Detecting Tissue-Specific Early Warning Signals for Complex Diseases

BRIEFINGS IN BIOINFORMATICS. VOL 15. NO 2. 229 ^243 doi:10.1093/bib/bbt027 AdvanceAccesspublishedon25April2013

Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis Meiyi Li*,TaoZeng*,RuiLiuandLuonanChen

Submitted: 12th February 2013; Received (in revised form): 25th March 2013 Downloaded from

Abstract Identifying early warning signals of critical transitions during disease progression is a key to achieving early diagnosis of complex diseases. By exploiting rich information of high-throughput data, a novel model-free method has been http://bib.oxfordjournals.org/ developed to detect early warning signals of diseases. Its theoretical foundation is based on dynamical network biomarker (DNB), which is also called as the driver (or leading) network of the disease because components or molecules in DNB actually drive the whole system from one state (e.g. normal state) to another (e.g. disease state). In this article, we first reviewed the concept and main results of DNB theory, and then applied the new method to the analysis of type 2 diabetes mellitus (T2DM). Specifically, based on the temporal-spatial gene expression data of T2DM, we identified tissue-specific DNBs corresponding to the critical transitions occurring in liver, adipose and muscle duringT2DM development and progression. Actually, we found that there are two different critical states during T2DM development characterized as responses to insulin resistance and serious inflammation, at Stanford University on September 20, 2014 respectively. Interestingly, a new T2DM-associated function, i.e. steroid hormone biosynthesis, was discovered, and those related genes were significantly dysregulated in liver and adipose at the first critical transition during T2DM deterioration. Moreover, the dysfunction of genes related to responding hormone was also detected in muscle at the similar period. Based on the functional and network analysis on pathogenic molecular mechanism of T2DM, we showed that most of DNB genes, in particular the core ones, tended to be located at the upstream of biological pathways, which implied that DNB genes act as the causal factors rather than the consequence to drive the downstream molecules to change their transcriptional activities. This also validated our theoretical prediction of DNB as the driver network. As shown in this study, DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level. Keywords: Type-2 diabetes; disease progression; critical transition; dynamical network biomarker (DNB); leading network; multi-tissues

Corresponding author. Luonan Chen, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: þ86 21-5492-0100; Fax: þ86 21-5497-2551; E-mail: [email protected] *These authors contributed equally to this work. Meiyi Li is a PhD student at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. Her main interest is in developing computational methods to understand disease progression at a molecular level. Tao Zeng is an Assistant Professor at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. His research interest includes network biology and computational biology. Rui Liu is an Assistant Professor at Department of Mathematics, South China University of Technology, China. His main interest includes computational systems biology and non-linear analysis on biological systems. Luonan Chen is Professor and Executive Director of Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. His research interest is computational systems biology and bioinformatics.

ß The Author 2013. Published by Oxford University Press. For Permissions, please email: [email protected] 230 Li et al.

INTRODUCTION stable state where the system undergoes gradual or Recent rapid advance in high-throughput technolo- slow change), pre-disease state (a limit of the normal gies has greatly expanded the availability of informa- state just before the drastic transition to the disease) tion at all levels of biological systems [1, 2], which and disease state (another stable state after the critical has significantly enhanced the research of transla- transition, where the system is considered to be tional medicine [3, 4], in particular on discovering seriously damaged) [15, 16]. Theoretically, the pre- new biomarkers of complex diseases, e.g. cancers and disease state corresponds to a critical point before the diabetes. Considerable work has been done on the system transits to an irreversible disease state accom- biomarkers for disease diagnosis or prognosis [5–12]. panying a drastic change of system dynamics [17–19]. These biomarkers or disease models mainly measure Thus, it is crucial to detect the pre-disease state so as disease status of patients or provide the necessary to achieve the early diagnosis of complex diseases. information for clinical judgments of disease states Traditional biomarkers are able to distinguish disease [13, 14]. But, they usually cannot be directly used state from normal state mainly based on the differ- to evaluate or predict a person in a pre-disease state ential expressions on individual molecules or a group (e.g. the state before the appearance of disease symp- of molecules, but are unable to detect the pre-disease Downloaded from toms), which is the key for achieving early diagnosis state owing to their static nature. Completely differ- of diseases. ent from the traditional methods, recently, a novel Generally, a disease progression can be divided method based on dynamical network biomarker

into three stages (Figure 1A), i.e. normal state (a (DNB) has been developed to distinguish the pre- http://bib.oxfordjournals.org/ at Stanford University on September 20, 2014

Figure 1: Differences between traditional molecular biomarker and dynamical network biomarker (DNB). (A) schematically illustrates the stages and progression of a complex disease. (B) indicates the differences between traditional molecular biomarker and DNB. Dynamical network biomarker explores the dynamical information of molecular fluctuations as well as the correlations between molecules (or network information), which is a new concept on biomarkers from both theoretical and biomedical viewpoints. Note that we focus on Pre-transition state (Pre-disease state), and thus Early-transition state (Early-disease state) is not analyzed in this paper, which is considered as similar to After-transition state (Disease state). Study of type 2 diabetes by cross-tissue analysis 231 disease state from the normal state. On the basis of induced by the complicated interplay of both genetic non-linear dynamical theory, DNB is able to detect factors and environmental factors [30], there are in- the early warning signals of the critical transitions tensive system-wide studies on T2DM [26, 28, 29, during the disease progression, by exploiting rich 31–33], e.g. GWAS. And as a result, many genetic information from the observable high-throughput and epigenetic factors involved in the disease pro- data [20, 21]. As shown in Figure 1B, in contrast gression at different tissues have been identified [22, to the static information of molecular expressions 34]. Despite these progresses, however, many ques- mainly adopted in traditional methods or bio- tions remain unanswered, i.e. (i) the critical transition markers, DNB explores the dynamical information points on each insulin-responsive tissue are still un- of molecular fluctuations as well as the correlations clear, (ii) the driver molecules of those transitions between molecules, which is a new concept on bio- that result in T2DM have not yet been fully revealed markers from both theoretical and biomedical view- and (iii) the dynamical roles of these insulin-respon- points. This method has been validated by theoretical sive tissues in T2DM development and progression analysis and experimental data on various diseases have not yet been clearly understood, especially in [17, 18]. In addition to the early diagnosis of diseases, the presence of pre-disease state. To answer these Downloaded from DNB can be also applied to detect key state changes questions, in this study, based on multi-tissue and their driving networks during many biological gene expression data of a T2DM animal model, we processes, e.g. cell differentiation processes and pro- identified the critical transitions and their driver (or leading) networks on muscle, adipose and liver by liferation processes [17, 19]. http://bib.oxfordjournals.org/ On the other hand, the globe figure of people tissue-specific DNBs and further analyzed the dy- with diabetes is increasing rapidly [22], especially namical regulations during T2DM development for type 2 diabetes mellitus (T2DM), which is a and progression by both intra-tissue and inter-tissue chronic disease with nature history lasting for studies. Specifically, in this article, we first gave a >20 years. Despite intensive studies on T2DM, its brief summary of the theoretical results of DNB molecular mechanism and its early molecular mar- and its computational algorithm [15, 16] for detect- kers still remain unclear. Generally, T2DM can be ing early warning signals of T2DM; then analyzed at Stanford University on September 20, 2014 divided into five stages: latent stage, transition stage, the dynamical behaviors of early warning signals in impaired glucose tolerance (IGT) stage, impaired each insulin-responsive tissue; next, discussed the fasting glucose stage and overt stage [23]. During biological significance (T2DM relevance) of tissue- the first four stages, the sub-health status of patient specific DNBs from various aspects, such as single is still able to return to normal, and IGT and im- disease genes, functional enriched gene sets and conditional rewired gene sub-networks; furthermore, paired fasting glucose stages are called pre-diabetes. investigated the spatial-temporal characteristics of Whereas, T2DM is always diagnosed when reaching DNBs and their relations with T2DM development the overt stage. Therefore, it is a key problem to and progression and finally, concluded this study by identify when and how the transition between dif- providing several general remarks. ferent stages happens at a molecular level, in particular, ahead of the diagnosis of T2DM [24–29]. Clearly, the early warning signals of critical transi- METHODOLOGY OVERVIEW tions during T2DM progression can be used for not Theoretical foundation on DNB only achieving prevention and personalized medi- As shown in Figure 1A, we consider that the biolo- cine, but also revealing molecular mechanisms of gical process of a transition from normal to disease the disease development. (or from early disease to advanced disease) can be T2DM is a well-known metabolic disease charac- divided into three stages [15], i.e. ‘before-transition terized by insulin resistance and beta cells failing to state’ (or normal state), which is a stable state and compensate, so that insulin-responsive tissues are im- where the system undergoes gradual or slow changes; portant organ targets of T2DM development and ‘pre-transition state’ (or pre-disease state), which is a progression [24–29]. Those tissues include muscle, limit of the ‘before-transition state’ just before the liver and adipose, each of which actually has its critical transition to another state and ‘after-transition own characteristics in disease progression. As state’ (or disease state), which is another stable state. T2DM (also known as non-insulin-dependent dia- When a biological system transfers from one state to betes) attributes to the metabolic disorders mainly another one during disease development and 232 Li et al. progression, some relevant molecules integrating into comparing with those for normal samples. In a network of this system will experience a drastic other words, the average SD of molecule con- transition, which drives the corresponding system centrations (denoted by SDd) is significantly to have a qualitative change. higher in the group. Then following the derivation of [15], we con- (2) Molecules in this group become closely related sider that the state dynamics of a living organism to each other, which means that their average can be described by a non-linear dynamical system Pearson correlation coefficient (PCC) in absolute Equation (1), value between all zi and zj (denoted by PCCd)in Zðk þ 1Þ¼f ðZðkÞ; PÞð1Þ the group drastically increases, compared with the value for normal samples. where the vector Z(k) ¼ (z1(k), ...,zn(k)) represents (3) Oppositely, the average PCC between molecules observed data with n observed variables, i.e. in this group and others decreases drastically, i.e. molecule concentrations (e.g. gene expressions or the corresponding average PCC of molecules in protein expressions) at time point k (k ¼ 0, 1, ...), absolute value (denoted by PCCo) becomes smal- e.g. minutes or days, and it is the variables indicating ler, contrary to the value for normal samples. Downloaded from the dynamical state of a biological system. Parameter P represents slowly varying factors such as genetic If there is one group of molecules satisfying all factors (e.g. SNP and CNV) and epigenetic factors three conditions simultaneously, it is the dominant (e.g. methylation and acetylation), which actually group, or so-called DNB, whose existence implies http://bib.oxfordjournals.org/ drive the system to change. f ¼ (f1, ..., fn) are general the imminent transition from one stable state to non-linear functions of Z(k) with a fixed point another. As shown in Figure 1B, in contrast to the Z ¼ Z, such that Z ¼ f ðZ; PÞ. Note that P indicates conventional molecular biomarkers distinguishing unknown parameters, which are not required to disease samples from normal samples by mainly be measured in this study, and the dynamics of using molecular expression information (i.e. static in- P are considered much slower than Z. formation), the DNB distinguishes not disease samples As the system is stable at the normal state (a stable but pre-disease samples from normal samples by using fixed point Z), we consider the linearized equations molecular fluctuation information (i.e. dynamical in- at Stanford University on September 20, 2014 of Equation (1) at Z. For simplicity, we assume that formation) and also network information (i.e. correl- all eigenvalues of the Jacobian matrix of f at Z are ation information among molecules). In addition, real values (for other cases, see [6, 15]). Then, there DNB is a model-free approach, which requires no exists a transformation matrix S so that the linearized settings on parameters or even models. In other Equation (1) can be transformed to the following words, patients may have different DNBs, even suf- Equation (2): fering from the same disease due to the personal vari- ations, and thus this method is a general measurement Yðk þ 1Þ¼ðPÞYðkÞþxðkÞð2Þ on diseases. Therefore, one advantage of DNB can be where new variable Y(k) ¼ (y1(k), ..., yn(k)) corres- expected to directly apply to the personalized medi- ponds to the original variable Z(k), i.e. cine. Note that theoretically there exist at most two YðkÞ¼S1ðZðkÞ ZÞ, (P) is the diagonalized DNBs for one critical transition [15, 16], either of

@f ðZ;PcÞ which can be used to signal the imminent transition. matrix of @Z and noise vector x(k) ¼ Z¼Z For a disease process, there may be multiple transitions (x (k), ..., x (k)) represents Gaussian noises with 1 n resulting in different deterioration stages. zero means and covariances kij ¼ Cov(xi, xj). Let l1 be the largest eigenvalue, and then at the normal state, j1j < 1. When the system approaches Algorithm for detecting early warning the critical point, jj1 ! 1. Then, those molecules, signal of a critical transition zi (e.g. genes, proteins or small compounds), affected The dominant group characterizes dynamical features by 1 form a dominant group of molecules, which of the underlying biological system and the molecules has been proven to satisfy the following three in the group are also strongly and dynamically corre- conditions: lated in the critical transition period (e.g. pre-disease state); the molecules in the dominant group are ex- (1) Concentrations of molecules (zi) in the dominant pected to form a sub-network of the biological group for pre-disease samples strongly fluctuate system, which is a dynamical network of biomarkers, Study of type 2 diabetes by cross-tissue analysis 233

or a DNB. DNB is also called as the driver (or leading) PCC of the DNB in absolute value and PCCo is the network because the members of the DNB make the average PCC of molecules between those in DNB first move from the one state to another. As stated in and other molecules in absolute value. The CI is the three conditions, the presence of DNB can be expected to increase sharply (i.e. reaches maximum) observed by the measurement of dynamics or mul- when the biological system approaches a critical tran- tiple samples. DNB appears in the critical transition sition or pre-disease state. Therefore, CI can indeed period but disappears in other periods, thereby signal- serve as an early warning signal to identify the pre- ing the upcoming critical transition. disease state effectively, although other index can be To obtain a strong signal at the critical transition, also constructed, provided that the three conditions the three criteria were further combined together to are satisfied. construct a composite index (CI) as follows: The detailed flowchart of computational algo- SD PCC rithm for detecting DNB according to CI is CI ¼ d d ð3Þ showed in Figure 2 and can be also stated as follows: PCCo Downloaded from where SDd is the average SD of the molecules of (1) Deviation test: select the genes with significantly the dominant group or DNB, PCCd is the average high deviations at each period or state based http://bib.oxfordjournals.org/ at Stanford University on September 20, 2014

Figure 2: Algorithm for detecting early warning signal of a critical transition. 234 Li et al.

on expression profiles (e.g. gene expressions or gov/geo), which were time-course gene expression protein expressions for a number of samples). data obtained from age-specific rats corresponding to Then, we have a group of molecules with high well-designed time series (five samples in 4, 8, 12, 16 SDs at each period t, i.e. a set of molecules, NtD; and 20 weeks, individually). (2) Intra-correlation test: cluster the previously After performing the aforementioned algorithm chosen genes from NtD at each period or state for detecting early warning signal, we found two based on correlations. Then, we have a smaller critical transitions for these tissues in the disease group whose molecules have high correlations at evolution of T2DM (Figure 3). As one of the most each period t, i.e. NtI, which is a subset of NtD; important energy metabolism systems, strong signal (3) Inter-correlation test: choose the genes from NtI, of CI for liver first appeared in rats at the age of whose correlations with other molecules outside 4 weeks (module L1) and then again at 16 weeks of NtI are significantly low, at each period. Then, (module L4). For the muscle, it likely responded we have a smaller group NtC at each period t, the signals from liver with the same critical transition which is a subset of NtI; periods at 4 and 16 weeks, respectively (i.e. module (4) DNB test: determine the critical transition point M1 and module M4). However, for adipose, there Downloaded from and its corresponding dominant group (or was only one critical transition period corresponding module) or DNB from NtC (t ¼ 1, ...,T) by CI to the disease deterioration at the age of 8 weeks significance analysis (e.g. CI score, or P-value), i.e. (module A2). Actually, these critical transition peri- let N be DNB if its corresponding CI is the ods were also consistent with the development of

tC http://bib.oxfordjournals.org/ largest among all periods t ¼ 1, ...,T; T2DM in GK rats, according to the phenotype- (5) Functional analysis on DNB: analyze the en- changing trends of the plasma glucose and the riched biological functions of the identified plasma insulin of concentrations from GK and DNB, e.g. by GO or network ontology analysis WKY rats at different ages (see Supplementary [35] or pathway analysis [6, 36]. Figure S1). This phenotype information was provided by the authors [37] with the experimental Based on the theoretical derivation [15, 16], both data. Their studies described that the concentrations at Stanford University on September 20, 2014 SDd and PCCd/PCCo should be maximal at the tran- of plasma insulin in GK were sharply elevated sition stage or point. However, for many cases, we between 4 and 8 weeks, but declined after usually have observed data not at exact critical tran- 12 weeks, and finally were lower than WKY at sition point but near the transition. Therefore, near 20 weeks. Meanwhile, the plasma glucose concen- the transition point, numerically, SDd and PCCd/ trations were presented at a higher level since PCCo may not simultaneously reach maximum. In 4 weeks of age and kept the increasing trend to 12 addition, to improve the computational efficiency weeks [37]. These observations definitely revealed or robustness, entropy or mutual information can two different and important critical transition peri- be also adopted to measure associations between ods, one for insulin resistance and the other for beta- two molecules [16] instead of PCCs in the afore- cell failure. This confirms our following results on mentioned computational algorithm. dysfunction of insulin sensitivity and serious inflammatory response correspondingly. Especially, either in tissue-specific analysis or in cross-tissue analysis, RESULTS a common functional cascade associated with T2DM has been found: (i) dysfunctions of insulin Early warning signals of T2DM in biosynthesis and response, (ii) resistance of plasma multi-tissues glucose uptake, (iii) energy supply, (iv) hormone Here, we applied the method of DNB to detect response and (v) lipid metabolism that appeared to the critical transitions during T2DM development be abnormal or inflammatory. and progression accompanying insulin resistance in adipose, gastrocnemius muscle and liver for rats Tissue-specific analysis [diabetes rats: Goto–Kakizaki (GK) rats; control To confirm the significant relation between tissue- rats: Wistar–Kyoto (WKY) rats]. The high- specific DNBs and T2DM progression, and to iden- throughput experimental data sets were downloaded tify what signal could be provided to pre-warn the from NCBI GEO database (access ID: GSE13268, disease transitions or deteriorations, the GO analysis GSE13269 and GSE13270) (www.ncbi.nlm.nih. by network ontology analysis [35] and KEGG [38] Study of type 2 diabetes by cross-tissue analysis 235 Downloaded from http://bib.oxfordjournals.org/ at Stanford University on September 20, 2014

Figure 3: The compositionindex (A)andtheDNB(B) foreach tissue of GKrats.L1and L4 are DNBs ofliver at the first period (4 weeks) and the fourth period (16 weeks), respectively.M1and M4 are DNBs of muscle at the first period (4 weeks) and the fourth period (16 weeks), respectively. A2 is the DNB of adipose at the second period (8 weeks). (C) visually illustrates dynamics of the identified DNBs compared with the whole molecular network. enrichments was conducted for each DNB to the inflammatory response. Specifically, in the first categorize the genes participating in different biolo- critical period, we found that the metabolism of gical functions or pathways (Tables 1 and 2 and xenobiotics by cytochrome P450 pathway played Supplementary Table S1). an important role in oxidation of organic substances, Here, we first analyzed results from liver tissue, whose involved enzymes in liver could generally act although both liver and muscle owned two as metabolic intermediates, e.g. lipids and steroid common critical points. For GK rats at the age of hormone [39, 40], whereas in the second critical 4 weeks, when the first critical point occurred, the period, the relevant biological processes and path- genes of the identified DNB were mainly enriched ways for T2DM were enriched, including the regu- in the basic metabolic processes and pathways, espe- lation of interleukin, the regulation of PI3-kinase cially in the parts of lipid metabolic processes and calcium ion transport function [41]. In details, and some hormone and ion regulatory processes. we further listed some interesting genes to explain However, at the second critical point (i.e. how aforementioned functions are related to T2DM 16 weeks), the functions of this DNB mainly were pathogenesis. The genes relevant to the first critical enriched in the metabolism and transport of lipid and point, such as CYP11A1, CYP11B1, CYP11B2, 236 Li et al.

Ta b l e 1: Functional enrichment of GO biological processes for each identified DNB

Tissue DNB Genes in DNB GO P-value Term name term

ARHGEF9; 0006694 3.60E-07 Steroid biosynthetic process SPTBN2;APC; 0060070 6.60E-05 Canonical wnt receptor signaling pathway CLASP2; CHGB; 0042981 1.50E-04 Regulation of apoptosis CYP11A1; 0030217 2.00E-04 T cell differentiation LI CYP2T1;JUN; 0051591 4.10E- 04 Response to camp (liver SHC3;ERBB2; 0048534 1.10E-03 Hemopoietic or lymphoid organ development 4 weeks) APC;CTNNB1; 0042325 3.00E-03 Regulation of phosphorylation NFKB1;MARK3; 0007179 4.10E-03 Transforming growth factor beta receptor signaling pathway DVL1;HSD3B1; 0048545 5.00E- 03 Response to steroid hormone stimulus IL24;CTGF; 0043408 5.60E-03 Regulation of MAPKKK cascade SLC8A3; ... 0050996 2.40E-04 Positive regulation of lipid catabolic process Liver PPP2R1B; 0022600 9.20E-04 Digestive system process Downloaded from LRP1;CAT; 0031333 1.20E- 03 Negative regulation of protein complex assembly SPTBN2;DDT; 0034633 1.30E-03 Retinol transport APOA2;FABP1; 0006639 2.20E- 03 Acylglycerol metabolic process Lr RBP4;ITGAD; 0006954 2.60E-03 Inflammatory response (liver LRRC10B; 0010901 5.40E- 03 Regulation of very-low-density lipoprotein particle remodeling 16 weeks) CEBPA;CLU; 0001561 5.40E-03 Fatty acid alpha-oxidation http://bib.oxfordjournals.org/ ARHGEF9; 0051046 8.70E-03 Regulation of secretion EGR4;PHYH; 0050798 1.35E-02 Activated T cell proliferation HES3;PTBP3; 0042981 4.20E- 08 Regulation of apoptosis CCDC43;ACP6; ... PPP2R2B; 0048511 1.70E-06 Rhythmic process RXRA;PPARG; 0045638 4.40E- 06 Negative regulation of myeloid cell differentiation GCGR;NFAM1; 0006165 8.30E-06 Nucleoside diphosphate phosphorylation MAPK13;PGR; 0045076 1.20E-05 Regulation of interleukin-2 biosynthetic process MI ESR1;ADCY10; 0030817 1.70E-05 Regulation of cAMP biosynthetic process (muscle at Stanford University on September 20, 2014 FOS;MAX;JUN; 0045761 1.20E-04 Regulation of adenylate cyclase activity 4 weeks) DDIT3;CD69; 0033189 1.50E-04 Response to vitamin A NPR3;TIAM1; 0048545 1.50E- 04 Response to steroid hormone stimulus CARD9; CEBPB; 0050727 3.60E-04 Regulation of inflammatory response RABIF;... 0006350 1.50E-06 Transcription Muscle GNG13;FLT3; 0007259 7.30E-06 JAK-STAT cascade SLIT2;CEBPA; 0009725 1.20E- 05 Response to hormone stimulus PLD1;STAT5A; 0019221 4.20E-05 Cytokine-mediated signaling pathway STAT5B;PLD2; 0002694 8.70E-05 Regulation of leukocyte activation M4 NFKB1;SCAF1; 0002521 2.20E-04 Leukocyte differentiation (muscle NR5A1;STAT3; 0045086 2.40E-04 Positive regulation of interleukin-2 biosynthetic process 16 weeks) FLT3;CISH; 0048511 8.50E-04 Rhythmic process SYNPR;CCR10; 0019915 9.10E-04 Lipid storage GABRQ; TRAF3; 0006644 1.50E-03 Phospholipid metabolic process SH2D2A; DMRT1; ... 0043434 6.50E- 08 Response to peptide hormone stimulus PIP5K1B; CYP11B2; 0006644 9.00E-08 Phospholipid metabolic process GSTT2;AKT1; 0006575 2.50E-06 Cellular amino acid derivative metabolic process AKT2;AGPAT4; 0046856 1.40E-05 Phosphoinositide dephosphorylation HSD11B2; 0006749 2.30E-05 Glutathione metabolic process A2 AANAT;EGR1; 0032868 3.20E- 05 Response to insulin stimulus Adipose (adipose GSTP1;PRDX3; 0006897 3.90E-05 Endocytosis 8 weeks) PPAP2C;CNR1; 0001961 9.40E- 05 Positive regulation of cytokine-mediated signaling pathway SMARCA1;CD9; 0006519 1.40E- 04 Cellular amino acid and derivative metabolic process OCRL;SGMS1; GSTM7; ... Study of type 2 diabetes by cross-tissue analysis 237

Ta b l e 2 : Functional enrichment of KEGG pathways for each identified DNB

Tissue DNB KEGG term Term name Overlap P-value

00140 Steroid hormone biosynthesis 5 1.95E- 08 L1 (liver 4 weeks) 04510 Focal adhesion 4 7.42E-05 04310 Wnt signaling pathway 4 7.42E-05 04010 MAPK signaling pathway 3 8.16E- 03 Liver 04012 ErbB signaling pathway 3 1.12E- 04 04146 Peroxisome 2 2.98E-05

00790 Folate biosynthesis 2 2.35E- 06 L4 (liver 16 weeks) 03320 PPAR signaling pathway 2 1.60E- 03 04350 TGF-beta signaling pathway 1 1.63E- 02 00190 Oxidative phosphorylation 1 1.62E- 02 04151 PI3K-Akt signaling pathway 8 3.51E- 05 04010 MAPK signaling pathway 7 3.88E- 05 M1(muscle4weeks) 00230 Purine metabolism 6 1.23E-05 00240 Pyrimidine metabolism 5 3.93E- 08 Downloaded from Muscle 03015 mRNA surveillance pathway 5 3.43E- 06 00564 Glycerophospholipid metabolism 5 2.16E- 07

04062 Chemokine signaling pathway 5 7.07E-05 M4 (muscle 16 weeks) 04630 Jak-STATsignaling pathway 4 2.18E-05 04666 Fc gamma R-mediated phagocytosis 3 3.44E-04 http://bib.oxfordjournals.org/ 04144 Endocytosis 3 7.62E- 04 00480 Glutathione metabolism 15 6.57E-23 04666 Fc gamma R-mediated phagocytosis 10 5.78E-10 Adipose A2 (adipose 8 weeks) 04062 Chemokine signaling pathway 10 1.57E-06 04151 PI3K-Akt signaling pathway 9 1.86E- 03 04144 Endocytosis 7 8.87E-06 at Stanford University on September 20, 2014

CYP21A1 and CYP2T1 as enzymes of the cyto- also promote lipid transport through the function chrome P450 superfamily participate in many meta- of PPAR-alpha; APOA2 associated with T2DM bolic reactions of steroids, cortisol and other lipids, [44] played a key role in fatty acid transport as well which is consistent with the fact that liver synthesizes as ITGAD in clearing lipoproteins. Interestingly, and releases some very-low-density lipoprotein for the retinol metabolisms have been found to play an T2DM [42]; CTNNB1 was also found to participate important role in obesity and T2DM [45], in which in the pathway of insulin internalization and SHC3 some key genes (e.g. RBP4) were also detected in to participate in the insulin signal transduction, from tissue-specific DNB. the GeneCards (http://www.genecards.org/). For muscle at the onset of T2DM (or the pre- Moreover, owing to lacking adequate glucose disease critical point), compared with liver at its uptake induced by dysfunction of insulin response, first critical point, regulatory signaling pathways most pathways related to the cellular regulatory were enriched, and these pathways mainly regulate signal transduction of basic energy metabolism the system response to stimulus and inflammatory became abnormal, such as Wnt, MAPK and ErbB response. Some detected genes are related to the signaling pathways and so on. For genes involved in processes of regulating cAMP, Ca2þ and gluco- the second critical point, the kinase cascade reactions corticoid, which are well-known regulators relevant involved in PI3 played an important role in the me- to glycometabolism in basal metabolism. For muscle tabolism of lipid, and calcium ion as an activator also at the second critical point, responses to hormone participated in these reactions; PI3K–Akt signaling stimulus and leukocyte activation were found in pathway was located at the upstream to regulate dysfunction, which were also observed in the first the inflammatory response, and this pathway was pre-disease critical point. At this latter critical point, also identified by our method; FABP1 in liver lipid metabolisms were significantly abnormal too. could activate PPAR signaling pathway regulating However, the similar functions enriched in two crit- gluconeogenesis and fatty acid oxidation [43] and ical periods of T2DM development and progression 238 Li et al. actually resulted from different DNB genes the disease gene. This supports again that DNB (Supplementary Table S2). As a further example, genes are key genes contributed to drive the critical the immune response mediated by those interleukins transition during disease development and progres- [regulated by several genes with sharply fluctuated sion, which cannot be detected by conventional bio- expressions (e.g., NFKB1, STAT3, STAT5A, markers and methods. STAT5B) participating in the regulation of interleukin biosynthesis] played an important role in the Cross-tissue analysis second critical period, but the B cell receptor signal- From the aforementioned functional enrichment ing pathway associating with PI3K–Akt regulation analysis, we have observed the comparative consist- decided the immune response in the first critical ence among tissue-specific critical points, DNBs and period. This function conservation rather than gene common T2DM-relevant pathways. This means that conservation might be induced by decrease of insulin there are cross-tissue functional relations between secretion and more severer resistance of glucose phenotype changes (e.g. blood glucose and insulin uptake (these clinical phenotypes will be discussed secretion) during the T2DM development and pro- in details in next sub-section). In addition, there Downloaded from gression in GK rats, although these tissue-specific are also many such phenomena in muscle consistent DNBs have few genes overlapped (Supplementary with liver and adipose, for instance, a few genes (e.g. Table S2). PGR, ESR1 and ESR2) related to responding hor- In the pre-disease period, the high blood glucose mone were also detected at 4 weeks, although ster- appeared in GK fed with the same food to two http://bib.oxfordjournals.org/ oid hormone biosynthesis does not belong to the strains, although the insulin secretion level in GK muscle-specific functions. This fact might suggest rats was similar to in WKY rats (Supplementary that there are certain coordinated interactions or re- Figure S1). Hence, it can be inferred that the insulin lations among those insulin-responsive tissues and receptors in GK rat could not correctly respond to the tissue-specific DNBs. For adipose at its critical period, the enriched func- the insulin regulation, such that the glucose could tions of its DNB mainly included lipid metabolisms not be transported successfully in cells. However, and the response to stimulus. Similar to the dysfunc- we have seen an interesting regulatory coordination at Stanford University on September 20, 2014 tions of liver, the DNB genes of adipose had signifi- on the biosynthesis and response to steroid hormone cant enrichments on the relevant biological processes in aforementioned analysis. For liver and adipose, and pathways of T2DM [41], such as the regulation of synthesizing and secreting steroid hormone had no interleukin biosynthesis, dephosphorylation, phos- tissue-specific functions, but they became abnor- phoinositide metabolism, the regulation of PI3- mally active under this condition compared with kinase, the activation of PKCs and calcium ion trans- WKY rats. Meanwhile, several genes participating port function. As a well-known T2DM-associated in response to steroid hormone stimulus were also mechanism, PKCs activated by diacylglycerol made detected in muscle. In fact, the relation between a contribution to the lipid-induced insulin resistance. steroids and insulin sensitivity has attracted increasing In addition, steroid hormone biosynthesis as a non- attention recently [51–53]. More importantly, for all specific function of adipose also became abnormal as tissues, the relevant function of fatty-acid transport observed in a liver-specific DNB. was regulated through the PPAR signaling pathway Furthermore, to check the significance of our activated by the upstream FABP family proteins, results by comparing the genes of aforementioned which might explain the significantly abnormal five DNBs with the genes (only 69 genes are lipid metabolisms under the lack of glucose uptake. included in this whole genomic expression profilers) In the pre-transition period, hyperglycemia still reported in previous studies [46–50] (Supplementary kept comparatively stable, but the concentration of Table S3), we found: RBP4 in DNB of liver at 16 plasma insulin was decreasing. According to the ana- weeks was identified to promote IGT (hypergeo- lysis of [24], the appearance of beta cells failing at metric test, P < 2.56e-2); 2 (GCGR and PPARG) 20 weeks might result from the increasingly serious of 58 DNB genes of muscle at the age of 4 weeks lack of glucose uptake. Consequently, many biolo- (hypergeometric test, P < 3.31e-3) have been vali- gical processes involved in lipid metabolisms were dated with significant relevance to T2DM; for adi- activated in liver and muscle, such as lipid oxidation. pose, there are also AKT2 of 146 genes And, many genes related to lipid transport became (hypergeometric test, P < 0.13) being reported as dysfunctional in liver. The previous studies showed Study of type 2 diabetes by cross-tissue analysis 239 that, consistent to the T2DM sufferance, abnormal of T2D and G protein-gamma combine GPCR to regulation of transporting with very-low-density regulate PI3K activation. lipoproteins was enriched in liver [42], and inflammatory response took place during the whole development of T2DM [54, 55]. However, the DISCUSSION AND CONCLUSION In this article, we first briefly summarized the theor- inflammatory response in liver and muscle tissues ac- etical foundation of a novel network-based dynam- tually resulted from different regulations, i.e. B/T ical biomarker and its computational algorithm cell actively participated in the first critical period, [15, 16] for detecting early warning signals of com- and interleukins in the second critical period. plex diseases, and then based on multi-tissue and multi-stage gene expression data during T2DM DNB features development and progression, we identified tissue- To further focus on the specific characteristics of specific DNBs and further elucidated the role of DNB as dynamical early warning signals, we com- DNBs as well as the molecular mechanism of pared DNBs with conventional biomarkers by their T2DM at a network level. Downloaded from The experiment data sets used in this work include expression levels and roles in regulation. First, the the multi-period gene expression profiles of three DNB genes rarely belonged to differentially ex- different tissues from the typical diabetes rat model pressed genes (DEG) detected by T-test statistics of the GK rats and the control model of the WKY with P < 0.05 at the same (critical) time point rats. The three tissues are adipose, liver and gastro- http://bib.oxfordjournals.org/ (Supplementary Table S4), and actually their over- cnemius muscle, which are all major metabolic tis- lapping significance measured by hypergeometric test sues with more risks to be affected by insulin was larger than 0.5. However, when checking DNB resistance [30]. Based on this carefully designed tem- genes’ expressions for all time points, it could be poral-spatial expression data, we showed that DNB found that these genes had drastic over-expression method not only detected the early warning signals or under-expression only at their corresponding crit- of stage transitions of each tissue in the development ical points (fold-change 1.3) (Supplementary Table of T2DM but also identified the common biological at Stanford University on September 20, 2014 S5). Second, we also found that many pathways functions or coordinated functions among multiple related to T2DM contain genes from both DNB tissues. GK rat model for T2DM study has many and DEG (Supplementary Table S4). This motivated advantages owing to its natural characteristics, such us to assume that there may exist strong relationships as high blood glucose, similar insulin resistance by between DNB and DEG as regulators and passen- the impaired insulin receptor and the multi-gene gers. Actually, many DNB genes are really located at dysfunctions. Thus, due to the conserved genetic the upstream of the network to regulate DEG in a background and under similar condition, GK rats number of key biological pathways related to T2DM (relatively) synchronously suffer first from pre-disease (Figure 4). Although DNB genes are not all in the symptom, then are in a disease state and finally reach upstream of DEGs, they stay at important places the serious disease stage. GK rat model is also similar to regulate some branch metabolism paths. For to the subset of type 2 diabetic human without obes- instance, several DNB genes are located at the up- ity [37, 56]. Hence, this model is useful for under- stream of the pathway of steroid hormone biosyn- standing the common mechanisms of T2DM thesis, and many genes at the pathway downstream development for multiple tissues between human really present significant over-expression or down- and rat. expression. Some other DNB genes as messengers or In this work, we focused on liver, muscle and receptors can help signals transduce into membrane, adipose (as three main target tissues of insulin) to e.g. DNB genes participate in the upstream regula- find their tissue-specific DNBs and further analyze tion of PI3K-AKT signaling transduction through the molecular mechanism of T2DM at respective GF–RTK interaction in muscle, whereas ECM af- tissue during the disease progression. As one main fects ITG and FAK in adipose. Moreover, some result, the tissue-specific DNBs as early warning sig- genes in DNB were also found to act as a part of nals for T2DM development and progression were the combined complex to regulate the downstream successfully detected. The results showed that DNB dynamics, e.g. RXR with PPAR as a complex to for each tissue and each transition point is actually regulate specific gene expressions in muscle at onset different, thus justifying the necessity to conduct 240 Li et al. Downloaded from http://bib.oxfordjournals.org/ at Stanford University on September 20, 2014

Figure 4: Key biological pathways with DNB genes and differentially expressed genes (DEG). Clearly, DNB genes are placed at the important positions in the pathways, i.e. core genes of the DNB are mainly located at the upstream to regulate DEG. tissue-specific analysis for complex diseases. resistance and the second (16 weeks) is related to Specifically, there are two critical states for both the immune system. The identified DNB genes are liver and muscle during T2DM development, the significantly associated with T2DM, some of which first (4 weeks) is involved in response to insulin have been reported to be the disease genes or Study of type 2 diabetes by cross-tissue analysis 241 increase the risk of T2DM. At the same time, these We identified early warning signals of critical transitions during T2DM progression based on DNB. DNB genes also participate in many important bio- Tissue-specific DNBs were detected in liver, adipose and muscle logical processes related to the T2DM development duringT2DM development and progression. and progression, such as response to insulin and in- DNB genes act as the causal factors rather than the conse- ositol stimuli, abnormal lipid metabolism and quence to drive the downstream molecules to change their transcription levels. immune system response. From the viewpoint of pathogenic mechanism explanation, DNB genes were also found to play an important role in regulating biological processes. These genes tend to be FUNDING located at the upstream of pathway so that they National Natural Science Foundation of China may drive the downstream molecules to have the under nos. 61134013, 61072149, 31200987 and transition change at a transcription level. Besides, 91029301; Shanghai Pujiang Program; Chief some inconspicuous functions identified in the cor- Scientist Program of Shanghai Institutes for responding tissues not only reveal new biological Biological Sciences, CAS under no. 2009CSP002; responses stimulated by insulin resistance but also and the FIRST program from JSPS initiated by Downloaded from signal the emergence of the critical transitions for CSTP. T2DM, e.g. steroid hormone biosynthesis is significantly dysregulated in liver and adipose at the critical transition from the normal stage to the pre-disease References stage and then final disease-deterioration stage. 1. Chen L, Wang RS, Zhang XS. Biomolecular Networks: http://bib.oxfordjournals.org/ Methods and Applications in Systems Biology. Hoboken, Furthermore, the warning signals of disease deteri- New Jersey: John Wiley & Sons Inc., 2009. oration detected in different tissues imply some 2. Chen L, Wang RQ, Li C, et al. Modelling Biomolecular common mechanisms or functional regulations for Networks in Cells: Structures and Dynamics. London: Springer- the similar abnormal situations. For instance, Verlag, 2010. although the dysfunction of steroid hormone biosyn- 3. Shah NH, Tenenbaum JD. The coming age of data-driven thesis does not exist in muscle owing to tissue spe- medicine: translational bioinformatics’ next frontier. JAm

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