RHINO: An Integrative Multi‐Omics Framework Linking Circadian Physiology to Precision Medicine
Source abstract
Abstract
ABSTRACT Circadian rhythms are involved in nearly all physiological processes, and their disruption increases disease risk. Aligning drug administration timing with the body's internal clock, known as circadian medicine, holds promise for improving treatment efficacy and safety. However, systematic strategies to identify circadian‐regulated therapeutic targets remain limited. Here, we develop RHINO (RHythmic Interacting Network for multi‐Omics), an integrative multi‐omics framework for exploring circadian regulation across diverse genetic and disease contexts, and prioritizing druggable circadian targets. Applying RHINO reveals that human genetic variation broadly shapes rhythmic gene expression, with approximately 80% of FDA‐approved drug targets exhibiting genotype‐dependent rhythmicity, thereby substantially expanding the druggable target landscape for more precise circadian medicine. Circadian analyses across cancer types and diseases further identify genes that consistently lose rhythmic expression, highlighting conserved circadian vulnerabilities linked to disease progression. Integrative regulatory analyses nominate upstream transcriptional regulators, including Estrogen Receptor α, associated with rhythmic disruption. Although the circadian rhythm of estrogen signaling has been recognized for decades, we show that this rhythmicity exhibits time‐of‐day–dependent metabolic responses in preclinical mouse models. Together, RHINO provides a community‐accessible multi‐omics platform that integrates genetic variation, disease‐associated circadian remodeling, and regulatory inference to advance circadian medicine ( https://hanlaboratory.com/RHINO ).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Circadian rhythm and melatonin · Dietary Effects on Health · Plant Molecular Biology Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Diabetes Endocrinology, and Metabolism Department of Medicine Baylor College of Medicine Houston Texas USA
Department of Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis Indiana USA
Funding
National Institutes of Health
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