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An in silico genome-wide screen for circadian clock strength in human samples. | LitMetric

An in silico genome-wide screen for circadian clock strength in human samples.

Bioinformatics

Divisions of Human Genetics and Immunobiology, Center for Circadian Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.

Published: December 2022

AI Article Synopsis

  • The study highlights a gap in understanding how 'non-clock' pathways affect circadian clock function in humans, despite extensive research on clock-controlled pathways.* -
  • A new computational tool called LTM was developed to analyze pathways linked to the circadian clock in human tissues, showing that the cell cycle is heavily associated with clock function in healthy skin and that extracellular matrix-related pathways relate to clock strength in various cancers.* -
  • LTM is accessible on GitHub and figshare, making it available for other researchers to utilize and further explore the connections between clock function and disease.*

Article Abstract

Motivation: Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function.

Results: We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues.

Availability And Implementation: LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750125PMC
http://dx.doi.org/10.1093/bioinformatics/btac686DOI Listing

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