Biology subjects

Soare, T.

Publications and source records attributed to Soare, T..

3 recordsLinked to original sources

bioRxiv2024

Causal considerations can determine the utility of machine learning assisted GWAS

Machine Learning (ML) is increasingly employed to generate phenotypes for genetic discovery, either by imputing existing phenotypes into larger cohorts or by creating novel phenotypes. While these ML-derived phenotypes can significantly increase sample size, and thereby empower genetic discovery, they can also inflate the false discovery rate (FDR). Recent research has focused on developing estimators that leverage both true and machine-learned phenotypes to properly control the type-I error. Our work complements these efforts by exploring how the true positive rate (TPR) and FDR depend on the causal relationships among the inputs to the ML model, the true phenotypes, and the environment. Using a simulation-based framework, we study architectures in which the machine-learned proxy phenotype is derived from biomarkers (i.e. inputs) either causally upstream or downstream of the target phenotype. We show that no inflation of the false discovery rate occurs when the proxy phenotype is generated from upstream biomarkers, but that false discoveries can occur when the proxy phenotype is generated from downstream biomarkers. Next, we show that power to detect variants truly associated with the target phenotype depends on its heritability and correlation with the proxy phenotype. However, the source of the correlation is key to evaluating a proxy phenotype's utility for genetic discovery. We demonstrate that evaluating machine-learned proxy phenotypes using out-of-sample predictive performance (e.g. phenotypic correlation) provides a poor lens on utility. This is because overall predictive performance does not differentiate between genetic and environmental correlation. In addition to parsing these properties of machine-learned phenotypes via simulations, we further illustrate them using real-world data from the UK Biobank.

genetics↗

bioRxiv2024

Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes

Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

neuroscience↗

bioRxiv2023

NMN Works in HFD-Induced T2DM by Interesting Effects in Adipose Tissue, and not by Mitochondrial Biogenesis

Nicotinamide mononucleotide (NMN) has emerged as a promising therapeutic intervention for age-related disorders, including Type 2 Diabetes. In this study, we investigated the effects of NMN treatment on glucose uptake and its underlying mechanisms in various tissue and cell lines. Through a comprehensive proteomic analysis, we uncovered a series of distinct organ-specific effects that contribute to the observed improvements in glucose metabolism. Notably, we observed the upregulation of thermogenic UCP1, promoting enhanced glucose utilization in muscle tissue. Additionally, liver and muscle cells displayed a unique response, characterized by spliceosome down-regulation and concurrent upregulation of chaperones, proteasomes, and ribosomes, leading to a mildly impaired and energy-inefficient protein synthesis machinery. Adipose tissue exhibited increased protein synthesis and degradation, fatty acid degradation, Lysosome and mTOR cell proliferation signalling up-regulation, while showing a surprising repressive effect on mitochondrial biogenesis. Furthermore, our findings revealed a remarkable metabolic rewiring in the brain, involving increased production of ketone bodies, down-regulation of mitochondrial OXPHOS components and the TCA cycle, and the induction of known fasting-associated effects. Collectively, our data elucidate the multifaceted nature of NMN action, highlighting its organ-specific effects and their role in modulating glucose metabolism. These findings deepen our understanding of NMNs therapeutic potential and pave the way for novel strategies in managing metabolic disorders.

biochemistry↗