Clemson University

BioSci Seminar Series Presents: Richard Laughlin

Department of Biological Sciences Seminar
March 7th, 2014; 3:30 pm
G-33 Jordan

"Describing and Predicting Host-Pathogen Interactions with Salmonella enterica Typhimurium in a Natural Host Model of Infection"

Dr. Richard Laughlin
Postdoctoral Research Associate
Veterinary Medicine & Biomedical Sciences
Texas A&M University
Non-typhoidal Salmonella (NTS) serotypes are a very common cause of food poisoning with serious symptoms of diarrhea, vomiting, fever and intestinal cramping. While the host inflammatory response helps to control non-typhoidal Salmonella serotypes in tissue, it also enhances growth of the pathogen in the intestinal lumen by dramatically altering this environment. To further understand these host-pathogens interactions, we employed the neonatal calf ligated jejunal-ileal loop procedure as an in vivo model of human salmonellosis, because humans and cattle, unlike mice, naturally both develop diarrhea and neutrophilic infiltrates in response to Salmonella enterica Typhimurium (STm). Our efforts to understand these interaction have taken two distinct, but related paths: Firstly, we have utilized recombinant strains of STm which conditionally express GFP to identify the spatio-temporal gene expression profile of the pathogen during a time course of acute infection. We also analyzed tissue biopsies by confocal and electron microscopy to identify STm in compromised vesicles within extruding intestinal epithelial cells. Secondly, we generated transcriptomic and proteomic data sets based on the in vivo response of both host and pathogen to each other during acute infection (0.25, 0.5, 1, 2, 4, 8h and 12h postinfection). We then integrated the in vivo time course transcriptomic and proteomic data from host and pathogen with a priori biological knowledge into dynamic Bayesian network (DBN) models and performed comprehensive systems level analyses for identifying both the host and pathogen temporal responses. Finally, we developed DBN based models for predicting host-pathogen protein-to-protein docking simulation interactions on time-course transcriptomic, proteomic data and prior biological knowledge to develop in silico predictive interactome models of STm infection biology. The in silico interactome model predicted 334 known and previously unknown STm genes to interact with 1887 host genes. We have phenotypically tested these predictions using gene-deletion strains of STm or short-interfering RNA technologies to narrow our focus, and are currently evaluating host-pathogen protein candidates for protein:protein interactions. These results provide biological evidence that in silico interactome predictive modeling is a useful tool to identify interactive host and pathogen genes in the molecular pathogenesis of salmonellosis.
Host: Dr. Rhonda Powell (

Friday, March 7, 2014 at 3:30pm to 5:00pm

Jordan Hall, G-33
130 Delta Epsilon Ct., Clemson, SC 29634, USA

Event Type

Lectures / Seminars / Speakers, Seminars


College of Agriculture, Forestry and Life Sciences, Biological Sciences

Target Audience

Students, Faculty, Staff


Contact Name:

Rhonda Powell

Contact Phone:


Contact Email:


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