Hoang, N. | bioRxiv Science

bioRxiv2025

Tissue-specific modulation of NADH consumption as an anti-aging intervention in Drosophila

Aging is characterized by extensive metabolic dysregulation. Redox coenzyme nicotinamide adenine dinucleotide (NAD) can exist in oxidized (NAD+) or reduced (NADH) states, which together form a key NADH/NAD+ redox pair. Total levels of NAD decline with age in a tissue-specific manner, thereby playing a significant role in the aging process. Supplementation with NAD precursors boosts total cellular NAD levels and provides some therapeutic benefits in human clinical trials. However, supplementation studies cannot determine tissue-specific effects of an altered NADH/NAD+ ratio. Here, we created transgenic Drosophila expressing a genetically encoded xenotopic tool LbNOX to directly manipulate the cellular NADH/NAD+ ratio. We found that LbNOX expression in Drosophila impacts both NAD(H) and NADP(H) metabolites in a sex-specific manner. LbNOX rescues neuronal cell death induced by the expression of mutated alpha-B crystallin in the Drosophila eye, a widely used system to study reductive stress. Utilizing LbNOX, we demonstrate that targeting redox NAD metabolism in different tissues may have drastically different outcomes, as the expression of LbNOX solely in the muscle is much more effective for rescuing paraquat-induced oxidative stress compared to whole-body expression. Excitingly, we demonstrate that perturbing NAD(P) metabolism in non-neuronal tissues is sufficient to rejuvenate sleep profiles in aged flies to a youthful state. In summary, we used xenotopic tool LbNOX to identify tissues and metabolic processes which benefited the most from the modulation of the NAD metabolism thereby highlighting important aspects of rebalancing the NAD and NADP pools, all of which can be translated into novel designs of NAD-related human clinical trials. Significance statementTotal levels of NAD decline with age in a tissue-specific manner, thereby playing a significant role in the aging process. Supplementation with NAD precursors boosts organismal NAD levels but cannot determine tissue-specific effects of altered NAD metabolism. Here, we created transgenic Drosophila expressing a genetically encoded xenotopic tool, LbNOX, to directly manipulate NAD metabolism. We demonstrate that targeting NAD metabolism in just one tissue may be more effective than altering whole-body metabolism and can reverse some aging-related manifestations in a sex-specific manner. We anticipate that our work will define the tissues that benefit the most from targeting NAD metabolism and aid in designing better human clinical trials.

synthetic biology↗

bioRxiv2022

Folate depletion induces erythroid differentiation through perturbation of de novo purine synthesis

All dividing cells require the essential vitamin folate. Hematopoietic cells harbor a unique sensitivity to folate deprivation, as implied by the development of folate-deficient anemia and the utility of anti-folate chemotherapy in blood cancer. To study this metabolic sensitivity, we applied mild folate depletion to human and mouse erythroid cell lines, as well as primary murine erythroid progenitors. We show that folate depletion induces early blockade of purine synthesis that is followed by enhanced heme metabolism, hemoglobin synthesis and erythroid differentiation. This finding is phenocopied by inhibition of folate metabolism using SHIN1, an inhibitor of the folate enzymes SHMT1/2. The metabolically-driven differentiation is rescued by supplementation of purine precursors, yet occurs independent of nucleotide sensing through mTORC1 and AMPK. Our work profiles the metabolic response to folate depletion in erythroid cells and suggest that premature differentiation of folate-deprived erythroid progenitor cells is a mechanistic etiology to folate-deficiency induced anemia.

molecular biology↗

bioRxiv2020

Automatic inference of demographic parameters using Generative Adversarial Networks

AO_SCPLOWBSTRACTC_SCPLOWPopulation genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since the evolutionary "ground truth" for real data is always limited, simulated data is crucial for training supervised machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. Here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg-gan, is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

genomics↗