Biology subjects

Rhoades, S. D.

Publications and source records attributed to Rhoades, S. D..

4 recordsLinked to original sources

bioRxiv2023

Glucose Challenge Uncovers Temporal Fungibility of Metabolic Homeostasis Throughout the Day

Rhythmicity is a cornerstone of behavioral and biological processes, especially metabolism, yet the mechanisms behind metabolite cycling remain elusive. This study uncovers a robust oscillation in key metabolite pathways downstream of glucose in humans. A purpose-built 13C6-glucose isotope tracing platform was used to sample Drosophila every 4h and probe these pathways, revealing a striking peak in biosynthesis shortly after lights-on in wild-type flies. A hyperactive mutant (fumin) demonstrates increased Krebs cycle labelling and dawn-specific glycolysis labelling. Surprisingly, neither underlying feeding rhythms nor the presence of food availability explain the rhythmicity of glucose processing across genotypes, suggesting a robust internal mechanism for metabolic control of glucose processing. These results align with clinical data highlighting detrimental effects of mistimed energy intake. Our approach offers a unique insight into the dynamic range of daily metabolic processing and provides a mechanistic foundation for exploring circadian metabolic homeostasis in disease contexts.

systems biology↗

bioRxiv2020

CRY1-CBS binding regulates circadian clock function and metabolism

Circadian disruption influences metabolic health. Metabolism modulates circadian function. However, the mechanisms coupling circadian rhythms and metabolism remain poorly understood. Here we report that Cystathionine {beta}-synthase (CBS), a central enzyme in one-carbon metabolism, functionally interacts with the core circadian protein Cryptochrome1 (CRY1). In cells, CBS augments CRY1 mediated repression of the CLOCK/BMAL1 complex and shortens circadian period. Notably, we find that mutant CBS-I278T protein, the most common cause of homocystinuria, does not bind CRY1 or regulate its repressor activity. Transgenic CbsZn/Zn mice, while maintaining circadian locomotor activity period, exhibit reduced circadian power and increased expression of E-BOX outputs. CBS function is reciprocally influenced by CRY1 binding. CRY1 modulates enzymatic activity of the CBS. Liver extracts from Cry1-/- mice show reduced CBS activity that normalizes after the addition of exogenous wild type (WT) CRY1. Metabolomic analysis of WT, CbsZn/Zn, Cry1-/-, and Cry2-/- samples highlights the metabolic importance of endogenous CRY1. We observed temporal variation in one-carbon and transsulfuration pathways attributable to CRY1 induced CBS activation. CBS-CRY1 binding provides a post-translational switch to modulate cellular circadian physiology and metabolic control.

molecular biology↗

bioRxiv2019

Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record

BackgroundThe growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for the times at which events occur. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).\n\nResultsUsing simulated data, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the electronic health records of 49 792 genotyped individuals. In terms of effect sizes, the hazard ratios estimated by Cox regression were nearly identical to the odds ratios estimated by logistic regression. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog.\n\nConclusionsAs longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.

bioinformatics↗

bioRxiv2018

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here we performed a retrospective EHR study and derived a cohort of 3,652 individuals with self-reported sleep behaviors, documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m2) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was one hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p<1{middle dot}10-12). Sleep duration also varied considerably as a function of age, and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q<0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n=555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p<0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.

bioinformatics↗