Research Project
10274355
Project Summary/Abstract Sepsis is responsible for one out of every five deaths worldwide. It is therefore essential to better understand better diagnose and treat sepsis. We propose to use deep RNA sequencing of the whole blood of sepsis patients to better identify the pathogen causing the disease, triage the appropriate resources, predict outcomes, and identify novel therapeutic targets. Utilizing novel methods of computational analysis of deep RNA sequencing data we will assess microbial populations, RNA biology (specifically RNA splicing entropy and RNA lariats) and identify novel treatment targets/identify patients likely to benefit. Some RNA sequencing data that does not match the species it came from (human) is typically discarded. We will look at this typically discarded data for microbial (bacteria and viruses) populations to improve diagnostic ability. To assess the host response we will study RNA biology. RNA splicing is a basic molecular function that occurs in all cells directly after RNA transcription, but before protein translation in which introns are removed and exons are joined together. Over 90% of human genes with multiple exons have alternative splicing events. We hope to assess if RNA splicing entropy could be a potential biomarker in sepsis. Introns are typically degraded rapidly after removal during splicing, however, the presence of these lariats could signify RNA metabolism dysfunction and we will correlate this to outcomes. RNA sequencing data will also allow for application of novel interventions, such as PD-1 antibodies, to patients most likely to benefit; essentially applying precision medicine to a critically ill patient. We will utilize whole blood from humans with sepsis and compare to control patients in the intensive care unit without sepsis. Samples will be collected serially over the course of the stay in the intensive care unit. We hypothesize that data from deep RNA sequencing obtained during sepsis can be quantified and improve care.
