Functional Gene Modules Research Articles

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.

The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. Supplementary data are available at Bioinformatics online.

Abstract P2072: Integrative Network Analysis Of Microbiome-Immune Axis In Metabolic Syndrome Patients During A Fasting Intervention

Fasting can prolong survival and reduce disease burden in rodent models, and possibly in humans. The relationship between diet, gut microbiota, immune system and host (patho)physiology has only recently been explored, and information is lacking on how periodic fasting affects the gut microbiome in patients with metabolic syndrome (MetS). We show a 5-day fast (FAST) in humans, followed by a modified DASH diet is more effective than DASH alone (DASH) at reducing systolic blood pressure (SBP change measured by ABPM, 95% CI; FAST: [-7.053,-1.142], DASH: [-5.880,1.477]), need for antihypertensive medication (FAST: n=15 of n=35, DASH: n=6 of n=36 patients), and body-mass index at three months post intervention. Fasting altered the gut microbiome, impacting bacterial taxa and functional gene modules associated with the production of short-chain fatty acids (e.g. Faecalibacterium prausnitzii , Eubacterium rectale, Coprococcus comes ), previously linked to vascular health and immunity. Immunophenotyping and cross-system analyses revealed that SBP changes correlated with circulating Il-2 + TNFα + mucosa-associated invariant T (MAIT) cells (FDR-corr P(q) =0.044, Spearman’s rho=0.44), Il-17 - IFNγ + MAITs (FDR-corr P(q) =0.022, Spearman’s rho=0.49), and effector CD4 + T cells (FDR-corr P(q)=0.047, Spearman’s rho=0.43). By stratifying the fasting group into BP responders and non-responders, we identified a set of 76 microbial and 99 immune responder-specific features. Machine learning algorithms could predict long-term SBP responsiveness from baseline immunome data, identifying changes in effector CD8 + T cells, Th17 cells and Tregs as discriminators (Single-subject prediction: 71%). This is the first high-resolution multi-omics characterization of fasting in MetS. Fasting induced long-term reduction in body weight and SBP, accompanied by changes in microbiome and immune homeostasis. Our data implicate fasting as a promising non-pharmacological intervention in MetS.

Measuring similarity between gene interaction profiles

BackgroundGene and protein interaction data are often represented as interaction networks, where nodes stand for genes or gene products and each edge stands for a relationship between a pair of gene nodes. Commonly, that relationship within a pair is specified by high similarity between profiles (vectors) of experimentally defined interactions of each of the two genes with all other genes in the genome; only gene pairs that interact with similar sets of genes are linked by an edge in the network. The tight groups of genes/gene products that work together in a cell can be discovered by the analysis of those complex networks.ResultsWe show that the choice of the similarity measure between pairs of gene vectors impacts the properties of networks and of gene modules detected within them. We re-analyzed well-studied data on yeast genetic interactions, constructed four genetic networks using four different similarity measures, and detected gene modules in each network using the same algorithm. The four networks induced different numbers of putative functional gene modules, and each similarity measure induced some unique modules. In an example of a putative functional connection suggested by comparing genetic interaction vectors, we predict a link between SUN-domain proteins and protein glycosylation in the endoplasmic reticulum.ConclusionsThe discovery of molecular modules in genetic networks is sensitive to the way of measuring similarity between profiles of gene interactions in a cell. In the absence of a formal way to choose the “best” measure, it is advisable to explore the measures with different mathematical properties, which may identify different sets of connections between genes.

MicroRNA‑198 suppresses prostate tumorigenesis by targeting MIB1

MicroRNAs are small non-coding RNA molecules which act as modulators of gene function, and have been identified as playing important roles in cancer as both tumor suppressors and oncogenes. The present study aimed to examine the role of miR-198 in prostate cancer aggression by analyzing how it influences several hallmarks of cancer. Abundance of miR-198 in prostate cancer and association with clinical characteristics was analyzed using a CPC-Gene prostate cancer dataset. Overexpression of miR-198 was performed using transient transfection of miR-198 mimic prior to assaying proliferation, cell cycle, and colony formation in LNCaP and DU145 cell lines using standard protocols. In vivo tumor formation in athymic nude mice was examined using LNCaP xenografts with stable overexpression conferred using lentiviral miR-198 transduction. Protein and mRNA abundance of MIB1 was determined using western blotting and RT-qPCR respectively, while miR-198 binding to MIB1 was validated using a luciferase reporter assay. miR-198 abundance was lower in high Gleason grade prostate cancer relative to intermediate and low-grade cancer. Overexpression of miR-198 diminished proliferation of prostate cancer cell lines, increased G0/G1 cell cycle arrest, and significantly impaired colony formation. Elevated miR-198 abundance was also demonstrated to impair tumor formation in vivo using LNCaP xenografts. Mindbomb E3 ubiquitin protein ligase 1 (MIB1) was demonstrated to be directly targeted by miR-198, and knockdown of MIB1 recapitulated the effects of miR-198 on proliferation and colony formation. The present evidence supports miR-198 as an important tumor suppressor in prostate cancer, and demonstrates for the first time that it acts by targeting MIB1. The present study reinforces the importance and complexity of miRNA in regulating prostate cancer aggression.

MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome.

Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary data are available at Bioinformatics online.

MicroRNAs contribute to ATP-binding cassette transporter- and autophagy-mediated chemoresistance in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) has an elevated mortality rate, largely because of high recurrence and metastasis. Additionally, the main obstacle during treatment of HCC is that patients usually develop resistance to chemotherapy. Cancer drug resistance involves many different mechanisms, including alterations in drug metabolism and processing, impairment of the apoptotic machine, activation of cell survival signaling, decreased drug sensitivity and autophagy, among others. Nowadays, miRNAs are emerging as master regulators of normal physiology- and tumor-related gene expression. In HCC, aberrant expression of many miRNAs leads to chemoresistance. Herein, we particularly analyzed miRNA impact on HCC resistance to drug therapy. Certain miRNAs target ABC (ATP-binding cassette) transporter genes. As most of these miRNAs are downregulated in HCC, transporter levels increase and intracellular drug accumulation decrease, turning cells less sensitive to death. Others miRNAs target autophagy-related gene expression, inhibiting autophagy and acting as tumor suppressors. Nevertheless, due to its downregulation in HCC, these miRNAs do not inhibit autophagy or tumor growth and, resistance is favored. Concluding, modulation of ABC transporter and/or autophagy-related gene expression or function by miRNAs could be determinant for HCC cell survival under chemotherapeutic drug treatment. Undoubtedly, more insights on the biological processes, signaling pathways and/or molecular mechanisms regulated by miRNAs are needed. Anyway, miRNA-based therapy together with conventional chemotherapeutic drugs has a great future in cancer therapy.

A Case of Identity: HOX Genes in Normal and Cancer Stem Cells.

Stem cells are undifferentiated cells that have the unique ability to self-renew and differentiate into many different cell types. Their function is controlled by core gene networks whose misregulation can result in aberrant stem cell function and defects of regeneration or neoplasia. HOX genes are master regulators of cell identity and cell fate during embryonic development. They play a crucial role in embryonic stem cell differentiation into specific lineages and their expression is maintained in adult stem cells along differentiation hierarchies. Aberrant HOX gene expression is found in several cancers where they can function as either oncogenes by sustaining cell proliferation or tumor-suppressor genes by controlling cell differentiation. Emerging evidence shows that abnormal expression of HOX genes is involved in the transformation of adult stem cells into cancer stem cells. Cancer stem cells have been identified in most malignancies and proved to be responsible for cancer initiation, recurrence, and metastasis. In this review, we consider the role of HOX genes in normal and cancer stem cells and discuss how the modulation of HOX gene function could lead to the development of novel therapeutic strategies that target cancer stem cells to halt tumor initiation, progression, and resistance to treatment.

Sox2 regulates palate development by promoting basal cell differentiation

With a frequency of 1:500–2,500, cleft lip with or without cleft palate (CL/P) is the most common congenital craniofacial defect. In mice and humans, a complex gene regulatory network (GRN) controls the different processes required for palatogenesis. Understanding how transcription factors act as the modulators of gene expression and function in palatogenesis will lead to better genetic counseling and genetic therapies for CL/P. Our group has shown that conditionally inactivating Sox2, one of the earliest transcription factors required during embryogenesis, results in clefting of the palate and fusion of the tongue to the mandible (ankylglossia).ObjectiveTo examine the role of Sox2, a stem cell factor, in palatogenesis.MethodsUsing Pitx2cre, Sox2 was conditionally inactivated in the oral epithelial tissue of developing murine embryos. Palate development was studied during the stages of embryogenesis.ResultsThe conditional ablation of Sox2 was sufficient to generate clefting in mice. Further examination revealed that Sox2 is expressed in the P63+ basal cell population of the oral epithelium as well as the periderm, which acts to prevent oral adhesions during development. Inactivating Sox2 resulted in defective periderm formation. The loss of an intact periderm resulted in intraoral adhesions that prevented complete palate shelf extension and ankylglossia.ConclusionsSox2 is required for periderm differentiation. Inactivating Sox2 results in cleft palate and ankylglossia.Support or Funding InformationUniversity of Iowa College of DentistryNIH, NIDCRThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Accelerated genome engineering of Pseudomonas putida by I-SceI-mediated recombination and CRISPR-Cas9 counterselection.

Summary Pseudomonas species have become reliable platforms for bioproduction due to their capability to tolerate harsh conditions imposed by large‐scale bioprocesses and their remarkable resistance to diverse physicochemical stresses. The last few years have brought forth a variety of synthetic biology tools for the genetic manipulation of pseudomonads, but most of them are either applicable only to obtain certain types of mutations, lack efficiency, or are not easily accessible to be used in different Pseudomonas species (e.g. natural isolates). In this work, we describe a versatile, robust and user‐friendly procedure that facilitates virtually any kind of genomic manipulation in Pseudomonas species in 3–5 days. The protocol presented here is based on DNA recombination forced by double‐stranded DNA cuts (through the activity of the I‐SceI homing meganuclease from yeast) followed by highly efficient counterselection of mutants (aided by a synthetic CRISPR‐Cas9 device). The individual parts of the genome engineering toolbox, tailored for knocking genes in and out, have been standardized to enable portability and easy exchange of functional gene modules as needed. The applicability of the procedure is illustrated both by eliminating selected genomic regions in the platform strain P. putida KT2440 (including difficult‐to‐delete genes) and by integrating different reporter genes (comprising novel variants of fluorescent proteins) into a defined landing site in the target chromosome.

Identification of Functional Gene Modules Associated With STAT-Mediated Antiviral Responses to White Spot Syndrome Virus in Shrimp.

White spot syndrome virus (WSSV) is one of the major threats to shrimp aquaculture. It has been found that the signal transducer and activator of transcription (STAT) protein plays an important role in the antiviral immunity of shrimp with a WSSV infection. However, the mechanism that underlies the STAT-mediated antiviral responses in shrimp, against WSSV infection, remains unclear. In this work, based on the gene expression profiles of shrimp with an injection of WSSV and STAT double strand RNA (dsRNA), we constructed a gene co-expression network for shrimp and identified the gene modules that are possibly responsible for STAT-mediated antiviral responses. These gene modules are found enriched in the regulation of the viral process, JAK-STAT cascade and the regulation of immune effector process pathways. The gene modules identified here provide insights into the molecular mechanism that underlies the STAT-mediated antiviral response of shrimp, against WSSV.

Podocyte-specific expression of Cre recombinase promotes glomerular basement membrane thickening.

Conditional gene targeting using Cre recombinase has offered a powerful tool to modify gene function precisely in defined cells/tissues and at specific times. However, in mammalian cells, Cre recombinase can be genotoxic. The importance of including Cre-expressing control mice to avoid misinterpretation and to maximize the validity of the experimental results has been increasingly recognized. While studying the role of podocytes in the pathogenesis of glomerular basement membrane (GBM) thickening, we used Cre recombinase driven by the podocyte-specific podocin promoter (NPHS2-Cre) to generate a conditional knockout. By conventional structural and functional measures (histology by periodic acid-Schiff staining, albuminuria, and plasma creatinine), we did not detect significant differences between NPHS2-Cre transgenic and wild-type control mice. However, surprisingly, the group that expressed Cre transgene alone developed signs of podocyte toxicity, including marked GBM thickening, loss of normal foot process morphology, and reduced Wilms tumor 1 expression. GBM thickening was characterized by altered expression of core structural protein laminin isoform α5β2γ1. RNA sequencing analysis of extracted glomeruli identified 230 genes that were significant and differentially expressed (applying a q < 0.05-fold change ≥ ±2 cutoff) in NPHS2-Cre mice compared with wild-type control mice. Many biological processes were reflected in the RNA sequencing data, including regulation of the extracellular matrix and pathways related to apoptosis and cell death. This study highlights the importance of including the appropriate controls for potential Cre-mediated toxicity in conditional gene-targeting experiments. Indeed, omitting the Cre transgene control can result in critical errors during interpretation of experimental data.

Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis

BackgroundAlternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3′ end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes.ResultsHere we proposed a computational framework, named PASCCA, for clustering genes from replicated or unreplicated poly(A) site data using canonical correlation analysis (CCA). PASCCA incorporates multiple layers of gene expression data from both the poly(A) site level and gene level and takes into account the number of replicates and the variability within each experimental group. Moreover, PASCCA characterizes poly(A) sites in various ways including the abundance and relative usage, which can exploit the advantages of 3′ end deep sequencing in quantifying APA sites. Using both real and synthetic poly(A) site data sets, the cluster analysis demonstrates that PASCCA outperforms other widely-used distance measures under five performance metrics including connectivity, the Dunn index, average distance, average distance between means, and the biological homogeneity index. We also used PASCCA to infer APA-specific gene modules from recently published poly(A) site data of rice and discovered some distinct functional gene modules. We have made PASCCA an easy-to-use R package for APA-related gene expression analyses, including the characterization of poly(A) sites, quantification of association between genes, and clustering of genes.ConclusionsBy providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. PASCCA could be used to elucidate the dynamic interplay of genes and their APA sites among various biological conditions from emerging 3′ end sequencing data to address the complex biological phenomenon.

Predicting Functional Modules of Liver Cancer Based on Differential Network Analysis.

Complex diseases are generally caused by disorders of biological networks or/and mutations in multiple genes. The efficient and systematic identification of functional modules can not only supply effective diagnosis and treatment in clinic, but also benefit in further in-depth analysis of the pathological mechanism of complex diseases. In this study, we applied the method of differential network to identify functional modules between control and disease samples, which are different from most of the current approaches that focus on differential expression. In particular, we applied our approach to analyze transcriptome data of liver cancer in The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/), and we obtained two modules associated with liver cancer. One is a functional gene module that contains a set of liver cancer-related genes, and another is an lncRNA (long non-coding RNA) module that includes liver cancer-related lncRNAs. The results of survival analysis and classification show that the functional modules cannot only be used as effective modular biomarkers to identifying liver cancer, but also predict the prognosis of liver cancer. The method can identify functional modules in genes and lncRNA from liver cancer, and these modules can be used to do prognosis detection and further study in mechanism of liver cancer.

RNA Interference-Based Cancer Drugs: The Roadblocks, and the "Delivery" of the Promise.

Nucleic acid-based therapeutics like synthetic small interfering RNAs have been exploited to modulate gene function, taking advantage of RNA interference (RNAi), an evolutionally conserved biological process. Recently, the world's first RNAi drug was approved for a rare genetic disorder in the liver. However, there are significant challenges that need to be resolved before RNAi can be translated in other genetic diseases like cancer. Current drug delivery platforms for therapeutic silencing RNAs are tailored to hepatic targets. RNAi therapies for nonhepatic conditions are still at early clinical phases. In this study, we discuss the critical design considerations in anticancer RNAi drug development, insights gained from initial clinical trials, and new strategies that are entering clinical development, shaping the future of RNAi in cancer.

Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development

Bamboo is one of the fastest-growing non-timber forest plants. Moso bamboo (Phyllostachys edulis) is the most economically valuable bamboo in Asia, especially in China. With the release of the whole-genome sequence of moso bamboo, there are increasing demands for refined annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data on the multiple growth stages of tissues. It is now feasible for us to construct co-expression networks to improve bamboo gene annotation and reveal the relationships between gene expression and growth traits. We integrated the genome sequence of moso bamboo and 78 transcriptome data sets to build genome-wide global and conditional co-expression networks. We overlaid the gene expression results onto the network with multiple dimensions (different development stages). Through combining the co-expression network, module classification and function enrichment tools, we identified 1,896 functional modules related to bamboo development, which covered functions such as photosynthesis, hormone biosynthesis, signal transduction, and secondary cell wall biosynthesis. Furthermore, an online database (http://bioinformatics.cau.edu.cn/bamboo) was built for searching the moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set enrichment analysis, and other tools. In summary, we integrated public and in-house bamboo transcriptome data sets and carried out co-expression network analysis and functional module identification. Through data mining, we have yielded some novel insights into the regulation of growth and development. Our established online database might be convenient for the bamboo research community to identify functional genes or modules with important traits.

Analysis of gene co-expression networks and function modules at different developmental stages of chicken breast muscle

The development of poultry muscle fibers after hatching is closely related to meat quality and production efficiency. It is necessary to identify functional modules (groups of functionally related genes) related to muscle development at different developmental stages, and to investigate their relationships based on the weighted gene co-expression network analysis (WGCNA) methods. Accordingly, we investigated the co-expression associations between genes related to chicken breast muscle at four different developmental stages (between 2 and 14 weeks of age), and systematically analyzed the network topology in Jinmao Hua chicken. As a result, 2341 differentially expressed genes were identified and subjected to co-expression analysis. Four modules were identified to be related to a particular growth stage for the development of breast muscle. A series of genes with the highest connectivity were identified in the pink (2 weeks), yellow (6 weeks), green (10 weeks) and black modules (14 weeks), respectively, and visualized by Cytoscape. These hub genes (FGF, MAPKAPK5, NRG1, SCD, ACSL1, PPAR etc.) were mainly enriched in 15 pathways, such as MAPK signaling pathway, NRG/ErbB signaling pathway, and insulin signaling pathway. They shared biological functions related to development of breast muscle and adipogenesis. This is the first study of gene network with different stages of muscle development in Jinmao Hua chicken to observe co-expression patterns. It may contribute to the underlied molecular mechanisms of chicken breast muscle development.

Abstract 16779: Mitochondrial Related Intragraft Gene Expression During Acute Rejection After Heart Transplantation

Introduction: Endomyocardial biopsies (EMB) for the surveillance of acute cellular rejection (ACR) after heart transplant (HT) is inconsistent and provides limited information. Mitochondrial dysfunction is associated with poor allograft prognosis and can overstimulate the inflammatory response. Mitochondrial related gene expression (GE) in EMB may be useful as a functional surrogate for EMB. Hypothesis: We hypothesize that down-regulated mitochondrial associated GE in EMB correlates with ACR. Methods: We collected 64 EMB from 47 post HT patients (mean age 50 ± 14.7 years). Purified mRNA was subjected to whole-genome next-generation sequencing. Weighted gene co-expression network analysis was applied to construct functional gene modules. Module derived eigengenes were correlated with ISHLT EMB criteria or using an unsupervised classification method we previously developed and B-type natriuretic peptide (BNP) levels. Biological significance and function of each module were assessed by gene ontology (GO) and pathway analyses. Highly relevant genes were identified by Hub gene analysis. Results: The reconstructed network resulted in 16 modules including two mitochondria-related modules (MRM) and one immune response module (IRM). Compared to 0R (n= 30/46.9%) samples, 2R (n= 11/17.2%) EMB showed enrichment of IRM and decreased expression of MRM. ISHLT 1R (n= 23/35.9%) samples did not show a significant correlation with IRM or MRM. Following the unsupervised classification method, findings were concordant, with higher phenotype-module eigengene correlation and significance level. MRM Hub genes included ATP5B, ATP5O, ACO2, COX14, COX4I1 , FH, NDUFV1, PDK2, SDHA and UQCR11 among others. GO categories enriched by these genes included oxidative phosphorylation, beta-oxidation, tricarboxylic acid cycle, glycolysis and gluconeogenesis. There was an inverse correlation between MRM and BNP level and positive correlation between IRM and BNP (Figure). Conclusions: EMB of patients with ISHLT 2R ACR show decrease activity of MRM, increased activity of IRM and elevated BNP suggesting an inverse functional relationship between immune activation, mitochondrial activity and cardiac allograft function.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

BackgroundWorldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).MethodsThe data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.ResultsUsing an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).ConclusionsLarge scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Biological networks integration based on dense module identification for gene prioritization from microarray data

BackgroundSelection of genes associated with disease plays an important role in understanding the disease pathogenesis and discovering the therapeutic targets. Network based approaches have been widely used for gene selection or functional module detection. Although microarray technology promotes the study of global changes in the gene expression, it is still not ready for clinical research because of lack of meticulous standards for data collection, analysis and validation. Integrating multiple omics data type can compensate for missing and unreliable information of any single data type and thus increases the reliability of newly discovered knowledge. ObjectiveThe work aims to design a novel dense subgraph based framework to select informative genes from the given gene expression data. The goal here is achieved by integrating the multiple types of biological networks and extracting densely connected components. The work focuses on identifying statistically significant and biologically enriched genes given a gene expression data. Methods and resultThe proposed framework identifies the modules of co-expressed genes and further integrates it with protein-protein interaction (PPI) network to identify modules of strongly interacting genes. The dense gene modules (DGM) embedded in the integrated co-expressed gene network are extracted representing tightly co-expressed gene modules. The proposed approach has been compared with the existing clustering based integration of expression data with PPI network. Besides achieving high prediction accuracy, our proposed approach underlines the robustness and consistency of identifying DGM and selects putative and functionally coherent genes. DiscussionThe co-expression based integration infers interesting biological information associated with the disease and the hub genes that are involved in many biological processes. Such a study reveals that integration of different biological networks for gene selection may provide greater insight in disease related biological process.

Cross‐species systems analysis of evolutionary toolkits of neurogenomic response to social challenge

Social challenges like territorial intrusions evoke behavioral responses in widely diverging species. Recent work has showed that evolutionary "toolkits"-genes and modules with lineage-specific variations but deep conservation of function-participate in the behavioral response to social challenge. Here, we develop a multispecies computational-experimental approach to characterize such a toolkit at a systems level. Brain transcriptomic responses to social challenge was probed via RNA-seq profiling in three diverged species-honey bees, mice and three-spined stickleback fish-following a common methodology, allowing fair comparisons across species. Data were collected from multiple brain regions and multiple time points after social challenge exposure, achieving anatomical and temporal resolution substantially greater than previous work. We developed statistically rigorous analyses equipped to find homologous functional groups among these species at the levels of individual genes, functional and coexpressed gene modules, and transcription factor subnetworks. We identified six orthogroups involved in response to social challenge, including groups represented by mouse genes Npas4 and Nr4a1, as well as common modulation of systems such as transcriptional regulators, ion channels, G-protein-coupled receptors and synaptic proteins. We also identified conserved coexpression modules enriched for mitochondrial fatty acid metabolism and heat shock that constitute the shared neurogenomic response. Our analysis suggests a toolkit wherein nuclear receptors, interacting with chaperones, induce transcriptional changes in mitochondrial activity, neural cytoarchitecture and synaptic transmission after social challenge. It shows systems-level mechanisms that have been repeatedly co-opted during evolution of analogous behaviors, thus advancing the genetic toolkit concept beyond individual genes.