Xtalk: a path-based approach for identifying crosstalk between signaling pathways

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Recurrent implantation failure (RIF) is an extremely thorny issue in in-vitro fertilization (IVF)-embryo transfer (ET). However, its intricate etiology and pathological mechanisms are still unclear. Nowadays, there has been extensive interest in cellular senescence in RIF, and its involvement in endometrial immune characteristics during the window of implantation (WOI) has captured scholars' growing concerns. Therefore, this study aims to probe into the pathological mechanism of RIF from cellular senescence and investigate the correlation between cellular senescence and endometrial immune characteristics during WOI based on bioinformatics combined with machine learning strategy, so as to elucidate the underlying pathological mechanisms of RIF and to explore novel treatment strategies for RIF. Firstly, the gene sets of GSE26787 and GSE111974 from the Gene Expression Omnibus (GEO) database were included for the weighted gene correlation network analysis (WGCNA), from which we concluded that the genes of the core module were closely related to cell fate decision and immune regulation. Subsequently, we identified 25 cellular senescence-associated differentially expressed genes (DEGs) in RIF by intersecting DEGs with cellular senescence-associated genes from the Cell Senescence (CellAge) database. Moreover, functional enrichment analysis was conducted to further reveal the specific molecular mechanisms by which these molecules regulate cellular senescence and immune pathways. Then, eight signature genes were determined by the machine learning method of support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were also validated in the testing gene set (GSE106602). Furthermore, distinct immune microenvironment abnormalities in the RIF endometrium during WOI were comprehensively explored and validated in GSE106602, including infiltrating immunocytes, immune function, and the expression profiling of human leukocyte antigen (HLA) genes and immune checkpoint genes. Moreover, the correlation between the eight signature genes with the endometrial immune landscape of RIF was also evaluated. After that, two distinct subtypes with significantly distinct immune infiltration characteristics were identified by consensus clustering analysis based on the eight signature genes. Finally, a "KEGG pathway-RIF signature genes-immune landscape" association network was constructed to intuitively uncover their connection. In conclusion, this study demonstrated that cellular senescence might play a pushing role in the pathological mechanism of RIF, which might be closely related to its impact on the immune microenvironment during the WOI phase. The exploration of the molecular mechanism of cellular senescence in RIF is expected to bring new breakthroughs for disease diagnosis and treatment strategies.

Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

Because there is no effective treatment for late-stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network-based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein-protein interactions to identify novel co-targets for PCa treatment. The results show that polo-like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)-related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5-Aza-2'-deoxycytidine (5-Aza) with inhibition of Plk1 suppresses PCa synergistically.

Identification of differentially expressed subpathways via a bilevel consensus scoring of network topology and gene expression

Identifying differentially expressed subpathways connected to the emergence of a disease that can be considered as candidates for pharmacological intervention, with minimal off-target effects, is a daunting task. In this direction, we present a bilevel subpathway analysis method to identify differentially expressed subpathways that are connected with an experimental condition, while taking into account potential crosstalks between subpathways which arise due to their connectivity in a combined multi-pathway network. The efficacy of the method is demonstrated on a hematopoietic stem cell aging dataset, with findings corroborated using recent literature.

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomics

The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9–11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual’s genetic admixture given exonic single nucleotide polymorphisms data.

PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI

Background

Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. In addition to identifying perturbed pathways individually, pathway analysis should consider interactions among pathways. Currently available pathway interaction prediction methods are based on the existence of overlapping genes between pathways, protein-protein interaction (PPI) or functional similarities. However, these approaches just consider the pathways as a set of genes, thus they do not take account of topological features. In addition, most of the existing approaches do not handle the explicit gene expression quantity information that is routinely measured by RNA-sequecing.

Results

To overcome these technical issues, we developed a new pathway interaction network construction method using PPI, closeness centrality and shortest paths. We tested our approach on three different high-throughput RNA-seq data sets: pregnant mice data to reveal the role of serotonin on beta cell mass, bone-metastatic breast cancer data and autoimmune thyroiditis data to study the role of IFN- α. Our approach successfully identified the pathways reported in the original papers. For the pathways that are not directly mentioned in the original papers, we were able to find evidences of pathway interactions by the literature search. Our method outperformed two existing approaches, overlapping gene-based approach (OGB) and protein-protein interaction-based approach (PB), in experiments with the three data sets.

Conclusion

Our results show that PINTnet successfully identified condition-specific perturbed pathways and the interactions between the pathways. We believe that our method will be very useful in characterizing biological mechanisms at the pathway level. PINTnet is available at http://biohealth.snu.ac.kr/software/PINTnet/.

XTalkDB: a database of signaling pathway crosstalk

Analysis of signaling pathways and their crosstalk is a cornerstone of systems biology. Thousands of papers have been published on these topics. Surprisingly, there is no database that carefully and explicitly documents crosstalk between specific pairs of signaling pathways. We have developed XTalkDB (http://www.xtalkdb.org) to fill this very important gap. XTalkDB contains curated information for 650 pairs of pathways from over 1600 publications. In addition, the database reports the molecular components (e.g. proteins, hormones, microRNAs) that mediate crosstalk between a pair of pathways and the species and tissue in which the crosstalk was observed. The XTalkDB website provides an easy-to-use interface for scientists to browse crosstalk information by querying one or more pathways or molecules of interest.