Dataset Information

The allometry of host-pathogen interactions.

ABSTRACT:

Background

Understanding the mechanisms that control rates of disease progression in humans and other species is an important area of research relevant to epidemiology and to translating studies in small laboratory animals to humans. Body size and metabolic rate influence a great number of biological rates and times. We hypothesize that body size and metabolic rate affect rates of pathogenesis, specifically the times between infection and first symptoms or death.

Methods and principal findings

We conducted a literature search to find estimates of the time from infection to first symptoms (t(S)) and to death (t(D)) for five pathogens infecting a variety of bird and mammal hosts. A broad sampling of diseases (1 bacterial, 1 prion, 3 viruses) indicates that pathogenesis is controlled by the scaling of host metabolism. We find that the time for symptoms to appear is a constant fraction of time to death in all but one disease. Our findings also predict that many population-level attributes of disease dynamics are likely to be expressed as dimensionless quantities that are independent of host body size.

Conclusions and significance

Our results show that much variability in host pathogenesis can be described by simple power functions consistent with the scaling of host metabolic rate. Assessing how disease progression is controlled by geometric relationships will be important for future research. To our knowledge this is the first study to report the allometric scaling of host/pathogen interactions.

SUBMITTER: Cable JM

PROVIDER: S-EPMC2042517 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The allometry of host-pathogen interactions.

Cable Jessica M JM Enquist Brian J BJ Moses Melanie E ME

PloS one 20071107 11

<h4>Background</h4>Understanding the mechanisms that control rates of disease progression in humans and other species is an important area of research relevant to epidemiology and to translating studies in small laboratory animals to humans. Body size and metabolic rate influence a great number of biological rates and times. We hypothesize that body size and metabolic rate affect rates of pathogenesis, specifically the times between infection and first symptoms or death.<h4>Methods and principal ...[more]

PMID: 17987117

Similar Datasets

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 allometry of host-pathogen interactions.

Background

Methods and principal findings

Conclusions and significance

Publications

The allometry of host-pathogen interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets