Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction”

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge. Here, the authors present a resource that contains pre-calculated biomedical descriptors derived from a very large knowledge graph.

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps using bioinformatics analysis

PURPOSE

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease of yet unknown etiology. The purpose of this study was to uncover key genes and pathways related to the pathogenesis of CRSwNP via bioinformatics approaches.

MATERIALS AND METHODS

The gene expression profile of GSE36830 extracted from Gene Expression Omnibus database was used to screen differentially expressed genes (DEGs) between nasal polyp samples and control samples. Furthermore, functional and pathway enrichment analysis was performed using the clusterProfiler package in R language. In addition, protein-protein interaction (PPI) network was constructed by STRING database and functional modules were detected using Molecular Complex Detection algorithm.

RESULTS

A total of 538 DEGs (326 up-regulated and 212 down-regulated) were identified. The most significantly enriched pathways for up-regulated and down-regulated genes were hematopoietic cell lineage and salivary secretion, respectively. Moreover, twenty hub genes with high connectivity degrees were selected from the PPI network, such as TYRO protein tyrosine kinase binding protein (TYROBP), G protein subunit gamma 2 (GNG2), CCR7, and CCR3. Besides, six important modules were obtained, which were highly associated with chemokine signaling pathway, Th1 and Th2 cell differentiation, complement and coagulation cascades, cell cycle, systemic lupus erythematosus, and Staphylococcus aureus infection.

CONCLUSIONS

The results of this study may provide new insights into potential molecular mechanisms of CRSwNP. Nevertheless, further experiments are needed to confirm these findings.

Machine Learning and Integrative Analysis of Biomedical Big Data

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

Role of 14-3-3 sigma in over-expression of P-gp by rifampin and paclitaxel stimulation through interaction with PXR

In this study, we presented the role of 14-3-3σ to activate CK2-Hsp90β-PXR-MDR1 pathway on rifampin and paclitaxel treated LS174T cells and in vivo LS174T cell-xenografted nude mouse model. Following several in vitro and in vivo experiments, rifampin and paclitaxel were found to be stimulated the CK2-Hsp90β-PXR-MDR1 pathway. Of the proteins in this pathway, Pregnane X receptor (PXR) is a representative transcription factor of multidrug resistance protein 1 (MDR1). We constructed FLAG-PXR-LS174T stable cell lines and discovered 22 proteins that interacted with PXR on rifampin treatment. Among them, Hsp90β and 14-3-3σ were isolated for further study. Both the proteins were found to be localized in cytoplasm on rifampin treatment by using confocal microscopy. On the other hand, PXR was found to be localized in nucleus after rifampin and paclitaxel treatment by using cell fractionation assay. In Western blot analysis, rifampin did not influence the expression of 14-3-3σ protein. Transient transfection of 14-3-3σ into LS174T cells induced overexpression of PXR; however, P-glycoprotein (P-gp) was not changed significantly. P-gp overexpression was induced only when 14-3-3σ transfected LS174T cells were treated with rifampin and paclitaxel, whereas 14-3-3σ inhibition by nonpeptidic inhibitor, BV02 and 14-3-3σ siRNA reduced rifampin induced PXR and P-gp expression. Cell survival rates were much higher at 14-3-3σ-LS174T stable cell lines than LS174T cells following paclitaxel and vincristine treatment. This data indicates that 14-3-3σ contributes to P-gp overexpression through interaction with PXR with rifampin and paclitaxel treatment.

Respiratory viral infections and host responses; insights from genomics

Respiratory viral infections are a leading cause of disease and mortality. The severity of these illnesses can vary markedly from mild or asymptomatic upper airway infections to severe wheezing, bronchiolitis or pneumonia. In this article, we review the viral sensing pathways and organizing principles that govern the innate immune response to infection. Then, we reconstruct the molecular networks that differentiate symptomatic from asymptomatic respiratory viral infections, and identify the underlying molecular drivers of these networks. Finally, we discuss unique aspects of the biology and pathogenesis of infections with respiratory syncytial virus, rhinovirus and influenza, drawing on insights from genomics.

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene-gene and attribute-attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation.Database URL: http://amp.pharm.mssm.edu/Harmonizome.

A survey of disease connections for CD4+ T cell master genes and their directly linked genes

Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein-protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes.