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In EBioMedicine

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 (∆LVEF) myocardial regeneration Responders (n=14; ∆LVEF +16% day 180/0) and Non-responders (n=9; ∆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 RvsNR preoperative gene-expression signatures in peripheral blood acorrelated to SH2B3 (p<0.05). Mutation analysis revealed increased specific variants in RvsNR. (R: 48 genes; NR: 224 genes). 2. Preclinical:SH2B3/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% RvsNR (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.

FUNDING : German Ministry of Research and Education (BMBF): Reference and Translation Center for Cardiac Stem Cell Therapy - FKZ0312138A and FKZ031L0106C, German Ministry of Research and Education (BMBF): Collaborative research center - DFG:SFB738 and Center of Excellence - DFG:EC-REBIRTH), European Social Fonds: ESF/IV-WM-B34-0011/08, ESF/IV-WM-B34-0030/10, and Miltenyi Biotec GmbH, Bergisch-Gladbach, Germany. Japanese Ministry of Health : Health and Labour Sciences Research Grant (H14-trans-001, H17-trans-002) TRIAL REGISTRATION: NCT00950274.

Wolfien Markus, Klatt Denise, Salybekov Amankeldi A, Ii Masaaki, Komatsu-Horii Miki, Gaebel Ralf, Philippou-Massier Julia, Schrinner Eric, Akimaru Hiroshi, Akimaru Erika, David Robert, Garbade Jens, Gummert Jan, Haverich Axel, Hennig Holger, Iwasaki Hiroto, Kaminski Alexander, Kawamoto Atsuhiko, Klopsch Christian, Kowallick Johannes T, Krebs Stefan, Nesteruk Julia, Reichenspurner Hermann, Ritter Christian, Stamm Christof, Tani-Yokoyama Ayumi, Blum Helmut, Wolkenhauer Olaf, Schambach Axel, Asahara Takayuki, Steinhoff Gustav


Angiogenesis induction, CABG, CHIP, Cardiac stem cell therapy, Clonal hematopoiesis of indeterminate pathology, Coronary bypass surgery, Machine learning, Myocardial regeneration, Post myocardial infarction heart failure, SH2B3