Biology subjects

Gumz, M. L.

Publications and source records attributed to Gumz, M. L..

2 recordsLinked to original sources

bioRxiv2022

Defining the age-dependent and tissue-specific circadian transcriptome in male mice

Cellular circadian clocks direct a daily transcriptional program that supports homeostasis and resilience. Emerging evidence supports age-associated changes in circadian functions. To define age-dependent changes at the systems level, we profiled the circadian transcriptome in the hypothalamus, lung, heart, kidney, skeletal muscle, and adrenal gland in 3 age groups. We found age-dependent and tissue-specific clock output changes. Aging reduced the number of rhythmically expressed genes (REGs), indicative of weakened circadian control. Many genes gained rhythmicity in old tissues, reflecting an adaptive response. REGs were enriched for the hallmarks of aging, adding a new dimension to our understanding of aging. Differential gene expression analysis found that there were temporally distinct clusters of genes in tissue-specific manner. Increased daily gene expression variability is a common feature of aged tissues. This novel analysis extends the landscape of the understanding of aging and highlights the impact of aging on circadian clock function and temporal changes in gene expression. HIGHLIGHTS- Rhythmically expressed genes (REGs) in Young, but not Old mice, are enriched for the aging hallmarks across all tissues. - The numbers of REGs decline across all tissues with age implicating the circadian clock in altered homeostasis. - Age- and tissue-specific differentially expressed genes (DEGs) cluster at specific times of the day. - Increase in gene expression variability over a day is a common feature of aging tissues.

genomics↗

bioRxiv2021

Likelihood-based Tests for Detecting CircadianRhythmicity and Differential Circadian Patterns in Transcriptomic Applications

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been founded to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level, and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR rhythmicity and LR diff was demonstrated in two real data applications, including a brain aging data (gene expression microarray data of human postmortem brain) and a time-restricted feeding data (RNA sequencing data of human skeletal muscles). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

bioinformatics↗