A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.
Ann Surg
; 275(6): 1094-1102, 2022 06 01.
Article
em En
| MEDLINE
| ID: mdl-35258509
ABSTRACT
OBJECTIVE:
To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing.BACKGROUND:
Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes.METHODS:
Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury.RESULTS:
The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints.CONCLUSIONS:
This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
/
Proteômica
Tipo de estudo:
Observational_studies
Limite:
Humans
Idioma:
En
Revista:
Ann Surg
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Nova Caledônia