A Bipartite Network Module-Based Project to Predict Pathogen–Host Association

Li, Jie; Wang, Shiming; Chen, Zhuo; Wang, Yadong

doi:10.3389/fgene.2019.01357

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…Bipartite networks/graphs are graphs in which the nodes of the graph can be divided into two independent sets, and the nodes from one set connect only with nodes in the other set ( 11 , 105 ). To visualize host-parasite interactions, bipartite networks were constructed with R package igraph v1.2.5 ( 106 ) and Cytoscape v3.8.0 ( 107 ) where the nodes were the genes and their interaction were represented with an edge.…”

Section: Methodsmentioning

confidence: 99%

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

2021

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Dual RNA sequencing (RNA-Seq) is the simultaneous transcriptomic analysis of interacting symbionts, for example, in malaria. Potential cross-species interactions identified by correlated gene expression might highlight interlinked signaling, metabolic, or gene regulatory pathways in addition to physically interacting proteins. Often, malaria studies address one of the interacting organisms—host or parasite—rendering the other “contamination.” Here we perform a meta-analysis using such studies for cross-species expression analysis. We screened experiments for gene expression from host and Plasmodium. Out of 171 studies in Homo sapiens, Macaca mulatta, and Mus musculus, we identified 63 potential studies containing host and parasite data. While 16 studies (1,950 samples) explicitly performed dual RNA-Seq, 47 (1,398 samples) originally focused on one organism. We found 915 experimental replicates from 20 blood studies to be suitable for coexpression analysis and used orthologs for meta-analysis across different host-parasite systems. Centrality metrics from the derived gene expression networks correlated with gene essentiality in the parasites. We found indications of host immune response to elements of the Plasmodium protein degradation system, an antimalarial drug target. We identified well-studied immune responses in the host with our coexpression networks, as our approach recovers known broad processes interlinked between hosts and parasites in addition to individual host and parasite protein associations. The set of core interactions represents commonalities between human malaria and its model systems for prioritization in laboratory experiments. Our approach might also allow insights into the transferability of model systems for different pathways in malaria studies. IMPORTANCE Malaria still causes about 400,000 deaths a year and is one of the most studied infectious diseases. The disease is studied in mice and monkeys as lab models to derive potential therapeutic intervention in human malaria. Interactions between Plasmodium spp. and its hosts are either conserved across different host-parasite systems or idiosyncratic to those systems. Here we use correlation of gene expression from different RNA-Seq studies to infer common host-parasite interactions across human, mouse, and monkey studies. First, we find a set of very conserved interactors, worth further scrutiny in focused laboratory experiments. Second, this work might help assess to which extent experiments and knowledge on different pathways can be transferred from models to humans for potential therapy.

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Section: Methodsmentioning

confidence: 99%

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

2021

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“…Bipartite networks/graphs are graphs in which the nodes of the graph can be divided into two independent sets, and the nodes from one set connect only with nodes in the other set [26,27]. To visualise host-parasite interactions, bipartite networks were constructed with R package igraph v1.2.5 [28] and Cytoscape v3.8.0 [29] where the nodes were the genes and their interaction were represented with an edge.…”

Section: Network and Functional Analysesmentioning

confidence: 99%

Publicly available transcriptomes provide the opportunity for dual RNA-Seq meta-analysis inPlasmodiuminfection

Mukherjee

Burgio

Heitlinger

2019

Preprint

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1Dual RNA-Seq is the simultaneous retrieval and analysis of host and pathogen transcriptomes. 2 It follows the rationale that cross-species interactions determine both pathogen virulence and host 3 tolerance, resistance or susceptibility. Correlated gene expression might help identify interlinked 4 signaling, metabolic or gene regulatory pathways in addition to potentially physically interacting 5 proteins. Numerous studies have used RNA-Seq to investigate Plasmodium infection with a focus 6 on only one organism, either the host or the parasite.7Here we propose a meta-analysis approach for dual RNA-Seq. We screened malaria transcrip-8 tome experiments for gene expression data from both Plasmodium and its host. Out of 105 malaria 9 studies in Homo sapiens, Macaca mulatta and Mus musculus, we identified 56 studies with the 10 potential to provide host and parasite data. While 15 studies (1,935 total samples) of these 56 11 explicitly aimed to generate dual RNA-Seq data, 41 (1,129 samples) had an original focus on either 12 the host or the parasite. We show that a total of up to 2,530 samples are suitable for dual RNA-Seq 13 analysis providing an unexplored potential for meta-analysis. 14 We argue that the multitude of variations in experimental conditions should help narrow down 15 a conserved core of cross-species interactions. Different hosts are infected by evolutionarily diverse 16 species of the genus Plasmodium. We propose to overlay interaction networks of different host-17 parasite systems based on orthologous genes. This might allow us to gauge the applicability of 18 model systems for different pathways in malaria infection and to address the evolution of parasite-19 host interactions.20 Word count: 2836, Target journal word limit: 3000 22 23Transcriptomes are often analyzed as a first attempt to understand cellular and organismic events, because a 24 comprehensive profile of RNA expression can be obtained at a reasonable cost and with high technical accuracy 25 [37]. Microarrays dominated transcriptomics since 1995 [11, 22, 44]. Microarrays quantify gene expression 26 based on hybridization of a target sequence to an immobilized probe of known sequence. Technical difficulties 27 associated with microarrays lie in probe-selection, cross-hybridization, and design cost of custom chips [51]. RNA 28 sequencing (RNA-Seq) eliminates these difficulties and provides deep and accurate expression estimates for all 29 RNAs in a sample. RNA-Seq has thus replaced microarrays as the predominant tool for transcriptomics [37,50]. 30RNA-Seq assesses host and parasite transcriptomes simultaneously, if RNA of both organisms is contained 31 in a sample. It has been proposed to analyze the transcriptomes of both organisms involved in an infection 32 as pathogen virulence in an infectious disease is often a result of interlinked processes of host and pathogen 33 (host-pathogen interactions) [32, 50, 51]. 34In case of malaria, unlike in bacterial infections, both the pathogen and the host are eukaryotic organisms 35 with sim...

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Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

Rosilan

Waiho

Fazhan

et al. 2023

Aquaculture Reports

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A Bipartite Network Module-Based Project to Predict Pathogen–Host Association

Cited by 7 publications

References 43 publications

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

Publicly available transcriptomes provide the opportunity for dual RNA-Seq meta-analysis inPlasmodiuminfection

Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

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