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Kulkarni, O.

Publications and source records attributed to Kulkarni, O..

2 recordsLinked to original sources

Cutibacterium acnes-derived short-chain fatty acids drive lipogenesis and induce holocrine secretion in human sebocytes

Sebum lipids are one of the key ecological determinants of skin microbiome composition, where sebaceous sites are enriched with sebum utilising microbes such as Cutibacterium acnes. While sebaceous gland (SG) activation and sebogenesis are classically viewed as host-regulated processes, the association of C. acnes expansion with sebum production in the skin suggests a possible bidirectional host-microbe regulation. Using bacterial supernatants, we combined image-based lipid quantification, GC-FID metabolite profiling, lipid secretion quantification, and transcriptomic analysis to determine the metabolic drivers of the interaction between C. acnes and sebocytes. We found that C. acnes secretome significantly increased lipid droplet accumulation in sebocytes and propionate as the primary driver of this lipogenic response. Mechanistically, propionate reprogrammed the central carbon metabolism, redirecting carbon flux toward energy production and generation of precursors for lipid biosynthesis. Propionate also promoted lipid assembly pathways and modulated the composition of the secreted lipids from the treated sebocytes. We further show that propionate promotes expression of late sebocyte differentiation markers associated with holocrine secretion. Together, these findings redefine the relationship between microbiome and sebaceous glands, and identify C. canes as an active regulator promoting both sebogenesis and holocrine secretion for the release of lipids in the pilosebaceous unit.

molecular biology↗

Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation

Long read sequencing technologies such as Oxford Nanopore (ONT) offer direct, simultaneous detection of DNA base modifications. The recent migration of ONT to the upgraded R10 chemistry has spurred the development of diverse methylation detection models. However, their performance and accuracy remain unclear. Here, leveraging diverse bacterial, plant, and mammalian datasets, we systematically evaluate the current landscape of tools and models for studying DNA methylation using nanopore sequencing. Our results demonstrate that the older models remain the best choice for studying CpG methylation. We note substantial improvement of newer tools in identifying 5-methylcytosine in non-CG contexts, 6-methyladenine, and 4-methylcytosine. We highlight the sensitivity of various tools to confounding methylation nearby. We also assess the computational performance of various tools, and effects of sequencing depth, methylation abundance, read quality, and basecalling mode. Our reusable pipelines and fully open access datasets provide a framework of resources to empower future benchmarking efforts. Our work thus details the strengths and limitations of the state-of-the-art methylation models and outlines practical guidelines for researchers using nanopore sequencing to study DNA modifications.

genomics↗