Inferring Infection Patterns Based on a Connectivity Map of Host Transcriptional Responses.

Lu Han,Haochen He,Dafei Xie,Hui Bai,Xiaochen Bo,Xiaofei Zheng,Yang Liu,Xiuliang Cui,Fei Li,Shengqi Wang

doi:10.1038/srep15820

Abstract

Host responses to infections represent an important pathogenicity determiner, and delineation of host responses can elucidate pathogenesis processes and inform the development of anti-infection therapies. Low cost, high throughput, easy quantitation, and rich descriptions have made gene expression profiling generated by DNA microarrays an optimal approach for describing host transcriptional responses (HTRs). However, efforts to characterize the landscape of HTRs to diverse pathogens are far from offering a comprehensive view. Here, we developed an HTR Connectivity Map based on systematic assessment of pairwise similarities of HTRs to 50 clinically important human pathogens using 1353 gene-expression profiles generated from >60 human cells/tissues. These 50 pathogens were further partitioned into eight robust “HTR communities” (i.e., groups with more consensus internal HTR similarities). These communities showed enrichment in specific infection attributes and differential gene expression patterns. Using query signatures of HTRs to external pathogens, we demonstrated four distinct modes of HTR associations among different pathogens types/class, and validated the reliability of the HTR community divisions for differentiating and categorizing pathogens from a host-oriented perspective. These findings provide a first-generation HTR Connectivity Map of 50 diverse pathogens, and demonstrate the potential for using annotated HTR community to detect functional associations among infectious pathogens.

Highlights

Host responses to infections represent an important pathogenicity determiner, and delineation of host responses can elucidate pathogenesis processes and inform the development of antiinfection therapies
We gathered 1,353 reference gene expression profiles from more than 60 human cells/tissues infected with 50 clinically important pathogen types and implemented an unbiased host transcriptional responses (HTRs) characterization strategy and rank-based expression profile comparisons[11,12] to evaluate 1,225 pairwise pathogen-pathogen HTR similarities (Fig. 1). We further divided these first 50 pathogens into groups with significant internal HTR similarity and characteristic modes of host gene expression patterns tagged with specific infection attributes, i.e., a reference resource known as HTR community
Using HTR signatures from external pathogens, we provided in the present study proof-of-concept evidence that HTR community scheme can be used to (i) recognize pathogen class related to common featured HTRs, (ii) discern the pathogenicity of pathogens with close phylogenetic relations (e.g., Streptococcus species), (iii) identify HTRs that are representative of particular microbiota and reflect a degree of host adaptation, and (iv) discover unknown common and unique HTRs to pathogens whose infections produce similar clinical presentations

Summary

Introduction

Host responses to infections represent an important pathogenicity determiner, and delineation of host responses can elucidate pathogenesis processes and inform the development of antiinfection therapies. We developed an HTR Connectivity Map based on systematic assessment of pairwise similarities of HTRs to 50 clinically important human pathogens using 1353 gene-expression profiles generated from >60 human cells/tissues These 50 pathogens were further partitioned into eight robust “HTR communities” (i.e., groups with more consensus internal HTR similarities). We gathered 1,353 reference gene expression profiles from more than 60 human cells/tissues infected with 50 clinically important pathogen types and implemented an unbiased HTR characterization strategy and rank-based expression profile comparisons[11,12] to evaluate 1,225 pairwise pathogen-pathogen HTR similarities (Fig. 1) We further divided these first 50 pathogens into groups with significant internal HTR similarity and characteristic modes of host gene expression patterns tagged with specific infection attributes, i.e., a reference resource known as HTR community. The annotations for community pathogens allowed us to propose, with an unprecedented host-oriented perspective, new associations for well-known pathogen taxonomy classes and novel associations for microenvironment-related and clinically relevant pathogens among these 50 infectious pathogens (Fig. 1)

Objectives

Methods

Conclusion