Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses

Identification of Novel Genes Associated with Partial Resistance to Aphanomyces Root Rot in Field Pea by BSR-Seq Analysis

Aphanomyces root rot, caused by Aphanomyces euteiches, causes severe yield loss in field pea (Pisum sativum). The identification of a pea germplasm resistant to this disease is an important breeding objective. Polygenetic resistance has been reported in the field pea cultivar '00-2067'. To facilitate marker-assisted selection (MAS), bulked segregant RNA-seq (BSR-seq) analysis was conducted using an F8 RIL population derived from the cross of 'Carman' × '00-2067'. Root rot development was assessed under controlled conditions in replicated experiments. Resistant (R) and susceptible (S) bulks were constructed based on the root rot severity in a greenhouse study. The BSR-seq analysis of the R bulks generated 44,595,510~51,658,688 reads, of which the aligned sequences were linked to 44,757 genes in a reference genome. In total, 2356 differentially expressed genes were identified, of which 44 were used for gene annotation, including defense-related pathways (jasmonate, ethylene and salicylate) and the GO biological process. A total of 344.1 K SNPs were identified between the R and S bulks, of which 395 variants were located in 31 candidate genes. The identification of novel genes associated with partial resistance to Aphanomyces root rot in field pea by BSR-seq may facilitate efforts to improve management of this important disease.

Intrapericardial Administration of Secretomes from Menstrual Blood-Derived Mesenchymal Stromal Cells: Effects on Immune-Related Genes in a Porcine Model of Myocardial Infarction

Acute myocardial infarction (AMI) is a manifestation of ischemic heart disease where the immune system plays an important role in the re-establishment of homeostasis. We hypothesize that the anti-inflammatory activity of secretomes from menstrual blood-derived mesenchymal stromal cells (S-MenSCs) and IFNγ/TNFα-primed MenSCs (S-MenSCs*) may be considered a therapeutic option for the treatment of AMI. To assess this hypothesis, we have evaluated the effect of S-MenSCs and S-MenSCs* on cardiac function parameters and the involvement of immune-related genes using a porcine model of AMI. Twelve pigs were randomly divided into three biogroups: AMI/Placebo, AMI/S-MenSCs, and AMI/S-MenSCs*. AMI models were generated using a closed chest coronary occlusion-reperfusion procedure and, after 72 h, the different treatments were intrapericardially administered. Cardiac function parameters were monitored by magnetic resonance imaging before and 7 days post-therapy. Transcriptomic analyses in the infarcted tissue identified 571 transcripts associated with the Gene Ontology term Immune response, of which 57 were differentially expressed when different biogroups were compared. Moreover, a prediction of the interactions between differentially expressed genes (DEGs) and miRNAs from secretomes revealed that some DEGs in the infarction area, such as STAT3, IGFR1, or BCL6 could be targeted by previously identified miRNAs in secretomes from MenSCs. In conclusion, the intrapericardial administration of secretome early after infarction has a significant impact on the expression of immune-related genes in the infarcted myocardium. This confirms the immunomodulatory potential of intrapericardially delivered secretomes and opens new therapeutic perspectives in myocardial infarction treatment.

Screening for Regulatory Network of miRNA–Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Background

Acute myocardial infarction (AMI), which commonly leads to heart failure, is among the leading causes of mortality worldwide. The aim of this study was to find potential regulatory network for miRNA-inflammation, oxidative stress and prognosis-related mRNA to uncover molecular mechanisms of AMI.

Methods

The expression profiles of miRNA and mRNA in the blood samples from AMI patients were downloaded from the Gene Expression Omnibus (GEO) dataset for differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to further identify important mRNAs. The negatively regulatory network construction of miRNA–inflammation, oxidative stress and prognosis-related mRNAs was performed, followed by protein–protein interaction (PPI) and functional analysis of mRNAs.

Results

A total of three pairs of negatively regulatory network of miRNA–inflammation and prognosis-related mRNAs (hsa-miR-636/hsa-miR-491-3p/hsa-miR-188-5p/hsa-miR-188-3p-AQP9, hsa-miR-518a-3p-C5AR1 and hsa-miR-509-3-5p/hsa-miR-127-5p-PLAUR), two pairs of negatively regulatory network of miRNA–oxidative stress and prognosis-related mRNAs (hsa-miR-604-TLR4 and hsa-miR-139-5p-CXCL1) and three pairs of negatively regulatory network of miRNA-inflammation, oxidative stress and prognosis-related mRNA (hsa-miR-634/hsa-miR-591-TLR2, hsa-miR-938-NFKBIA and hsa-miR-520h/hsa-miR-450b-3p-ADM) were identified. In the KEGG analysis, some signaling pathways were identified, such as complement and coagulation cascades, pathogenic Escherichia coli infection, chemokine signaling pathway and cytokine–cytokine receptor interaction and Toll-like receptor signaling pathway.

Conclusion

Identified negatively regulatory network of miRNA-inflammation/oxidative stress and prognosis-related mRNA may be involved in the process of AMI. Those inflammation/oxidative stress and prognosis-related mRNAs may be diagnostic and prognostic biomarkers for AMI.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

Identification of protein complexes associated with myocardial infarction using a bioinformatics approach

Myocardial infarction (MI) is a leading cause of mortality and disability worldwide. Determination of the molecular mechanisms underlying the disease is crucial for identifying possible therapeutic targets and designing effective treatments. On the basis that MI may be caused by dysfunctional protein complexes rather than single genes, the present study aimed to use a bioinformatics approach to identifying complexes that may serve important roles in the development of MI. By investigating the proteins involved in these identified complexes, numerous proteins have been reported that are related to MI, whereas other proteins interacted with MI-related proteins, which implied that these protein complexes may indeed be related to the development of MI. The protein complexes detected in the present study may aid in our understanding of the molecular mechanisms that underlie MI pathogenesis.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Data integration to prioritize drugs using genomics and curated data

Background

Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature.

Results

We have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3.

Conclusions

Our results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0097-1) contains supplementary material, which is available to authorized users.

Education, collaboration, and innovation: intelligent biology and medicine in the era of big data

Here we present a summary of the 2014 International Conference on Intelligent Biology and Medicine (ICIBM 2014) and the editorial report of the supplement to BMC Genomics and BMC Systems Biology that includes 20 research articles selected from ICIBM 2014. The conference was held on December 4-6, 2014 at San Antonio, Texas, USA, and included six scientific sessions, four tutorials, four keynote presentations, nine highlight talks, and a poster session that covered cutting-edge research in bioinformatics, systems biology, and computational medicine.