Dataset Information

Scale dependencies and generalism in host use shape virus prevalence.

ABSTRACT: Processes that generate the distribution of pathogens and their interactions with hosts are not insensitive to changes in spatial scale. Spatial scales and species traits are often selected intentionally, based on practical considerations, ignoring biases that the scale and type of observation may introduce. Specifically, these biases might change the interpretation of disease-diversity relationships that are reported as either 'dilution' or 'amplification' effects. Here, we combine field data of a host-pathogen community with empirical models to test the effects that (i) spatial scale and (ii) host range have on the relationship between plant-virus infection prevalence and diversity. We show that prevalence-diversity relationships are scale-dependent and can produce opposite effects associated with different habitats at sub-ecosystem scales. The total number of host species of each virus reflected generalism at the ecosystem scale. However, plasticity in host range resembled habitat-specific specialization and also changed model predictions. We show that habitat heterogeneity, ignored at larger (ecosystem) spatial scales, influences pathogen distributions. Hence, understanding disease distributions and the evolution of pathogens requires reconciling specific hypotheses of the study with an appropriate spatial scale, or scales, and consideration of traits, such as host range, that might strongly contribute to biotic interactions.

SUBMITTER: McLeish M

PROVIDER: S-EPMC5745412 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Scale dependencies and generalism in host use shape virus prevalence.

McLeish Michael M Sacristán Soledad S Fraile Aurora A García-Arenal Fernando F

Proceedings. Biological sciences 20171201 1869

Processes that generate the distribution of pathogens and their interactions with hosts are not insensitive to changes in spatial scale. Spatial scales and species traits are often selected intentionally, based on practical considerations, ignoring biases that the scale and type of observation may introduce. Specifically, these biases might change the interpretation of disease-diversity relationships that are reported as either 'dilution' or 'amplification' effects. Here, we combine field data o ...[more]

PMID: 29263286

Similar Datasets

Project description:Recent functional genomics studies including genome-wide small interfering RNA (siRNA) screens demonstrated that hepatitis C virus (HCV) exploits an extensive network of host factors for productive infection and propagation. How these co-opted host functions interact with various steps of HCV replication cycle and exert pro- or antiviral effects on HCV infection remains largely undefined. Here we present an unbiased and systematic strategy to functionally interrogate HCV host dependencies uncovered from our previous infectious HCV (HCVcc) siRNA screen. Applying functional genomics approaches and various in vitro HCV model systems, including HCV pseudoparticles (HCVpp), single-cycle infectious particles (HCVsc), subgenomic replicons, and HCV cell culture systems (HCVcc), we identified and characterized novel host factors or pathways required for each individual step of the HCV replication cycle. Particularly, we uncovered multiple HCV entry factors, including E-cadherin, choline kinase α, NADPH oxidase CYBA, Rho GTPase RAC1 and SMAD family member 6. We also demonstrated that guanine nucleotide binding protein GNB2L1, E2 ubiquitin-conjugating enzyme UBE2J1, and 39 other host factors are required for HCV RNA replication, while the deubiquitinating enzyme USP11 and multiple other cellular genes are specifically involved in HCV IRES-mediated translation. Families of antiviral factors that target HCV replication or translation were also identified. In addition, various virologic assays validated that 66 host factors are involved in HCV assembly or secretion. These genes included insulin-degrading enzyme (IDE), a proviral factor, and N-Myc down regulated Gene 1 (NDRG1), an antiviral factor. Bioinformatics meta-analyses of our results integrated with literature mining of previously published HCV host factors allows the construction of an extensive roadmap of cellular networks and pathways involved in the complete HCV replication cycle. This comprehensive study of HCV host dependencies yields novel insights into viral infection, pathogenesis and potential therapeutic targets.

Project description:BACKGROUND:Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. RESULTS:We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~?2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. CONCLUSIONS:Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

Scale dependencies and generalism in host use shape virus prevalence.

Publications

Scale dependencies and generalism in host use shape virus prevalence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure