miR-369-3p ameliorates diabetes-associated atherosclerosis by regulating macrophage succinate-GPR91 signaling.

AIMS: Diabetes leads to dysregulated macrophage immunometabolism, contributing to accelerated atherosclerosis progression. Identifying critical factors to restore metabolic alterations and promote resolution of inflammation remains an unmet goal. MicroRNAs (miRs) orchestrate multiple signaling events in macrophages, yet their therapeutic potential in diabetes-associated atherosclerosis remains unclear. METHODS AND RESULTS: MiRNA profiling revealed significantly lower miR-369-3p expression in aortic intimal lesions from Ldlr-/- mice on a high-fat sucrose containing (HFSC) diet for 12 weeks. miR-369-3p was also reduced in peripheral blood mononuclear cells (PBMCs) from diabetic patients with coronary artery disease (CAD). Cell-type expression profiling showed miR-369-3p enrichment in aortic macrophages. In vitro, oxLDL treatment reduced miR-369-3p expression in mouse bone marrow-derived macrophages (BMDMs). Metabolic profiling in BMDMs revealed that miR-369-3p overexpression blocked the oxLDL-mediated increase in the cellular metabolite succinate and reduced mitochondrial respiration (OXPHOS) and inflammation (lL-1ß, TNF-a, IL-6). Mechanistically, miR-369-3p targeted the succinate receptor (GPR91) and alleviated the oxLDL-induced activation of inflammasome signaling pathways. Therapeutic administration of miR-369-3p mimics in HFSC-fed Ldlr-/- mice reduced GPR91 expression in lesional macrophages and diabetes-accelerated atherosclerosis, evident by a decrease in plaque size and pro-inflammatory Ly6Chi monocytes. RNA-seq analyses showed more pro-resolving pathways in plaque macrophages from miR-369-3p treated mice, consistent with an increase in macrophage efferocytosis in lesions. Finally, a GPR91 antagonist attenuated oxLDL-induced inflammation in primary monocytes from human subjects with diabetes. CONCLUSION: These findings establish a therapeutic role for miR-369-3p in halting diabetes-associated atherosclerosis by regulating GPR91 and macrophage succinate metabolism.

Deficiency of lncRNA MERRICAL abrogates macrophage chemotaxis and diabetes-associated atherosclerosis.

Diabetes-associated atherosclerosis involves excessive immune cell recruitment and plaque formation. However, the mechanisms remain poorly understood. Transcriptomic analysis of the aortic intima in Ldlr-/- mice on a high-fat, high-sucrose-containing (HFSC) diet identifies a macrophage-enriched nuclear long noncoding RNA (lncRNA), MERRICAL (macrophage-enriched lncRNA regulates inflammation, chemotaxis, and atherosclerosis). MERRICAL expression increases by 249% in intimal lesions during progression. lncRNA-mRNA pair genomic mapping reveals that MERRICAL positively correlates with the chemokines Ccl3 and Ccl4. MERRICAL-deficient macrophages exhibit lower Ccl3 and Ccl4 expression, chemotaxis, and inflammatory responses. Mechanistically, MERRICAL guides the WDR5-MLL1 complex to activate CCL3 and CCL4 transcription via H3K4me3 modification. MERRICAL deficiency in HFSC diet-fed Ldlr-/- mice reduces lesion formation by 74% in the aortic sinus and 86% in the descending aorta by inhibiting leukocyte recruitment into the aortic wall and pro-inflammatory responses. These findings unveil a regulatory mechanism whereby a macrophage-enriched lncRNA potently inhibits chemotactic responses, alleviating lesion progression in diabetes.

Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance.

Despite numerous reports on the altered sphingolipids metabolism in human cancers, their clinical significance in breast cancer remains obscure. Previously, we identified the high levels of sphingolipids, ceramide phosphates and sphingosine phosphates, and the genes involved in their synthesis, CERK and SPHK1, in breast cancer patients. The present study aimed to determine the correlations of CERK and SPHK1 with clinical outcomes as well as metastasis and drug resistance markers. Both local and TCGA cohorts were analysed. High-confidence regulatory interaction network was constructed to find association of target genes with metastasis and drug resistance. Furthermore, correlations of CERK and SPHK1 with selected metastasis and drug resistance markers were validated in both cohorts. Overexpression of CERK and SPHK1 was associated with nodal metastasis, late tumor stage and high proliferation potency. In addition, increased CERK expression was also indicative of poor patient survival. Computational network analysis revealed the association of CERK and SPHK1 with known metastasis markers MMP-2 and MMP-9 and drug resistance markers ABCC1 and ABCG2. Correlation analysis confirmed the associations of target genes with these markers in both local as well as TCGA cohort. The above findings suggest clinical utility of CERK and SPHK1 as potential biomarkers in breast cancer patients and thus could provide novel leads in the development of therapeutics.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics.

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging.

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Analysis of aging-related protein interactome and cross-network module comparisons across tissues provide new insights into aging.

Delaying the human aging process and thus eliminating the risk factors for age-related diseases is one of the prime objectives. While various aging-associated genes and proteins have been characterized, which provide a significant understanding of the human aging process, a significant success in regulating aging is not achieved yet. Understanding how aging proteins interact with each other and also with other proteins could provide important insights into the underlying mechanisms governing the aging process. Therefore, in this work, information of gene expression was included to the static aging-related protein interactome to understand the network-based relationships among aging-related essential (AE) proteins, aging-related non-essential (ANE) proteins, and housekeeping-proteins that could regulate or influence aging. Comprehensive analyses provided various systems-level insights into the regulatory characteristics of aging; for example, (i) network-based correlation analysis predicted functional relationships among AE proteins and ANE proteins; (ii) network variability analysis predicted aging to affect different tissues in strikingly different ways by differentially regulating various regulatory interactions; (iii) cross-network comparisons identified two aging-related modules to be significantly conserved across most of the tissues. Overall, the findings obtained during this study could be helpful for researchers to delay, prevent, or even reverse various aspects of the aging.

Abundance and Diversity of Phages, Microbial Taxa, and Antibiotic Resistance Genes in the Sediments of the River Ganges Through Metagenomic Approach.

In this study, we have analyzed the metagenomic DNA from the pooled sediment sample of the river Ganges to explore the abundance and diversity of phages, microbial community, and antibiotic resistance genes (ARGs). Utilizing data from Illumina platform, 4,174 (∼0.0013%) reads were classified for the 285 different DNA viruses largely dominated by the group of 260 distinctive phages (3,602 reads, ∼86.3%). Among all, Microcystis (782 hits), Haemophilus (403), Synechococcus (386), Pseudomonas (279), Enterococcus (232), Bacillus (196), Rhodococcus (166), Caulobacter (163), Salmonella (146), Enterobacteria (143), Mycobacterium and (128) phages show the highest abundance and account for ∼90% of the total identified phages. In addition, we have also identified corresponding host pertaining to these phages. Mainly, Proteobacteria (∼69.3%) dominates the microbial population structure. Primarily, orders such as Caulobacterales (∼28%), Burkholderiales (∼13.9%), Actinomycetales (∼13.7%), and Pseudomonadales (∼7.5%) signify the core section. Furthermore, 21,869 (∼0.00695%) reads were classified in 20 ARG types (classes) and 240 ARGs (subtypes), among which 4 ARG types, namely multidrug resistance (12,041 reads, ∼55%), bacitracin (3,202 reads, ∼15%), macrolide-lincosamide-streptogramin (1,744 reads, ∼7.98%), and fosmidomycin (990 reads, ∼4.53%), have the highest abundance. Simultaneously, six resistance mechanisms were also recognized with the dominance of antibiotic efflux (72.8%, 15,919 reads). The results unveil the distribution of (pro)-phages; microbial community; and various ARGs in the Ganges river sediments.

Expression of miR-18a-5p, miR-144-3p, and miR-663b in colorectal cancer and their association with cholesterol homeostasis.

OBJECTIVE: Though cholesterol accumulation is an established hallmark of a tumor cell, the relationship between the two is still not clear. Previously, we identified 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), Sterol Regulatory Element BindingTranscription Factor 2 (SREBF2), Nuclear Receptor Subfamily 1 Group H Member 3 (NR1H3), and Nuclear Receptor Subfamily 1 Group H Member 2 (NR1H2) as the key cholesterol homeostasis genes involved in colorectal cancer (CRC). In the present study, we aimed to identify microRNAs regulating these key genes in CRC. METHODS: miR-18a-5p, miR-144-3p, and miR-663b were selected as the miRNAs targeting NR1H2, HMGCR, and SREBF2, respectively, based on the bioinformatic prediction tools and literature review. Their expression was evaluated in the local and The Cancer Genome Atlas (TCGA) cohorts. Receiver Operating Characteristic Curves and Kaplan Meier analysis were performed to elucidate their diagnostic and prognostic potential. Pearson or Spearman's correlations were used to evaluate the relationship between miRNAs and their target genes. Protein-protein interaction networks and Gene Ontology analyses were performed to investigate the potential molecular mechanism of these miRNAs. RESULTS: Deregulated expression of miR-18a-5p, miR-144-3p, and miR-663b was associated with various clinicopathological features. miR-18a-5p exhibited an inverse correlation with NR1H2. miR-18a-5p and miR-144-3p also had a significant direct correlation with miR-33a-5p, an important modulator of cholesterol homeostasis. These miRNAs also exhibited high centrality in the mirna-protein interaction network. miR-144-3p and miR-663b exhibited the potential to be used as diagnostic biomarkers. CONCLUSIONS: miR-18a-5p and miR-144-3p exhibited the potential to modulate cholesterol homeostasis in CRC. miR-663b is an interesting candidate in CRC pathophysiology.

Design, synthesis, molecular docking, anti-quorum sensing, and anti-biofilm activity of pyochelin-zingerone conjugate.

In this article, we report the chemical synthesis of pyochelin-zingerone conjugate via a hydrolysable ester linkage for drug delivery as a "Trojan Horse Strategy." It is a new therapeutic approach to combat microbial infection and to address the issue of multi drug resistance in Gram-negative, nosocomial pathogen Pseudomonas aeruginosa. Pyochelin (Pch) is a catecholate type of phenolate siderophore produced and utilized by the pathogen P. aeruginosa to assimilate iron when colonizing the vertebrate host. Zingerone, is active component present in ginger, a dietary herb known for its anti-virulent approach against P. aeruginosa. In the present study, zingerone was exploited to act as a good substitute for existing antibiotics, known to have developed resistance by most pathogens. Encouraging results were obtained by docking analysis of pyochelin-zingerone conjugate with FptA, the outer membrane receptor of pyochelin. Conjugate also showed anti-quorum sensing activity and also inhibited swimming, swarming, and twitching motilities as well as biofilm formation in vitro.

Functional characterization of two type-1 diacylglycerol acyltransferase (DGAT1) genes from rice (Oryza sativa) embryo restoring the triacylglycerol accumulation in yeast.

KEY MESSAGE: Two OsDGAT1 genes showed the ability to restore TAG and LB synthesis in yeast H1246. Alterations in the N-terminal region of OsDGAT1-1 gene revealed its regulatory role in gene function. Accumulation of triacylglycerol (TAG) or oil in vegetative tissues has emerged as a promising approach to meet the global needs of food, feed, and fuel. Rice (Oryza sativa) has been recognized as an important cereal crop containing nutritional rice bran oil with high economic value for renewable energy production. To identify the key component involved in storage lipid biosynthesis, two type-1 diacylglycerol acyltransferases (DGAT1) from rice were characterized for its in vivo function in the H1246 (dga1, lro1, are1 and are2) yeast quadruple mutant. The ectopic expression of rice DGAT1 (designated as OsDGAT1-1 and OsDGAT1-2) genes restored the capability of TAG synthesis and lipid body (LB) formation in H1246. OsDGAT1-1 showed nearly equal substrate preferences to C16:0-CoA and 18:1-CoA whereas OsDGAT1-2 displayed substrate selectivity for C16:0-CoA over 18:1-CoA, indicating that these enzymes have contrasting substrate specificities. In parallel, we have identified the intrinsically disordered region (IDR) at the N-terminal domains of OsDGAT1 proteins. The regulatory role of the N-terminal domain was dissected. Single point mutations at the phosphorylation sites and truncations of the N-terminal region highlighted reduced lipid accumulation capabilities among different OsDGAT1-1 variants.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge.

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

LC-HRMS based approach to identify novel sphingolipid biomarkers in breast cancer patients.

Perturbations in lipid metabolic pathways to meet the bioenergetic and biosynthetic requirements is a principal characteristic of cancer cells. Sphingolipids (SPLs) are the largest class of bioactive lipids associated to various aspects of tumorigenesis and have been extensively studied in cancer cell lines and experimental models. The clinical relevance of SPLs in human malignancies however is still poorly understood and needs further investigation. In the present study, we adopted a UHPLC-High resolution (orbitrap) Mass spectrometry (HRMS) approach to identify various sphingolipid species in breast cancer patients. A total of 49 SPLs falling into 6 subcategories have been identified. Further, integrating the multivariate analysis with metabolomics enabled us to identify an elevation in the levels of ceramide phosphates and sphingosine phosphates in tumor tissues as compared to adjacent normal tissues. The expression of genes involved in the synthesis of reported metabolites was also determined in local as well as TCGA cohort. A significant upregulation in the expression of CERK and SPHK1 was observed in tumor tissues in local and TCGA cohort. Sphingomyelin levels were found to be high in adjacent normal tissues. Consistent with the above findings, expression of SGMS1 in tumor tissues was downregulated in TCGA cohort only. Clinical correlations of the selected metabolites and their performance as biomarkers was also evaluated. Significant ROC and positive correlation with Ki67 index highlight the diagnostic potential and clinical relevance of ceramide phosphates in breast cancer.

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network.

Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads - Nilotinib, Deslanoside and Acetyldigitoxin - on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.Communicated by Ramaswamy H. Sarma.

Comparative phylogenetic analysis and transcriptional profiling of MADS-box gene family identified DAM and FLC-like genes in apple (Malusx domestica).

The MADS-box transcription factors play essential roles in various processes of plant growth and development. In the present study, phylogenetic analysis of 142 apple MADS-box proteins with that of other dicotyledonous species identified six putative Dormancy-Associated MADS-box (DAM) and four putative Flowering Locus C-like (FLC-like) proteins. In order to study the expression of apple MADS-box genes, RNA-seq analysis of 3 apical and 5 spur bud stages during dormancy, 6 flower stages and 7 fruit development stages was performed. The dramatic reduction in expression of two MdDAMs, MdMADS063 and MdMADS125 and two MdFLC-like genes, MdMADS135 and MdMADS136 during dormancy release suggests their role as flowering-repressors in apple. Apple orthologs of Arabidopsis genes, FLOWERING LOCUS T, FRIGIDA, SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 and LEAFY exhibit similar expression patterns as reported in Arabidopsis, suggesting functional conservation in floral signal integration and meristem determination pathways. Gene ontology enrichment analysis of predicted targets of DAM revealed their involvement in regulation of reproductive processes and meristematic activities, indicating functional conservation of SVP orthologs (DAM) in apple. This study provides valuable insights into the functions of MADS-box proteins during apple phenology, which may help in devising strategies to improve important traits in apple.

Amino acids flanking the central core of Cu,Zn superoxide dismutase are important in retaining enzyme activity after autoclaving.

Enzymes are known to be denatured upon boiling, although Cu,Zn superoxide dismutase of Potentilla atrosanguinea (Pot-SOD) retains significant catalytic activity even after autoclaving (heating at 121 °C at a pressure of 1.1 kg per square cm for 20 min). The polypeptide backbone of Pot-SOD consists of 152 amino acids with a central core spanning His45 to Cys145 that is involved in coordination of Cu(2+) and Zn(2+) ions. While the central core is essential for imparting catalytic activity and structural stability to the enzyme, the role of sequences flanking the central core was not understood. Experiments with deletion mutants showed that the amino acid sequences flanking the central core were important in retaining activity of Pot-SOD after autoclaving. Molecular dynamics simulations demonstrated the unfavorable structure of mutants due to increased size of binding pocket and enhanced negative charge on the electrostatic surface, resulting in unavailability of the substrate superoxide radical ([Formula: see text]) to the catalytic pocket. Deletion caused destabilization of structural elements and reduced solvent accessibility that further produced unfavorable structural geometry of the protein.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina.

BACKGROUND: Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as a basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Rauvolfia serpentina is an important medicinal plant, endemic to Himalayan mountain ranges of Indian subcontinent, reported to be of immense therapeutic value against various diseases. DESCRIPTION: We present SerpentinaDB, a structured compilation of 147 R. serpentina PDMs, inclusive of their plant part source, chemical classification, IUPAC, SMILES, physicochemical properties, and 3D chemical structures with associated references. It also provides refined search option for identification of analogs of natural molecules against ZINC database at user-defined cut-off. CONCLUSION: SerpentinaDB is an exhaustive resource of R. serpentina molecules facilitating prospection for therapeutic molecules from a medicinally important source of natural products. It also provides refined search option to explore the neighborhood of chemical space against ZINC database to identify analogs of natural molecules obtained as leads. In a previous study, we have demonstrated the utility of this resource by identifying novel aldose reductase inhibitors towards intervention of complications of diabetes.

Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful. METHODS: The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset. RESULTS: The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy. CONCLUSIONS: The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

Identification of key nodes of type 2 diabetes mellitus protein interactome and study of their interactions with phloridzin.

Network biology-inspired approaches could be used effectively in probing regulatory processes by which small molecules intervene with disease mechanisms. The present study aims at identification of key targets of type 2 diabetes mellitus (T2DM) by network analysis of the underlying protein interactome, and probing for mechanisms by which phloridzin could be critical at altering the disease phenotype. Towards this goal, we constructed a protein-protein interaction network associated with T2DM, starting from candidate genes and systems-level interactions data available. The relevance of the network constructed was verified with the help of gene ontology, node deletion, and biological essentiality studies. Using a network analysis method, MAPK1, EP300, and SMAD2 were identified as the most central proteins of potential therapeutic value. Phloridzin, a known antidiabetic agent, potentially interacts with proteins central to T2DM mechanisms. The structural understanding of interaction of phloridzin with these proteins of relevance to T2DM could provide better insight into its regulatory mechanisms and help in developing better therapeutic agents. The molecular docking results suggest that phloridzin is potentially involved in making critical interactions with MAPK1. These results could further be validated by experimental studies and could be used to design therapeutic agents for T2DM intervention.