Hematopoietic Stem-Cell Senescence and Myocardial Repair: Coronary Artery Disease Genotype/Phenotype Analysis of Post-MI Myocardial Regeneration Response Induced by CABG/CD133+ Bone Marrow Hematopoietic Stem Cell Treatment in RCT PERFECT Phase 3

Wolfien, Markus; Klatt, Denise; Salybekov, Amankeldi A.; Ii, Masaaki; Komatsu-Horii, Miki; Gaebel, Ralf; Philippou‐Massier, Julia; Schrinner, Eric; Investigators, Vascular Regeneration; Blum, Helmut; Schambach, Axel; Asahara, Takayuki; Steinhoff, Gustav; Akimaru, Hiroshi; Akimaru, Erika; Dávid, Róbert; Garbade, Jens; Gummert, Jan; Haverich, Axel; Hennig, Holger; Iwasaki, Hiroto; Kaminski, Alexander; Kawamoto, Atsuhiko; Kowallick, Johannes T.; Krebs, Stefan; Nesteruk, Julia; Ritter, Christian; Stamm, Christof; Tani-Yokoyama, Ayumi; Wagner, Florian Mathias

doi:10.2139/ssrn.3564409

Cited by 3 publications

(3 citation statements)

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“…Thus, to equip HSCs with a selective advantage is not only of interest for X-CGD gene therapy, but also for other pathologies that benefit from gene therapy or from bone marrow transplantation in general. Recently, HSCs with somatic mutations in LNK were identified in patients that responded with an improved myocardial regeneration after infarction and CD133 + cell/coronary artery bypass graft surgery [ 53 ]. The findings indicate that modulation of LNK function in HSCs might also be beneficial for non-hematologic disorders.…”

Section: Discussionmentioning

confidence: 99%

Competitive sgRNA Screen Identifies p38 MAPK as a Druggable Target to Improve HSPC Engraftment

Klatt

Schinke

et al. 2020

Cells

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Previous gene therapy trials for X-linked chronic granulomatous disease (X-CGD) lacked long-term engraftment of corrected hematopoietic stem and progenitor cells (HSPCs). Chronic inflammation and high levels of interleukin-1 beta (IL1B) might have caused aberrant cell cycling in X-CGD HSPCs with a concurrent loss of their long-term repopulating potential. Thus, we performed a targeted CRISPR-Cas9-based sgRNA screen to identify candidate genes that counteract the decreased repopulating capacity of HSPCs during gene therapy. The candidates were validated in a competitive transplantation assay and tested in a disease context using IL1B-challenged or X-CGD HSPCs. The sgRNA screen identified Mapk14 (p38) as a potential target to increase HSPC engraftment. Knockout of p38 prior to transplantation was sufficient to induce a selective advantage. Inhibition of p38 increased expression of the HSC homing factor CXCR4 and reduced apoptosis and proliferation in HSPCs. For potential clinical translation, treatment of IL1B-challenged or X-CGD HSPCs with a p38 inhibitor led to a 1.5-fold increase of donor cell engraftment. In summary, our findings demonstrate that p38 may serve as a potential druggable target to restore engraftment of HSPCs in the context of X-CGD gene therapy.

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Section: Discussionmentioning

confidence: 99%

Competitive sgRNA Screen Identifies p38 MAPK as a Druggable Target to Improve HSPC Engraftment

Klatt

Schinke

et al. 2020

Cells

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“…This suggests another potentially promising avenue for future research or a complementary approach with DNA-based methods to reduce misclassification. 94 Machine learning formulation. Given the aligned sequencing data ([1] read pileup image, which is a matrix of dimension M and N, with M the number of reads and N the length of reads; or [2] the raw reads, which are a set of sequences strings) for each locus, classify the multi-class variant status.…”

Section: Variant Callingmentioning

confidence: 99%

Machine learning applications for therapeutic tasks with genomics data

Huang

Xiao²,

Glass

et al. 2021

Patterns

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Summary Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

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“…In this article of EBioMedicine, Wolfien et al. propose a diagnostic strategy to predict the response of CABG surgery patients in terms of myocardial repair induced by bone marrow stem cells (BMSC) [ 1 ]. Predictive analyses in such a scenario would provide much needed information to identify those individuals most likely to benefit from BMSC treatment with patient-specific diagnostic characteristics.…”

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confidence: 99%