Hu, Li

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

Abstract Clinical and preclinical research has demonstrated that iPSC-derived NK (iNK) cells have a high therapeutic potential, yet poor understanding of the detailed process of their differentiation in vitro and their counterpart cell development in vivo has hindered therapeutic iNK cell production and engineering. Here we dissect the crucial differentiation of both fetal liver NK cells and iNK cells to enable the rational design of advanced iNK production protocols. We use a comparative analysis of single-cell RNA-seq (scRNA-seq) to pinpoint key factors lacking in the induced setting which we hypothesized would hinder iNK differentiation and/ or functionality. By analyzing key transcription factor regulatory networks, we discovered the importance of TBX21, EOMES, and STAT5A in the differentiation timeline. This analysis provides a blueprint for further engineering new iPSC lines to obtain iNK cells with enhanced functions. We validated this approach by creating a new line of STAT5A-iPSCs which can be differentiated to STAT5A-expressing macrophages with both NK cell and macrophage features such as perforin production, phagocytosis, and anti-tumor functions.

ABSTRACT His domain protein tyrosine phosphatase (HD-PTP; also known as PTPN23) facilitates function of the endosomal sorting complexes required for transport (ESCRTs) during multivesicular body (MVB) formation. To uncover its role in physiological homeostasis, embryonic lethality caused by a complete lack of HD-PTP was bypassed through generation of hypomorphic mice expressing reduced protein, resulting in animals that are viable into adulthood. These mice exhibited marked lipodystrophy and decreased receptor-mediated signaling within white adipose tissue (WAT), involving multiple prominent pathways including RAS/MAPK, phosphoinositide 3-kinase (PI3K)/AKT and receptor tyrosine kinases (RTKs), such as EGFR. EGFR signaling was dissected in vitro to assess the nature of defective signaling, revealing decreased trans-autophosphorylation and downstream effector activation, despite normal EGF binding. This corresponds to decreased plasma membrane cholesterol and increased lysosomal cholesterol, likely resulting from defective endosomal maturation necessary for cholesterol trafficking and homeostasis. The ESCRT components Vps4 and Hrs have previously been implicated in cholesterol homeostasis; thus, these findings expand knowledge on which ESCRT subunits are involved in cholesterol homeostasis and highlight a non-canonical role for HD-PTP in signal regulation and adipose tissue homeostasis.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

Key Points The type III TGF-β receptor is a marker that distinguishes “early” and “late” BFU-Es. TGF-β inhibitors increase early BFU-E cell self-renewal and total erythroblast production.