Charting the Molecular Terrain of Exercise: The Power of Multi-Omic Mapping.

Physical activity plays a fundamental role in human health and disease. Exercise has been shown to improve a wide variety of disease states, and the scientific community is committed to understanding the precise molecular mechanisms that underlie the exquisite benefits. This review provides an overview of molecular responses to acute exercise and chronic training, particularly energy mobilization and generation, structural adaptation, inflammation, and immune regulation. Further it offers a detailed discussion on known molecular signals and systemic regulators activated during various forms of exercise and their role in orchestrating health benefits. Critically, the increasing use of multi-omic technologies is explored with an emphasis on how multi-omic and multi-tissue studies contribute to a more profound understanding of exercise biology. These data inform anticipated future advancement in the field and highlight the prospect of integrating exercise with pharmacology for personalized disease prevention and treatment.

Local read haplotagging enables accurate long-read small variant calling.

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

The mitochondrial multi-omic response to exercise training across rat tissues.

Mitochondria have diverse functions critical to whole-body metabolic homeostasis. Endurance training alters mitochondrial activity, but systematic characterization of these adaptations is lacking. Here, the Molecular Transducers of Physical Activity Consortium mapped the temporal, multi-omic changes in mitochondrial analytes across 19 tissues in male and female rats trained for 1, 2, 4, or 8 weeks. Training elicited substantial changes in the adrenal gland, brown adipose, colon, heart, and skeletal muscle. The colon showed non-linear response dynamics, whereas mitochondrial pathways were downregulated in brown adipose and adrenal tissues. Protein acetylation increased in the liver, with a shift in lipid metabolism, whereas oxidative proteins increased in striated muscles. Exercise-upregulated networks were downregulated in human diabetes and cirrhosis. Knockdown of the central network protein 17-beta-hydroxysteroid dehydrogenase 10 (HSD17B10) elevated oxygen consumption, indicative of metabolic stress. We provide a multi-omic, multi-tissue, temporal atlas of the mitochondrial response to exercise training and identify candidates linked to mitochondrial dysfunction.

Loss of function of FAM177A1, a Golgi complex localized protein, causes a novel neurodevelopmental disorder.

PURPOSE: The function of FAM177A1 and its relationship to human disease is largely unknown. Recent studies have demonstrated FAM177A1 to be a critical immune-associated gene. One previous case study has linked FAM177A1 to a neurodevelopmental disorder in 4 siblings. METHODS: We identified 5 individuals from 3 unrelated families with biallelic variants in FAM177A1. The physiological function of FAM177A1 was studied in a zebrafish model organism and human cell lines with loss-of-function variants similar to the affected cohort. RESULTS: These individuals share a characteristic phenotype defined by macrocephaly, global developmental delay, intellectual disability, seizures, behavioral abnormalities, hypotonia, and gait disturbance. We show that FAM177A1 localizes to the Golgi complex in mammalian and zebrafish cells. Intersection of the RNA sequencing and metabolomic data sets from FAM177A1-deficient human fibroblasts and whole zebrafish larvae demonstrated dysregulation of pathways associated with apoptosis, inflammation, and negative regulation of cell proliferation. CONCLUSION: Our data shed light on the emerging function of FAM177A1 and defines FAM177A1-related neurodevelopmental disorder as a new clinical entity.

Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease.

Rare structural variants (SVs) - insertions, deletions, and complex rearrangements - can cause Mendelian disease, yet they remain difficult to accurately detect and interpret. We sequenced and analyzed Oxford Nanopore long-read genomes of 68 individuals from the Undiagnosed Disease Network (UDN) with no previously identified diagnostic mutations from short-read sequencing. Using our optimized SV detection pipelines and 571 control long-read genomes, we detected 716 long-read rare (MAF < 0.01) SV alleles per genome on average, achieving a 2.4x increase from short-reads. To characterize the functional effects of rare SVs, we assessed their relationship with gene expression from blood or fibroblasts from the same individuals, and found that rare SVs overlapping enhancers were enriched (LOR = 0.46) near expression outliers. We also evaluated tandem repeat expansions (TREs) and found 14 rare TREs per genome; notably these TREs were also enriched near overexpression outliers. To prioritize candidate functional SVs, we developed Watershed-SV, a probabilistic model that integrates expression data with SV-specific genomic annotations, which significantly outperforms baseline models that don't incorporate expression data. Watershed-SV identified a median of eight high-confidence functional SVs per UDN genome. Notably, this included compound heterozygous deletions in FAM177A1 shared by two siblings, which were likely causal for a rare neurodevelopmental disorder. Our observations demonstrate the promise of integrating long-read sequencing with gene expression towards improving the prioritization of functional SVs and TREs in rare disease patients.

Regional Variation in Cardiovascular Genes Enables a Tractable Genome Editing Strategy.

BACKGROUND: To realize the potential of genome engineering therapeutics, tractable strategies must be identified that balance personalized therapy with the need for off-the-shelf availability. We hypothesized that regional clustering of pathogenic variants can inform the design of rational prime editing therapeutics to treat the majority of genetic cardiovascular diseases with a limited number of reagents. METHODS: We collated 2435 high-confidence pathogenic/likely pathogenic (P/LP) variants in 82 cardiovascular disease genes from ClinVar. We assessed the regional density of these variants by defining a regional clustering index. We then combined a highly active base editor with prime editing to demonstrate the feasibility of a P/LP hotspot-directed genome engineering therapeutic strategy in vitro. RESULTS: P/LP variants in cardiovascular disease genes display higher regional density than rare variants found in the general population. P/LP missense variants displayed higher average regional density than P/LP truncating variants. Following hypermutagenesis at a pathogenic hotspot, mean prime editing efficiency across introduced variants was 57±27%. CONCLUSIONS: Designing therapeutics that target pathogenic hotspots will not only address known missense P/LP variants but also novel P/LP variants identified in these hotspots as well. Moreover, the clustering of P/LP missense rather than truncating variants in these hotspots suggests that prime editing technology is particularly valuable for dominant negative disease. Although prime editing technology in relation to cardiac health continues to improve, this study presents an approach to targeting the most impactful regions of the genome for inherited cardiovascular disease.

APOE loss-of-function variants: Compatible with longevity and associated with resistance to Alzheimer's disease pathology.

The ε4 allele of apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). Knockdown of ε4 may provide a therapeutic strategy for AD, but the effect of APOE loss of function (LoF) on AD pathogenesis is unknown. We searched for APOE LoF variants in a large cohort of controls and patients with AD and identified seven heterozygote carriers of APOE LoF variants. Five carriers were controls (aged 71-90 years), one carrier was affected by progressive supranuclear palsy, and one carrier was affected by AD with an unremarkable age at onset of 75 years. Two APOE ε3/ε4 controls carried a stop-gain affecting ε4: one was cognitively normal at 90 years and had no neuritic plaques at autopsy; the other was cognitively healthy at 79 years, and lumbar puncture at 76 years showed normal levels of amyloid. These results suggest that ε4 drives AD risk through the gain of abnormal function and support ε4 knockdown as a viable therapeutic option.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.

Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.

Mobile Health Fitness Interventions: Impact of Features on Routine Use and Data Sharing Acceptability.

Background: Mobile health (mHealth) interventions are increasingly being used for cardiovascular research and physical activity promotion. Objectives: As a result, the authors aimed to evaluate which features facilitate and impede routine engagement with mobile fitness applications. Methods: We distributed a pan-Canadian online questionnaire via the behavioral research platform Prolific.co to evaluate what features are associated with the use and routine engagement (ie, daily or weekly use) of mHealth fitness applications and attitudes about data sharing. Binary logistic regression was used to quantify the association between these endpoints and exploratory factors such as the perceived utility of various mHealth application features. Results: The survey received 694 responses. Most people were women (62%), the median age was 28 years (range: 18-78 years), and most people reported current use of an mHealth fitness application (48%). The perceived importance of personal health (OR: 2.40; 95% CI: 1.34-4.50) was the factor most associated with the current use of an mHealth fitness application. The feature most associated with routine engagement was the ability to track progress toward a goal (OR: 5.10; 95% CI: 2.73-9.61) while the most significant barrier was the absence of goal customization features (OR: 0.44; 95% CI: 0.25-0.81). The acceptance of sharing health data for research was high (56%), and privacy concerns did not significantly affect routine engagement (OR: 0.81; 95% CI: 0.40-1.77). Results were consistent across race and gender. Conclusions: mHealth interventions have the potential to be scaled across populations. Optimizing applications to improve self-monitoring and personalization could increase routine engagement and, thus, user retention and intervention effectiveness.