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The diverse roles of RIP kinases in host-pathogen interactions.

ABSTRACT: Receptor Interacting Protein Kinases (RIPKs) are cellular signaling molecules that are critical for homeostatic signaling in both communicable and non-communicable disease processes. In particular, RIPK1, RIPK2, RIPK3 and RIPK7 have emerged as key mediators of intracellular signal transduction including inflammation, autophagy and programmed cell death, and are thus essential for the early control of many diverse pathogenic organisms. In this review, we discuss the role of each RIPK in host responses to bacterial and viral pathogens, with a focus on studies that have used pathogen infection models rather than artificial stimulation with purified pathogen associated molecular patterns. We also discuss the intricate mechanisms of host evasion by pathogens that specifically target RIPKs for inactivation, and finally, we will touch on the controversial issue of drug development for kinase inhibitors to treat chronic inflammatory and neurological disorders, and the implications this may have on the outcome of pathogen infections.

SUBMITTER: Eng VV

PROVIDER: S-EPMC7448748 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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The diverse roles of RIP kinases in host-pathogen interactions.

Eng Vik Ven VV Wemyss Madeleine A MA Pearson Jaclyn S JS

Seminars in cell & developmental biology 20200826

Receptor Interacting Protein Kinases (RIPKs) are cellular signaling molecules that are critical for homeostatic signaling in both communicable and non-communicable disease processes. In particular, RIPK1, RIPK2, RIPK3 and RIPK7 have emerged as key mediators of intracellular signal transduction including inflammation, autophagy and programmed cell death, and are thus essential for the early control of many diverse pathogenic organisms. In this review, we discuss the role of each RIPK in host resp ...[more]

PMID: 32859501

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Project description:The goal of this project is to understand the etiology and pathology of host-pathogen interaction using high-throughput cellular phenotyping. By combining the expertise in pathogen biology, informatics and stem cell biology across the institute, this project will use targeted genetic modification in mouse embryonic stem cells to identify functions of host genes in innate immunity. We will also explore the biology of mouse embryonic stem cells and their differentiation into immune competent cells. To this end we will generate homozygous and inducible knockout lines in mouse embryonic stem cells, which will be differentiated into immune competent cells, such as macrophages or dendritic cells. These will be subjected to a panel of immune challenge and stimulation assays to characterise the functions of the disrupted genes. To identify new functions for mammalian genes in innate immunity we will:\Generate a panel of homozygous inducible gene knockout mouse ES cell lines working from a list of ca. 80 candidate genes (MGP phenotyping and GWAS hits). \Scale up differentiation protocols for immune challenge and stimulation assays.\Benchmark in vitro differentiated ES cell models against primary immune cells from selected MGP mouse lines and eventually against human iPS cells.\Screen a panel of 30 genetically modified cell lines in immunological challenge and stimulation assays, defining phenotypes at the cellular level by high content imaging, biochemically by assaying cytokine production, and at the molecular level by transcriptome analysis.\Define novel gene functions in innate immunity through network analysis of gene expression data from all challenged cell lines.\In the longer term scale up the production and phenotyping of genetically modified cell lines (mouse or human as appropriate).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Project description:MotivationAn important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host-pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called 'multitask learning', which considers the problem of building models across tasks that are related to each other. A 'task' in our scenario is the set of host-pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.ResultsOur current work on host-pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex-Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host-pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.AvailabilityThe predictions and code are available at: http://www.cs.cmu.edu/?mkshirsa/ismb2013_paper320.html .Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

The diverse roles of RIP kinases in host-pathogen interactions.

Publications

The diverse roles of RIP kinases in host-pathogen interactions.

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