Infections caused by antibiotic-resistant bacterial pathogens are exceedingly common in immunocompromised hosts. Patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) are particularly susceptible to these infections and are the patient population our studies will focus upon. Our goal is to extend and further develop systems biology approaches that our group has pioneered to identify mechanisms by which the intestinal microbiota confers resistance to infection by Vancomycin-resistant Enterococcus (VRE), antibiotic-resistant Klebsiella pneumoniae (arKp) and Clostridium difficile (C. diff).
Aim 1 of our project is to establish a clinical database from the hospital recrds of allo-HSCT patients during their initial hospitalization that will include all laboratory values,vital signs, pharmacy data, dietary data, symptoms and physical exam findings.
Aim 2 will expand our fecal bank by collecting fecal samples from approximately 160 allo-HSCT patients per year and determining the presence/absence of VRE, arKp and C. diff by culture and PCR. We will use NGS of 16S rRNA genes to determine microbiota composition on each sample, will perform metagenomic and RNA sequencing to determine the bacterial transcriptome and perform metabolomic analyses on a selected subset of fecal samples.
Aim 3 is to extend our mathematical modeling to identify specific members of the microbiota, metabolic pathways and metabolic products that correlate with resistance to VRE or arKp expansion in the GI tract or are associated with resistance to C. diff infection. The clinical database will be used to establish correlations between clinical treatments or events and changes in the intestinal microbiota or the expression of bacterial metabolic pathways. Ultimately, the computational platforms developed in aim 3 will identify bacterial species or consortia that are associated with resistance to infection and Aim 4 will test these associations in germ-free mouse models. We will culture bacterial species associated with resistance, colonize mice with these protective bacteria and test for resistance against VRE, arKp and C. diff. Samples obtained from these experimental studies will be subjected to metagenomic and metabolomic analyses to further refine, in an iterative fashion, computational models developed in aim 3. Our proposed studies will develop new and extend existing computational models to identify bacterial species and molecular mechanisms that confer resistance to antibiotic-resistant bacterial infections.
The normal bacteria inhabiting the human intestine provide a high level of resistance against antibiotic-resistant bacterial pathogens. We are investigating the intestinal flora of hospitalized patients and using mathematical modeling to identify bacterial species and their metabolic products that reduce the risk of infection by three prevalent antibiotic-resistant bacteria. These studies may lead to the development of new approaches to treat and prevent antibiotic-resistant infections.
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