Abstract
Background: Bone marrow stem cell clonal dysfunction by somatic mutation is suspected to affect post-infarction myocardial regeneration after coronary bypass surgery (CABG). Methods: Transcriptome and variant expression analysis was studied in the phase 3 PERFECT trial post myocardial infarction CABG and CD133(+) bone marrow derived hematopoetic stem cells showing difference in left ventricular ejection fraction (Delta LVEF) myocardial regeneration Responders (n=14;Delta LVEF +16% day 180/0) and Non-responders (n=9;Delta LVEF -1.1% day 180/0). Subsequently, the findings have been validated in an independent patient cohort (n=14) as well as in two preclinical mouse models investigating SH2B3/LNK antisense or knockout deficient conditions. Findings: 1. Clinical: R differed from NR in a total of 161 genes in differential expression (n=23, q<0.05) and 872 genes in coexpression analysis (n=23, q<0.05). Machine Learning clustering analysis revealed distinct RysNR preoperative gene -expression signatures in peripheral blood acorrelated to SH2B3 (p <0.05). Mutation analysis revealed increased specific variants in RysNR. (R: 48 genes;NR: 224 genes). 2. Preclinical: SH2B3I LNK-silenced hematopoietic stem cell (HSC) clones displayed significant overgrowth of myeloid and immune cells in bone marrow, peripheral blood, and tissue at day 160 after competitive bone-marrow transplantation into mice. SH2B3/LNK / mice demonstrated enhanced cardiac repair through augmenting the kinetics of bone marrow-derived endothelial progenitor cells, increased capillary density in ischemic myocardium, and reduced left ventricular fibrosis with preserved cardiac function. 3. Validation: Evaluation analysis in 14 additional patients revealed 85% RysNR (12/14 patients) prediction accuracy for the identified biomarker signature. Interpretation: Myocardial repair is affected by HSC gene response and somatic mutation. Machine Learning can be utilized to identify and predict pathological HSC response.
Item Type: | Journal article |
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Faculties: | Chemistry and Pharmacy > Department of Biochemistry |
Subjects: | 500 Science > 540 Chemistry |
ISSN: | 2352-3964 |
Language: | English |
Item ID: | 89729 |
Date Deposited: | 25. Jan 2022, 09:32 |
Last Modified: | 25. Jan 2022, 09:32 |