2011 study visit). The gene expression data were generated with 20,869 probes from the Illumina HumanHT-12 v4 Expression BeadChip (Illumina, Inc., San Diego, CA). In addition, untargeted metabolomics was measured from archived serum samples using liquid chromatography–mass spectrometry (LC-MS) analysis. After quality control of gene expression data and metabolomics data, we used integrated network analysis to investigate the association among air pollution exposure, genes, and metabolites (10). Based on the network results connecting genes and metabolites with specific air pollution exposures, we further used pathway enrichment analysis to examine gene-metabolite pathways related to different air pollution exposures. ❧ Results: By integrating air pollution exposures, genes and metabolites, a total of 5 sub-networks were found. In one subnetwork involving childhood average exposure to O₃, metabolites including oxoglutaric acid, L-Glutamic acid Choline, and glycerophospholipid were also included, which indicated metabolic pathways related to oxidative stress (arginine biosynthesis, alanine, aspartate and glutamate metabolism, histidine metabolism, D-Glutamine and D-glutamate metabolism). In addition, genes involved in signaling pathways regulating pluripotency of stem cells were also connected to O₃-related sub-network. Besides, we also observed that perturbations in glycerophospholipid was linked to long-term exposures to PM₁₀ and PM₂.₅. The subnetwork including long-term exposures PM₁₀, PM₂.₅ were also connected to genes in several genetic pathways including cancer related pathways (small-cell and non-small cell lung cell pathways, etc.), signaling pathways (mTOR signaling pathway, T cell receptor signaling pathway, B cell receptor signaling pathway, etc.), and inflammation pathways (pathogenic Escherichia coli infection, human immunodeficiency virus 1 infection, etc.). Furthermore, childhood average exposure to freeway NOₓ could induce perturbations of some amino acid metabolism such as biosynthesis of unsaturated fatty acids. By contrast, short-term exposures to total NOₓ and NO₂ were mainly associated with specific gene pathways, including signaling pathways (mTOR signaling pathway, phosphatidylinositol signaling system, sphingolipid signaling pathway, chemokine signaling pathway, etc.), and certain inflammation pathways (yersinia infection, human cytomegalovirus infection, etc.). In addition, short-term exposures were shown to be associated with dysregulated fatty acid metabolism, such as linoleic acid, gamma-linolenic acid, palmitic acid, stearic acid, and arachidic acid. Short-term exposures to PM₁₀ and PM₂.₅ were connected with a group of genes (TSC2, MAP2K1, SLC38A9, TTI1, etc.) and two metabolites (mannose and cysteinyl glycine) in one subnetwork. These gene and metabolite findings suggested short-term exposures to PM₁₀ and PM₂.₅ were associated with altered mannose, sugar and glutathione metabolisms. ❧ Conclusions: Both long-term and short-term exposures to regional and traffic-related air pollutants are associated with a broad spectrum of alterations in amino acid and fatty acid metabolism, inflammation and oxidative stress pathways. Specifically, childhood exposure of long-term air pollution exposures (O₃, nonfreeway NOₓ, PM₁₀, PM₂.₅ and total acids) were associated with dysregulation of glycerophospholipid metabolism. mTOR signaling pathway was affected by both long-term and short-term PM₁₀ and PM₂.₅ exposure with the disturbance of genes including TTI1, SLC38A9, RRAGB and MAP2K1. Furthermore, oxidative phosphorylation was associated with short-term exposures to total NOₓ and NO₂, which was consistent with previous studies suggesting associations between NO₂ and oxidative stress.
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Creator
Li, Xin
(author)
Core Title
Linking air pollution to integrative gene and metabolites networks in young adult with asthma
School
Keck School of Medicine
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Master of Science
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Biostatistics
Publication Date
05/03/2020
Defense Date
05/02/2020
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adults,Air pollution,asthma,Gene,integrative networks,metabolites,OAI-PMH Harvest
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