Machine learning-based identification and cellular validation of Tropomyosin 1 as a genetic inhibitor of hematopoiesis

Thom CS, Jobaliya CD, Lorenz K, Maguire JA, Gagne A, Gadue P, French DL, Voight BF

Jun 13, 2019
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Received Date: 3rd June 19

A better understanding of the genetic mechanisms regulating hematopoiesis are necessary, and could augment translational efforts to generate red blood cells (RBCs) and/or platelets in vitro. Using available genome-wide association data sets, we applied a machine-learning framework to identify genomic features enriched at established platelet trait associations and score variants genome-wide to identify biologically plausible gene candidates. We found that high-scoring SNPs marked relevant loci and genes, including an expression quantitative trait locus for Tropomyosin 1 (TPM1). CRISPR/Cas9-mediated TPM1 knockout in ­human induced pluripotent stem cells (iPSCs) unexpectedly enhanced early hematopoietic progenitor development. Our findings may help explain human genetics associations and identify a novel genetic strategy to enhance in vitro hematopoiesis, increasing RBC and MK yield.

Read in full at bioRxiv.

This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

Nature Communications

Nature Research, Springer Nature