Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications

Insights into the radiation and oxidative stress mechanisms in genus Deinococcus

Deinococcus species, noted for their exceptional resistance to DNA-damaging environmental stresses, have piqued scientists' interest for decades. This study dives into the complex mechanisms underpinning radiation resistance in the Deinococcus genus. We have examined the genomes of 82 Deinococcus species and classified radiation-resistance proteins manually into five unique curated categories: DNA repair, oxidative stress defense, Ddr and Ppr proteins, regulatory proteins, and miscellaneous resistance components. This classification reveals important information about the various molecular mechanisms used by these extremophiles which have been less explored so far. We also investigated the presence or lack of these proteins in the context of phylogenetic relationships, core, and pan-genomes, which offered light on the evolutionary dynamics of radiation resistance. This comprehensive study provides a deeper understanding of the genetic underpinnings of radiation resistance in the Deinococcus genus, with potential implications for understanding similar mechanisms in other organisms using an interactomics approach. Finally, this study reveals the complexities of radiation resistance mechanisms, providing a comprehensive understanding of the genetic components that allow Deinococcus species to flourish under harsh environments. The findings add to our understanding of the larger spectrum of stress adaption techniques in bacteria and may have applications in sectors ranging from biotechnology to environmental research.

Hyaluronic acid modified extracellular vesicles targeting hepatic stellate cells to attenuate hepatic fibrosis

RATIONALE

Transforming growth factor-beta1 (TGF-β1) plays a pivotal role in promoting hepatic fibrosis, pirfenidone (PFD) could inhibit TGF-β1 signaling pathway to alleviate hepatic stellate cells (HSC) activation mediated hepatic fibrosis. The targeting delivery strategy of PFD to hepatic stellate cells is a challenge. Extracellular vesicles (EVs), cell-derived membranous particles are intraluminal nano-vesicles that play a vital role in intercellular communication, they also be considered as an ideal nano-carrier.

METHODS

In this study, we developed a target strategy to deliver PFD to HSC with CD44 over-expression by EVs, hyaluronic acid (HA) modified DSPE-PEG2000 endows the active targeting ability of activated HSCs to PFD-loaded EVs.

RESULTS

In both rat hepatic stellate cell line HSC-T6 and rat hepatocyte cell line BRL, HA@EVs-PFD demonstrated the capacity to down-regulate the expression of collagen-synthesis-related proteins and showed superior inhibition efficacy of HSC-T6 activation compared to free PFD. In hepatic fibrosis model, 4 weeks of HA@EVs-PFD treatment resulted in a reduction in liver collagen fibers, significant improvement in hepatic cell morphology, and amelioration of hepatic fibrosis.

CONCLUSIONS

HA@EVs-PFD, as a drug delivery system that effectively targets and inhibits activated HSCs to treat hepatic fibrosis, holds promise as a potential therapeutic agent against hepatic fibrosis.

DbKB a knowledge graph dataset for diabetes: A system biology approach

Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions. The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships. Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes. These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.

Evolving cognition of the JAK-STAT signaling pathway: autoimmune disorders and cancer

The Janus kinase (JAK) signal transducer and activator of transcription (JAK-STAT) pathway is an evolutionarily conserved mechanism of transmembrane signal transduction that enables cells to communicate with the exterior environment. Various cytokines, interferons, growth factors, and other specific molecules activate JAK-STAT signaling to drive a series of physiological and pathological processes, including proliferation, metabolism, immune response, inflammation, and malignancy. Dysregulated JAK-STAT signaling and related genetic mutations are strongly associated with immune activation and cancer progression. Insights into the structures and functions of the JAK-STAT pathway have led to the development and approval of diverse drugs for the clinical treatment of diseases. Currently, drugs have been developed to mainly target the JAK-STAT pathway and are commonly divided into three subtypes: cytokine or receptor antibodies, JAK inhibitors, and STAT inhibitors. And novel agents also continue to be developed and tested in preclinical and clinical studies. The effectiveness and safety of each kind of drug also warrant further scientific trials before put into being clinical applications. Here, we review the current understanding of the fundamental composition and function of the JAK-STAT signaling pathway. We also discuss advancements in the understanding of JAK-STAT-related pathogenic mechanisms; targeted JAK-STAT therapies for various diseases, especially immune disorders, and cancers; newly developed JAK inhibitors; and current challenges and directions in the field.

In Silico Molecular Docking of Phytochemicals for Type 2 Diabetes Mellitus Therapy: A Network Pharmacology Approach

Identification of potential lead molecules in herbal medicines is crucial not only for validation but also for drug discovery. This study was focused on identifying the therapeutic mechanisms of 10 common herbs used to treat type 2 diabetes mellitus (T2DM) using network pharmacology and docking studies. Details pertaining to medicinal plants and their phytoconstituents were obtained from Indian Medicinal Plants, Phytochemistry, and Therapeutics and Dr. Duke's database, respectively. MolSoft was used to assess their drug likeness. Prediction of protein targets for the screened phytochemicals and the list of target genes involved in T2DM were obtained using Swiss TargetPrediction and GeneCards respectively. STRING; Cytoscape; Database for Annotation, Visualization, and Integrated Discovery; and PyRx were used for network construction, network analysis, gene ontology analysis, and molecular docking, respectively. The protein targets MAPK1, AKT1, PI3K, and EGFR were identified to play a crucial role in the progression of T2DM. Furthermore, molecular docking indicated that nimbaflavone exhibited high binding affinities for MAPK1 (-8.7 kcal/mole) and PI3K (-9.6 kcal/mole), whereas rutin and 10-hydroxyaloin-B showed high binding affinities for AKT1 (-7.4 kcal/mole) and EGFR (-8.1 kcal/mole), respectively. The findings from this study suggest that flavonoids are the major phytoconstituents that display antidiabetic activity by interacting with key protein molecules related to the MAPK and PI3K-AKT signaling pathways, thereby aiding in the treatment of T2DM. The activation of these pathways alters Ras-GTPase activity and enhances the expression of GLUT4, a glucose transporter, resulting in the uptake of glucose from the bloodstream.

SUMOylation targeting mitophagy in cardiovascular diseases

Small ubiquitin-like modifier (SUMO) plays a key regulatory role in cardiovascular diseases, such as cardiac hypertrophy, hypertension, atherosclerosis, and cardiac ischemia-reperfusion injury. As a multifunctional posttranslational modification molecule in eukaryotic cells, SUMOylation is essentially associated with the regulation of mitochondrial dynamics, especially mitophagy, which is involved in the progression and development of cardiovascular diseases. SUMOylation targeting mitochondrial-associated proteins is admittedly considered to regulate mitophagy activation and mitochondrial functions and dynamics, including mitochondrial fusion and fission. SUMOylation triggers mitochondrial fusion to promote mitochondrial dysfunction by modifying Fis1, OPA1, MFN1/2, and DRP1. The interaction between SUMO and DRP1 induces SUMOylation and inhibits lysosomal degradation of DRP1, which is further involved in the regulation of mitochondrial fission. Both SUMOylation and deSUMOylation contribute to the initiation and activation of mitophagy by regulating the conjugation of MFN1/2 SERCA2a, HIF1α, and PINK1. SUMOylation mediated by the SUMO molecule has attracted much attention due to its dual roles in the development of cardiovascular diseases. In this review, we systemically summarize the current understanding underlying the expression, regulation, and structure of SUMO molecules; explore the biochemical functions of SUMOylation in the initiation and activation of mitophagy; discuss the biological roles and mechanisms of SUMOylation in cardiovascular diseases; and further provide a wider explanation of SUMOylation and deSUMOylation research to provide a possible therapeutic strategy for cardiovascular diseases. Considering the precise functions and exact mechanisms of SUMOylation in mitochondrial dysfunction and mitophagy will provide evidence for future experimental research and may serve as an effective approach in the development of novel therapeutic strategies for cardiovascular diseases. Regulation and effect of SUMOylation in cardiovascular diseases via mitophagy. SUMOylation is involved in multiple cardiovascular diseases, including cardiac hypertrophy, hypertension, atherosclerosis, and cardiac ischemia-reperfusion injury. Since it is expressed in multiple cells associated with cardiovascular disease, SUMOylation can be regulated by numerous ligases, including the SENP family proteins PIAS1, PIASy/4, UBC9, and MAPL. SUMOylation regulates the activation and degradation of PINK1, SERCA2a, PPARγ, ERK5, and DRP1 to mediate mitochondrial dynamics, especially mitophagy activation. Mitophagy activation regulated by SUMOylation further promotes or inhibits ventricular diastolic dysfunction, perfusion injury, ventricular remodelling and ventricular noncompaction, which contribute to the development of cardiovascular diseases.

Differential expression of aerobic oxidative metabolism-related proteins in diabetic urinary exosomes

BACKGROUND

As a metabolic disease, any abnormality in the aerobic oxidation pathway of glucose may lead to the occurrence of diabetes. This study aimed to investigate the changes in proteins related to aerobic oxidative metabolism in urinary exosomes of diabetic patients and normal controls of different ages, and to further verify their correlation with the pathogenesis of diabetes.

METHODS

Samples were collected, and proteomic information of urinary exosomes was collected by LC-MS/MS. ELISA was used to further detect the expression of aerobic and oxidative metabolism-related proteins in urinary exosomes of diabetic patients and normal controls of different ages, and to draw receiver operating characteristic (ROC) curve to evaluate its value in diabetes monitoring.

RESULTS

A total of 17 proteins involved in aerobic oxidative metabolism of glucose were identified in urinary exosome proteins. Compared with normal control, the expressions of PFKM, GAPDH, ACO2 and MDH2 in diabetic patients were decreased, and the expression of IDH3G was increased. The concentrations of PFKM, GAPDH and ACO2 in urinary exosomes were linearly correlated with the expression of MDH2 (P<0.05). These four proteins vary with age, with the maximum concentration in the 45-59 age group. PFKM, GAPDH, ACO2, and MDH2 in urinary exosomes have certain monitoring value. When used in combination, the AUC was 0.840 (95% CI 0.764-0.915).

CONCLUSIONS

In diabetic patients, aerobic oxidative metabolism is reduced, and the expression of aerobic oxidative metabolism-related proteins PFKM, GAPDH, ACO2, and MDH2 in urinary exosomes is reduced, which may become potential biomarkers for monitoring changes in diabetes.

Differential mass spectrometry-based proteome analyses unveil major regulatory hubs in rifamycin B production in Amycolatopsis mediterranei

Rifamycin B is produced by Amycolatopsis mediterranei S699 as a secondary metabolite. Its semi-synthetic derivatives have been used for curing tuberculosis caused by Mycobacterium tuberculosis. But the emergence of rifampicin-resistant strains required analogs of rifamycin B to be developed by rifamycin biosynthetic gene cluster manipulation. In 2014 genetic engineering of the rifamycin polyketide synthase gene cluster in S699 led to a mutant, A. mediterranei DCO#34, that produced 24-desmethylrifamycin B. Unfortunately, the productivity was strongly reduced to 20 mgL^-1 as compared to 50 mgL^-1 of rifamycin B. To understand the mechanisms leading to reduced productivity and rifamycin biosynthesis by A. mediterranei S699 during the early and late growth phase we performed a proteome study for wild type strain S699, mutant DCO#34, and the non-producer strain SCO2-2. Proteins identification and relative label-free quantification were performed by nLC-MS/MS. Data are available via ProteomeXchange with identifier PXD016416. Also, in-silico protein-protein interaction approach was used to determine the relationship between different structural and regulatory proteins involved in rifamycin biosynthesis. Our studies revealed RifA, RifK, RifL, Rif-Orf19 as the major regulatory hubs. Relative abundance expression values revealed that genes encoding RifC-RifI and the transporter RifP, down-regulated in DCO#34 and genes encoding RifR, RifZ, other regulatory proteins up-regulated. SIGNIFICANCE: The study is designed mainly to understand the underlying mechanisms of rifamycin biosynthesis in Amycolatopsis mediterranei. This resulted in the identification of regulatory hubs which play a crucial role in regulating secondary metabolism. It elucidates the complex mechanism of secondary metabolite biosynthesis and their conversion and extracellular transportation in temporal correlation with the different growth phases. The study also elucidated the mechanisms leading to reduced production of analog, 24-desmethylrifamycin B by the genetically modified strain DCO#34, derivatives of which have been found effective against rifampicin-resistant strains of Mycobacterium tuberculosis. These results can be useful while carrying out genetic manipulations to improve the strains of Amycolatopsis to produce better analogs/drugs and promote the eradication of TB. Thus, this study is contributing significantly to the growing knowledge in the field of the crucial drug, rifamycin B biosynthesis by an economically important bacterium Amycolatopsis mediterranei.

Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC

With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.

has_circ_CCNB1 and has_circ_0009024 function as potential biomarkers for the diagnosis of type 2 diabetes mellitus

Circular RNAs (circRNAs) have been reported to be associated with various diseases, including type 2 diabetes mellitus (T2DM). The aim of present study was to investigate the clinical value of has_circ_CCNB1 and has_circ_0009024 in T2DM. Serum samples from patients with T2DM (n = 166) and healthy volunteers (n = 166) were recruited. Then, real‐time quantitative reverse transcription‐polymerase chain reaction (RT‐qPCR) analysis and enzyme‐linked immunosorbent (ELISA) assays were conducted to detect the expression levels of circRNAs and inflammatory factors. Furthermore, the correlation analysis and receiver operating characteristic (ROC) curve were used to evaluate diagnostic accuracy. From the results, circ_CCNB1 was significantly increased while circ_0009024 was decreased in serum samples from T2DM patients. Moreover, has_circ_CCNB1 was positively correlated with glucose (GLU), glycosylated hemoglobin (GHb), interleukin 6 (IL‐6), and tumor necrosis factor‐α (TNF‐α) while has_circ_0009024 was negatively correlated with them. Importantly, the AUC of has_circ_CCNB1 and has_and circ_0009024 was 0.9255 (95% CI = 0.8909‐0.9601) and 0.9592 (95% CI = 0.9381‐0.9803), while the AUC of combinative curve is 0.8875 (95% CI = 0.8204‐0.8547). In a word, has_circ_CCNB1 and has_circ_0009024 may exhibit as potential biomarkers for the diagnosis of T2DM.

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach

Background

Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.

Objective

In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks.

Methodology

In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.

Results

The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network.

Conclusion

The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Computational medicine: quantitative modeling of complex diseases

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Multi-Target Drugs Against Metabolic Disorders

BACKGROUND

Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat.

OBJECTIVE

The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders.

METHODS

In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles.

DISCUSSION

In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies.

CONCLUSION

In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

Comparative genomics of Sphingopyxis spp. unravelled functional attributes

Members of genus Sphingopyxis are known to thrive in diverse environments. Genomes of 21 Sphingopyxis strains were selected. Phylogenetic analysis was performed using GGDC, AAI and core-SNP showed agreement at sub-species level. Based on our results, we propose that both S. baekryungensis DSM16222 and Sphingopyxis sp. LPB0140 strains should not be included under genus Sphingopyxis. Core-analysis revealed, 1422 genes were shared which included essential pathways and genes for conferring adaptation against stress environment. Polyhydroxybutyrate degradation, anaerobic respiration, type IV secretion were notable abundant pathways and exopolysaccharide, hyaluronic acid production and toxin-antitoxin system were differentially present families. Interestingly, genome of S. witflariensis DSM14551, Sphingopyxis sp. MG and Sphingopyxis sp. FD7 provided a hint of probable pathogenic abilities. Protein-Protein Interactome depicted that membrane proteins and stress response has close integration with core-proteins while aromatic compounds degradation and virulence ability formed a separate network. Thus, these should be considered as strain specific attributes.

LCN2-interacting proteins and their expression patterns in brain tumors

Lipocalin 2 (LCN2) is a member of the lipocalin family. Elevated expression of LCN2 has been observed in many human tumors, suggesting it might be a potential biomarker and/or therapeutic target in malignancies. In this study, we aimed to explore LCN2 interacting proteins through bioinformatics, as well as their biological functions. Protein-protein interaction networks (PPIN) were constructed using LCN2 and its interacting proteins as the core node. These PPINs were scale free biological networks in which LCN2 and its interacting proteins could connect or cross-talk with at least one partner protein. Both functional and KEGG pathway enrichment analyses identified the known and potential biological functions of the PPIN, such as cell migration and cancer-related pathways. Expression levels of the PPIN proteins, as well as their expression correlations, in five types of brain tumor, were analyzed and integrated into the PPIN to illustrate a dynamic change. A significant correlation was found between the survival time of glioblastoma patients and the expression level of 10 genes (LCN2, MMP9, MMP2, PDE4DIP, L2HGDH, HNRNPA1, DDX31, LOXL2, FAM60A and RNF25). Taken together, our results suggest that LCN2 and its interacting proteins are mostly differentially expressed and have a distinguishing co-expression pattern. They might promote proliferation and migration via cell migration signaling and cancer-related pathways. LCN2 and its interacting proteins might be potential biomarkers in glioblastoma.

Effects of CPAP on the transcriptional signatures in patients with obstructive sleep apnea via coexpression network analysis

A growing number of studies provide epidemiological evidence linking obstructive sleep apnea (OSA) with a number of chronic disorders. Transcriptional analyses have been conducted to analyze the gene expression data. However, the weighted gene coexpression network analysis (WGCNA) method has not been applied to determine the transcriptional consequence of continuous positive airway pressure (CPAP) therapy in patients with severe OSA. The aim of this study was to identify key pathways and genes in patients with OSA that are influenced by CPAP treatment and uncover/unveil potential molecular mechanisms using WGCNA. We analyzed the microarray data of OSA (GSE 49800) listed in the Gene Expression Omnibus database. Coexpression modules were constructed using WGCNA. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were also conducted. After the initial data processing, 5101 expressed gene profiles were identified. Next, a weighted gene coexpression network was established and 16 modules of coexpressed genes were identified. The interaction analysis demonstrated a relative independence of gene expression in these modules. The black module, tan module, midnightblue module, pink module, and greenyellow module were significantly associated with the alterations in circulating leukocyte gene expression at baseline and after exposure to CPAP. The five hub genes were considered to be candidate OSA-related genes after CPAP treatment. Functional enrichment analysis revealed that steroid biosynthesis, amino sugar and nucleotide sugar metabolism, protein processing in the endoplasmic reticulum, and the insulin signaling pathway play critical roles in the development of OSA in circulating leukocyte gene expression at baseline and after exposure to CPAP. Using this new systems biology approach, we identified several genes and pathways that appear to be critical to OSA after CPAP treatment, and these findings provide a better understanding of OSA pathogenesis.

The analyses of SRCR genes based on protein-protein interaction network in esophageal squamous cell carcinoma

The scavenger receptor cysteine-rich (SRCR) proteins, with one to several SRCR domains, play important roles in human diseases. A full view of their functions in esophageal squamous cell carcinoma (ESCC) remain unclear. Sequence alignment and phylogenetic tree for all human SRCR domains were performed. Differentially-expressed SRCR genes were identified in ESCC, followed by protein-protein interaction (PPI) network construction, topological parameters, subcellular distribution, functional enrichment and survival analyses. The variation of conserved cysteines in each SRCR domain suggested a requirement for new classification of the SRCR family. Six genes (LGALS3BP, MSR1, CD163, LOXL2, LOXL3 and LOXL4) were upregulated, and four genes (DMBT1, PRSS12, TMPRSS2 and SCARA5) were downregulated in ESCC. These 10 SRCR genes form a unique biological network. Functional enrichment analyses provided important clues to investigate the biological functions for SRCR gene network in ESCC, such as extracellular structure organization and the PI3K-Akt signaling pathway. Kaplan-Meier curves confirmed that high expression of SCARA5, LOXL2, LOXL3, LOXL4 were related to poor survival, whereas high expression of DMBTI and PRSS12 showed the opposite result. SRCR genes promote the development of ESCC through its network and could serve as potential prognostic factors and therapy targets of ESCC.

Screening of circular RNAs and validation of circANKRD36 associated with inflammation in patients with type 2 diabetes mellitus

Circular RNAs (circRNAs) are an abundant class of endogenous non-coding RNAs and are associated with numerous diseases, including cancer, cardiovascular diseases, and type 2 diabetes mellitus (T2DM). However, the association between circRNAs and inflammation or inflammatory cytokines in patients with T2DM remains to be fully elucidated. The purpose of the present study was to investigate the expression profiles of circRNAs in peripheral leucocytes of patients with T2DM and their association with inflammatory cytokines. Peripheral blood from patients with T2DM (n=43) and healthy individuals (n=45) were collected for RNA sequencing and later verification. Reverse transcription-polymerase chain reaction (RT-PCR) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analyses were used to detect the expression levels of circRNAs. The expression of inflammatory factors, including interleukin (IL)-1, (IL)-6, and tumor necrosis factor (TNF)-α were measured via enzyme-linked immunosorbent assay. Furthermore, the mRNA expression level of ankyrin repeat domain 36 (ANKRD36), a protein located at 2q11.2 that interacts with the GAPDH gene, was measured using RT-qPCR analysis. The circRNA/microRNA (miRNA) interaction was predicted using RegRNA and mirPath software. In total, 220 circRNAs were found to be differentially expressed between patients with T2DM and healthy individuals, of which 107 were upregulated and 113 were downregulated. Among the nine selected circRNAs, circANKRD36 was significantly upregulated in patients with T2DM compared with control subjects (P=0.02). The expression level of circANKRD36 was positively correlated with glucose and glycosylated hemoglobin (r=0.3250, P=0.0047 and r=0.3171, P=0.0056, respectively). The expression level of IL-6 was significantly different between the T2DM group and control group (P=0.028) and was positively correlated with circANKRD36. The difference of circANKRD36 host gene expression between patients with T2DM and healthy controls was significant (P=0.04). Taken together, circANKRD36 may be involved in T2DM and inflammation-associated pathways via interaction with miRNAs, including hsa-miR-3614-3p, hsa-miR-498, and hsa-miR-501-5p. The expression of circANKRD36 was up regulated in peripheral blood leucocytes and was correlated with chronic inflammation in T2DM. Therefore, circANKRD36 can be used as a potential biomarker for screening chronic inflammation in patients with T2DM.

The protein-protein interaction network and clinical significance of heat-shock proteins in esophageal squamous cell carcinoma

Heat-shock proteins (HSPs), one of the evolutionarily conserved protein families, are widely found in various organisms, and play important physiological functions. Nevertheless, HSPs have not been systematically analyzed in esophageal squamous cell carcinoma (ESCC). In this study, we applied the protein-protein interaction (PPI) network methodology to explore the characteristics of HSPs, and integrate their expression in ESCC. First, differentially expressed HSPs in ESCC were identified from our previous RNA-seq data. By constructing a specific PPI network, we found differentially expressed HSPs interacted with hundreds of neighboring proteins. Subcellular localization analyses demonstrated that HSPs and their interacting proteins distributed in multiple layers, from membrane to nucleus. Functional enrichment annotation analyses revealed known and potential functions for HSPs. KEGG pathway analyses identified four significant enrichment pathways. Moreover, three HSPs (DNAJC5B, HSPA1B, and HSPH1) could serve as promising targets for prognostic prediction in ESCC, suggesting these HSPs might play a significant role in the development of ESCC. These multiple bioinformatics analyses have provided a comprehensive view of the roles of heat-shock proteins in esophageal squamous cell carcinoma.

Co-expression analysis reveals key gene modules and pathway of human coronary heart disease

Coronary heart disease is a kind of disease which causes great injury to people world-widely. Although gene expression analyses had been performed previously, to our best knowledge, systemic co-expression analysis for this disease is still lacking to date. Microarray data of coronary heart disease was downloaded from NCBI with the accession number of GSE20681. Co-expression modules were constructed by WGCNA. Besides, the connectivity degree of eigengenes was analyzed. Furthermore, GO and KEGG enrichment analysis was performed on these eigengenes in these constructed modules. A total of 11 co-expression modules were constructed by the 3000 up-regulated genes from the 99 samples with coronary heart disease. The average number of genes in these modules was 270. The interaction analysis indicated the relative independence of gene expression in these modules. The functional enrichment analysis showed that there was a significant difference in the enriched terms and degree among these 11 modules. The results showed that modules 9 and 10 played critical roles in the occurrence of coronary disease. Pathways of hsa00190 (oxidative phosphorylation) and (hsa01130: biosynthesis of antibiotics) were thought to be closely related to the occurrence and development of coronary heart disease. Our result demonstrated that modules 9 and 10 were the most critical modules in the occurrence of coronary heart disease. Pathways as hsa00190 (oxidative phosphorylation) and (hsa01130: biosynthesis of antibiotics) had the potential to serve as the prognostic and predictive marker of coronary heart disease.

T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders

We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genes AGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRP and NOS3 are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients' skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes, VEGFA is associated with several complications of T2DM. A few genes ACE2, ADCYAP1, HDAC4, NCF1, NFE2L2, OSM, SMAD1, TGFB1, BDNF, SYVN1, TXNIP, CD36, CYP2J2, NLRP3 with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available at www.http://t2diacod.igib.res.in/ .

Association of donor and recipient SUMO4 rs237025 genetic variant with new-onset diabetes mellitus after liver transplantation in a Chinese population

BACKGROUNDS & AIMS

New-onset diabetes mellitus (NODM) is a common complication after liver transplantation (LT). The small ubiquitin-like modifier 4 (SUMO4) rs237025 polymorphism has been reported to be associated with type 2 diabetes mellitus (T2DM). In this study, we aimed to evaluate the association of donor and recipient SUMO4 rs237025 polymorphisms with NODM and the long-term consequences of NODM after LT.

METHODS

A total of 126 liver transplant patients were enrolled in the study. One single nucleotide polymorphism, SUMO4 rs237025, was genotyped in both donors and recipients.

RESULTS

Both donor and recipient SUMO4 rs237025 polymorphisms were found to be significantly associated with NODM after LT. In multivariate analysis, recipient age>50 years, tacrolimus trough concentrations>10ng/mL at 1month after LT, donor and recipient rs237025 genetic variant, and the combined donor and recipient rs237025 genetic variant were independent predictive factors of NODM. Area under the receiver operating characteristic curve (AUROC) analysis indicated the higher predictive ability of the model containing combined donor and recipient rs237025 polymorphisms than the clinical model (p=0.046). Furthermore, Kaplan-Meier survival analysis demonstrated that NODM was related to significantly poorer patient survival in comparison with non-NODM patients (p=0.041).

CONCLUSIONS

Both donor and recipient SUMO4 rs237025 polymorphisms contribute to the development of NODM after LT and NODM is a frequent complication that negatively affects patient survival.

Lysine 63 ubiquitination is involved in the progression of tubular damage in diabetic nephropathy

The purpose of our study was to evaluate how hyperglycemia (HG) influences Lys63 protein ubiquitination and its involvement in tubular damage and fibrosis in diabetic nephropathy (DN). Gene and protein expression of UBE2v1, a ubiquitin-conjugating E2-enzyme variant that mediates Lys63-linked ubiquitination, and Lys63-ubiquitinated proteins increased in HK2 tubular cells under HG. Matrix-assisted laser desorption/ionization-time of flight/tandem mass spectrometry identified 30 Lys63-ubiquitinated proteins, mainly involved in cellular organization, such as β-actin, whose Lys63 ubiquitination increased under HG, leading to cytoskeleton disorganization. This effect was reversed by the inhibitor of the Ubc13/UBE2v1 complex NSC697923. Western blot analysis confirmed that UBE2v1 silencing in HK2 under HG, restored Lys63-β-actin ubiquitination levels to the basal condition. Immunohistochemistry on patients with type 2 diabetic (T2D) revealed an increase in UBE2v1- and Lys63-ubiquitinated proteins, particularly in kidneys of patients with DN compared with control kidneys and other nondiabetic renal diseases, such as membranous nephropathy. Increased Lys63 ubiquitination both in vivo in patients with DN and in vitro, correlated with α-SMA expression, whereas UBE2v1 silencing reduced HG-induced α-SMA protein levels, returning them to basal expression. In conclusion, UBE2v1- and Lys63-ubiquitinated proteins increase in vitro under HG, as well as in vivo in T2D, is augmented in patients with DN, and may affect cytoskeleton organization and influence epithelial-to-mesenchymal transition. This process may drive the progression of tubular damage and interstitial fibrosis in patients with DN.-Pontrelli, P., Conserva, F., Papale, M., Oranger, A., Barozzino, M., Vocino, G., Rochetti, M. T., Gigante, M., Castellano, G., Rossini, M., Simone, S., Laviola, L., Giorgino, F., Grandaliano, G., Di Paolo, S., Gesualdo, L. Lysine 63 ubiquitination is involved in the progression of tubular damage in diabetic nephropathy.

Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis

Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis strategy. The biomarker-target interrelated molecules associated with diabetes and its complications were identified via the Comparative Toxicogenomics Database (CTD); the Search Tool for Recurring Instances of Neighboring Genes was utilized for network construction. Functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery software to investigate connections between diabetes and its complications. A total of 142 (including 122 biomarkers, 10 therapeutic targets and 10 overlapping molecules) biomarker-target interrelated molecules associated with diabetes and its complications were identified via the CTD database, and analysis of the network yielded 1,087 biological processes and fifteen Kyoto Encyclopedia of Genes and Genomes pathways with significant P‑values. Various critical aspects of the networks were examined in the present study: a) Intermolecular horizontal and vertical combinations in biomarkers and therapeutic targets associated with diabetes and its complicationb) network topology properties associated with molecular pathological responsec) contribution of key molecules to integrated regulation; and d) crosstalk between multiple pathways. Based on a multi-dimensional analysis, it was concluded that the integrated molecular pathological development of diabetes and its complications does not proceed randomly, which suggests a requirement for integrated, multi-target intervention.

Comparative genomic analysis of novel Acinetobacter symbionts: A combined systems biology and genomics approach

The increasing trend of antibiotic resistance in Acinetobacter drastically limits the range of therapeutic agents required to treat multidrug resistant (MDR) infections. This study focused on analysis of novel Acinetobacter strains using a genomics and systems biology approach. Here we used a network theory method for pathogenic and non-pathogenic Acinetobacter spp. to identify the key regulatory proteins (hubs) in each strain. We identified nine key regulatory proteins, guaA, guaB, rpsB, rpsI, rpsL, rpsE, rpsC, rplM and trmD, which have functional roles as hubs in a hierarchical scale-free fractal protein-protein interaction network. Two key hubs (guaA and guaB) were important for insect-associated strains, and comparative analysis identified guaA as more important than guaB due to its role in effective module regulation. rpsI played a significant role in all the novel strains, while rplM was unique to sheep-associated strains. rpsM, rpsB and rpsI were involved in the regulation of overall network topology across all Acinetobacter strains analyzed in this study. Future analysis will investigate whether these hubs are useful as drug targets for treating Acinetobacter infections.

Understanding Genetic Heterogeneity in Type 2 Diabetes by Delineating Physiological Phenotypes: SIRT1 and its Gene Network in Impaired Insulin Secretion

Type 2 diabetes (T2D) is a chronic metabolic disease which shows an exponential increase in all parts of the world. However, the disease is controllable by early detection and modified lifestyle. A series of factors have been associated with the pathogenesis of diabetes, and genes are considered to play a critical role. The individual risk of developing T2D is determined by an altered genetic background of the en-zymes involved in several metabolism-related biological mechanisms, including glucose homeostasis, insulin metab-olism, the glucose and ion transporters involved in glucose uptake, transcription factors, signaling intermediates of insulin signaling pathways, insulin production and secretion, pancreatic tissue development, and apoptosis. However, many candidate genes have shown heterogeneity of associations with the disease in different populations. A possible approach to resolving this complexity and under-standing genetic heterogeneity is to delineate the physiological phenotypes one by one as studying them in combination may cause discrepancies in association studies. A systems biology approach involving regulatory proteins, transcription factors, and microRNAs is one way to understand and identify key factors in complex diseases such as T2D. Our earlier studies have screened more than 100 single nucleotide polymorphisms (SNPs) belonging to more than 60 globally known T2D candidate genes in the Indian population. We observed that genes invariably involved in the activity of pancreatic β-cells provide susceptibility to type 2 diabetes (T2D). Encouraged by these results, we attempted to delineate in this review one of the commonest physiological phenotypes in T2D, namely impaired insulin secretion, as the cause of hyperglycemia. This review is also intended to explain the genetic basis of the pathophysiology of insulin secretion in the context of variations in the SIRT1 gene, a major switch that modulates insulin secretion, and a set of other genes such as HHEX, PGC-α, TCF7L2, UCP2, and ND3 which were found to be in association with T2D. The review aims to look at the genotypic and transcriptional regulatory relationships with the disease phenotype.

Molecular disease presentation in diabetic nephropathy

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

TGF-β/SMAD Pathway and Its Regulation in Hepatic Fibrosis

Transforming growth factor-beta1 (TGF-β1), a key member in the TGF-β superfamily, plays a critical role in the development of hepatic fibrosis. Its expression is consistently elevated in affected organs, which correlates with increased extracellular matrix deposition. SMAD proteins have been studied extensively as pivotal intracellular effectors of TGF-β1, acting as transcription factors. In the context of hepatic fibrosis, SMAD3 and SMAD4 are pro-fibrotic, whereas SMAD2 and SMAD7 are protective. Deletion of SMAD3 inhibits type I collagen expression and blocks epithelial-myofibroblast transition. In contrast, disruption of SMAD2 upregulates type I collagen expression. SMAD4 plays an essential role in fibrosis disease by enhancing SMAD3 responsive promoter activity, whereas SMAD7 negatively mediates SMAD3-induced fibrogenesis. Accumulating evidence suggests that divergent miRNAs participate in the liver fibrotic process, which partially regulates members of the TGF-β/SMAD signaling pathway. In this review, we focus on the TGF-β/SMAD and other relative signaling pathways, and discussed the role and molecular mechanisms of TGF-β/SMAD in the pathogenesis of hepatic fibrosis. Moreover, we address the possibility of novel therapeutic approaches to hepatic fibrosis by targeting to TGF-β/SMAD signaling.

Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy

In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.

Multi-Target Analysis and Design of Mitochondrial Metabolism

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.

A systematic analysis of human lipocalin family and its expression in esophageal carcinoma

The lipocalin proteins (lipocalins) are a large family of small proteins characterized by low sequence similarity and highly conserved crystal structures. Lipocalins have been found to play important roles in many human diseases. For this reason, a systemic analysis of the molecular properties of human lipocalins is essential. In this study, human lipocalins were found to contain four structurally conserved regions (SCRs) and could be divided into two subgroups. A human lipocalin protein-protein interaction network (PPIN) was constructed and integrated with their expression data in esophageal carcinoma. Many lipocalins showed obvious co-expression patterns in esophageal carcinoma. Their subcellular distributions also suggested these lipocalins may transfer signals from the extracellular space to the nucleus using the pathway-like paths. These analyses also expanded our knowledge about this human ancient protein family in the background of esophageal carcinoma.

L1 cell adhesion molecule expression is associated with pelvic lymph node metastasis and advanced stage in diabetic patients with endometrial cancer: a matched case control study

Background:

Diabetic patients with endometrial cancer had more lymph node metastasis than non-diabetic patients with endometrial cancer. L1 cell adhesion molecule (L1CAM) could be possibly associated with lymph node metastasis in diabetic patients with endometrial cancer via epithelial-mesenchymal transition. We aimed to investigate the association between L1CAM expression and lymph node metastasis in diabetic patients with endometrial cancer.

Methods:

We conducted a matched case control study of 68 endometrial cancer patients who comprise each 34 diabetic and non-diabetic patients. L1CAM expression was evaluated by immunohistochemistry using fresh formalin-fixed paraffin-embedded tissue block of the patients. The association between L1CAM expression and pelvic lymph node metastasis was assessed according to the presence of diabetes.

Results:

Of the 68 patients, 13 (19.1%) were positive for L1CAM immunostaining. Positive rate of L1CAM expression in diabetic endometrial cancer patients was similar to that in non-diabetic endometrial cancer patients (14.7% vs. 23.5%, P = 0.355). Tumor recurred more frequently in patients with positive L1CAM expression than those with negative L1CAM expression (33.3% vs. 1.6%, P = 0.019). However, we failed to find any significant association between L1CAM expression and lymph node metastasis. Only for the diabetic patients (n = 34), patients with pelvic lymph node metastasis had more L1CAM expression than those without lymph node metastasis (50.0% vs. 3.6%, P = 0.035). Advanced stage was the only risk factor for recurrence that showed a significant association with L1CAM expression for the diabetic endometrial cancer patients (P = 0.006), as well as all the enrolled patients (P = 0.014).

Conclusion:

L1CAM expression is associated with pelvic lymph node metastasis and advanced stage in diabetic patients with endometrial cancer.

Apoptosis regulatory protein-protein interaction demonstrates hierarchical scale-free fractal network

Dysregulation or inhibition of apoptosis favors cancer and many other diseases. Understanding of the network interaction of the genes involved in apoptotic pathway, therefore, is essential, to look for targets of therapeutic intervention. Here we used the network theory methods, using experimentally validated 25 apoptosis regulatory proteins and identified important genes for apoptosis regulation, which demonstrated a hierarchical scale-free fractal protein-protein interaction network. TP53, BRCA1, UBIQ and CASP3 were recognized as a four key regulators. BRCA1 and UBIQ were also individually found to control highly clustered modules and play an important role in the stability of the overall network. The connection among the BRCA1, UBIQ and TP53 proteins was found to be important for regulation, which controlled their own respective communities and the overall network topology. The feedback loop regulation motif was identified among NPM1, BRCA1 and TP53, and these crucial motif topologies were also reflected in high frequency. The propagation of the perturbed signal from hubs was found to be active upto some distance, after which propagation started decreasing and TP53 was the most efficient signal propagator. From the functional enrichment analysis, most of the apoptosis regulatory genes associated with cardiovascular diseases and highly expressed in brain tissues were identified. Apart from TP53, BRCA1 was observed to regulate apoptosis by influencing motif, propagation of signals and module regulation, reflecting their biological significance. In future, biochemical investigation of the observed hub-interacting partners could provide further understanding about their role in the pathophysiology of cancer.

Discovering study-specific gene regulatory networks

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.

Human proteins with target sites of multiple post-translational modification types are more prone to be involved in disease

Many proteins can be modified by multiple types of post-translational modifications (Mtp-proteins). Although some post-translational modifications (PTMs) have recently been found to be associated with life-threatening diseases like cancers and neurodegenerative disorders, the underlying mechanisms remain enigmatic to date. In this study, we examined the relationship of human Mtp-proteins and disease and systematically characterized features of these proteins. Our results indicated that Mtp-proteins are significantly more inclined to participate in disease than proteins carrying no known PTM sites. Mtp-proteins were found significantly enriched in protein complexes, having more protein partners and preferred to act as hubs/superhubs in protein-protein interaction (PPI) networks. They possess a distinct functional focus, such as chromatin assembly or disassembly, and reside in biased, multiple subcellular localizations. Moreover, most Mtp-proteins harbor more intrinsically disordered regions than the others. Mtp-proteins carrying PTM types biased toward locating in the ordered regions were mainly related to protein-DNA complex assembly. Examination of the energetic effects of PTMs on the stability of PPI revealed that only a small fraction of single PTM events influence the binding energy of >2 kcal/mol, whereas the binding energy can change dramatically by combinations of multiple PTM types. Our work not only expands the understanding of Mtp-proteins but also discloses the potential ability of Mtp-proteins to act as key elements in disease development.

A design automation framework for computational bioenergetics in biological networks

The bioenergetic activity of mitochondria can be thoroughly investigated by using computational methods. In particular, in our work we focus on ATP and NADH, namely the metabolites representing the production of energy in the cell. We develop a computational framework to perform an exhaustive investigation at the level of species, reactions, genes and metabolic pathways. The framework integrates several methods implementing the state-of-the-art algorithms for many-objective optimization, sensitivity, and identifiability analysis applied to biological systems. We use this computational framework to analyze three case studies related to the human mitochondria and the algal metabolism of Chlamydomonas reinhardtii, formally described with algebraic differential equations or flux balance analysis. Integrating the results of our framework applied to interacting organelles would provide a general-purpose method for assessing the production of energy in a biological network.

Node interference and robustness: performing virtual knock-out experiments on biological networks: the case of leukocyte integrin activation network

The increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node and as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis.

Familial young-onset diabetes, pre-diabetes and cardiovascular disease are associated with genetic variants of DACH1 in Chinese

In Asia, young-onset type 2 diabetes (YOD) is characterized by obesity and increased risk for cardiovascular disease (CVD). In a genome-wide association study (GWAS) of 99 Chinese obese subjects with familial YOD diagnosed before 40-year-old and 101 controls, the T allele of rs1408888 in intron 1 of DACH1(Dachshund homolog 1) was associated with an odds ratio (OR) of 2.49(95% confidence intervals:1.57-3.96, P = 8.4 × 10(-5)). Amongst these subjects, we found reduced expression of DACH1 in peripheral blood mononuclear cells (PBMC) from 63 cases compared to 65 controls (P = 0.02). In a random cohort of 1468 cases and 1485 controls, amongst top 19 SNPs from GWAS, rs1408888 was associated with type 2 diabetes with a global P value of 0.0176 and confirmation in a multiethnic Asian case-control cohort (7370/7802) with an OR of 1.07(1.02-1.12, P(meta)  = 0.012). In 599 Chinese non-diabetic subjects, rs1408888 was linearly associated with systolic blood pressure and insulin resistance. In a case-control cohort (n = 953/953), rs1408888 was associated with an OR of 1.54(1.07-2.22, P = 0.019) for CVD in type 2 diabetes. In an autopsy series of 173 non-diabetic cases, TT genotype of rs1408888 was associated with an OR of 3.31(1.19-9.19, P = 0.0214) and 3.27(1.25-11.07, P = 0.0184) for coronary heart disease (CHD) and coronary arteriosclerosis. Bioinformatics analysis revealed that rs1408888 lies within regulatory elements of DACH1 implicated in islet development and insulin secretion. The T allele of rs1408888 of DACH1 was associated with YOD, prediabetes and CVD in Chinese.

A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.

Complex disease interventions from a network model for type 2 diabetes

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network are informative to reveal significant metabolic processes and disease's associations with other complex disorders. In the current study, Type 2 diabetes associated functional linkage network (T2DFN) containing 2770 proteins and 15041 linkages was constructed. The functional modules in this network were scored and evaluated in terms of shared pathways, co-localization, co-expression and associations with similar diseases. The assembly of top scoring overlapping members in the functional modules revealed that, along with the well known biological pathways, circadian rhythm, diverse actions of nuclear receptors in steroid and retinoic acid metabolisms have significant occurrence in the pathophysiology of the disease. The disease's association with other metabolic and neuromuscular disorders was established through shared proteins. Nuclear receptor NRIP1 has a pivotal role in lipid and carbohydrate metabolism, indicating the need to investigate subsequent effects of NRIP1 on Type 2 diabetes. Our study also revealed that CREB binding protein (CREBBP) and cardiotrophin-1 (CTF1) have suggestive roles in linking Type 2 diabetes and neuromuscular diseases.

Regulation of lipid and glucose homeostasis by mango tree leaf extract is mediated by AMPK and PI3K/AKT signaling pathways

Ethanolic extract of Mangifera indica (mango) dose-dependently decreased serum glucose and triglyceride in KK-A(y) mice. Our in vitro and in vivo investigations revealed that the effect of mango leave extract (ME) on glucose and lipid homeostasis is mediated, at least in part, through the PI3K/AKT and AMPK signaling pathway. ME up-regulated the expression of PI3K, AKT and GYS genes by 2.0-fold, 3.2-fold, and 2.7-fold, respectively, leading to a decrease in glucose level. On the other hand, ME up-regulated AMPK and altered lipid metabolism. ME also down-regulated ACC (2.8-fold), HSL (1.6-fold), FAS (1.8-fold) and PPAR-γ (4.0-fold). Finally, we determined that active metabolites of benzophenone C-glucosides, Iriflophenone 3-C-β-glucoside and Foliamangiferoside A from ME, may play a dominant role in this integrated regulation of sugar and lipid homeostasis.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases

The metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.

Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation.

Network biology approach for identifying key regulatory genes by expression based study of breast cancer

The use of high-throughput array technology is omnipresent in diverse areas specifically, early diagnosis of disease, discovery of infectious agents, search for biological markers and screening of potential drug candidates. Here, we integrated gene expression data with the network-based approach to identify novel genes that were playing central role in the network through interconnecting to a number of differentially expressed breast cancer genes. The 62 cancerous genes retrieved from the Breast Cancer Gene Database (BCGD) were mapped in the normalized data accessed from Stanford Microarray Database (SMD) to analyze their pattern. Interaction networks for each gene were constructed to understand the biology of the metastasis at systems level. The individual networks were fused together for the detection of interacting hubs, 38 novel genes were found to be deeply intermingled with the central hub node. Gene Ontology studies were made to depict the biology of the hub nodes not alone through gene ranking but by applying the Hyper geometric test with the Benjamini Hochberg False Discovery Rate (FDR) correction method at a significance level of 0.05. Analyzing p-values from the statistical test indicated that most of the novel genes were involved in the same biological function as the disordered genes like signal transducer, transcription regulator, enzyme binding, molecular transducer and receptor signaling protein activity and same pathway as MAPK signaling, Apoptosis, Wnt Signaling, ErbB signaling and Cell Cycle. Lastly, we identified 3 novel genes CHUK, INSR and CREBBP showing high connections with the 12 novel genes reported in literatures as well with the perturbed genes. As a result, these genes can be considered as significant finding in revealing the basis and pathways responsible for breast cancer.

The emerging paradigm of network medicine in the study of human disease

The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.