Network screening of Goto-Kakizaki rat liver microarray data during diabetic progression

RNA-Seq transcriptome profiling of Nile rat livers reveals novel insights on the anti-diabetic mechanisms of Water-Soluble Palm Fruit Extract

Water-Soluble Palm Fruit Extract (WSPFE) has been shown to confer anti-diabetic effects in the Nile rat (NR) (Arvicanthis niloticus). Liquid and powder WSPFE both deterred diabetes onset in NRs fed a high-carbohydrate (hiCHO) diet, but the liquid form provided better protection. In this study, NRs were fed either a hiCHO diet or the same diet added with liquid or powder WSPFE. Following feeding of the diets for 8 weeks, random blood glucose levels were measured to categorize NRs as either diabetes-resistant or diabetes-susceptible, based on a cut-off value of 75 mg/dL. Livers were then obtained for Illumina HiSeq 4000 paired end RNA-sequencing (RNA-Seq) and the data were mapped to the reference genome. Consistent with physiological and biochemical parameters, the gene expression data obtained indicated that WSPFE was associated with protection against diabetes. Among hepatic genes upregulated by WSPFE versus controls, were genes related to insulin-like growth factor binding protein, leptin receptor, and processes of hepatic metabolism maintenance, while those downregulated were related to antigen binding, immunoglobulin receptor, inflammation- and cancer-related processes. WSPFE supplementation thus helped inhibit diabetes progression in NRs by increasing insulin sensitivity and reducing both the inflammatory effects of a hiCHO diet and the related DNA-damage compensatory mechanisms contributing to liver disease progression. In addition, the genetic permissiveness of susceptible NRs to develop diabetes was potentially associated with dysregulated compensatory mechanisms involving insulin signaling and oxidative stress over time. Further studies on other NR organs associated with diabetes and its complications are warranted.

Prediabetes uncovers differential gene expression at fasting and in response to oral glucose load in immune cells

BACKGROUND AND OBJECTIVE

Metabolic disorders including diabetes are associated with immune cell dysfunction. However, the effect of normal glucose metabolism or impairment thereof on immune cell gene expression is not well known. Hence, in this cross-sectional pilot study, we sought to determine the differences in gene expression in the peripheral blood mono-nuclear cells (PBMCs) of normal glucose tolerant (NGT) and prediabetic (PD) Asian Indian men, at fasting and in response to 75 g oral glucose load.

METHODS

Illumina HT12 bead chip-based microarray was performed on PBMCs at fasting and 2-h post load conditions for NGT (N = 6) and PD (N = 9) subjects. Following normalization and due quality control of the raw data, differentially expressed genes (DEGs) under different conditions within and across the two groups were identified using GeneSpring GX V12.0 software. Paired and unpaired Student's t-tests were applied along with fold change cut-offs for appropriate comparisons. Validation of the microarray data was carried out through real-time qPCR analysis. Significantly regulated biological pathways were analyzed by employing DEGs and DAVID resource. Deconvolution of the DEGs between NGT and PD subjects at fasting was performed using CIBERSORT and genes involved in regulatory T-cell (Treg) function were further analyzed for biological significance.

RESULTS

Glucose load specifically altered the expression of 112 genes in NGT and 356 genes in PD subjects. Biological significance analysis revealed transient up-regulation of innate and adaptive immune response related genes following oral glucose load in NGT individuals, which was not observed in PD subjects. Instead, in the PD group, glucose load led to an increase in the expression of pro-atherogenic and anti-angiogenic genes. Comparison of gene expression at fasting state in PD versus NGT revealed 21,707 differentially expressed genes. Biological significance analysis of the immune function related genes between these two groups (at fasting) revealed higher gene expression of members of the TLR signaling, MHC class II molecules, and T-cell receptor, chemotaxis and adhesion pathways in PD subjects. Expression of interferon-γ (IFN-γ) and TNFα was higher and that of type-1 interferons and TGF-β was lower at fasting state in PD subjects compared to NGT. Additionally, expression of multiple proteasome subunits and protein arginine methyl transferase genes (PRMTs) were higher and that of Treg specific genes was significantly distinct at fasting in PD subjects compared to NGT.

CONCLUSION

Prediabetes uncovers constitutive TLR activation, enhanced IFN-γ signaling, and Treg dysfunction at fasting along with altered gene expression response to oral glucose load.

Aging, Metabolism, and Cancer Development: from Peto's Paradox to the Warburg Effect

Medical advances made over the last century have increased our lifespan, but age-related diseases are a fundamental health burden worldwide. Aging is therefore a major risk factor for cardiovascular disease, cancer, diabetes, obesity, and neurodegenerative diseases, all increasing in prevalence. However, huge inter-individual variations in aging and disease risk exist, which cannot be explained by chronological age, but rather physiological age decline initiated even at young age due to lifestyle. At the heart of this lies the metabolic system and how this is regulated in each individual. Metabolic turnover of food to energy leads to accumulation of co-factors, byproducts, and certain proteins, which all influence gene expression through epigenetic regulation. How these epigenetic markers accumulate over time is now being investigated as the possible link between aging and many diseases, such as cancer. The relationship between metabolism and cancer was described as early as the late 1950s by Dr. Otto Warburg, before the identification of DNA and much earlier than our knowledge of epigenetics. However, when the stepwise gene mutation theory of cancer was presented, Warburg's theories garnered little attention. Only in the last decade, with epigenetic discoveries, have Warburg's data on the metabolic shift in cancers been brought back to life. The stepwise gene mutation theory fails to explain why large animals with more cells, do not have a greater cancer incidence than humans, known as Peto's paradox. The resurgence of research into the Warburg effect has given us insight to what may explain Peto's paradox. In this review, we discuss these connections and how age-related changes in metabolism are tightly linked to cancer development, which is further affected by lifestyle choices modulating the risk of aging and cancer through epigenetic control.

RNA-seq analysis of the hypothalamic transcriptome reveals the networks regulating physiopathological progress in the diabetic GK rat

The Goto-Kakizaki (GK) rat is an animal model of non-obese type 2 diabetes (T2D). The GK rat was generated through the introduction of various genetic mutations from continuous inbreeding; these rats develop diabetes spontaneously. The mutated genes in GK rats may play key roles in the regulation of diabetes. The hypothalamus plays a central role in systematic energy homeostasis. Here, the hypothalamic transcriptomes in GK and Wistar rats at 4, 8 and 12 weeks were investigated by RNA-seq, and multiple variants and gene expression profiles were obtained. The number of variants identified from GK rats was significantly greater than that of Wistar rats, indicating that many variants were fixed and heritable in GK rats after selective inbreeding. The differential gene expression analysis indicated that GK rats had a dysfunctional hypothalamic melanocortin system and attenuation of the hypothalamic glucose-sensing pathway. In addition, we generated integrated gene network modules by combining the protein-protein interaction (PPI) network, co-expression network and mutations in GK and Wistar rats. In the modules, GK-specific genes, such as Bad, Map2k2, Adcy3, Adcy2 and Gstm6, may play key roles in hypothalamic regulation in GK rats. Our research provides a comprehensive map of the abnormalities in the GK rat hypothalamus, which reveals the new mechanisms of pathogenesis of T2D.

Transcriptomic alterations in the heart of non-obese type 2 diabetic Goto-Kakizaki rats

Background

There is a spectacular rise in the global prevalence of type 2 diabetes mellitus (T2DM) due to the worldwide obesity epidemic. However, a significant proportion of T2DM patients are non-obese and they also have an increased risk of cardiovascular diseases. As the Goto-Kakizaki (GK) rat is a well-known model of non-obese T2DM, the goal of this study was to investigate the effect of non-obese T2DM on cardiac alterations of the transcriptome in GK rats.

Methods

Fasting blood glucose, serum insulin and cholesterol levels were measured at 7, 11, and 15 weeks of age in male GK and control rats. Oral glucose tolerance test and pancreatic insulin level measurements were performed at 11 weeks of age. At week 15, total RNA was isolated from the myocardium and assayed by rat oligonucleotide microarray for 41,012 genes, and then expression of selected genes was confirmed by qRT-PCR. Gene ontology and protein–protein network analyses were performed to demonstrate potentially characteristic gene alterations and key genes in non-obese T2DM.

Results

Fasting blood glucose, serum insulin and cholesterol levels were significantly increased, glucose tolerance and insulin sensitivity were significantly impaired in GK rats as compared to controls. In hearts of GK rats, 204 genes showed significant up-regulation and 303 genes showed down-regulation as compared to controls according to microarray analysis. Genes with significantly altered expression in the heart due to non-obese T2DM includes functional clusters of metabolism (e.g. Cyp2e1, Akr1b10), signal transduction (e.g. Dpp4, Stat3), receptors and ion channels (e.g. Sln, Chrng), membrane and structural proteins (e.g. Tnni1, Mylk2, Col8a1, Adam33), cell growth and differentiation (e.g. Gpc3, Jund), immune response (e.g. C3, C4a), and others (e.g. Lrp8, Msln, Klkc1, Epn3). Gene ontology analysis revealed several significantly enriched functional inter-relationships between genes influenced by non-obese T2DM. Protein–protein interaction analysis demonstrated that Stat is a potential key gene influenced by non-obese T2DM.

Conclusions

Non-obese T2DM alters cardiac gene expression profile. The altered genes may be involved in the development of cardiac pathologies and could be potential therapeutic targets in non-obese T2DM.

Electronic supplementary material

The online version of this article (doi:10.1186/s12933-016-0424-3) contains supplementary material, which is available to authorized users.

POLD1: Central mediator of DNA replication and repair, and implication in cancer and other pathologies

The evolutionarily conserved human polymerase delta (POLD1) gene encodes the large p125 subunit which provides the essential catalytic activities of polymerase δ (Polδ), mediated by 5′–3′ DNA polymerase and 3′–5′ exonuclease moieties. POLD1 associates with three smaller subunits (POLD2, POLD3, POLD4), which together with Replication Factor C and Proliferating Nuclear Cell Antigen constitute the polymerase holoenzyme. Polδ function is essential for replication, with a primary role as the replicase for the lagging strand. Polδ also has an important proofreading ability conferred by the exonuclease activity, which is critical for ensuring replicative fidelity, but also serves to repair DNA lesions arising as a result of exposure to mutagens. Polδ has been shown to be important for multiple forms of DNA repair, including nucleotide excision repair, double strand break repair, base excision repair, and mismatch repair. A growing number of studies in the past decade have linked germline and sporadic mutations in POLD1 and the other subunits of Polδ with human pathologies. Mutations in Polδ in mice and humans lead to genomic instability, mutator phenotype and tumorigenesis. The advent of genome sequencing techniques has identified damaging mutations in the proofreading domain of POLD1 as the underlying cause of some inherited cancers, and suggested that mutations in POLD1 may influence therapeutic management. In addition, mutations in POLD1 have been identified in the developmental disorders of mandibular hypoplasia, deafness, progeroid features and lipodystrophy and atypical Werner syndrome, while changes in expression or activity of POLD1 have been linked to senescence and aging. Intriguingly, some recent evidence suggests that POLD1 function may also be altered in diabetes. We provide an overview of critical Polδ activities in the context of these pathologic conditions.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Distinct Hepatic Macrophage Populations in Lean and Obese Mice

Obesity is a complex metabolic disorder associated with the development of non-communicable diseases such as cirrhosis, non-alcoholic fatty liver disease, and type 2 diabetes. In humans and rodents, obesity promotes hepatic steatosis and inflammation, which leads to increased production of pro-inflammatory cytokines and acute-phase proteins. Liver macrophages (resident as well as recruited) play a significant role in hepatic inflammation and insulin resistance (IR). Interestingly, depletion of hepatic macrophages protects against the development of high-fat-induced steatosis, inflammation, and IR. Kupffer cells (KCs), liver-resident macrophages, are the first-line defense against invading pathogens, clear toxic or immunogenic molecules, and help to maintain the liver in a tolerogenic immune environment. During high fat diet feeding and steatosis, there is an increased number of recruited hepatic macrophages (RHMs) in the liver and activation of KCs to a more inflammatory or M1 state. In this review, we will focus on the role of liver macrophages (KCs and RHMs) during obesity.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Systematic profiling of mRNA and miRNA expression in the pancreatic islets of spontaneously diabetic Goto-Kakizaki rats

Type 2 diabetes (T2DM) is a complex multifactorial metabolic disorder that affects >100 million individuals worldwide, yet the mechanisms involved in the development and progression of the disease have not yet been fully elucidated. The present study examined the mRNA and micro (mi)RNA expression profiles by microarray analysis in the pancreas islets of spontaneously diabetic Goto-Kakizaki rats with the aim to identify regulatory mechanisms underlying the pathogenesis of T2DM. A total of 9 upregulated and 10 downregulated miRNAs were identified, including miR-150, miR-497, miR-344-3p and let-7f, which were independently validated by quantitative polymerase chain reaction assays. In addition, differential expression of 670 genes was detected by mRNA microarray analysis, including 370 upregulated and 247 downregulated genes. The differentially expressed genes were statistically associated with major cellular pathways, including the immune response pathway and the extracellular matrix (ECM)-receptor interaction pathway. Finally, a reverse regulatory association of differentially expressed miRNAs and their predicted target genes was constructed, supported by analysis of their mRNA and miRNA expression profiles. A number of key pairs of miRNA-mRNA was proposed to have significant roles in the pathogenesis of T2DM rats based on bioinformatics analysis, one example being the let-7f/collagen, type II, alpha 1 pair that may regulate ECM-receptor interactions.

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.

Association of Rev-erbα in adipose tissues with Type 2 diabetes mellitus amelioration after gastric bypass surgery in Goto-Kakizaki rats

We estimated the key molecules related to Type 2 diabetes mellitus (T2DM) in adipose, liver, and muscle tissues, from nonobese diabetic Goto-Kakizaki (GK) rats and their Wistar controls, by computationally analyzing the expression profiles in open source data. With the aid of information from previous reports, Rev-erbα in adipose tissue emerged as one of the most plausible candidates. Here, in animal models, including GK rats surgically treated to ameliorate T2DM, we examined the association of Rev-erbα in adipose tissue with T2DM progression. After analyses of the Rev-erbα mRNA expression in the adipose tissue of our animal models, we compared the Rev-erbα protein expression levels in the adipose, liver, and muscle tissues of GK and Wistar controls at the ages of 1 mo (M), 3M, and 6M. The Rev-erbα protein levels in adipose tissue showed a distinctive pattern, with the negative correlation of an increasing trend in GK rats, and a decreasing trend in Wistar rats during aging, from those in liver and muscle tissues. Moreover, dysregulation of the circadian Rev-erbα expression in the adipose tissue of 6-mo-old GK rats was also observed. In particular, we ameliorated T2DM in GK rats by gastric bypass surgery, and revealed that T2DM amelioration in diabetic GK rats was associated with improved circadian Rev-erbα expression, in a comparison between the surgically treated and untreated GK rats. The roles of Rev-erbα in adipose tissue were further investigated by observations of Rev-erbα-related molecules, with reference to previous reports.

Effects on high cholesterol-fed to liver, retina, hippocampus, and Harderian gland in Goto-Kakizaki rat

To understand the relationship among cholesterolemia, hyperglycemic stage in non obese type 2 diabetes mellitus, and histological perturbations on liver, retina, hippocampus, and Harderian gland, we maintained rat on a diet high in cholesterol for fourteen weeks, then analyzed blood lipid profiles, blood glucose, hepatic enzymes, and microscopic lesion of those tissues. We observed that high cholesterol-treated rat elevated in cholesterol and low density lipoprotein with not correlated to hyperglycemia. Histopathological changing in Goto-Kakizaki rat on liver (microvesicular steatosis) and Harderain gland (tubular lesions) were related to hyperglycemic effect rather than cholesterolemic effect. These may be related to hypoinsulinemic characteristic of this diabetic model. However increasing pyknotic nuclei on hippocampus and reducing of retinal ganglionic cell were related to the high level of cholesterol loaded with synergized effect due to diabetic stage.

Gene expression profile analysis of type 2 diabetic mouse liver

Liver plays a key role in glucose metabolism and homeostasis, and impaired hepatic glucose metabolism contributes to the development of type 2 diabetes. However, the precise gene expression profile of diabetic liver and its association with diabetes and related diseases are yet to be further elucidated. In this study, we detected the gene expression profile by high-throughput sequencing in 9-week-old normal and type 2 diabetic db/db mouse liver. Totally 12132 genes were detected, and 2627 genes were significantly changed in diabetic mouse liver. Biological process analysis showed that the upregulated genes in diabetic mouse liver were mainly enriched in metabolic processes. Surprisingly, the downregulated genes in diabetic mouse liver were mainly enriched in immune-related processes, although all the altered genes were still mainly enriched in metabolic processes. Similarly, KEGG pathway analysis showed that metabolic pathways were the major pathways altered in diabetic mouse liver, and downregulated genes were enriched in immune and cancer pathways. Analysis of the key enzyme genes in fatty acid and glucose metabolism showed that some key enzyme genes were significantly increased and none of the detected key enzyme genes were decreased. In addition, FunDo analysis showed that liver cancer and hepatitis were most likely to be associated with diabetes. Taken together, this study provides the digital gene expression profile of diabetic mouse liver, and demonstrates the main diabetes-associated hepatic biological processes, pathways, key enzyme genes in fatty acid and glucose metabolism and potential hepatic diseases.

Diabetes and nonalcoholic Fatty liver disease: a pathogenic duo

Recent data increasingly support a complex interplay between the metabolic condition diabetes mellitus and the pathologically defined nonalcoholic fatty liver disease (NAFLD). NAFLD predicts the development of type 2 diabetes and vice versa, and each condition may serve as a progression factor for the other. Although the association of diabetes and NAFLD is likely to be partly the result of a "common soil," it is also probable that diabetes interacts with NAFLD through specific pathogenic mechanisms. In particular, through interrelated metabolic pathways currently only partly understood, diabetes appears to accelerate the progression of NAFLD to nonalcoholic steatohepatitis, defined by the presence of necroinflammation, with varying degrees of liver fibrosis. In the research setting, obstacles that have made the identification of clinically significant NAFLD, and particularly nonalcoholic steatohepatitis, difficult are being addressed with the use of new imaging techniques combined with risk algorithms derived from peripheral blood profiling. These techniques are likely to be used in the diabetes population in the near future. This review examines the pathogenic links between NAFLD and diabetes by exploring the epidemiological evidence in humans and also through newer animal models. Emerging technology to help screen noninvasively for differing pathological forms of NAFLD and the potential role of preventive and therapeutic approaches for NAFLD in the setting of diabetes are also examined.

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

A computational procedure for identifying master regulator candidates: a case study on diabetes progression in Goto-Kakizaki rats

Background

We have recently identified a number of active regulatory networks involved in diabetes progression in Goto-Kakizaki (GK) rats by network screening. The networks were quite consistent with the previous knowledge of the regulatory relationships between transcription factors (TFs) and their regulated genes. To study the underlying molecular mechanisms directly related to phenotype changes, such as diseases, we also previously developed a computational procedure for identifying transcriptional master regulators (MRs) in conjunction with network screening and network inference, by effectively perturbing the phenotype states.

Results

In this work, we further improved our previous method for identifying MR candidates, by listing them in a more reliable manner, and applied the method to reveal the MR candidates for diabetes progression in GK rats from the active networks. Specifically, the active TF-gene pairs for different time periods in GK rats were first extracted from the networks by network screening. Another set of active TF-gene pairs was selected by network inference, by considering the gene expression signatures for those periods between GK and Wistar-Kyoto (WKY) rats. The TF-gene pairs extracted by the two methods were then further selected, from the viewpoints of the emergence specificity of TF in GK rats and the regulated-gene coverage of TF in the expression signature. Finally, we narrowed all of the genes down to only 5 TFs (Etv4, Fus, Nr2f1, Sp2, and Tcfap2b) as the candidates of MRs, with 54 regulated genes, by merging the selected TF-gene pairs.

Conclusions

The present method has successfully identified biologically plausible MR candidates, including the TFs related to diabetes in previous reports. Although the experimental verifications of the candidates and the present procedure are beyond the scope of this study, we narrowed down the candidates to 5 TFs, which can be used to perform the verification experiments relatively easily. The numerical results showed that our computational method is an efficient way to detect the key molecules responsible for biological phenomena.