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Biomedical subjects

Melina Claussnitzer

Publications and source records attributed to Melina Claussnitzer.

3 recordsLinked to original sources

Loss of Mtarc1 Protects Against Steatotic Liver Disease in Mice.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) spans from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH) and can progress to cirrhosis or hepatocellular carcinoma. Despite its prevalence, effective therapies are lacking. Recent genome-wide association studies identified a common missense variant (rs2642438) in the Mitochondrial Amidoxime Reducing Component 1 (MTARC1) gene that protects against liver cirrhosis without increasing cardiovascular disease risk. Biochemical and disease risk signatures associated with carriers of this missense variant also aligned with those of a known loss-of-function MTARC1 variant, suggesting mARC1 inhibition as a potential MASLD treatment. METHODS: To validate mARC1 loss-of-function as protective against MASLD, we generated Mtarc1 knockout (KO) mice and placed them on a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD). Effects of Mtarc1 KO on obesity and type 2 diabetes were explored using a high-fat diet. Hepatocytes from Mtarc1 KO mice were isolated to explore the molecular mechanisms by which Mtarc1 KO impacts lipid metabolism. RESULTS: Mtarc1 KO mice exhibited no vital growth or development defects. With a high-fat diet-induced obesity model, obese Mtarc1 KO mice exhibited reduced liver mass and lower cholesterol levels, with no effect on glucose homeostasis. In a CDAHFD-induced MASLD model, mARC1 deficiency significantly reduced liver steatosis, profibrosis, and inflammation. Untargeted metabolomics profiling further showed hepatic enrichment of phospholipids in Mtarc1 KO mice. Primary hepatocytes isolated from Mtarc1 KO mice exhibited reduced lipid droplet accumulation, decreased fatty acid uptake, and increased lipid secretion. CONCLUSIONS: These findings support mARC1 inhibition as a promising therapeutic strategy for MASLD/MASH.

Animals

Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy.

BACKGROUND: White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic architecture of adipocyte morphology remain poorly explored. METHODS: We introduce Adipocyte U-Net 2.0, an advanced deep learning method for the semantic segmentation of adipose tissue histology, enabling analysis of over 27 million adipocytes from 2,667 individuals. FINDINGS: Our approach revealed that adipocyte hypertrophy associates with metabolic dysfunction, including increased fasting glucose, glycated hemoglobin, leptin, and triglycerides, with decreased adiponectin and HDL cholesterol levels. Through the largest genome-wide association study of adipocyte size to date (NSubcutaneous = 2,066, NVisceral = 1,878), we identified four genome-wide significant loci: two in sex-combined analysis (rs73184721 in NAALADL2 and rs200047724 in NRXN3) and two female-specific variants (rs140503338 and rs11656704 in ULK2). Notably, these genetic associations showed congruent relationships with cardiometabolic traits, suggesting shared biological mechanisms. INTERPRETATION: Our findings demonstrate the utility of deep learning for adipocyte phenotyping at scale and provide novel insights into the genetic basis of adipocyte morphology and its relationship to metabolic disease.

Journal Article

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Humans