Biology subjects

Sen, S. K.

Publications and source records attributed to Sen, S. K..

3 recordsLinked to original sources

bioRxiv2026

Circadian feeding rhythm loss drives sex-specific β-cell dysfunction in diet-induced obesity

Diet-induced obesity (DIO), modeled by a high-fat diet (HFD), adversely affects circadian glucose homeostasis partly by dysregulating fasting/feeding cycles. However, its impact on circadian {beta}-cell function and relevant transcriptional programs remains unclear. To investigate this, we exposed male and female C57BL/6 mice to either chow or HFD for eight weeks and assessed behavioral (feeding/activity), physiological (glucose tolerance/insulin secretion), transcriptomic (RNA sequencing (RNA-seq)), and epigenomic (Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-seq)) circadian rhythms. Additionally, we tested whether normalizing fasting/feeding cycles via time-restricted feeding (tRF) during the dark- or light-phase of the circadian cycle could mitigate DIO's deleterious effects. DIO disrupted circadian fasting/feeding rhythms, with greater disruption in males than females (p<0.05 vs. CHOW). In males, DIO abolished circadian regulation of glucose tolerance and in vivo glucose-stimulated insulin secretion (GSIS) (p<0.05 vs. chow), whereas female mice maintained circadian glucose homeostasis. RNA-seq analysis of male islets revealed a 42.1% reduction in circadian-regulated transcripts under DIO, with weakened rhythms in key {beta}-cell pathways such as insulin secretion. Moreover, ATAC-Seq analysis indicated reduced chromatin accessibility of genes/pathways related to insulin secretion and glucose hemostasis under DIO (p<0.05 vs. chow) during both dark- or light-phase of the circadian cycle. Notably, dark-phase time-restricted feeding (tRF) restored circadian glucose tolerance, GSIS, and islet gene expression, whereas light-phase tRF enhanced {beta}-cell function during the normally inactive/light phase of the circadian cycle, further suppressing circadian rhythmicity of the islet transcriptome. Motif analysis indicated that DIO suppressed the activity of D-box transcription factors (TFs) (DBP and TEF) while enhancing the activity of ETS family TFs (ELK1, ETS2, ELF2, and GABPA), which was partly mitigated by dark-phase tRF. Our study demonstrates that DIO disrupts circadian {beta}-cell function and gene expression by dysregulating fasting/feeding cycles in male mice and reveals significant sex differences in how DIO affects circadian glucose homeostasis and {beta}-cell function.

physiology↗

bioRxiv2020

Hidden components of microbiome diversity revealed by cell wall lysis with Adaptive Focused Acoustics

Within the rapidly evolving field of microbiome sequencing, a primary need exists for experimentally capturing microbiota in a manner as close as possible to their in vivo composition. During microbiome profiling, the first step necessarily involves lysis of the cell wall, releasing nucleic acids for next-generation sequencing. Microbial cell wall thicknesses can vary between 5nm to 80nm; while some species are quite easy to lyse, others are particularly resistant to lysis. Despite this, current chemical/mechanical lysis protocols ignore the possibility that species with different cell wall thicknesses are lysed at differential rates. This creates noise in species compositions and possibly skews current microbiome results in ways that are not currently understood. To develop a cell wall thickness-agnostic lysis protocol, we used Adaptive Focused Acoustics (AFA), a tunable acoustic methodology for processing of biological samples. Using identical aliquots of mouse stool homogenate as the lysis substrate, we compared AFA with chemical/mechanical lysis methodology routinely used in microbiome studies and found that AFA-mediated lysis substantially increases both microbial DNA yield as well as alpha and beta diversity. By starting with lower AFA energy levels, sequentially removing aliquots at each step, and subjecting the remainder to progressively stronger AFA treatment, we developed a sequential lysis method that accounts for differences in cell wall thickness. This method revealed even greater levels of diversity than single-timepoint AFA treatment. 16S sequencing results from the above experiments were verified by shotgun metagenome sequencing of a subset of the AFA samples. We found that lysis-induced noise affects not just species compositaions, but also functional characterization of shotgun metagenome data. AFA samples also showed a higher detection of eukaryotic and fungal DNA. We suggest that AFA-mediated lysis produces a truer representation of the native microbiota, and that this method deserves consideration as a potential addition to microbiome lysis protocols.

genomics↗

bioRxiv2017

Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq(R) and Framingham Heart Study cohorts

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits and requires systems level approaches. To this end, we employed random forests (RF) and neural networks (NN) for predictive modeling of coronary artery calcification (CAC), which is an intermediate end-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq(R) discovery cohort (n=16) and the FHS replication cohort (n=36) from 89th-99th CAC score percentile range, and age-matching controls (ClinSeq(R) n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables (CLIN), genotypes of 57 SNPs associated with CAC in past GWAS (SNP Set-1), and an alternative set of 56 SNPs (SNP Set-2) ranked highest in terms of their nominal correlation with advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristics curves (AUC). Within the discovery cohort, RF models generated AUC values of 0.69 with CLIN, 0.72 with SNP Set-1, and 0.77 with their combination. In the replication cohort, SNP Set-1 was again more predictive (AUC=0.78) than CLIN (AUC=0.61), but also more predictive than the combination (AUC=0.75). In contrast, in both cohorts, SNP Set-2 generated enhanced predictive performance with or without CLIN (AUC> 0.8). Using the 21 SNPs of SNP Set-2 that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq(R) data and tested with FHS data and replicated the high predictive accuracy (AUC>0.8) with several topologies, thereby identifying several potential susceptibility loci for advanced CAD. Several CAD-related biological processes were found to be enriched in the network of genes constructed from these loci. In both cohorts, SNP Set-1 derived from past CAC GWAS yielded lower performance than SNP Set-2 derived from \"extreme\" CAC cases within the discovery cohort. Machine learning tools hold promise for surpassing the capacity of conventional GWAS-based approaches for creating predictive models utilizing the complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders.

genomics↗