Adhideb Ghosh

Single-Nucleus Analysis of Human White Adipose Tissue Reveals Adipocyte Subsets with Distinct Metabolic Profiles

The paper identifies distinct adipocyte subsets in human white adipose tissue with different metabolic profiles and links TSHZ3 to their regulation and metabolic disease associations.

p63 and PITX1 sustain a pre-invasive malignant keratinocyte population in squamous cell carcinoma precursors

The paper identifies a pre-invasive malignant keratinocyte population in actinic keratosis, characterized by p63 and PITX1 regulation, and distinguishes early oncogenic programs from invasion effectors in squamous cell carcinoma.

A conserved transcriptomic model defines metabolic resilience and vulnerability in obesity

The paper constructs a transcriptomic BMI model to identify molecular differences between metabolically healthy and unhealthy obesity in humans and validates it in mice.

Slit3 Fragments Orchestrate Neurovascular Expansion and Thermogenesis in Brown Adipose Tissue

This study identifies Slit3 fragments that coordinate neurovascular expansion and thermogenesis in brown adipose tissue by regulating angiogenesis and sympathetic innervation.

Sex-specific role of epigenetic modification of a leptin upstream enhancer in the adipose tissue

The paper investigates how maternal thyroid hormone exposure affects DNA methylation at a leptin upstream enhancer in adipose tissue, with sex-specific impacts on body fat percentage.

Control of cholesterol-induced adipocyte inflammation by the Nfe2l1-Atf3 pathway

This study identifies the Nfe2l1-Atf3 pathway as a regulator of cholesterol-induced adipocyte inflammation and insulin resistance in both human and mouse models.

The obesity-linked human lncRNA AATBC regulates adipocyte plasticity by stimulating mitochondrial dynamics and respiration

This study identifies AATBC as a human-specific lncRNA that regulates adipocyte plasticity and mitochondrial function, linking it to metabolic health.

Biological function polarity prediction of missense variants using machine learning

This paper develops a machine learning classifier to predict whether missense variants cause gain or loss of protein function, achieving high accuracy on independent data.