PATHWAY AND NETWORK ANALYSIS IN ADIPOSE TISSUE RELATED TO BMI AND GLUCOSE TOLERANCE
Felicidade, I1,2; Coort, SL²; Mantovani, MS3; Ribeiro, LR¹; Evelo, CT2,4
1- Department of Pathology, Medical School of Botucatu (FMB), São Paulo State University (UNESP), Botucatu, Brazil
2 - Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands
3 - Department of General Biology, State University of Londrina (UEL), Londrina, Brazil
4 - Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
[email protected]
INTRODUCTION
Table 3. Biological processes associated with obesity and relevant
disorders and metabolism.
Table 2. Pathways significantly changed in obese individuals.
Obesity is classified and defined by body mass index (BMI), being a heterogeneous condition.
Defining obesity based only on BMI is not a sufficiently sensitive marker for risk of metabolic
disorders. Therefore it is needed to investigate biological pathways and find markers for an early
prediction of metabolic disorders.
Subcutaneous adipose tissue (SAT) is the largest adipose tissue depots in humans and also the
preferred site to store excess fat. The capacity of SAT to accommodate excess fat is regulated by the
ability of the existing adipose cells to expand (hypertrophy) and/or recruit precursor cells into
adipogenic differentiation (hyperplasia). The expansion of fat cells, pre-existing, leads to inflamed,
dysregulated, and dysfunctional adipose tissue promoting ectopic fat accumulation, and insulin
resistance, while the ability to recruit new adipose cells is protective. Therefore, the adipose tissue play
a key role on metabolic homeostasis and insulin resistance.
OBJECTIVES
The aim of the present study was find differences in glucose sensitivity related processes in adipose
tissue from individuals with different BMIs, and to identify a functional biology network based on
obesity-related genes and pathways.
MATERIALS AND METHODS
Pathway
Z-score p-value
Change
d genes
Complement Activation, Classical Pathway
6.660
0.000
6
miRNA targets in ECM and membrane receptors
4.930
0.001
6
IL1 and megakaryotyces in obesity
4.630
0.000
6
Complement and Coagulation Cascades
4.590
0.000
10
Fatty Acid Biosynthesis
4.230
0.000
5
Pathogenic Escherichia coli infection
3.310
0.004
7
Adipogenesis
2.950
0.004
13
Angiogenesis
2.940
0.007
4
Folate Metabolism
2.930
0.003
7
Vitamin B12 Metabolism
2.810
0.004
6
Allograft Rejection
2.800
0.008
9
IL-3 Signaling Pathway
2.560
0.014
6
Focal Adhesion
2.530
0.010
16
Oxidative Stress
2.530
0.018
4
Constitutive Androstane Receptor Pathway
2.530
0.021
4
Hypertrophy Model
2.240
0.022
3
Glycerophospholipid Biosynthetic Pathway
2.240
0.025
3
Farnesoid X Receptor Pathway
2.240
0.036
3
Spinal Cord Injury
2.180
0.031
10
Selenium Micronutrient Network
2.170
0.022
7
Fatty Acid Beta Oxidation
2.110
0.023
4
Genes
↑ C1QA, C1QB, C1QC, C1R, C1S
↓ C6
↑ COL1A2, COL5A1, COL6A2, ITGB5, THBS1, THBS2
↑ CCL2, FCER1A, IL18, MMP9, PLA2G7, TIMP1
↑ C1QA, C1QB, C1QG, C1S, C1R, C3AR1, C5R1, CFH, CLU
↓ C6
↓ ACACB, ACAS2, ECHDC2, ECHDC3, HADHSC
↑ ARPC1B, CD14, HCLS1, LY96, TLR5, TUBB2A, TUBB2B
↑
↓
↑
↓
↑
↓
↑
↓
↑
↓
↑
LEP, GDF10, SFRP4
CEBPD, FOXO1A, IRS2, LPIN1, NR2F1, NR3C1, NRIP1, PCK2, RXRA, TWIST1
MMP9, MMP2
FGF2, VEGFA
FOLR2, MCP1
GPX3, HBB, CTH, APOB, SHMT1
MCP1, CCL5
APOB, CTH, HBB, MUT
CCL19,COL5, HLA-DRB1, HLA-DMB, HLA-DPB1, HLA-DPA1, HLA-DQB1, HLA-DQA1
VEGFA
CSF2RB, HCK, LYN, PTPN6, SYK, VAV1
↑
↓
↑
↓
↑
↓
↑
↓
↑
↓
COL11A1, COL1A2, COL5A1, COL6A2, FGR, HCK, ITGAM, ITGB2, ITGB5, PDGFD, THBS1, THBS2, VAV1
KDR, PTEN, VEGF
NQO1, NOX4, HMOX1
GPX3
ABCC3, SULT1A1
FOXO1, RXRA
IL18
EIF4EBP1, VEGF
PTDSS1
CRLS1, LPIN1
↓ FKBP5, IRS2, RXRA
↑ AIF1, AQP1, C1QB, CCL2, LEP, MMP9, NOX4, SELP, SLIT2, TNFSF13B
↑ ALOX5, ALOX5AP, CCL2
↓ APOB, CTH, HBB, GPX3
↓ ACAS2, ECI1, HADH, PNPLA2
Transcriptomics dataset
Figure 1. Visualization of gene
expression on adipogenesis pathway
‒ Publicly available from Gene Expression Omnibus on accession number GSE27951.
‒ Adipose tissues were obtained by biopsies, free from visible blood and connective tissue, from subcutaneous abdominal.
‒ A general health examination and an oral glucose tolerance test (OGTT) were performed and WHO diagnostic criteria were
applied (WHO, 1999) for glucose tolerance classification.
Table 1. Characteristics of obese and lean subjects
Obese
Lean
p-value
‒ Body Mass Index:
n
8
5
‒ Lean < 25 Kg/m² → 5 subjects
Age (y)
49.38 ± 12.14 54.20 ± 13.99 0.543
Fasting insulin (pmol/L) 115.75 ± 58.83 30.80 ± 13.59 0.004**
‒ Obese > 30 Kg/m² → 8 subjects
Fasting glucose (mg/dL) 128.48 ± 24.05 92.16 ± 4.10
HbA1c (%)
6.16 ± 0.53 5.56 ± 0.27
BMI (Kg/m²)
39.33 ± 5.86 21.92 ± 2.93
In the adipogenesis pathway selected based
on the pathway analysis 13 genes were
significantly different in adipose tissue from
obese subjects compared to lean subjects.
0.003**
0.020*
2E-05***
LEP is upregulated, and LPIN1, NRIP1,
TWIST1,
NR3C1
and
NR2F1
are
downregulated in obese adipose tissue, and
the genetic expression is inversely correlated
with adiposity.
The data are present as Mean ± SD. Student’s t test was
done for test the significant difference between obese and
lean subjects. *p < 0.05, **p < 0.01 and ***p < 0.001.
Statistical analysis in normalized data
‒ Selected human adipose tissue dataset 20,111 genes → Affymetrix Human Genome U133 Plus 2.0
‒ 590 genes differentially expressed (absolute logFC > 0.58 and p-value < 0.05):
‒ 343 were upregulated
‒ 247 were downregulated
Pathway analysis
‒ To interpret and visualize the molecular changes on a
pathway level.
‒ PathVisio 3.2.0 → WikiPathways collection
‒ Criteria: (1) Z-score > 1.96, (2) permutated p-value < 0.05
and (3) minimum number of changed genes is 3
The expression of GDF10 had a similar
mode of action as observed in murine cells,
where
GDF10
inhibited
adipocyte
differentiation
by
affecting
this
transcriptional cascade.
Gene Ontology analysis
‒ Identify biological processes for the differentially expressed
genes in the normalized dataset.
‒ GO-Elite
‒ Settings:(1) 2000 permutations, (2) Z-score pruning algorithm,
(3) Z-score threshold > 1.96, (4) p-value threshold < 0.05 and
(5) minimum number of changed genes is 3
Figure 2.
Biological functional
network of the genes differentially
expressed in adipogenesis pathway
from obese subjects.
The network shows which genes are linked
to adipogenesis and whether they are nuclear
receptors. The biological processes linked to
the differentially expressed genes and
obtained with GO analysis are shown in the
functional network.
Network analysis
‒ Comprehensive visualization functionality for adipogenesis pathway
‒ Cytoscape 3.2.1
‒ 195 edges connecting 59 nodes, consisting in 13 genes products, 2 pathways
and 44 biological process.
CONCLUSION
RESULTS AND DISCUSSION
The anthropometric and metabolic characteristics of the subjects selected are demonstrated in Table 1.
The pathway statistical analysis resulted 21 significantly changed pathways (Table 2). The most
relevant biological process for obesity and metabolic disorders associate are demonstrated in Table 3.
After analysis of the pathways and biological process related with the genes differentially expressed in
obese subjects. The adipogenesis pathway was selected and in this pathway 13 genes were
differentially expressed in obese subjects, compared with lean, where 3 were upregulated and 10 were
downregulated (Figure 1). The functional biology network, for these genes, is shown in Figure 2.
In this study, the results were able to confirm many pathways related to insulin resistance and T2DM in
obese adipose tissue. In addition it was demonstrated that in humans the GDF10 expression is changed
in SAT from obese subjects. The pathway and network analysis also demonstrated the importance of
LEP in obesity, and the cascade molecular action. Obesity increases the risk of developing serious
health complications including hypertension, steatohepatitis, fatty liver, dyslipidemia and type 2
diabetes mellitus (T2DM), where adipose tissue has a big impact. Therefore, understand and underlying
the mechanisms, and network, of the key genes involved in this process is essential for early
interventions and prevention of obesity complications.
REFERENCES
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Supported by CAPES, CNPq and the EU FP7 project MICROGENNET (31.96.42.94 E, www.microgennet.org).