Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets

MiRNA/mRNA network topology in hepatitis virus B-related liver cirrhosis reveals miR-20a-5p/340-5p as hubs initiating fibrosis

Background

The pathophysiology of hepatitis B-related liver cirrhosis (HBV-LC) remains unclear. This study aimed to explore the disease mechanisms using topological analysis of the miRNA/mRNA network.

Methods

Paired miRNA/mRNA sequencing was performed with thirty-three peripheral blood mononuclear cell samples (LC, n = 9; chronic hepatitis B, n = 12; normal controls, n = 12) collected from a prospective cohort to identify the miRNA/mRNA network. Topological features and functional implications of the network were analyzed to capture pathophysiologically important miRNAs/mRNAs, whose expression patterns were confirmed in the validation group (LC, n = 15; chronic hepatitis B, n = 15; normal controls, n = 10), and functional potentials initiating fibrogenesis were demonstrated in vitro.

Results

The miRNA/mRNA network contained 3121 interactions between 158 differentially expressed (DE) miRNAs and 442 DE-mRNAs. The topological analysis identified a core module containing 99 miRNA/mRNA interactions and two hub nodes (miR-20a-5p/miR-340-5p), which connected to 75 DE-mRNAs. The expression pattern along the disease progression of the core module was found associated with a continuous increase in wound healing, inflammation, and leukocyte migration but an inflection of immune response and lipid metabolic regulation, consistent with the pathophysiology of HBV-LC. MiR-20a-5p/miR-340-5p were found involved in macrophage polarization and hepatic stellate cell (HSC) activation in vitro (THP-1, LX-2 cell lines), and their expression levels were confirmed in the validation group independently.

Conclusion

Topological analysis of the miRNA/mRNA network in HBV-LC revealed the association between fibrosis and miR-20a-5p/miR-340-5p involving initiating activations of macrophage and HSC. Further validations should be performed to confirm the HSC/macrophage activations and the interactions between miR-20a-5p/miR-340-5p and their potential targets, which may help to develop non-invasive prognostic markers or intervention targets for HBV-LC.

Predicted mouse interactome and network-based interpretation of differentially expressed genes

The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.

PBMC transcriptomics identifies immune-metabolism disorder during the development of HBV-ACLF

OBJECTIVE

Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) pathophysiology remains unclear. This study aims to characterise the molecular basis of HBV-ACLF using transcriptomics.

METHODS

Four hundred subjects with HBV-ACLF, acute-on-chronic hepatic dysfunction (ACHD), liver cirrhosis (LC) or chronic hepatitis B (CHB) and normal controls (NC) from a prospective multicentre cohort were studied, and 65 subjects (ACLF, 20; ACHD, 10; LC, 10; CHB, 10; NC, 15) among them underwent mRNA sequencing using peripheral blood mononuclear cells (PBMCs).

RESULTS

The functional synergy analysis focusing on seven bioprocesses related to the PBMC response and the top 500 differentially expressed genes (DEGs) showed that viral processes were associated with all disease stages. Immune dysregulation, as the most prominent change and disorder triggered by HBV exacerbation, drove CHB or LC to ACHD and ACLF. Metabolic disruption was significant in ACHD and severe in ACLF. The analysis of 62 overlapping DEGs further linked the HBV-based immune-metabolism disorder to ACLF progression. The signatures of interferon-related, neutrophil-related and monocyte-related pathways related to the innate immune response were significantly upregulated. Signatures linked to the adaptive immune response were downregulated. Disruptions of lipid and fatty acid metabolism were observed during ACLF development. External validation of four DEGs underlying the aforementioned molecular mechanism in patients and experimental rats confirmed their specificity and potential as biomarkers for HBV-ACLF pathogenesis.

CONCLUSIONS

This study highlights immune-metabolism disorder triggered by HBV exacerbation as a potential mechanism of HBV-ACLF and may indicate a novel diagnostic and treatment target to reduce HBV-ACLF-related mortality.

HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis

To facilitate biomedical studies of disease mechanisms, a high-quality interactome that connects functionally related genes is needed to help investigators formulate pathway hypotheses and to interpret the biological logic of a phenotype at the biological process level. Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes (23.2% functional interactions of HIR V2 are in common with the previous version of HIR), representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at http://human.biomedtzc.cn.

Predicted Drosophila Interactome Resource and web tool for functional interpretation of differentially expressed genes

Drosophila melanogaster is a well-established model organism that is widely used in genetic studies. This species enjoys the availability of a wide range of research tools, well-annotated reference databases and highly similar gene circuitry to other insects. To facilitate molecular mechanism studies in Drosophila, we present the Predicted Drosophila Interactome Resource (PDIR), a database of high-quality predicted functional gene interactions. These interactions were inferred from evidence in 10 public databases providing information for functional gene interactions from diverse perspectives. The current version of PDIR includes 102 835 putative functional associations with balanced sensitivity and specificity, which are expected to cover 22.56% of all Drosophila protein interactions. This set of functional interactions is a good reference for hypothesis formulation in molecular mechanism studies. At the same time, these interactions also serve as a high-quality reference interactome for gene set linkage analysis (GSLA), which is a web tool for the interpretation of the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that the PDIR/GSLA system was able to produce a more comprehensive and concise interpretation of the collective functional impact of multiple simultaneously changed genes compared with the widely used gene set annotation tools, including PANTHER and David. PDIR and its associated GSLA service can be accessed at http://drosophila.biomedtzc.cn.

Predicted functional interactome of Caenorhabditis elegans and a web tool for the functional interpretation of differentially expressed genes

BACKGROUND

The nematode worm, Caenorhabditis elegans, is a saprophytic species that has been emerging as a standard model organism since the early 1960s. This species is useful in numerous fields, including developmental biology, neurobiology, and ageing. A high-quality comprehensive molecular interaction network is needed to facilitate molecular mechanism studies in C. elegans.

RESULTS

We present the predicted functional interactome of Caenorhabditis elegans (FIC), which integrates functional association data from 10 public databases to infer functional gene interactions on diverse functional perspectives. In this work, FIC includes 108,550 putative functional associations with balanced sensitivity and specificity, which are expected to cover 21.42% of all C. elegans protein interactions, and 29.25% of these associations may represent protein interactions. Based on FIC, we developed a gene set linkage analysis (GSLA) web tool to interpret potential functional impacts from a set of differentially expressed genes observed in transcriptome analyses.

CONCLUSION

We present the predicted C. elegans interactome database FIC, which is a high-quality database of predicted functional interactions among genes. The functional interactions in FIC serve as a good reference interactome for GSLA to annotate differentially expressed genes for their potential functional impacts. In a case study, the FIC/GSLA system shows more comprehensive and concise annotations compared to other widely used gene set annotation tools, including PANTHER and DAVID. FIC and its associated GSLA are available at the website http://worm.biomedtzc.cn .

Predicted rat interactome database and gene set linkage analysis

Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights. Database URL: http://rat.biomedtzc.cn.

Challenges and future of precision medicine strategies for breast cancer based on a database on drug reactions

Breast cancer (BC) is a malignancy with the highest incidence in women. Great progress has been made in research related to traditional precision medicine for BC. However, many reports have suggested that patients with BC have not benefited a lot from such progress. Thus, we analyze traditional precision medicine strategies for BC, sum up their limitations and challenges, and preliminarily propose future orientations of precision medicine strategies based on a database on drug reaction of patients with BC. According to related research, traditional precision medicine strategies for BC, which are based on molecular subtypes, perform pertinent treatments, new drug research and development according to molecular typing results. Nevertheless, these strategies still have some deficiencies. First, there are very few patients with each molecular subtype, the match ratio of drugs is low. Second, these strategies can not solve the problem of poor drug sensitivity resulting from heterogeneity. The main strategy we put forward in the present paper is based on patients’ varying drug reactions. Focusing on treating existing patients and maximizing the utilization of existing drugs, it is expected to not have deficiencies of traditional precision medicine for BC, including low match rate and poor therapeutic efficacy arising from tumor heterogeneity of BC.

Utilization of information from gene networks towards a better understanding of functional similarities between complex traits: a dairy cattle model

Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore — trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.

DCDB 2.0: a major update of the drug combination database

Experience in clinical practice and research in systems pharmacology suggested the limitations of the current one-drug-one-target paradigm in new drug discovery. Single-target drugs may not always produce desired physiological effects on the entire biological system, even if they have successfully regulated the activities of their designated targets. On the other hand, multicomponent therapy, in which two or more agents simultaneously interact with multiple targets, has attracted growing attention. Many drug combinations consisting of multiple agents have already entered clinical practice, especially in treating complex and refractory diseases. Drug combination database (DCDB), launched in 2010, is the first available database that collects and organizes information on drug combinations, with an aim to facilitate systems-oriented new drug discovery. Here, we report the second major release of DCDB (Version 2.0), which includes 866 new drug combinations (1363 in total), consisting of 904 distinctive components. These drug combinations are curated from ∼140,000 clinical studies and the food and drug administration (FDA) electronic orange book. In this update, DCDB collects 237 unsuccessful drug combinations, which may provide a contrast for systematic discovery of the patterns in successful drug combinations. Database URL: http://www.cls.zju.edu.cn/dcdb/