Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein-protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-β1 (TGF-β1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future.

Upregulation of key genes Eln and Tgfb3 were associated with the severity of cardiac hypertrophy

Background

Hypertension-induced cardiac hypertrophy is one of the most common pre-conditions that accompanies heart failure. This study aimed to identify the key pathogenic genes in the disease process.

Methods

GSE18224 was re-analyzed and differentially expressed genes (DEGs) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Networks of transcription factor (TF)-mRNA, microRNA (miRNA)-mRNA and Protein-Protein interaction (PPI) were constructed, and a key module was further screened out from PPI network. GSE36074 dataset and our transverse aortic constriction (TAC) mouse model were used to validate gene expression in the module. Finally, the correlation between the genes and biomarkers of cardiac hypertrophy were evaluated.

Results

Totally, there were 348 DEGs in GSE18224, which were mainly enriched in biological processes including collagen fibril organization, cellular response to transforming growth factor-beta stimulus and were involved in ECM-receptor interaction and Oxytocin signaling pathway. There were 387 miRNAs targeted by 257 DEGs, while 177 TFs targeted 71 DEGs. The PPI network contained 222 nodes and 770 edges, with 18 genes screened out into the module. After validation, 8 genes, which were also significantly upregulated in the GSE36074 dataset, were selected from the 18 DEGs. 2 of the 8 DEGs, including Eln and Tgfb3 were significantly upregulated in our mouse model of myocardial hypertrophy. Finally, the expression of Eln and Tgfb3 were found to be positively correlated with the level of the disease biomarkers.

Conclusions

Upregulated key genes Eln and Tgfb3 were positively correlated with the severity of cardiac hypertrophy, which may provide potential therapeutic targets for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08778-0.

Transcriptomic Profile of Genes Regulating the Structural Organization of Porcine Atrial Cardiomyocytes during Primary In Vitro Culture

Numerous cardiovascular diseases (CVD) eventually lead to severe myocardial dysfunction, which is the most common cause of death worldwide. A better understanding of underlying molecular mechanisms of cardiovascular pathologies seems to be crucial to develop effective therapeutic options. Therefore, a worthwhile endeavor is a detailed molecular characterization of cells extracted from the myocardium. A transcriptomic profile of atrial cardiomyocytes during long-term primary cell culture revealed the expression patterns depending on the duration of the culture and the heart segment of origin (right atrial appendage and right atrium). Differentially expressed genes (DEGs) were classified as involved in ontological groups such as: “cellular component assembly”, “cellular component organization”, “cellular component biogenesis”, and “cytoskeleton organization”. Transcriptomic profiling allowed us to indicate the increased expression of COL5A2, COL8A1, and COL12A1, encoding different collagen subunits, pivotal in cardiac extracellular matrix (ECM) structure. Conversely, genes important for cellular architecture, such as ABLIM1, TMOD1, XIRP1, and PHACTR1, were downregulated during in vitro culture. The culture conditions may create a favorable environment for reconstruction of the ECM structures, whereas they may be suboptimal for expression of some pivotal transcripts responsible for the formation of intracellular structures.

lncRNA BZRAP1-AS1 alleviates rheumatoid arthritis by regulating miR-1286/COL5A2 axis

BACKGROUND

Dysregulation of BZRAP1-AS1 was associated with immune statuses of cancer or Alzheimer's disease patients, yet little is known about its role in rheumatoid arthritis.

METHODS

RT-qPCR and western blot were applied to assess the expression of indicated expression. CCK-8 and BrdU proliferation assays were used to measure the proliferation of RA-HFLS. Apoptosis in RA-HFLS was evidenced by the alteration of caspase-3 activity and apoptosis-related factors. ELISA was performed to detect IL-6, IL-1β, and TNF-α level. Luciferase reporter, RIP, and pull-down assays were used to confirm the BZRAP1-AS1/miR-1286/COL5A2 cascade predicted by bioinformatics analysis.

RESULTS

BZRAP1-AS1 and COL5A2 were downregulated in RA tissues and RA-HFLS while miR-1286 was amplified. Overexpression of BZRAP1-AS1 reduced the RA-HFLS proliferation, IL-6, IL-1β, and TNF-α level and induced cell apoptosis while BZRAP1-AS1 silence produced an opposite effect. Overexpression of BZRAP1-AS1 reduced the miR-1286 expression which in turn increased the COL5A2 expression, thereby relieving the excessive proliferation and limited apoptosis in RA-HFLS.

CONCLUSION

Our findings suggested that BZRAP1-AS1 sequestered miR-1286 and reshaped the COL5A2 expression, thereby suppressed RA-HFLS proliferation and inflammation, and triggered cell apoptosis, resulting in the attenuation of RA progression.

Potential biomarkers of acute myocardial infarction based on co‑expression network analysis

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

A developmental lineage-based gene co-expression network for mouse pancreatic β-cells reveals a role for Zfp800 in pancreas development

To gain a deeper understanding of pancreatic β-cell development, we used iterative weighted gene correlation network analysis to calculate a gene co-expression network (GCN) from 11 temporally and genetically defined murine cell populations. The GCN, which contained 91 distinct modules, was then used to gain three new biological insights. First, we found that the clustered protocadherin genes are differentially expressed during pancreas development. Pcdhγ genes are preferentially expressed in pancreatic endoderm, Pcdhβ genes in nascent islets, and Pcdhα genes in mature β-cells. Second, after extracting sub-networks of transcriptional regulators for each developmental stage, we identified 81 zinc finger protein (ZFP) genes that are preferentially expressed during endocrine specification and β-cell maturation. Third, we used the GCN to select three ZFPs for further analysis by CRISPR mutagenesis of mice. Zfp800 null mice exhibited early postnatal lethality, and at E18.5 their pancreata exhibited a reduced number of pancreatic endocrine cells, alterations in exocrine cell morphology, and marked changes in expression of genes involved in protein translation, hormone secretion and developmental pathways in the pancreas. Together, our results suggest that developmentally oriented GCNs have utility for gaining new insights into gene regulation during organogenesis.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

Purpose

Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment.

Materials and Methods

We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.

Results

A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues.

Conclusion

The multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Fluid-based assays and precision medicine of cardiovascular diseases: the ‘hope’ for Pandora’s box?

Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.

Identification of the Biomarkers and Pathological Process of Osteoarthritis: Weighted Gene Co-expression Network Analysis

Osteoarthritis (OA) is a joint disease resulting in high rates of disability and low quality of life. The initial site of OA (bone or cartilage) is uncertain. The aim of the current study was to explore biomarkers and pathological processes in subchondral bone samples. The gene expression profile GSE51588 was downloaded from the Gene Expression Omnibus database. Fifty subchondral bone [knee lateral tibial (LT) and medial tibial (MT)] samples from 40 OA and 10 non-OA subjects were analyzed. After data preprocessing, 5439 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 19 modules. The yellow module was found to be highly correlated with OA (r = 0.71, p = 1e-08) and the brown module was most associated with the differences between the LT and MT regions (r = 0.77, p = 1e-10). Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the yellow module was enriched in a variety of components including proteinaceous extracellular matrix and collagen trimers, involved in protein digestion and absorption, axon guidance, ECM-receptor interaction, and the PI3K-Akt signaling pathway. In addition, the brown module suggests that the differences between the early (LT) and end (MT) stage of OA are associated with extracellular processes and lipid metabolism. Finally, 45 hub genes in the yellow module (COL24A1, COL5A2, COL3A1, MMP2, COL6A1, etc.) and 72 hub genes in the brown module (LIPE, LPL, LEP, SLC2A4, FABP4, ADH1B, ALDH4A1, ADIPOQ, etc.) were identified. Hub genes were validated using samples from cartilage (GSE57218). In summary, 45 hub genes and 72 hub genes in two modules are associated with OA. These hub genes could provide new biomarkers and drug targets in OA. Further studies focusing on subchondral bone are required to validate these hub genes and better understand the pathological process of OA.

Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis

The study aimed to seek potential biomarkers for acute myocardial infarction (AMI) detection and treatment.The dataset GSE48060 was used, consisting of 52 peripheral blood samples (31 AMI samples and 21 normal controls). By limma package, differentially expressed genes (DEGs) between 2 kinds of samples were identified, followed by enrichment analysis, subpathway analysis, protein-protein interaction (PPI) network analysis, and transcription factor network (TFN) analysis. Weighted gene co-expression network analysis was used to further extract key modules relating to AMI, followed by enrichment and TFN analyses. Expression validation was performed via meta-analysis of 2 datasets, GSE22229 and GSE29111.A set of 428 DEGs in AMI were screened out, and the upregulated toll-like receptor (TLR) family genes (TLR1, TLR2, and TLR10) were enriched in wound response, immune response and inflammatory response functions, and downregulated genes (GBP5, CXCL5, GZMA, CCL5, and CCL4) were correlated with immune response. CCL5, GZMA, GZMB, TLR2, and formyl peptide receptor 1 (FPR1) were predicted as crucial nodes in the PPI network. Signal transducer and activator of transcription 1 (STAT1) was the key transcription factor (TF) with multiple targets. The grey module was highly related to AMI. Genes in this module were closely related to regulation of macrophage activation, and spermatogenic leucine zipper 1 (SPZ1) was identified as a TF. Expressions of TLR2 and FPR1 were confirmed via the integrated matrix.Several potential biomarkers for AMI detection were identified, such as GZMB, GBP5, FPR1, TLR2, STAT1, and SPZ1. They might exert their functions via regulation of immune and inflammation responses. Genes in grey module play significant roles in AMI via regulation of macrophage activation.

Identifying key genes associated with acute myocardial infarction

BACKGROUND

This study aimed to identify key genes associated with acute myocardial infarction (AMI) by reanalyzing microarray data.

METHODS

Three gene expression profile datasets GSE66360, GSE34198, and GSE48060 were downloaded from GEO database. After data preprocessing, genes without heterogeneity across different platforms were subjected to differential expression analysis between the AMI group and the control group using metaDE package. P < .05 was used as the cutoff for a differentially expressed gene (DEG). The expression data matrices of DEGs were imported in ReactomeFIViz to construct a gene functional interaction (FI) network. Then, DEGs in each module were subjected to pathway enrichment analysis using DAVID. MiRNAs and transcription factors predicted to regulate target DEGs were identified. Quantitative real-time polymerase chain reaction (RT-PCR) was applied to verify the expression of genes.

RESULT

A total of 913 upregulated genes and 1060 downregulated genes were identified in the AMI group. A FI network consists of 21 modules and DEGs in 12 modules were significantly enriched in pathways. The transcription factor-miRNA-gene network contains 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p. RT-PCR validations showed that expression levels of FOXO3 and MYBL2 were significantly increased in AMI, and expression levels of hsa-miR-21-5p and hsa-miR-30c-5p were obviously decreased in AMI.

CONCLUSION

A total of 41 DEGs, such as SOCS3, VAPA, and COL5A2, are speculated to have roles in the pathogenesis of AMI; 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p may be involved in the regulation of the expression of these DEGs.

GWAB: a web server for the network-based boosting of human genome-wide association data

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

Decoding the complex genetic causes of heart diseases using systems biology

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

GeneFriends: a human RNA-seq-based gene and transcript co-expression database

Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts and genome-wide association studies often identify loci containing novel genes and there is a need to infer their functions and interaction partners. To facilitate this we have expanded GeneFriends, an online database that allows users to identify co-expressed genes with one or more user-defined genes. This expansion entails an RNA-seq-based co-expression map that includes genes and transcripts that are not present in the microarray-based co-expression maps, including over 10 000 non-coding RNAs. The results users obtain from GeneFriends include a co-expression network as well as a summary of the functional enrichment among the co-expressed genes. Novel insights can be gathered from this database for different splice variants and ncRNAs, such as microRNAs and lincRNAs. Furthermore, our updated tool allows candidate transcripts to be linked to diseases and processes using a guilt-by-association approach. GeneFriends is freely available from http://www.GeneFriends.org and can be used to quickly identify and rank candidate targets relevant to the process or disease under study.

Robust coordination of cardiac functions from gene co-expression reveals a versatile combinatorial transcriptional control

The necessary overall coordination of cardiac cellular functions is little known at the mRNA level. Focusing on energy production and cardiac contraction, we analyzed microarray data from heart tissue obtained in groups of mice and rats in normal conditions and with a left ventricular dysfunction. In each group and for each function, we identified genes positively or negatively correlated with numerous genes of the function, which were called coordinated or inversely coordinated with the function. The genes coordinated with energy production or cardiac contraction showed the coupling of these functions in all groups. Among coordinated or inversely coordinated genes common to the two functions, we proposed a fair number of transcriptional regulators as potential determinants of the energy production and cardiac contraction coupling. Although this coupling was constant across the groups and unveiled a stable gene core, the combinations of transcriptional regulators were very different between the groups, including one half that has never been linked to heart function. These results highlighted the stable coordination of energy production or cardiac contraction at the mRNA level, and the combinatorial and versatile nature of potential transcriptional regulation. In addition, this work unveiled new transcriptional regulators potentially involved in normal or altered cardiac functional coupling.

Coexpression within Integrated Mitochondrial Pathways Reveals Different Networks in Normal and Chemically Treated Transcriptomes

As energy producers, mitochondria play a pivotal role in multiple cellular processes. Although several lines of evidence suggest that differential expression of mitochondrial respiratory complexes (MRCs) has a significant impact on mitochondrial function, the role of integrated MRCs in the whole coexpression network has yet to be revealed. In this study, we construct coexpression networks based on microarray datasets from different tissues and chemical treatments to explore the role of integrated MRCs in the coexpression network and the effects of different chemicals on the mitochondrial network. By grouping MRCs as one seed target, the hypergeometric distribution allowed us to identify genes that are significantly coexpress with whole MRCs. Coexpression among 46 MRC genes (approximately 78% of MRC genes tested) was significant in the normal tissue transcriptome dataset. These MRC genes are coexpressed with genes involved in the categories "muscle system process," "metabolic process," and "neurodegenerative disease pathways," whereas, in the chemically treated tissues, coexpression of these genes mostly disappeared. These results indicate that chemical stimuli alter the normal coexpression network of MRC genes. Taken together, the datasets obtained from the different coexpression networks are informative about mitochondrial biogenesis and should contribute to understanding the side effects of drugs on mitochondrial function.

Selecting biologically informative genes in co-expression networks with a centrality score

BACKGROUND

Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.

RESULTS

The method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.

CONCLUSIONS

A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.

Transcriptional profiling of left ventricle and peripheral blood mononuclear cells in a rat model of postinfarction heart failure

Background

Myocardial infarction (MI) often results in left ventricular (LV) remodeling followed by heart failure (HF). It is of great clinical importance to understand the molecular mechanisms that trigger transition from compensated LV injury to HF and to identify relevant diagnostic biomarkers. The aim of this study was to investigate gene expression in the LV and to evaluate their reflection in peripheral blood mononuclear cells (PBMCs).

Methods

MI was induced in rats by ligation of the proximal left coronary artery. Rats with small, moderate, and large MI size were included into the experiment two months after the operation. The development of heart failure was estimated by echocardiography and catheterization. Microarrays were used to compare the LV and PBMCs transcriptomes of control and experimental animals.

Results

Only rats with a large MI developed extensive LV remodeling and heart failure. 840 transcripts were altered in LV of failing hearts, and especially numerous were those associated with the extracellular matrix. In contrast, no significant gene expression changes were seen in LVs of rats with moderate or small MI that had compensated LV injury. We showed that ceruloplasmin was similarly overexpressed in the heart and blood in response to HF, whereas downregulation of tetraspanin 12 was significant only in the PBMCs.

Conclusion

A large size of infarcted area is critical for progression of LV remodeling and HF development, associated with altered gene expression in the heart. Ceruloplasmin and tetraspanin 12 are potential convenient markers in readily obtainable PBMCs.

Connecting the dots: applications of network medicine in pharmacology and disease

In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease.(1) Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting "big picture" allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.