ISSN: 0970-938X (Print) | 0976-1683 (Electronic)

Biomedical Research

An International Journal of Medical Sciences

Research Article - Biomedical Research (2017) Volume 28, Issue 3

Dysregulated pathways in type 2 diabetes mellitus
Li H1, Li H2, Ran J3 and Mao X4*

1Central Laboratory of Xinjiang Medical University, China

2Cancer Institute, the Affiliated Cancer Hospital of Xinjiang Medical University, China

3Clinical Laboratory Diagnostic Center, General Hospital of Xinjiang Military Region, China

4Chinese Medicine School of Xinjiang Medical University, China

*Corresponding Author:
Mao X
Chinese Medicine School
Xinjiang Medical University, China

Accepted on September 13, 2016

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Abstract

Objective: Type 2 Diabetes Mellitus (T2DM) is a leading health problem worldwide. In this study, we aimed to identify the pathways significantly relevant to T2DM, and a brief review based on these pathways was made.

Materials and Methods: The genes associated with T2DM were extracted by text mining tool from literature database. Then we employed the Fisher’s exact test based on the cumulative hypergeometric distribution to evaluate the pathways relevant to T2DM.

Result: A total of 135 genes associated with T2DM were confirmed and 76 pathways significantly relevant to T2DM were identified from 880 cellular pathways. These 76 pathways could be classified mainly in five classes: adipocytokine, inflame, PPAR, insulin and T2DM pathway. Adipocytokine pathway from KEGG database was the most relevant pathway.

Conclusion: Disregulation of adipocytokine and inflammatory pathway is the hallmark of T2DM originated from the organism in the status of excess energy over long time span.

Keywords

Type 2 diabetes mellitus, Pathways, Dysregulation, Adipocytokine, Insulin.

Introduction

Type 2 Diabetes (T2DM) has become a leading health problem worldwide. According to the International Diabetes Federation, the total number of the patients with diabetes is predicted to rise to 552 million by the year of 2030 [1]. Furthermore, the patients with T2DM tended to develop cardiovascular disease and other vascular complications such as peripheral vascular disease, diabetic nephropathy, and diabetic retinopathy and so on. It is widely accepted that T2DM is a complex disease as a joint action of genetic background and environmental factor. Therefore, great efforts have been made to identify the genes associated with T2DM, and a number of genes have been identified. The genes always performed biological function in cooperation way rather than alone, particularly in complex diseases. In the development of T2DM, the signaling pathway of pathological mechanism was more important than individual genes. Admittedly, there were a great number of signaling pathways related to T2DM, which were discussed in the medicine literature database.

In this study, we reviewed signaling pathways related to T2DM, which was identified as dysregulated pathway associated with disease [2-5]. As refer to system review, meta-analysis was also employed to evaluate the parameters from different studies. The genes associated with T2DM by text mining tool were extracted from literature database. Then we employed the Fisher’s exact test based on the cumulative hypergeometric distribution to evaluate the pathways relevant to T2DM.

Method

Construction of cellular pathways database

All of the pathways with the gene members were downloaded from an integrated pathway database Molecular Signatures Database (MSigDB) [6], which is a large collection of annotated functional gene sets. There are 880 canonical pathways with 6804 genes members in the database, including the metabolic and signaling pathways collected from Biocarta (www.Biocarta.com), KEGG [7], and Reactome [8].

Extraction of genes related to T2DM from the literature

We searched candidate genes associated with T2DM by PolySearch text mining system, which can produce a list of concepts relevant to the user’s query by analyzing multiple information sources including PubMed, OMIM, DrugBank and Swiss-Prot. It covers many types of biomedical concepts including diseases, genes/proteins, drugs, metabolites, SNPs, pathways and tissues [9]. The query type is ‘Disease-Gene/ Protein Association’ and the query keyword is ‘type 2 diabetes’. PolySearch system returns 1325 literatures. To check the accuracy, we manually confirmed whether these genes were associated with the T2DM. Finally a total of 135 candidate genes were obtained (Table 1).

SN Pathway Name Class p-value
1 KEGG adipocytokine signaling pathway Adipocytokine 3.17E-07
2 KEGG type ii diabetes mellitus T2DM 4.88E-07
3 BIOCARTA PPARA pathway PPAR 6.56E-07
4 BIOCARTA cytokine pathway Inflammation 1.91E-06
5 BIOCARTA INFLAM pathway Inflammation 1.68085E-05
6 Reactome regulation of lipid metabolism by peroxisome proliferator activated
receptor alpha		 PPAR 2.28286E-05
7 BIOCARTA IL5 pathway Inflammation 2.74777E-05
8 Reactome signal attenuation Insulin 4.28557E-05
9 BIOCARTA DC pathway Inflammation 5.60284E-05
10 KEGG PPAR signaling pathway PPAR 5.65648E-05

Table 1. Top 10 statistically relevant pathways with T2DM ranked by P-value

Identification of dysregulated pathways associated with T2DM

To examine the association of the pathway with T2DM, Fisher’s exact test based on the cumulative hypergeometric distribution was employed. The P-value was calculated to evaluate statistical significance of a given pathway by the formula as follow.

image

In this formula, N represents the total number of genes in the background population; n represents the number of genes related to T2DM extracted from literatures; m denotes the number of genes within the given pathways. The number of genes that overlapped with both T2DM related genes and this pathway are denoted as k. In this study, the pathway is considered association with T2DM if its P-value is less than 0.05.

Result

Pathways associated with T2DM

Of eight hundreds and eighty pathways, three hundreds and seventy four pathways contain at least one gene of the one hundred and thirty five genes related to T2DM (Table 2). Seventy six pathways statistically associated with T2DM were identified (p<0.05, Table 2).

SN Pathway name P-value Number of genes in the pathway Number of overlapped genes
1 KEGG adipocytokine signaling pathway 3.17E-07 67 12
2 KEGG type ii diabetes mellitus 4.88E-07 47 9
3 BIOCARTA PPARA pathway 6.56E-07 58 9
4 BIOCARTA cytokine pathway 1.91E-06 21 6
5 BIOCARTA INFLAM Pathway 1.68085E-05 29 6
6 Reactome regulation of lipid metabolism by peroxisome proliferator activated receptor alpha 2.28286E-05 61 8
7 BIOCARTA IL5 pathway 2.74777E-05 10 4
8 Reactome signal attenuation 4.28557E-05 11 4
9 BIOCARTA DC pathway 5.60284E-05 22 5
10 KEGG PPAR signaling pathway 5.65648E-05 69 8
11 KEGG leishmania infection 7.72476E-05 72 8
12 Reactome PI3K cascade 8.67844E-05 38 6
13 KEGG maturity onset diabetes of the young 0.00010699 25 5
14 Reactome IRS related events 0.000150919 79 8
15 Reactome downstream signaling of activated FGFR 0.000177383 43 6
16 BIOCARTA GH pathway 0.000190377 28 5
17 KEGG JAK STAT signaling pathway 0.000228524 155 11
18 BIOCARTA IL10 pathway 0.000283718 17 4
19 Reactome chylomicron mediated lipid transport 0.000283718 17 4
20 KEGG cytokine cytokine receptor interaction 0.000754833 267 14
21 BIOCARTA insulin pathway 0.000808775 22 4
22 BIOCARTA leptin pathway 0.00110817 11 3
23 BIOCARTA NTHI pathway 0.001138568 24 4
24 KEGG aldosterone regulated sodium reabsorption 0.001321971 42 5
25 KEGG hematopoietic cell lineage 0.001688182 88 7
26 Reactome lipoprotein metabolism 0.001797438 27 4
27 BIOCARTA granulocytes pathway 0.002341151 14 3
28 SIG insulin receptor pathway in cardiac myocytes 0.002654552 49 5
29 BIOCARTA stem pathway 0.002884567 15 3
30 KEGG toll like receptor signaling pathway 0.003910244 102 7
31 BIOCARTA PML pathway 0.00418812 17 3
32 Reactome regulation of insulin like growth factor activity by insulin like growth factor binding proteins 0.00418812 17 3
33 KEGG cytosolic DNA sensing pathway 0.004764438 56 5
34 Reactome metabolism of lipids and lipoproteins 0.005288124 228 11
35 KEGG insulin signaling pathway 0.005574524 137 8
36 KEGG allograft rejection 0.006395459 38 4
37 SIG PIP3 signaling in cardiac myoctes 0.007857084 63 5
38 BIOCARTA HER2 pathway 0.00882709 22 3
39 KEGG graft versus host disease 0.009127855 42 4
40 Reactome GPCR ligand binding 0.010184646 392 15
41 KEGG type I diabetes mellitus 0.010740936 44 4
42 KEGG intestinal immune network for IGA production 0.014493763 48 4
43 KEGG adherens junction 0.016001105 75 5
44 Reactome nuclear receptor transcription pathway 0.016645491 50 4
45 BIOCARTA NKT pathway 0.017231882 28 3
46 ST STAT3 pathway 0.018967271 11 2
47 KEGG T cell receptor signaling pathway 0.01971209 108 6
48 KEGG asthma 0.020755172 30 3
49 BIOCARTA NFAT pathway 0.021524131 54 4
50 BIOCARTA ASBCELL pathway 0.022469461 12 2
51 BIOCARTA ACE2 pathway 0.026215434 13 2
52 BIOCARTA TCRA pathway 0.026215434 13 2
53 Reactome SOS mediated signalling 0.026215434 13 2
54 BIOCARTA IL1R pathway 0.026726425 33 3
55 Reactome platelet adhesion to exposed collagen 0.03019464 14 2
56 Reactome SHC related events 0.03019464 14 2
57 BIOCARTA carm ER pathway 0.031163931 35 3
58 KEGG prion diseases 0.031163931 35 3
59 Reactome regulation of insulin secretion by glucagon like peptide 1 0.031983614 61 4
60 KEGG nod like receptor signaling pathway 0.033682644 62 4
61 BIOCARTA longevity pathway 0.034396291 15 2
62 BIOCARTA nuclearrs pathway 0.034396291 15 2
63 Reactome class C3 metabotropic glutamate pheromone receptors 0.034396291 15 2
64 SA MMP cytokine connection 0.034396291 15 2
65 BIOCARTA toll pathway 0.035963655 37 3
66 Reactome PI3K akt signalling 0.035963655 37 3
67 Reactome G alpha S signalling events 0.038177788 126 6
68 Reactome glucose transport 0.038497865 38 3
69 BIOCARTA rela pathway 0.038809538 16 2
70 Reactome diabetes pathways 0.038839459 383 13
71 BIOCARTA IL17 pathway 0.043426096 17 2
72 BIOCARTA IL7 pathway 0.043426096 17 2
73 BIOCARTA lair pathway 0.043426096 17 2
74 BIOCARTA NO2IL12 pathway 0.043426096 17 2
75 KEGG renin angiotensin system 0.043426096 17 2
76 Reactome regulation of gene expression in beta cells 0.049148321 101 5
77 BIOCARTA biopeptides pathway 0.052485943 43 3
78 BIOCARTA TH1TH2 pathway 0.053227544 19 2
79 BIOCARTA TID pathway 0.053227544 19 2
80 Reactome CD28 dependent PI3K AKT signaling 0.053227544 19 2
81 Reactome MYD88 cascade 0.053227544 19 2
82 Reactome downstream events in GPCR signaling 0.058232248 448 14
83 Reactome regulation of insulin secretion by free fatty acids 0.05839479 20 2
84 Reactome G alpha I signalling events 0.060669959 177 7
85 Reactome class A1 rhodopsin like receptors 0.062787056 292 10
86 BIOCARTA IL6 pathway 0.069217801 22 2
87 Reactome regulation of beta cell development 0.074677825 114 5
88 BIOCARTA IL12 pathway 0.074856758 23 2
89 BIOCARTA NFKB pathway 0.074856758 23 2
90 Reactome phospholipase cmediated cascade 0.074856758 23 2
91 Reactome toll like receptor 9 cascade 0.074856758 23 2
92 BIOCARTA CSK pathway 0.080636382 24 2
93 BIOCARTA PGC1A pathway 0.080636382 24 2
94 KEGG nicotinate and nicotinamide metabolism 0.080636382 24 2
95 Reactome activated TLR4 signalling 0.080636382 24 2
96 KEGG hypertrophic cardiomyopathy HCM 0.086981297 85 4
97 KEGG neuroactive ligand receptor interaction 0.088943124 272 9
98 KEGG TGF beta signaling pathway 0.0898965 86 4
99 Reactome platelet degranulation 0.0898965 86 4
100 Reactome hemostasis 0.092072368 274 9
101 Reactome FGFR ligand binding and activation 0.098749518 27 2
102 Reactome FRS2mediated cascade 0.098749518 27 2
103 SIG IL4receptor in B lyphocytes 0.098749518 27 2
104 Reactome the role of NEF in HIV1 replication and disease pathogenesis 0.105020165 28 2
105 Reactome toll like receptor 4 cascade 0.105020165 28 2
106 KEGG thyroid cancer 0.111395597 29 2
107 Reactome CD28 co stimulation 0.111395597 29 2
108 Reactome regulation of glucokinase by glucokinase regulatory protein 0.111395597 29 2
109 ST PAC1 receptor pathway 0.112893641 6 1
110 Reactome cell surface interactions at the vascular wall 0.114852428 94 4
111 BIOCARTA HDAC pathway 0.117870092 30 2
112 Reactome peptide ligand binding receptors 0.127151728 173 6
113 KEGG alanine aspartate and glutamate metabolism 0.131089449 32 2
114 Reactome glucagon type ligand receptors 0.137822509 33 2
115 ST G alpha I pathway 0.144630194 34 2
116 Reactome TRKA signalling from the plasma membrane 0.146132767 103 4
117 KEGG regulation of autophagy 0.15150708 35 2
118 Reactome costimulation by the CD28 family 0.160167933 70 3
119 Reactome formation of platelet plug 0.161866188 186 6
120 ST interferon gamma pathway 0.164501309 9 1
121 KEGG RIG I like receptor signaling pathway 0.164970398 71 3
122 BIOCARTA barrestin pathway 0.181032002 10 1
123 BIOCARTA EPHA4 pathway 0.181032002 10 1
124 BIOCARTA FREE pathway 0.181032002 10 1
125 BIOCARTA SARS pathway 0.181032002 10 1
126 BIOCARTA SODD pathway 0.181032002 10 1
127 KEGG limonene and pinene degradation 0.181032002 10 1
128 Reactome calcitonin like ligand receptors 0.181032002 10 1
129 Reactome ethanol oxidation 0.181032002 10 1
130 Reactome regulation of RHEB GTPASE activity by AMPK 0.181032002 10 1
131 Reactome removal of the flap intermediate from the C strand 0.181032002 10 1
132 KEGG tryptophan metabolism 0.186746657 40 2
133 BIOCARTA EPONFKB pathway 0.197238684 11 1
134 BIOCARTA IL4 pathway 0.197238684 11 1
135 BIOCARTA monocyte pathway 0.197238684 11 1
136 BIOCARTA SET pathway 0.197238684 11 1
137 BIOCARTA TCAPOPTOSIS pathway 0.197238684 11 1
138 Reactome CD28 dependent VAV1 pathway 0.197238684 11 1
139 Reactome HDL mediated lipid transport 0.197238684 11 1
140 Reactome recycling of bile acids and salts 0.197238684 11 1
141 Reactome signaling by VEGF 0.197238684 11 1
142 Reactome G ALPHA Q signalling events 0.198766887 157 5
143 KEGG pathways in cancer 0.199838877 328 9
144 Reactome metabolism of vitamins and cofactors 0.201152563 42 2
145 ST differentiation pathway in PC12 cells 0.201152563 42 2
146 KEGG FC EPSILON RI signaling pathway 0.204794347 79 3
147 Reactome metabolism of carbohydrates 0.208609462 119 4
148 BIOCARTA VDR pathway 0.213126719 12 1
149 Reactome amino acid synthesis and interconversion 0.213126719 12 1
150 Reactome facilitative NA independent glucose transporters 0.213126719 12 1
151 Reactome hormone sensitive lipase HSL mediated triacylglycerol hydrolysis 0.213126719 12 1
152 Reactome PECAM1 interactions 0.213126719 12 1
153 BIOCARTA CHREBP2 pathway 0.215684056 44 2
154 KEGG amino sugar and nucleotide sugar metabolism 0.215684056 44 2
155 BIOCARTA CARM1 pathway 0.228702605 13 1
156 Reactome death receptor signalling 0.228702605 13 1
157 Reactome early phase of HIV life cycle 0.228702605 13 1
158 Reactome notch HLH transcription pathway 0.228702605 13 1
159 Reactome platelet activation 0.234452963 167 5
160 Reactome class B2 secretin family receptors 0.235974908 85 3
161 Reactome toll receptor cascades 0.241254985 86 3
162 Reactome regulation of insulin secretion 0.24157095 212 6
163 BIOCARTA PS1 pathway 0.243972659 14 1
164 BIOCARTA THELPER pathway 0.243972659 14 1
165 Reactome polymerase switching 0.243972659 14 1
166 Reactome regulation of AMPK activity via LKB1 0.243972659 14 1
167 Reactome removal of the flap intermediate 0.243972659 14 1
168 Reactome SEMA3A plexin repulsion signaling by inhibiting integrin adhesion 0.243972659 14 1
169 KEGG glycerolipid metabolism 0.252357662 49 2
170 KEGG prostate cancer 0.257208586 89 3
171 BIOCARTA ERYTH pathway 0.258942366 15 1
172 BIOCARTA HIF pathway 0.258942366 15 1
173 BIOCARTA HSP27 pathway 0.258942366 15 1
174 BIOCARTA PITX2 pathway 0.258942366 15 1
175 Reactome repair synthesis of patch 27 30 bases long by dna polymerase 0.258942366 15 1
176 Reactome SEMA3A PAK dependent axon repulsion 0.258942366 15 1
177 BIOCARTA CDMAC pathway 0.273617327 16 1
178 BIOCARTA GATA3 pathway 0.273617327 16 1
179 BIOCARTA IL22BP pathway 0.273617327 16 1
180 BIOCARTA P53 pathway 0.273617327 16 1
181 KEGG pantothenate and COA biosynthesis 0.273617327 16 1
182 KEGG riboflavin metabolism 0.273617327 16 1
183 Reactome CRMPS in SEMA3A signaling 0.273617327 16 1
184 Reactome trafficking OF GLUR2 containing AMPA receptors 0.273617327 16 1
185 KEGG MTOR signaling pathway 0.274461925 52 2
186 KEGG starch and sucrose metabolism 0.274461925 52 2
187 KEGG amyotrophic lateral sclerosis als 0.281827569 53 2
188 KEGG autoimmune thyroid disease 0.281827569 53 2
189 BIOCARTA 41BB pathway 0.288004756 17 1
190 Reactome activated AMPK stimulates fatty acid oxidation in muscle 0.288004756 17 1
191 Reactome energy dependent regulation of MTOR by LKB1 AMPK 0.288004756 17 1
192 Reactome chemokine receptors bind chemokines 0.29654026 55 2
193 Reactome signaling in immune system 0.297591031 366 9
194 BIOCARTA CARDIACEGF PATHWAY 0.302108705 18 1
195 BIOCARTA CCR5 pathway 0.302108705 18 1
196 Reactome synthesis and interconversion of nucleotide DI and triphosphates 0.302108705 18 1
197 Reactome TIE2 signaling 0.302108705 18 1
198 BIOCARTA MAL pathway 0.315935373 19 1
199 BIOCARTA TGFB pathway 0.315935373 19 1
200 Reactome unfolded protein response 0.315935373 19 1
201 ST WNT CA2 CYCLIC GMP pathway 0.315935373 19 1
202 KEGG pathogenic Escherichia coli infection 0.325816274 59 2
203 BIOCARTA NKCELLS pathway 0.329490364 20 1
204 Reactome lagging strand synthesis 0.329490364 20 1
205 KEGG acute myeloid leukemia 0.333091259 60 2
206 BIOCARTA IGF1 pathway 0.342778444 21 1
207 BIOCARTA TOB1 pathway 0.342778444 21 1
208 Reactome CTLA4 inhibitory signaling 0.342778444 21 1
209 Reactome NEF mediates down modulation of cell surface receptors by recruiting them to clathrin adapters 0.342778444 21 1
210 KEGG colorectal cancer 0.347572803 62 2
211 KEGG glycolysis gluconeogenesis 0.347572803 62 2
212 KEGG beta alanine metabolism 0.355805397 22 1
213 Reactome E2F transcriptional targets AT G1 S 0.355805397 22 1
214 Reactome regulation of insulin secretion by acetylcholine 0.355805397 22 1
215 BIOCARTA IGF1R pathway 0.368575513 23 1
216 KEGG mismatch repair 0.368575513 23 1
217 Reactome collagen mediated activation cascade 0.368575513 23 1
218 Reactome cytosolic TRNA aminoacylation 0.368575513 23 1
219 Reactome integrin ALPHAIIBBETA3 signaling 0.368575513 23 1
220 ST MYOCYTE AD pathway 0.368575513 23 1
221 BIOCARTA CXCR4 pathway 0.381093919 24 1
222 BIOCARTA ECM pathway 0.381093919 24 1
223 BIOCARTA EIF4 pathway 0.381093919 24 1
224 BIOCARTA TPO pathway 0.381093919 24 1
225 Reactome further platelet releasate 0.381093919 24 1
226 Reactome translocation of ZAP70 to immunological synapse 0.381093919 24 1
227 BIOCARTA stress pathway 0.393366933 25 1
228 KEGG ascorbate and aldarate metabolism 0.393366933 25 1
229 ST granule cell survival pathway 0.393366933 25 1
230 KEGG pancreatic cancer 0.404331386 70 2
231 KEGG renal cell carcinoma 0.404331386 70 2
232 BIOCARTA WNT pathway 0.40539825 26 1
233 KEGG galactose metabolism 0.40539825 26 1
234 KEGG glycosaminoglycan biosynthesis heparan sulfate 0.40539825 26 1
235 Reactome phosphorylation of CD3 and TCR zeta chains 0.40539825 26 1
236 Reactome platelet aggregation plug formation 0.40539825 26 1
237 KEGG melanoma 0.411269367 71 2
238 KEGG leukocyte transendothelial migration 0.4139359 118 3
239 BIOCARTA GSK3 pathway 0.417192519 27 1
240 Reactome GS alpha mediated events in glucagon signalling 0.417192519 27 1
241 Reactome metabolism of bile acids and bile salts 0.417192519 27 1
242 Reactome mtor signalling 0.417192519 27 1
243 ST GAQ pathway 0.417192519 27 1
244 KEGG viral myocarditis 0.425026476 73 2
245 BIOCARTA ERK pathway 0.428754508 28 1
246 Reactome extension of telomeres 0.428754508 28 1
247 ST tumor necrosis factor pathway 0.428754508 28 1
248 Reactome transmembrane transport of small molecules 0.43253684 218 5
249 BIOCARTA TNFR1 pathway 0.440088451 29 1
250 BIOCARTA VEGF pathway 0.440088451 29 1
251 KEGG histidine metabolism 0.440088451 29 1
252 Reactome PD1 signaling 0.440088451 29 1
253 KEGG arrhythmogenic right ventricular cardiomyopathy arvc 0.445347369 76 2
254 KEGG O glycan biosynthesis 0.45119977 30 1
255 Reactome inhibition of insulin secretion by adrenaline noradrenaline 0.45119977 30 1
256 Reactome trafficking of AMPA receptors 0.45119977 30 1
257 KEGG neurotrophin signaling pathway 0.455945611 126 3
258 KEGG peroxisome 0.458673954 78 2
259 BIOCARTA EGF pathway 0.462091982 31 1
260 BIOCARTA NO1 pathway 0.462091982 31 1
261 Reactome dna strand elongation 0.462091982 31 1
262 BIOCARTA PDGF pathway 0.472768962 32 1
263 Reactome integration of energy metabolism 0.475275159 229 5
264 Reactome integrin cell surface interactions 0.478314638 81 2
265 KEGG propanoate metabolism 0.483236372 33 1
266 Reactome E2F Mediated Regulation OF dna replication 0.483236372 33 1
267 Reactome global genomic NER 0.483236372 33 1
268 ST phosphoinositide 3 kinase pathway 0.483236372 33 1
269 Reactome glucose and other sugar SLC transporters 0.484766066 82 2
270 BIOCARTA AT1R pathway 0.493496954 34 1
271 BIOCARTA MPR pathway 0.493496954 34 1
272 KEGG butanoate metabolism 0.493496954 34 1
273 Reactome glucagon signaling in metabolic regulation 0.493496954 34 1
274 ST adrenergic 0.493496954 34 1
275 KEGG ECM receptor interaction 0.497520685 84 2
276 KEGG huntingtons disease 0.499771774 185 4
277 KEGG base excision repair 0.503555775 35 1
278 KEGG primary immunodeficiency 0.503555775 35 1
279 Reactome downstream signal transduction 0.503555775 35 1
280 Reactome generic transcription pathway 0.503555775 35 1
281 Reactome innate immunity signaling 0.506625831 136 3
282 KEGG progesterone mediated oocyte maturation 0.510074735 86 2
283 KEGG natural killer cell mediated cytotoxicity 0.511562943 137 3
284 BIOCARTA AGR pathway 0.513415813 36 1
285 KEGG dna replication 0.513415813 36 1
286 KEGG apoptosis 0.522423923 88 2
287 BIOCARTA ALK pathway 0.52308166 37 1
288 BIOCARTA MET pathway 0.52308166 37 1
289 Reactome generation of second messenger molecules 0.52308166 37 1
290 KEGG systemic lupus erythematosus 0.526218712 140 3
291 BIOCARTA IL2RB pathway 0.532556772 38 1
292 BIOCARTA integrin pathway 0.532556772 38 1
293 ST JNK MAPK pathway 0.532556772 38 1
294 ST B cell antigen receptor 0.541845322 39 1
295 KEGG dilated cardiomyopathy 0.546496153 92 2
296 KEGG pyruvate metabolism 0.550950289 40 1
297 Reactome tRNA aminoacylation 0.550950289 40 1
298 KEGG bladder cancer 0.568625212 42 1
299 KEGG fatty acid metabolism 0.568625212 42 1
300 Reactome amine ligand binding receptors 0.568625212 42 1
301 KEGG ABC transporters 0.585609257 44 1
302 KEGG lysine degradation 0.585609257 44 1
303 KEGG nucleotide excision repair 0.585609257 44 1
304 KEGG valine leucine and isoleucine degradation 0.585609257 44 1
305 KEGG vasopressin regulated water reabsorption 0.585609257 44 1
306 Reactome transcription coupled NER 0.585609257 44 1
307 ST T cell signal transduction 0.585609257 44 1
308 BIOCARTA keratinocyte pathway 0.601929247 46 1
309 SIG BCR signaling pathway 0.601929247 46 1
310 KEGG melanogenesis 0.602930248 102 2
311 BIOCARTA TCR pathway 0.60984844 47 1
312 KEGG NOTCH signaling pathway 0.60984844 47 1
313 Reactome downstream TCR signaling 0.60984844 47 1
314 KEGG MAPK signaling pathway 0.612801313 267 5
315 KEGG proteasome 0.61761117 48 1
316 Reactome signalling by NGF 0.619395375 215 4
317 Reactome glucose regulation of insulin secretion 0.621608973 161 3
318 KEGG regulation of actin cytoskeleton 0.623069227 216 4
319 Reactome nucleotide excision repair 0.625220656 49 1
320 KEGG endometrial cancer 0.64715898 52 1
321 KEGG taste transduction 0.64715898 52 1
322 Reactome hormone biosynthesis 0.64715898 52 1
323 KEGG alzheimers disease 0.654370368 169 3
324 KEGG arginine and proline metabolism 0.661071062 54 1
325 KEGG oocyte meiosis 0.663511038 114 2
326 KEGG basal cell carcinoma 0.667821884 55 1
327 KEGG steroid hormone biosynthesis 0.667821884 55 1
328 KEGG vibrio cholerae infection 0.674438596 56 1
329 BIOCARTA HIVNEF pathway 0.687282801 58 1
330 Reactome host interactions of HIV factors 0.690930426 120 2
331 Reactome platelet activation triggers 0.693515301 59 1
332 Reactome toll like receptor 3 cascade 0.693515301 59 1
333 Reactome steroid metabolism 0.711481452 62 1
334 Reactome signaling by PDGF 0.722874284 64 1
335 Reactome TCR signaling 0.722874284 64 1
336 KEGG cell cycle 0.724623084 128 2
337 KEGG axon guidance 0.728611171 129 2
338 Reactome insulin synthesis and secretion 0.728611171 129 2
339 KEGG chemokine signaling pathway 0.730600953 190 3
340 Reactome transmission across chemical synapses 0.732550442 130 2
341 Reactome semaphorin interactions 0.73382026 66 1
342 Reactome phase 1 functionalization of compounds 0.739131451 67 1
343 KEGG epithelial cell signaling in helicobacter pylori infection 0.744336963 68 1
344 KEGG cell adhesion molecules CAMS 0.747829139 134 2
345 Reactome NCAM signaling for neurite out growth 0.749439776 69 1
346 KEGG long term potentiation 0.754441381 70 1
347 KEGG metabolism of xenobiotics by cytochrome P450 0.754441381 70 1
348 Reactome metablism of nucleotides 0.759343803 71 1
349 KEGG VEGF signaling pathway 0.782436311 76 1
350 Reactome telomere maintenance 0.786783814 77 1
351 ST integrin signaling pathway 0.79104501 78 1
352 KEGG WNT signaling pathway 0.804703653 151 2
353 Reactome neuroransmitter receptor binding and downstream transmission in the postsynaptic cell 0.814893723 84 1
354 KEGG ERBB signaling pathway 0.825784147 87 1
355 KEGG antigen processing and presentation 0.832688689 89 1
356 Reactome synthesis of DNA 0.832688689 89 1
357 Reactome SLC mediated transmembrane transport 0.852308095 169 2
358 Reactome gene expression 0.853637636 425 6
359 KEGG GNRH signaling pathway 0.868767738 101 1
360 Reactome G1 S transition 0.87139976 102 1
361 KEGG calcium signaling pathway 0.871946394 178 2
362 Reactome HIV life cycle 0.87397939 103 1
363 Reactome S phase 0.87397939 103 1
364 Reactome DNA repair 0.876507699 104 1
365 Reactome HIV infection 0.881798148 183 2
366 KEGG focal adhesion 0.911776602 201 2
367 KEGG lysosome 0.912539721 121 1
368 Reactome biological oxidations 0.922582686 127 1
369 Reactome apoptosis 0.925669074 129 1
370 KEGG purine metabolism 0.959681332 159 1
371 Reactome axon guidance 0.96129626 161 1
372 Reactome metabolism of amino acids 0.962079525 162 1
373 KEGG endocytosis 0.975333571 183 1
374 Reactome cell cycle mitotic 0.998067737 306 1

Table 2. 374 pathways which contain at least one gene related to T2DM and their P-value.

Description of top 10 pathways ranked by p-value

These ten pathways could be attributed to five classes signaling pathways including adipocytokine, inflammatory, Peroxisome Proliferators Activated Receptor (PPAR), insulin and T2DM pathway (Table 1). Of the other 66 pathways associated with T2DM, there were only 23 pathways that could not be attributed to the five classes signaling pathways mentioned previously (Tables 3 and 4).

Class Name of Pathway Frequency
Adipocytokine pathway 1
inflammatory pathway 21
Insulin and its up-downstream pathway 17
PPAR and Lipid metabolism pathway 3
T2DM pathway 1
Other pathway 23

Table 3. The categorical frequency of other 66 pathways associated with T2DM.

SN Pathway name Class ID *
1 KEGG leishmania infection 4
2 Reactome PI3K cascade 5
3 KEGG maturity onset diabetes of the young 5
4 Reactome IRS related events 5
5 Reactome downstream signaling of activated FGFR 5
6 BIOCARTA GH pathway 5
7 KEGG JAK STAT signaling pathway 0
8 BIOCARTA IL10 pathway 4
9 Reactome chylomicron mediated lipid transport 3
10 KEGG cytokine cytokine receptor interaction 4
11 BIOCARTA insulin pathway 5
12 BIOCARTA leptin pathway 1
13 BIOCARTA NTHI pathway 4
14 KEGG aldosterone regulated sodium reabsorption 0
15 KEGG hematopoietic cell lineage 0
16 Reactome lipoprotein metabolism 3
17 BIOCARTA granulocytes pathway 4
18 SIG insulin receptor pathway in cardiac myocytes 5
19 BIOCARTA stem pathway 0
20 KEGG toll like receptor signaling pathway 4
21 BIOCARTA PML pathway 0
22 Reactome regulation of insulin like growth factor activity by insulin like growth factor binding proteins 5
23 KEGG cytosolic DNA sensing pathway 0
24 Reactome metabolism of lipids and lipoproteins 3
25 KEGG insulin signaling pathway 5
26 KEGG allograft rejection 4
27 SIG PIP3 signaling in cardiac myoctes 5
28 BIOCARTA HER2 pathway 0
29 KEGG graft versus host disease 4
30 Reactome GPCR ligand binding 0
31 KEGG TYPE I diabetes mellitus 5
32 KEGG intestinal immune network for IGA production 4
33 KEGG adherens junction 0
34 Reactome nuclear receptor transcription pathway 0
35 BIOCARTA NKT pathway 4
36 ST STAT3 pathway 0
37 KEGG T cell receptor signaling pathway 4
38 KEGG asthma 4
39 BIOCARTA NFAT pathway 0
40 BIOCARTA ASBCELL pathway 4
41 BIOCARTA ACE2 pathway 0
42 BIOCARTA TCRA pathway 0
43 Reactome SOS mediated signalling 5
44 BIOCARTA IL1R pathway 4
45 Reactome platelet adhesion to exposed collagen 0
46 Reactome SHC related events 5
47 BIOCARTA carm ER pathway 0
48 KEGG prion diseases 0
49 Reactome regulation of insulin secretion by glucagon like peptide 1 5
50 KEGG NOD like receptor signaling pathway 0
51 BIOCARTA longevity pathway 0
52 BIOCARTA nuclearrs pathway 0
53 Reactome class C3 metabotropic glutamate pheromone receptors 0
54 SA MMP cytokine connection 4
55 BIOCARTA toll pathway 4
56 Reactome PI3K akt signalling 5
57 Reactome G ALPHA S signalling events 0
58 Reactome glucose transport 5
59 BIOCARTA RELA pathway 4
60 Reactome diabetes pathways 2
61 BIOCARTA IL17 pathway 4
62 BIOCARTA IL7 pathway 4
63 BIOCARTA LAIR pathway 4
64 BIOCARTA NO2IL12 pathway 4
65 KEGG renin angiotensin system 0
66 Reactome regulation of gene expression in beta cells 5
*:1: Adipocytokine pathway; 2:T2D pathway; 3: PPAR and Lipid metabolism pathway; 4: Flammation pathway; 5: Insulin and its up-downstream pathway; 0: Other pathway.

Table 4. Class of other 66 pathways associated with T2D.

Adipocytokine pathway

Adipose tissue is a heterogeneous mix of adipocytes, pre-adipocytes, immune cells, and endothelium [10]. Adipocytokine refer to the cytokines secreted from adipose tissue. There are three kinds of adipocytokines (Leptin, Adiponectin and TNFα) in the adipocytokine pathways from KEGG database. Increased adiposity is associated with decreased adiponectin secretion and positively correlated with leptin production. Leptin and its receptor play roles in food intake and energy balance [11]. The binding of leptin to its receptor initiates a phosphorylation cascade that results in transcriptional activation of target genes of STAT5 and STAT3 and activation of the PI3K pathway, the MAPK/ERK pathway, and mTOR/S6K pathway. Leptin regulates energy intake and metabolic rate primarily by its effect on hypothalamic nuclei and exerts its anorectic effects by modulating the levels of neuropeptides such as NPY, AGRP, and alpha-MSH. Adiponectin has beneficial effects on insulin sensitivity to lower plasma glucose and Free Fat Acids (FFAs) improvement. These effects are partly induced by adiponectin-induced AMPK activation, which in turn stimulates skeletal muscle fatty acid oxidation and glucose uptake. The proinflammatory cytokine TNF-α has been implicated as a link between obesity and insulin resistance. TNF-α may inhibit IRS1 tyrosine phosphorylation by promoting its serine phosphorylation.

Inflammatory pathways

Inflammation is a protective response to infection or injury by immune system that requires communication between different classes of immune cells to coordinate their actions. Each of these cell types communicates with other immune cells using secreted factors called cytokines, including interleukins, TNF, and the interferons. Inflammation can be classified as acute or chronic, local or systemic. Chronic low-grade inflammation, which is characterized by the production of abnormal adipocytokine such as TNF-α, IL-1, IL-6, leptin and adiponectin, is frequently observed in obese individuals. These factors inhibit insulin signaling and are involved in the development of insulin resistance, which increases the risk of T2DM. MCP-1 is one of the chemo attractants secreted from adipocytes, which plays an important role in the recruitment of macrophages to the adipose tissues. Moreover, obesity is associated with increased plasma levels of MCP-1 and overexpression in adipose tissue [12,13]. The macrophages which reside in adipose tissue are responsible for the expression of most tissue’s TNF-α and IL-6. The expression of macrophage markers in human adipose tissue is high in the subjects with obesity and insulin resistance, and is correlated with the expression of TNF-α and IL-6 [12,14].

PPAR pathways

PPARs are ligand activated nuclear hormone receptors that are activated by fatty acids and their derivatives. There are three kinds of identified PPARs, PPAR-α, PPAR-β and PPAR-γ. They work as the master regulators of glucose, lipids metabolism, energy balance and inflammation [15-17]. PPAR- α is highly expressed in tissues such as liver and skeletal muscle, where activation of PPAR-α results in the clearance of circulating or cellular lipids via the regulation of gene expression involved in lipid uptake, catabolism and homeostasis [18]. The primary effects of PPAR-β are involved in lipid oxidation and cell proliferation. PPAR-γ is highly expressed in adipocytes, skeletal muscle, liver and kidney. PPAR-γ enhances blood glucose and fatty acid uptake by regulating expression of genes that mediate adipocyte differentiation, energy metabolism and insulin action [19,20]. Accordingly, PPAR-γ activation results in an increase in insulin sensitivity and anti-inflammatory effects [21-23]. Dysregulation of PPARs contributes to the development of T2DM and Metabolic syndrome (Mets). Therefore, PPARs are important therapeutic targets in the clinical management of T2DM, obesity and Mets. PPARα agonists, such as fenofibrate, clofibrate and gemfibrozil, act as hypolipidemic agents and are clinically used for the treatment of hyperlipidemia, particularly hypertriglyceridemia associated with MetS, diabetes and diabetes-linked disease [24,25]. Likewise, Thiazolidinediones (TZDs), such as pioglitazone and rosiglitazone, which are specific ligands for PPARγ, act as insulin sensitizers and are currently marketed for the treatment of hyperglycemia in patients with T2DM [23,26].

Interplay of adipocytokine, inflammatory, PPAR and insulin pathway

T2DM pathway is the interplay of adipocytokine, inflammatory, PPAR and insulin pathway. T2DM pathway from KEGG database consists from 47 proteins, of which there are 20 members overlapped with these pathways (Figure 1 and Table 5). The significant overlap members, in another word, the nodes of these pathways are TNF (from 4 pathways: adipocytokine, inflammatory, PPAR and T2DM), ADIPOQ (from 3 pathways: adipocytokine, PPAR and T2DM) and IRSs (from 3 pathways: adipocytokine, insulin and T2DM) (Figure 1). FFA and TNF-α induce insulin resistance through inhibition of IRS1 functions by various kinases including ERK, JNK, IKK-β, PKC and mTOR. Adiponectin has beneficial effects on improving insulin sensitivity. Increased adiposity is associated with decreased adiponectin secretion and leads to insulin resistance. Insulin resistance leads to chronic hyperglycemia. The combined effects of excess nutrient load, hyperglycemia and cytokines induce multiple defects in beta-cells [27].

biomedres-interplay-adipocytokine

Figure 1. T2DM pathway is the interplay of adipocytokine, inflammatory, PPAR and insulin pathway. This figure is stemmed from the top 10 statistically relevant pathways with T2DM ranked by P-value. Inflammatory pathways contain 4 pathways (See Table 1).

Pathway ID* Members of the pathway
1 PRKAG3,PPARA,PRKAG1,LEPR,PRK,AG2,NFKB1,CAMKK1,CAMKK2,AKT1,SLC2A4,SLC2A1,CHUK,AKT3,AKT2,IRS4,IRS2,SOCS3,RXRB,RXRA,RELA,PRKAB2,RXRG,ADIPOR2,PRKAB1,ADIPOR1,IRS1,PPARGC1A,PRKCQ,G6PC,CD36,MAPK9,MAPK8,TRAF2,TNF,NFKBIE,STK11,NFKBIB,NFKBIA,POMC,G6PC2,TNFRSF1A,TNFRSF1B,ACSL1,PRKAA1,PRKAA2,ACSL4,ACSL3,AGRP,ACSL6,ACSL5,CPT1C,CPT1B,MAPK10,ACACB,PCK2,ADIPOQ,STAT3,CPT1A,TRADD,PTPN11,PCK1,LEP,NPY,IKBKG,JAK2,MTOR,IKBKB
2 PRKCZ,TNF,HK2,HK1,PDX1,KCNJ11,SLC2A4,INS,SLC2A2,HK3,PIK3CA,PIK3R5,PIK3R3,INSR,PIK3R1,PIK3R2,IRS4,PIK3CG,IRS2,SOCS2,PIK3CB,SOCS3,PIK3CD,SOCS1,SOCS4,MAPK10,PRKCE,IRS1,ADIPOQ,PRKCD,MAPK1,GCK,PKM2,PKLR,MAPK3,CACNA1G,MAPK9,MAPK8,CACNA1E,MTOR,MAFA,IKBKB,CACNA1C,ABCC8,CACNA1D,CACNA1A,CACNA1B
3 ACOX1,PPARA,PTGS2,PDGFA,STAT5A,EHHADH,STAT5B,CITED2,PRKACG,PRKAR2B,PRKAR2A,APOA2,APOA1,PIK3CA,NOS2,PRKACB,MYC,NR2F1,PIK3CG,PRKCA,HSP90AA1,RXRA,RELA,NR0B2,RB1,PPARGC1A,PRKCB,NRIP1,MAPK1,NCOA1,CD36,EP300,JUN,MAPK3,MED1,ME1,TNF,FRA8B,NFKBIA,HSPA1A,MRPL11,INS,FAT1,HSD17B4,PIK3R1,NR1H3,CPT1B,LPL,CREBBP,SRA1,DUSP1,SP1,PRKAR1B,PRKAR1A,FABP1,NCOR1,NCOR2,DUT
4 IL4,IL3,IL6,IL5,TNF,IL8,IL16,IL18,IL9,IL13,IL15,IL10,IFNA1,IL17A,IFNB1,IFNG,IL12A,IL12B,LTA,IL1A,IL2
5 CSF3,CSF2,TNF,HLA-DRB1,PDGFA,CSF1,TGFB3,IL13,IL15,TGFB1,IL10,IL11,TGFB2,IFNA1,IFNG,CD4,LTA,IL1A,IL4,IL3,IL6,IL5,IL8,IL7,IFNB1,IL12A,IL12B,HLA-DRA,IL2
6 ACOX1,SLC27A1,PPARA,CPT2,EHHADH,APOA2,SIN3B,APOA1,SIN3A,SMARCD3,SLC2A2,CYP7A1,APOA5,TGS1,TBL1XR1,ACADM,SULT2A1,RXRA,NCOA1,NCOA2,CD36,DGAT1,NCOA3,NCOA6,PRIC285,CARM1,MED1,ME1,ABCA1,CHD9,UGT1A9,ACSL1,CYP4A22,PLIN2,PLIN1,AGT,FASN,PLTP,NR1H3,ANGPTL4,SREBF1LPL,SCD,FADS1,CREBBP,ACACA,FADS2,UCP1,SLC10A2,CPT1A,MTTPABCB4,PEX11A,CYP4A11,HDAC3,UGT2B4,FABP4,TBL1X,NCOR1,SCP2,NCOR2
7 IL4,CCL11,IL6,IL5,HLA-DRB1,CCR3,IL1B,CD4,IL5RA,HLA-DRA
8 DOK1,IRS2,GRB10,INS,GRB2,SOS1,MAPK3,SHC1,INSR,IRS1,CRK
9 IL4,CSF2,IL3,IL5,TLR2,IL13,TLR4,ANPEP,CD40,TLR7,IL10,TLR9,IFNA1,ITGAX,IFNB1,CD33,IFNG,IL12A,CD2,IL12B,CD5,CD7
10 ACOX2,ACOX1,SLC27A1,PPARA,PPARD,CPT2,EHHADH,PPARG,AQP7,MMP1,ACOX3,APOA2,PDPK1,APOA1,CYP7A1,APOA5,ILK,SCD5,ACADM,RXRB,RXRA,RXRG,ACADL,CD36,CYP27A1,UBC,SLC27A6,SLC27A2,SLC27A5,SLC27A4,ACAA1,ME1,GK2,ACSL1,CYP4A22,SORBS1,PLIN1,APOC3,ACSL4,ACSL3,ACSL6,PLTP,ACSL5,NR1H3,ANGPTL4,CPT1C,CPT1B,LPL,OLR1,SCD,FADS2,UCP1,PCK2,ADIPOQ,DBI,CPT1A,PCK1,CYP4A11,HMGCS2,FABP3,FABP4,FABP1,GK,FABP2,FABP7,CYP8B1,SCP2,FABP5,FABP6

Table 5. Members in the top 10 pathways.

Discussion

Energy metabolism exists in all organisms. Mammals obtain energy from nutriment. The supply of nutriment and the energy demand for animal is variety. Thus, a delicate mechanism for energy metabolism to regulate energy balance has been developed in the long journey of biological evolution. For most wild animal and even human in the ancient times, scarcity of food and obtaining food by arduous physical activity are their theme. How did they survive from the short period of food scarcity? Adipose tissue acted as an optimum pattern to store energy occurred. In fed states, high levels of nutrients and growth factors drive lipid uptake in adipose tissue, while in fasting state, adipose tissue release fatty acids into the circulation. These fatty acids are generated by the breaking down of triacylglycerols, which contain more energy per unit mass than carbohydrates and can essentially be stored anhydrously. Triacylglycerols are stored as energy material in adipose tissue, which is very efficient. Phenotype of obesity is a result of enough triacylglycerols stored in adipose tissue. It is a beneficial trait as a result of nature selection for organism’s survival under rigorous condition [28,29].

Glucose is a common energy material and can be utilized by almost all organs. To maintain a security level of blood glucose is important. Glucose homeostasis in mammals is primarily maintained through a tight regulation of glucose uptake in peripheral tissues and the glucose production in liver. This complicate process is under the control of hormones and energy sensors. We guess that insulin resistance is a compromise strategy for mammals to keep a security level of blood glucose. As referrer to insulin resistance associated with T2DM, it is often defined as status of a decreased uptake and utilization of glucose in liver, muscles and adipose tissue by reduction of insulin sensitivity. As mentioned before, insulin resistance often happens in the status of obesity and inflammation. Two main molecules, fat acid and TNF-α induce insulin resistance through inhibition of IRS1’s functions by various pathways [30]. Fat acid is used as fuel when glucose is limiting, which happen in the condition of food scarcity. Therefore, we consider that insulin resistance is a compromise strategy for mammals to keep a security level of blood glucose to cope with the condition of food scarcity and maintain enough energy sources to cope with the other unexpected biological event such as inflammation, a protective response to infection or injury [31].

Phenotype of obesity considered as a beneficial trait and insulin resistance considered as a beneficial mechanism are the results of nature selection in the long journey of biological evolution [32]. However, they became arch-criminal of T2DM and Mets currently. The reason should be that the mammals adapt to the rigorous condition of food scarcity and obtain food by arduous physical activity. All molecular mechanisms have been tailored in this condition. Suddenly, this scenario changed. People nowadays enjoy the redundant food with sedentary life style. Organism cannot adapt to the status of excess energy over a long time span. It coincides with our result in this study. Obesity leads to dysregulation of adipocytokine pathway, moreover, it leads to insulin resistance by impaired insulin signaling pathway. Insulin resistance leads to chronic hyperglycemia. The combined effects of excess nutrient load, hyperglycemia and cytokines induce multiple defects in beta-cells [27]. Insulin resistance is a major factor in the pathogenesis of T2DM.

Conclusion

Our result suggests that analysis of pathway is a helpful way to facilitate us to understand the mechanism of T2DM. T2DM is the result of dysregulated energy metabolism originated from the status of excess energy over a long time span. Dysregulation of the adipocytokine and inflammatory pathway is the hallmark of this status, which lead to insulin resistance by impaired insulin signaling pathway. Chronic hyperglycemia induces defects in beta-cells and T2DM.

Acknowledgement

This work has received support from Open Subject of Key Laboratory of the Xinjiang Uygur Autonomous Region, Subject Number: 2015KL006.

References