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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Parasitic Diseases Laboratory » Research » Research Project #442578

Research Project: Comprehensive Mining of the Blood Cell Transcriptome for Improved Phenomics in Swine

Location: Animal Parasitic Diseases Laboratory

Project Number: 8042-32000-117-002-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Apr 1, 2022
End Date: Mar 31, 2025

This grant is aimed at developing a whole blood molecular assay to predict piglet quantitative traits for improved health and growth potential. The objctive are: 1. Develop and validate cell-type-specific gene expression signatures for comprehensive deconvolution of whole blood RNA data 2: Test multiple deconvolution methods to maximize re-use of multiple existing large-scale RNA expression data 3: Develop and validate cost-effective methods for practical whole blood RNA expression phenotyping

ARS will generate comprehensive transcriptomes of all cell types in whole blood, including single cell expression data of lymphocytes and monocytes, as well as isolated granulocytes, platelets and erythrocytes. ARS will collect these transcriptomes across multiple biological conditions such as pre- and post-weaning of healthy pigs as well as during bacterial and viral challenges. We will then jointly analyze these data to identify specific gene sets whose expression pattern across all cell types can be used as a means to estimate the composition of whole blood samples. These gene lists will be validated on an independent set of samples. Validated gene sets will then be used to estimate cell composition in whole blood-based RNA expression datasets in many published projects studying the genetic basis for phenotypic variation in response to infectious disease, differences in feed efficiency, and blood cell traits. Such deconvolution will allow the separation of RNA level differences due to composition from those due to transcriptional differences at the cellular level and will increase accuracy and clarify biological interpretation of current and future eQTL and GWAS whole-blood based studies. Finally, gene sets predictive of cell composition will be converted to NanoString assays, which do not require bioinformatic analysis of RNAseq data. We will also test whether NanoString assays can be significantly scaled down, to eliminate the requirement of on-farm venous puncture and broaden the applicability of RNA biomarkers across pig phenotyping research.