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Nowak, R. A.

Publications and source records attributed to Nowak, R. A..

2 recordsLinked to original sources

Genome-wide CRISPRi screen identifies basigin loss as protective in cardiac hypoxia

Cardiac function depends on continuous oxidative metabolism, rendering cardiomyocytes highly vulnerable to oxygen deprivation. Here, we performed a genome-wide CRISPR interference (CRISPRi) screen in human iPSC-derived cardiomyocytes to identify genes that modulate survival during chronic hypoxia. This screen revealed that knockdown of basigin (BSG), a chaperone for the monocarboxylate transporters MCT1 and MCT4, confers robust protection. Canonically, hypoxic cells suppress pyruvate dehydrogenase (PDH) activity to reduce the oxidation of major fuel sources, thereby limiting TCA cycle flux, lowering oxygen consumption, and minimizing reactive oxygen species generated by an overly reduced electron transport chain (ETC). In contrast, we found that BSG inhibition reverses this response, prioritizing ATP maintenance during hypoxia and enhancing cardiomyocyte survival. Mechanistically, BSG loss restricts lactate efflux, leading to decreased PDH phosphorylation and increased glucose uptake for oxidation. Consistent with this, ETC subunits are more essential under hypoxia, highlighting cardiomyocytes unusual reliance on aerobic ATP production even when oxygen is limited. These findings challenge prevailing models of hypoxic adaptation by revealing cardiomyocyte-specific bioenergetic requirements and motivating future therapeutic efforts.

cell biology↗

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

bioengineering↗