Biology subjects

Ruben, M. D.

Publications and source records attributed to Ruben, M. D..

8 recordsLinked to original sources

bioRxiv2022

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

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 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 are 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. LTM is available on GitHub to facilitate its use.

bioinformatics↗

bioRxiv2021

Normalized coefficient of variation (nCV): a method to evaluate circadian clock robustness in population scale data

Robust oscillation of clock genes is a core feature of the circadian system. Relative amplitude (rAMP) measures the robustness of clock gene oscillations, but only works for longitudinal samples. We lack a method for estimating robust oscillations from human samples without labeled time. We show that the normalized coefficient of variation (nCV) is linearly correlated with rAMP, independent of time labels. Using nCV, we found that clock gene oscillations are consistently dampened in tumors compared to non-tumors, suggesting a new therapeutic target in cancer treatment by enhancing clock robustness. nCV can provide a simple measure of the robustness of clock gene oscillations in any population-level dataset. Availability and implementationThe nCV web application is available on the GitHub repository (https://github.com/gangwug/nCV).

systems biology↗

bioRxiv2021

Machine learning approaches to identify sleep genes

Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. We built machine learning (ML) models to predict genes based on their similarity to known sleep genes. Using a manually curated list of 109 labeled sleep genes, we trained a prediction model on thousands of published datasets, representing circadian, immune, sleep deprivation, and many other processes. Our predictions fit with prior knowledge of sleep regulation and also identify several key genes/pathways to pursue in follow-up studies. We tested one of our findings, the NF-{kappa}B pathway, and showed that its genetic alteration affects sleep duration in mice. Our study highlights the power of ML to integrate prior knowledge and genome-wide data to study genetic regulation of sleep and other complex behaviors.

genomics↗

bioRxiv2020

Short-term exposure to intermittent hypoxia in mice leads to changes in gene expression seen in chronic pulmonary disease

Obstructive sleep apnea (OSA) results from episodes of airway collapse and intermittent hypoxia and is associated with a host of health complications including dementia, diabetes, heart failure, and stroke. Although the lung is the first organ to sense changes in inspired oxygen levels, little is known about the consequences of IH to the lung hypoxia-inducible factor (HIF)-responsive pathways. Furthermore, cellular mechanisms causing disease progression across multiple systems in OSA are unknown. We hypothesized that exposure to IH would lead to up- and down-regulation of diverse expression pathways and that individual cell populations would show distinctive responses to IH. We identify changes in circadian and immune pathways in lungs from mice exposed to IH. Among all cell types, endothelial cells show the most prominent transcriptional changes. Interestingly, up-regulated genes in endothelial, fibroblast, and myofibroblast cells were enriched for genes associated with pulmonary fibrosis and pulmonary hypertension. These genes include targets of several drugs currently used to treat chronic pulmonary diseases. Our results reveal potential candidates for cell-targeted therapy seeking to minimize pulmonary effects of OSA. A better understanding of the pathophysiologic mechanisms underlying diseases associated with OSA could improve our therapeutic approaches, directing therapies to the most relevant cells and molecular pathways.

pathology↗

bioRxiv2019

A single-sample circadian biomarker that performs across populations and platforms

BackgroundFor circadian medicine to influence health, such as when to take a drug or undergo a procedure, a practical way to measure body time is needed. Recent machine learning algorithms show that gene expression data from blood and skin can provide reliable estimates of body time. However, for clinical viability, a biomarker must be easily measured and generalizable to a broad population. It is not clear that any circadian biomarker yet satisfies these criteria.\n\nResultsWe analyzed 24 h molecular rhythms in human dermis and epidermis at three distinct body sites, leveraging both longitudinal and population data. Circadian clock function was strongest in the epidermis, regardless of body site. We identified a 12-gene biomarker set that reported circadian phase to within 3 hours from a single sample of epidermis--the skins most superficial layer. This set performed well across body sites, ages, sexes, and detection platforms.\n\nConclusionsThis research shows that the clock in epidermis is more robust than dermis regardless of body site. To encourage ongoing validation of this biomarker in diverse populations, diseases, and experimental designs, we developed SkinPhaser--a user-friendly app to test biomarker performance in datasets (https://github.com/gangwug/SkinPhaser).

systems biology↗

bioRxiv2019

A Large-scale Study Reveals 24 hour Operational Rhythms in Hospital Treatment

Hospitals operate 24 hours a day, and it is assumed that critical decisions occur continuously around the clock. However, many aspects of hospital operation occur at particular times of day, including medical team rounding and shift changes. It is unclear if this impacts patient care, as an empirical account of 24 h treatment patterns is lacking. We analyzed the daily distribution of ~120K doses of 12 separate drugs in 1,486 inpatients at a major childrens hospital in the U.S. Treatment orders and administration were strongly time-of-day-dependent, marked by distinct morning time surges and overnight lulls. These 24 h rhythms in treatment were remarkably consistent across drugs, diagnoses, and hospital units. In sum, nearly one-third of all 116,975 orders for treatment were placed between 8 AM and 12 PM. This rhythm in hospital medicine coincided with medical team rounding time, not necessarily immediate medical need. Lastly, we show that the clinical response to hydralazine, an acute antihypertensive, is dosing time-dependent and greatest at night, when the fewest doses were administered. The prevailing dogma is that hospital treatment is administered as needed regardless of time of day. Our findings challenge this notion and reveal a potential operational barrier to best clinical care.\n\nSIGNIFICANCE STATEMENTThe order and administration of hospital treatment was characterized by morning time surges and overnight lulls, regardless of drug type, diagnosis or care unit. As the first large-scale account of 24 h rhythms in hospital medicine, this study identifies a potential operational barrier to best clinical care. Critical clinical decisions should be made around the clock; pain, infection, hypertensive crisis, and other conditions do not occur selectively in the morning. Systemic bias in the timing of medicine is also at odds with circadian biology, which can influence when certain treatments are most effective or safe. Prevailing dogma is that hospital treatment is administered as needed regardless of time of day. Our findings challenge this notion and suggest that time of day in hospital operations deserves further consideration.

epidemiology↗

bioRxiv2019

Dosing Time Matters

Trainees in medicine are taught to diagnose and administer treatment as needed; time-of-day is rarely considered. Yet accumulating evidence shows that [~]half of human genes and physiologic functions follow daily rhythms. Circadian medicine aims to incorporate knowledge of these rhythms to enhance diagnosis and treatment. Interest in this approach goes back at least six decades, but the path to the clinic has been marked by starts, stops, and ambiguity. How do we move the field forward to impact clinical practice? To gain insight into successful strategies, we studied the results of more than 100 human trials that evaluated time-of-administration of drugs.

epidemiology↗

bioRxiv2018

A population-based human enCYCLOPedia for circadian medicine

The discovery that half of the mammalian protein-coding genome is clock-regulated has clear implications for medicine. Indeed, recent studies demonstrate time-of-day impact on therapeutic outcomes in human heart disease and cancer. Yet biological time is rarely given clinical consideration. A key barrier is the absence of information on the what and where of molecular rhythms in the human body. Here, we have applied CYCLOPS, an algorithm designed to reconstruct sample order in the absence of time-of-day information, to the GTEx collection of 632 human donors contributing 4,292 RNA-seq samples from 13 distinct human tissue types. We identify rhythms in expression across the body that persist at the population-level. This includes a set of ubiquitous cyclers comprised of well-established circadian clock factors but also many genes without prior circadian context. Among thousands of tissue-divergent rhythms, we discover a set of genes robustly oscillating in cardiovascular tissue, including key drug targets relevant to heart disease. These results also have implications for genetic studies where circadian variability may have masked genetic influence. It is our hope that the human enCYCLOPedia helps drive the translation of circadian biology into prospective clinical trials in cardiology and many other therapeutic areas.\n\nOne Sentence SummaryBioinformatic analyses on thousands of human tissue samples reveals an enCYCLOPedia of rhythmic gene expression across the body and identifies key translational opportunities for circadian medicine in cardiovascular disease.

bioinformatics↗