Publications

Abstract

Consumer wearables provide continual physiological data, including multiple cardiovascular metrics, yet their use in detecting physiological changes that precede cardiac events remains limited.A 76-year-old man with structural heart disease experienced sudden cardiac death minutes after high-intensity indoor cycling. Change-point analysis of wearable-captured physiology revealed a coordinated step-change, with most changes clustering 4-6 months before the sudden cardiac death: heart rate variability halved, resting and nocturnal heart rate rose, deep sleep and 6-minute walk distance fell. These coincided with a fatigue complaint and a murmur prompting echocardiography, which revealed severe left ventricular dilation with preserved ejection fraction (approximately 60%), severe mitral regurgitation, moderate aortic regurgitation, and trace tricuspid regurgitation, consistent with advanced structural heart disease.The key signal was not any single abnormal value but a coordinated, sustained step-change from the patient's own baseline that preceded imaging. Data of this kind could complement the episodic measurements cardiovascular care now relies on, informing both when to re-evaluate patients with known heart disease and how wearable heart-rate kinetics might guide their exercise.Embedding continuous digital physiology monitoring into traditional cardiology has the potential to aid clinicians in timely evaluation and intervention.

View details for DOI 10.1016/j.jaccas.2026.110397

View details for PubMedID 42788912

Abstract

The contribution of the gut microbiome to chronic heart failure (HF) remains poorly defined. We performed integrated microbiome and host multi-omic profiling of adults with chronic systolic HF due to nonischemic cardiomyopathy (n = 59) and healthy participants (n = 50), including longitudinal sampling in some cases. Here we show that patients with HF have depletion of anti-inflammatory Bifidobacterium and reduced short-chain fatty acid biosynthetic potential, alongside altered microbiome-immune system interactions. Longitudinal analysis revealed that higher Bifidobacterium abundance was associated with clinical improvement. Multi-omic integration prioritized indole-3-propionic acid (IPA), a gut metabolite with cardioprotective properties, as a candidate mechanistic link. We confirmed that Bifidobacterium species can produce IPA in vitro and circulating IPA levels were associated with milder HF severity. Together, these findings define gut microbiome alterations in chronic systolic HF, identify candidate microbiome biomarkers of disease severity and trajectory, and suggest microbiome-derived metabolites as potential therapeutic targets.

View details for DOI 10.1038/s44161-026-00854-y

View details for PubMedID 42786289

View details for PubMedCentralID 12101525

Abstract

Publicly available continuous glucose monitoring (CGM) datasets are fragmented across schemas, limiting reuse, pooling, and machine learning development. Although large biobanks provide harmonized resources, many smaller CGM studies remain difficult to access because ingestion and standardization require substantial manual effort.We developed INSIGHT (Ingest and Standardize Glucose Harmonization Tool), an open-source large language model (LLM)-assisted harmonization pipeline that standardizes public CGM datasets into a minimal common schema: Subject_ID, Timestamp, and Glucose. INSIGHT combines deterministic rules with structured LLM assistance to identify CGM files, classify file roles, generate schema-specific read plans, normalize timestamps and glucose units, resolve subject identifiers, detect overlapping exports, merge outputs, and retain auditable JSON artifacts. We evaluated INSIGHT on 14 public CGM datasets with curated reference outputs using pre-specified training and held-out testing partitions. Performance was assessed using file-level precision/recall, subject recovery, temporal alignment, glucose fidelity, scalar CGM feature accuracy, and composite INSIGHT scores.INSIGHT is open-source at https://github.com/benehlert/CGMHarmonization. Across 90 model-dataset evaluations spanning 9 model variants and 10 datasets, top configurations achieved near-perfect held-out harmonization fidelity. GPT-5.4 and Gemini 3.1 Pro Preview achieved mean INSIGHT scores of 0.990, with glucose mean absolute error (MAE) below 0.03 mg/dL and within 5 mg/dL agreement of 0.9996. Errors primarily reflected complex subject-timestamp recovery rather than glucose conversion, with failures concentrated in deeply nested datasets containing overlapping or co-located non-CGM exports.INSIGHT demonstrates that LLM-assisted code generation can generalize CGM harmonization across heterogeneous datasets while producing auditable loaders. By reducing reuse costs, INSIGHT complements curated repositories and supports large-scale pooled analyses and foundation model development for glycemic physiology.

View details for DOI 10.1177/19322968261467778

View details for PubMedID 42509666

Abstract

Despite extensive research, molecular differences in human populations and the influence of ancestry, age, geography, and diet are poorly understood. We performed comprehensive multiomics profiling (including genomics, transcriptomics, proteomics, metabolomics, lipidomics, metallomics, glycomics, and microbiomics) on samples from 322 healthy individuals of European, East Asian, and South Asian ancestry across multiple continents. We identified ethnicity-associated molecular features linked to host metabolism, autoimmune disease risk, drug metabolism, and neurodegenerative pathways. We uncovered ancestry- and geography-related molecular changes affecting metabolism, immune function, microbiome composition, and biological aging. Specific genetic variants and gene expression differences were associated with lipid metabolism and immune regulation. Geography influenced biological age: East Asians showed lower biological age in their ancestral regions, whereas individuals of European ancestry exhibited lower biological age in the US/Canada than in Europe. Diet-microbiome metabolism interactions displayed ethnicity-specific patterns, many related to health. This open access resource advances understanding of ethnicity-environment interactions and supports precision medicine.

View details for DOI 10.1016/j.cell.2026.04.032

View details for PubMedID 42134306

Abstract

Identifying the proteins that interact with sequence-defined chromatin segments is a critical step in understanding gene expression. Most procedures perform bulk analysis of samples to identify the general interactions that occur in a cellular population and thereby do not detect the proteins that operate at a single locus. To circumvent this limitation, we developed a modified method that uses genetically targeted proximity labeling with dCas9-APEX2 to specifically biotinylate the promoter proximal proteome of the single copy locus FOXP2 in live HEK293 cells. After capture of the tagged proteins with streptavidin and isobaric labeling of the peptides produced from on-bead digestion with tandem mass tags, we used quantitative 2D-LC-MS3 on a tribrid mass spectrometer to identify 373 significantly enriched proteins at the active promoter relative to control samples (Storey-q<.05, FC>1.2). These proteins were enriched for transcription factors and components of the spliceosome. To validate our candidate transcriptional regulators, we utilized computationally predicted transcription factor binding and the >200 ChIP-Seq experiments performed in HEK293 cells by ENCODE. In addition to validating dozens of candidate transcription factors as binders of the targeted genomic locus, we newly identify IRF2BP2 and glucocorticoid signaling as negative regulators of FOXP2 transcription, suggesting they each play a key role in FOXP2 gene expression. We further demonstrate that MS detects approximately one third of both binders and non-binders, with more highly expressed genes significantly more likely to be detected regardless of binding status or locus specificity. ENCODE ChIP-Seq binders not detected by MS show significantly lower expression compared to non-binders only at the targeted FOXP2 promoter and not at off target loci.

View details for DOI 10.1016/j.mcpro.2026.101570

View details for PubMedID 42025763

Abstract

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), beta-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring (CGM) and wearable technologies enable a paradigm shift toward non-invasive, dynamic metabolic phenotyping. We show evidence that machine learning models can leverage high-resolution glucose data from at-home, CGM-enabled oral glucose tolerance tests to accurately predict gold-standard measures of muscle IR and beta-cell function. This personalized characterization extends to real-world nutrition, where an individual's unique postprandial glycemic response (PPGR) to standardized meals, such as the relative glucose spike to potatoes versus grapes, could serve as a biomarker for their metabolic subtype. Moreover, integrating wearable data reveals that habitual diet, sleep, and physical activity patterns, particularly their timing, are uniquely associated with specific metabolic dysfunctions, informing precision lifestyle interventions. The efficacy of dietary mitigators in attenuating PPGR is also shown to be phenotype-dependent. Collectively, this evidence demonstrates that CGM can deconstruct the complexity of early dysglycemia into distinct, actionable subphenotypes. This approach moves beyond simple glycemic control, paving the way for targeted nutritional, behavioral, and pharmacological strategies tailored to an individual's core metabolic defects, thereby paving the way for a new era of precision diabetes prevention.

View details for DOI 10.1177/19322968261431860

View details for PubMedID 41978474

Abstract

The postprandial glycemic response (PPGR) is associated with diabetes and cardiovascular disease and is highly individualized. The PPGR is affected by both physiological and behavioral factors. Attention to the PPGR has dramatically increased recently with the widespread use of continuous glucose monitors. It is expected that individualized control of PPGRs will be important in the prevention of diabetes and its associated complications. In this article, we discuss six modifiable factors associated with the PPGRs, including (1) the glucoregulatory hormones, (2) gastric emptying, (3) salivary or pancreatic amylase, (4) diet, (5) physical exercise, and (6) sleep and circadian rhythm. Modifying these factors may allow for personalized intervention strategies to control the PPGR-to reduce the risk for cardiovascular disease in individuals with varying degrees of glycemia.

View details for DOI 10.1177/19322968261418614

View details for PubMedID 41660725

Abstract

Profiling protein abundance and dynamics at single-cell resolution in complex human tissues is challenging. Given the discordance between transcript and protein abundance observed in studies of the human cerebral cortex, we developed an optimized workflow that combines label-free single-cell mass spectrometry with precise sample preparation to resolve quantitative proteomes of individual cells from the developing human brain. Our method achieves deep proteomic coverage (~800 proteins per cell) even in small immature prenatal human neurons (diameter ~7-10 μm, ~50 pg protein), capturing major brain cell types and enabling proteome-wide characterization at single-cell resolution. We document extensive transcriptome-proteome discordance across cell types, particularly in genes associated with neurodevelopmental disorders. Proteins exhibit markedly higher cell-type specificity than their mRNA counterparts, underscoring the importance of proteomic-level analysis. By reconstructing developmental trajectories from radial glia to excitatory neurons at the proteomic level, we identify dynamic, stage-specific protein co-expression modules and pinpoint the intermediate progenitor-to-neuron transition as a genetically vulnerable phase associated with autism.

View details for DOI 10.1038/s41587-025-02980-7

View details for PubMedID 41593231

View details for PubMedCentralID 2728742

Abstract

Metabolites present in the mother traverse the placenta to supply energy, essential nutrients, and communication signals to the fetus. To gain a deeper understanding of fetal metabolism and the impacts of maternal metabolic health and medications on the fetus, we have created CordDB. Using mass spectrometry, we systematically document the metabolites and medications that enter and leave the fetus during birth, as well as the associated health records of the mother and newborn. These data reveal the metabolites consumed by the fetus, microbial metabolites (e.g., 3-indolepropionic acid), metabolites obtained from diet, and medications, as well as create a healthy newborn signature. Our study demonstrates that the mother's microbial interactions and nutrition, premature birth, and the mother's use of drugs such as bupivacaine and betamethasone are linked to variations in the metabolic profiles and health of newborns.

View details for DOI 10.1016/j.xcrm.2025.102548

View details for PubMedID 41592564

Abstract

The interaction between the genome and the exposome is increasingly recognized as central to human health and disease. While exposome research has generally focused on adverse exposures such as pollutants and toxins, the concept of the beneficial exposome-positive environmental exposures that promote health-remains underexplored. Among the most promising beneficial exposures are plant-derived phytochemicals, a rich class of bioactive compounds with therapeutic potential. Phytoncides, a specific subset of volatile organic compounds released by plants, exemplify this beneficial potential through their antimicrobial, anti-inflammatory, antioxidant, and neuroprotective effects. Historically utilized in traditional medicine across cultures, plant-based remedies containing these compounds are now being examined through modern genomics, exposomics, and systems biology approaches to understand the specific contributions of phytoncides and other bioactive constituents. Emerging data suggest that phytochemicals modulate gene expression, immune function, and metabolic pathways across multiple organ systems, contributing to immune, neurological, endocrine, cardiovascular, respiratory, integumentary, and mental health improvements. However, the evidence base is predominantly preclinical, with limited human validation, considerable heterogeneity in plant-extract composition, and incompletely characterized molecular mechanisms. This review synthesizes current evidence on genome-exposome interactions (GxE) related to plant-derived compounds, highlighting recent mechanistic insights and exploring translational applications-including forest bathing, green space integration in urban design, and bioengineering approaches-while addressing the challenges of clinical translation. As environmental change accelerates, understanding beneficial GxE offers new opportunities for preventative and precision public health interventions and calls for integrating nature-based solutions into modern healthcare paradigms.

View details for DOI 10.1186/s40246-025-00882-y

View details for PubMedID 41572398

Abstract

Cancer is generally thought to be caused by expansion of a single mutant cell1. However, analyses of early colorectal cancer lesions suggest that tumors may instead originate from multiple, genetically distinct cell populations2,3. Detecting polyclonal tumor initiation is challenging in patients, as it requires profiling early-stage lesions before clonal sweeps obscure diversity. To investigate this, we analyzed normal colorectal mucosa, benign and dysplastic premalignant polyps, and malignant adenocarcinomas (123 samples) from six individuals with familial adenomatous polyposis (FAP). Individuals with FAP have a germline heterozygous APC mutation, predisposing them to colorectal cancer and numerous premalignant polyps by early adulthood4. Whole-genome and/or whole-exome sequencing revealed that many premalignant polyps-40% with benign histology and 28% with dysplasia-were composed of multiple genetic lineages that diverged early, consistent with polyclonal origins. This conclusion was reinforced by whole-genome sequencing of single crypts from multiple polyps in additional patients which showed limited sharing of mutations among crypts within the same lesion. In some cases, multiple distinct APC mutations co-existed in different lineages of a single polyp, consistent with polyclonality. These findings reshape our understanding of early neoplastic events, demonstrating that tumor initiation can arise from the convergence of diverse mutant clones. They also suggest that cell-intrinsic growth advantages alone may not fully explain tumor initiation, highlighting the importance of microenvironmental and tissue-level factors in early cancer evolution.

View details for DOI 10.1038/s41586-025-09930-y

View details for PubMedID 41291291

Abstract

Despite the millions of individuals struggling with persistent symptoms, Long COVID has remained difficult to diagnose due to limited objective biomarkers, often leading to underdiagnosis or even misdiagnosis. To bridge this gap, we investigated the potential of utilizing wearable sensor data to aid in the diagnosis of Long COVID. We analyzed longitudinal heart rate (HR) data from 126 individuals with acute SARS-CoV-2 infections to develop machine learning models capable of predicting Long COVID status using derived HR features, symptom features, or a combination of both feature sets. The HR features were derived across six analytical categories, including time-domain, Poincaré nonlinear, raw signal, Kullback-Leibler (KL) divergence, variational mode decomposition (VMD), and the Shannon energy envelope (SEE), enabling the capture of heart rate dynamics over various temporal scales and the quantification of day-to-day shifts in HR distributions. The symptom features used in the final models included chest pain, vomiting, excessive sweating, memory loss, brain fog, heart palpitations, and loss of smell. The combined HR- and symptom-feature model demonstrated robust predictive performance, achieving an area under the Receiver Operating Characteristic curve (ROC-AUC) of 95.1% and an area under the Precision-Recall curve (PR-AUC) of 85.9%. These values represent a significant improvement of approximately 5% in both the ROC-AUC and PR-AUC over the symptoms-only model. At the population level, this improvement in discrimination could lead to clinically meaningful reductions in misclassification and improved patient outcomes, achieved through a non-invasive diagnostic tool. These findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

View details for DOI 10.1371/journal.pdig.0001093

View details for PubMedID 41264615

Abstract

Background and Aims: Continuous glucose monitoring (CGM) devices provide real-time actionable data on blood glucose levels, making them essential tools for effective glucose management. Integrating blood glucose data with food log data is crucial for understanding how dietary choices impact glucose levels. Despite their utility, many CGM applications lack integration with other external services, such as food trackers, and do not generate useful glycemic variability (GV) metrics or advanced visualizations. Existing solutions vary in functionality: some are proprietary, many require additional user programming or custom preprocessing to meet diverse research needs, and few have created solutions to connect CGM data with external services. Recent reviews highlight gaps such as insufficient postprandial analytics, absence of composite indices, and inadequate tools for nontechnical users. Methods: Glucose360 and commonly used alternative CGM applications and tools were compared by calculating GV metrics on 60 participant datasets and by contrasting their general applications for research workflows. Results: To address limitations, we developed Glucose360, featuring (1) an open-source python framework for event-based CGM data integration and analysis; (2) automated calculation of glucose metrics specific for meals and exercise events and other short-interval events; and (3) a user-friendly web application, designed for users with minimal programming experience and accessible at vurhd2.shinyapps.io/glucose360/. Discussion: Overall, Glucose360 provides a holistic analysis pipeline that is useful for both individuals and researchers to track and analyze CGM data. The source code for Glucose360 can be found at github.com/vurhd2/Glucose360.

View details for DOI 10.1177/15209156251374711

View details for PubMedID 40900178

Abstract

Polygenic risk scores (PRSs) predict an individual's genetic risk for complex diseases, yet their utility in elucidating disease biology remains limited. We introduce scPRS, a graph neural network-based framework that computes single-cell-resolved PRSs by integrating reference single-cell chromatin accessibility profiles. scPRS outperforms traditional PRS approaches in genetic risk prediction, as demonstrated across multiple diseases including type 2 diabetes, hypertrophic cardiomyopathy, Alzheimer disease and severe COVID-19. Beyond risk prediction, scPRS prioritizes disease-critical cells and, when combined with a layered multiomic analysis, links risk variants to gene regulation in a cell-type-specific manner. Applied to these diseases, scPRS fine-maps causal cell types and cell-type-specific variants and genes, demonstrating its ability to bridge genetic risk with cell-specific biology. scPRS provides a unified framework for genetic risk prediction and mechanistic dissection of complex diseases, laying a methodological foundation for single-cell genetics.

View details for DOI 10.1038/s41587-025-02725-6

View details for PubMedID 40715455

View details for PubMedCentralID 10284707

Abstract

Distinct metabolic susceptibilities (beta-cell dysfunction, insulin resistance (IR), and impaired incretin response) underlie type 2 diabetes (T2D). However, their relationships with habitual lifestyle behaviors are underexplored. This study integrated high-resolution lifestyle data from wearable devices, continuous glucose monitoring, and smartphone-based food logs with gold-standard physiological tests in 36 individuals at risk for T2D (ClinicalTrials.Gov; NCT03919877; 2019-04-18). Over 6400 timestamped records of diet, sleep, and physical activity were analyzed with in participants with measures of beta-cell function, tissue-specific IR (muscle, hepatic, adipose), and incretin response. We found that lifestyle timing and variability were strongly associated with metabolic subphenotypes: (1) eating timing was associated with muscle IR and incretin function; (2) irregular sleep correlated to IR and incretin function; and (3) Time-of-day effects of physical activity varied by subphenotype. These findings were validated in an independent cohort. Our results highlight novel physiological links between daily behaviors and metabolic risk, informing potential lifestyle modifications for T2D prevention.

View details for DOI 10.1038/s41746-025-01728-6

View details for PubMedID 40500312

View details for PubMedCentralID PMC12159136

Abstract

Elevated postprandial glycemic responses (PPGRs) are associated with type 2 diabetes and cardiovascular disease. PPGRs to the same foods have been shown to vary between individuals, but systematic characterization of the underlying physiologic and molecular basis is lacking. We measured PPGRs using continuous glucose monitoring in 55 well-phenotyped participants challenged with seven different standard carbohydrate meals administered in replicate. We also examined whether preloading a rice meal with fiber, protein or fat ('mitigators') altered PPGRs. We performed gold-standard metabolic tests and multi-omics profiling to examine the physiologic and molecular basis for interindividual PPGR differences. Overall, rice was the most glucose-elevating carbohydrate meal, but there was considerable interindividual variability. Individuals with the highest PPGR to potatoes (potato-spikers) were more insulin resistant and had lower beta cell function, whereas grape-spikers were more insulin sensitive. Rice-spikers were more likely to be Asian individuals, and bread-spikers had higher blood pressure. Mitigators were less effective in reducing PPGRs in insulin-resistant as compared to insulin-sensitive participants. Multi-omics signatures of PPGR and metabolic phenotypes were discovered, including insulin-resistance-associated triglycerides, hypertension-associated metabolites and PPGR-associated microbiome pathways. These results demonstrate interindividual variability in PPGRs to carbohydrate meals and mitigators and their association with metabolic and molecular profiles.

View details for DOI 10.1038/s41591-025-03719-2

View details for PubMedID 40467897

View details for PubMedCentralID 4266395

Abstract

The short-chain fatty acids (SCFAs) propionate and butyrate have beneficial health effects, are produced in large amounts by microbial metabolism and have been identified as unique acyl lysine histone marks. To better understand the function of these modifications, we used chromatin immunoprecipitation followed by sequencing to map the genome-wide location of four short-chain acyl histone marks, H3K18pr, H3K18bu, H4K12pr and H4K12bu, in treated and untreated colorectal cancer (CRC) and normal cells as well as in mouse intestines in vivo. We correlate these marks with open chromatin regions and gene expression to access the function of the target regions. Our data demonstrate that propionate and butyrate bind and act as promoters of genes involved in growth, differentiation and ion transport. We propose a mechanism involving direct modification of specific genomic regions by SCFAs resulting in increased chromatin accessibility and, in the case of butyrate, opposing effects on the proliferation of normal versus CRC cells.

View details for DOI 10.1038/s42255-024-01191-9

View details for PubMedID 39789354

View details for PubMedCentralID 4393657

Abstract

This retrospective cohort study evaluates the impact of an AI-supported continuous glucose monitoring (CGM) mobile app ("January V2") on glycemic control and weight management in 944 users, including healthy individuals and those with prediabetes or type 2 diabetes (T2D). The app, leveraging AI to personalize feedback, tracked users' food intake, activity, and glucose responses over 14 days. Significant improvements in time in range (TIR) were observed, particularly in users with lower baseline TIR. Healthy users' TIR increased from 74.7% to 85.5% (p<0.0001), while T2D users' TIR improved from 49.7% to 57.4% (p<0.0004). Higher app engagement correlated with greater TIR improvements. Users also experienced an average weight reduction of 3.3 lbs over 33 days. These findings suggest that AI-enhanced digital health interventions can improve glycemic control and promote weight loss, particularly when users are actively engaged.

View details for DOI 10.1038/s41746-025-01430-7

View details for PubMedID 39789102

Abstract

The classification of type 2 diabetes and prediabetes does not consider heterogeneity in the pathophysiology of glucose dysregulation. Here we show that prediabetes is characterized by metabolic heterogeneity, and that metabolic subphenotypes can be predicted by the shape of the glucose curve measured via a continuous glucose monitor (CGM) during standardized oral glucose-tolerance tests (OGTTs) performed in at-home settings. Gold-standard metabolic tests in 32 individuals with early glucose dysregulation revealed dominant or co-dominant subphenotypes (muscle or hepatic insulin-resistance phenotypes in 34% of the individuals, and β-cell-dysfunction or impaired-incretin-action phenotypes in 40% of them). Machine-learning models trained with glucose time series from OGTTs from the 32 individuals predicted the subphenotypes with areas under the curve (AUCs) of 95% for muscle insulin resistance, 89% for β-cell deficiency and 88% for impaired incretin action. With CGM-generated glucose curves obtained during at-home OGTTs, the models predicted the muscle-insulin-resistance and β-cell-deficiency subphenotypes of 29 individuals with AUCs of 88% and 84%, respectively. At-home identification of metabolic subphenotypes via a CGM may aid the risk stratification of individuals with early glucose dysregulation.

View details for DOI 10.1038/s41551-024-01311-6

View details for PubMedID 39715896

View details for PubMedCentralID 11057359

Abstract

Pregnancy is a vital period affecting both maternal and fetal health, with impacts on maternal metabolism, fetal growth, and long-term development. While the maternal metabolome undergoes significant changes during pregnancy, longitudinal shifts in maternal urine have been largely unexplored. In this study, we applied liquid chromatography-mass spectrometry-based untargeted metabolomics to analyze 346 maternal urine samples collected throughout pregnancy from 36 women with diverse backgrounds and clinical profiles. Key metabolite changes included glucocorticoids, lipids, and amino acid derivatives, indicating systematic pathway alterations. We also developed a machine learning model to accurately predict gestational age using urine metabolites, offering a non-invasive pregnancy dating method. Additionally, we demonstrated the ability of the urine metabolome to predict time-to-delivery, providing a complementary tool for prenatal care and delivery planning. This study highlights the clinical potential of urine untargeted metabolomics in obstetric care.

View details for DOI 10.1093/bib/bbaf059

View details for PubMedID 39955767

View details for PubMedCentralID PMC11830194

Abstract

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected persons and is an ideal model to study transitions of early precancer states to colorectal cancer (CRC). We performed deep multiomic profiling of 93 samples, including normal mucosa, benign polyps and dysplastic polyps, from six persons with FAP. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during precancerous transitions toward cancer formation. These involve processes such as cell proliferation, immune response, metabolic alterations (including amino acids and lipids), hormones and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin and other nonsteroidal anti-inflammatory drugs, a preventative treatment under investigation in persons with FAP. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in CRC formation and potential mechanisms of pharmaceutical prophylaxis.

View details for DOI 10.1038/s43018-024-00831-z

View details for PubMedID 39478120

View details for PubMedCentralID 2706149

Abstract

Although three-dimensional (3D) genome architecture is crucial for gene regulation, its role in disease remains elusive. We traced the evolution and malignant transformation of colorectal cancer (CRC) by generating high-resolution chromatin conformation maps of 33 colon samples spanning different stages of early neoplastic growth in persons with familial adenomatous polyposis (FAP). Our analysis revealed a substantial progressive loss of genome-wide cis-regulatory connectivity at early malignancy stages, correlating with nonlinear gene regulation effects. Genes with high promoter-enhancer (P-E) connectivity in unaffected mucosa were not linked to elevated baseline expression but tended to be upregulated in advanced stages. Inhibiting highly connected promoters preferentially represses gene expression in CRC cells compared to normal colonic epithelial cells. Our results suggest a two-phase model whereby neoplastic transformation reduces P-E connectivity from a redundant state to a rate-limiting one for transcriptional levels, highlighting the intricate interplay between 3D genome architecture and gene regulation during early CRC progression.

View details for DOI 10.1038/s43018-024-00823-z

View details for PubMedID 39478119

View details for PubMedCentralID 7541718

Abstract

Aging is a complex process associated with nearly all diseases. Understanding the molecular changes underlying aging and identifying therapeutic targets for aging-related diseases are crucial for increasing healthspan. Although many studies have explored linear changes during aging, the prevalence of aging-related diseases and mortality risk accelerates after specific time points, indicating the importance of studying nonlinear molecular changes. In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years. The participants resided in California, United States, and were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. The analysis revealed consistent nonlinear patterns in molecular markers of aging, with substantial dysregulation occurring at two major periods occurring at approximately 44 years and 60 years of chronological age. Distinct molecules and functional pathways associated with these periods were also identified, such as immune regulation and carbohydrate metabolism that shifted during the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. Overall, this research demonstrates that functions and risks of aging-related diseases change nonlinearly across the human lifespan and provides insights into the molecular and biological pathways involved in these changes.

View details for DOI 10.1038/s43587-024-00692-2

View details for PubMedID 39143318

View details for PubMedCentralID 3341616

Abstract

Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear.We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269).CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9).These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels.Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

View details for DOI 10.1002/alz.13848

View details for PubMedID 38809917

Abstract

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

View details for DOI 10.1038/s41586-023-06877-w

View details for PubMedID 38693412

View details for PubMedCentralID PMC11062907

Abstract

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

View details for DOI 10.1016/j.chom.2024.02.012

View details for PubMedID 38479397

Abstract

Proteomics data sharing has profound benefits at individual level as well as at community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regards to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Datasets which will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

View details for DOI 10.1016/j.mcpro.2024.100731

View details for PubMedID 38331191

Abstract

The effectiveness of lifestyle interventions in reducing caloric intake and increasing physical activity for preventing Type 2 Diabetes (T2D) has been previously demonstrated. The use of modern technologies can potentially further improve the success of these interventions, promote metabolic health, and prevent T2D at scale. To test this concept, we built a remote program that uses continuous glucose monitoring (CGM) and wearables to make lifestyle recommendations that improve health. We enrolled 2,217 participants with varying degrees of glucose levels (normal range, and prediabetes and T2D ranges), using continuous glucose monitoring (CGM) over 28 days to capture glucose patterns. Participants logged food intake, physical activity, and body weight via a smartphone app that integrated wearables data and provided daily insights, including overlaying glucose patterns with activity and food intake, macronutrient breakdown, glycemic index (GI), glycemic load (GL), and activity measures. The app furthermore provided personalized recommendations based on users' preferences, goals, and observed glycemic patterns. Users could interact with the app for an additional 2 months without CGM. Here we report significant improvements in hyperglycemia, glucose variability, and hypoglycemia, particularly in those who were not diabetic at baseline. Body weight decreased in all groups, especially those who were overweight or obese. Healthy eating habits improved significantly, with reduced daily caloric intake and carbohydrate-to-calorie ratio and increased intake of protein, fiber, and healthy fats relative to calories. These findings suggest that lifestyle recommendations, in addition to behavior logging and CGM data integration within a mobile app, can enhance the metabolic health of both nondiabetic and T2D individuals, leading to healthier lifestyle choices. This technology can be a valuable tool for T2D prevention and treatment.

View details for DOI 10.1038/s41746-023-00956-y

View details for PubMedID 38001287

View details for PubMedCentralID 3891203

Abstract

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions. Expected final online publication date for the Annual Review of Medicine, Volume 75 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

View details for DOI 10.1146/annurev-med-052422-020437

View details for PubMedID 37983384

Abstract

PURPOSE OF REVIEW: Metabolic syndrome (MetS) refers to a group of risk factors, which increase the risk of cardiovascular disease (CVD), type 2 diabetes (T2D), and other chronic diseases. Dietary fiber has been shown to mitigate many of the effects of various risk factors associated with MetS. Our review summarizes the recent findings on the association between dietary fiber deficiency and MetS.RECENT FINDINGS: A number of studies have shown that dietary fiber deficiency is associated with an increased risk of MetS. The main mechanisms by which dietary fiber may reduce the risk of MetS include reduction of cholesterol levels; improvement of blood sugar control; reduction of inflammation; and promotion of weight loss.SUMMARY: Literature suggests that a deficiency in dietary fiber consumption is a risk factor for MetS. An increase in dietary fiber intake may help to reduce the risk of developing MetS and its associated chronic diseases.

View details for DOI 10.1097/MCO.0000000000000971

View details for PubMedID 37751374

Abstract

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

View details for DOI 10.1038/s42255-023-00880-1

View details for PubMedID 37697054

View details for PubMedCentralID 7736650

Abstract

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

View details for DOI 10.1016/j.cell.2023.08.003

View details for PubMedID 37657418

Abstract

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

View details for DOI 10.1038/s41556-023-01194-w

View details for PubMedID 37468756

View details for PubMedCentralID 8238499

Abstract

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

View details for DOI 10.1038/s41586-023-05915-x

View details for PubMedID 37468586

View details for PubMedCentralID PMC10356619

View details for DOI 10.1038/s41551-023-01005-5

View details for Web of Science ID 000920584900001

View details for PubMedID 36697922

Abstract

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

View details for DOI 10.1038/s41551-022-00999-8

View details for PubMedID 36658343

Abstract

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases1,2. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer3-8. Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis-regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study.

View details for DOI 10.1038/s41586-022-05515-1

View details for PubMedID 36517591

Abstract

To understand what determines the success of short- and long-term weight loss, we conduct a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we find that dietary adherence and diet quality, not just caloric restriction, are important for short-term weight loss in both diets. Interestingly, we observe minimal dietary differences between those who succeeded in long-term weight loss and those who did not. Instead, proteomic and gut microbiota signatures significantly differ between these two groups at baseline. Moreover, the baseline respiratory quotient may suggest a specific diet for better weight-loss outcomes. Overall, the identification of these dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, provides a roadmap for developing individualized weight-loss strategies.

View details for DOI 10.1016/j.xcrm.2022.100870

View details for PubMedID 36516846

Abstract

Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.

View details for DOI 10.1371/journal.pdig.0000161

View details for PubMedID 36812648

View details for PubMedCentralID PMC9931296

Abstract

Cell type- and differentiation-specific gene expression is precisely controlled by genomic non-coding regulatory elements (NCREs), which include promoters, enhancers, silencers and insulators. It is estimated that more than 90% of disease-associated sequence variants lie within the non-coding part of the genome, potentially affecting the activity of NCREs. Consequently, the functional annotation of NCREs is a major driver of genome research. Compared with our knowledge of other regulatory elements, our knowledge of silencers, which are NCREs that repress the transcription of genes, is largely lacking. Multiple recent studies have reported large-scale identification of transcription silencer elements, indicating their importance in homeostasis and disease. In this Review, we discuss the biology of silencers, including methods for their discovery, epigenomic and other characteristics, and modes of function of silencers. We also discuss important silencer-relevant considerations in assessing data from genome-wide association studies and shed light on potential future silencer-based therapeutic applications.

View details for DOI 10.1038/s41580-022-00549-9

View details for PubMedID 36344659

Abstract

To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked.The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them.It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.

View details for DOI 10.1093/jamiaopen/ooac080

View details for Web of Science ID 000868349400001

View details for PubMedID 36267121

View details for PubMedCentralID PMC9566305

Abstract

Despite a strong genetic component, only a few genes have been identified in congenital heart diseases (CHDs). We introduced systems analyses to uncover the hidden organization on biological networks of mutations in CHDs and leveraged network analysis to integrate the protein interactome, patient exomes, and single-cell transcriptomes of the developing heart. We identified a CHD network regulating heart development and observed that a sub-network also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHDs and neurodevelopmental conditions. At a small scale, we experimentally verified uncharacterized cardiac functions of several proteins. At a global scale, our study revealed developmental dynamics of the network and observed its association with the hypoplastic left heart syndrome (HLHS), which was further supported by the dysregulation of the network in HLHS endothelial cells. Overall, our work identified previously uncharacterized CHD factors and provided a generalizable framework applicable to studying many other complex diseases. A record of this paper's Transparent Peer Review process is included in the supplemental information.

View details for DOI 10.1016/j.cels.2022.09.001

View details for PubMedID 36167075

Abstract

As our closest living relatives, non-human primates uniquely enable explorations of human health, disease, development, and evolution. Considerable effort has thus been devoted to generating induced pluripotent stem cells (iPSCs) from multiple non-human primate species. Here, we establish improved culture methods for chimpanzee (Pan troglodytes) and pig-tailed macaque (Macaca nemestrina) iPSCs. Such iPSCs spontaneously differentiate in conventional culture conditions, but can be readily propagated by inhibiting endogenous WNT signaling. As a unique functional test of these iPSCs, we injected them into the pre-implantation embryos of another non-human species, rhesus macaques (Macaca mulatta). Ectopic expression of gene BCL2 enhances the survival and proliferation of chimpanzee and pig-tailed macaque iPSCs within the pre-implantation embryo, although the identity and long-term contribution of the transplanted cells warrants further investigation. In summary, we disclose transcriptomic and proteomic data, cell lines, and cell culture resources that may be broadly enabling for non-human primate iPSCs research.

View details for DOI 10.1016/j.celrep.2022.111264

View details for PubMedID 36044843

Abstract

SUMMARY: One of the major challenges in LC-MS data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Materials.AVAILABILITY AND IMPLEMENTATION: https://massdatabase.tidymass.org/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

View details for DOI 10.1093/bioinformatics/btac546

View details for PubMedID 35944213

Abstract

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.

View details for DOI 10.1038/s41467-022-32155-w

View details for PubMedID 35902589

Abstract

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

View details for DOI 10.1038/s41588-022-01088-x

View details for PubMedID 35726067

View details for Web of Science ID 000892509504476

Abstract

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

View details for DOI 10.1101/gr.276521.121

View details for PubMedID 35667843

Abstract

The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.

View details for DOI 10.1016/j.cels.2022.05.007

View details for PubMedID 35690068

Abstract

Dietary fibers act through the microbiome to improve cardiovascular health and prevent metabolic disorders and cancer. To understand the health benefits of dietary fiber supplementation, we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multiomic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel, and clinical measurements on healthy and insulin-resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose, there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation and the mechanisms behind fiber-induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.

View details for DOI 10.1016/j.chom.2022.03.036

View details for PubMedID 35483363