Epidemiological and clinical features of a large blastomycosis outbreak at a paper mill in Michigan.

BACKGROUND: Blastomycosis is an environmentally acquired fungal infection that can result in severe pulmonary illness and high hospitalization rates. In 2023, a blastomycosis outbreak was detected among workers at a paper mill in Delta County, Michigan. METHODS: We included patients with clinical and laboratory evidence of blastomycosis who had spent ≥40 hours in Delta County since September 1, 2022 and had illness onset December 1, 2022-July 1, 2023. We assessed epidemiological and clinical features of patients and evaluated factors associated with hospitalization. We performed whole-genome sequencing to characterize genetic relatedness of clinical isolates from eight patients. RESULTS: In total, 131 patients were identified; all had worked at or visited the mill. Sixteen patients (12%) were hospitalized; one died. Compared with non-hospitalized patients, more hospitalized patients had diabetes (p=0.03) and urine antigen titers above the lower limit of quantification (p<0.001). Hospitalized patients were also more likely to have had ≥1 healthcare visits before receiving a blastomycosis diagnostic test (p=0.02) and to have been treated with antibiotics prior to antifungal prescription (p=0.001). All sequenced isolates were identified as Blastomyces gilchristii and clustered into a distinct outbreak cluster. CONCLUSIONS: This was the largest documented blastomycosis outbreak in the United States. Epidemiologic evidence indicated exposures occurred at or near the mill, and genomic findings suggested a common exposure source. Patients with diabetes may have increased risk for hospitalization, and elevated urine antigen titers could indicate greater disease severity. Early suspicion of blastomycosis may prompt earlier diagnosis and treatment, potentially reducing unnecessary antibiotic prescriptions and improving patient outcomes.

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders.

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.

Multiomic analysis of familial adenomatous polyposis reveals molecular pathways associated with early tumorigenesis.

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.

Global loss of promoter-enhancer connectivity and rebalancing of gene expression during early colorectal cancer carcinogenesis.

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.

Lifestyle Profiling Using Wearables and Prediction of Glucose Metabolism in Individuals with Normoglycemia or Prediabetes.

This study examined the relationship between lifestyles (diet, sleep, and physical activity) and glucose responses at a personal level. 36 healthy adults in the Bay Area were monitored for their lifestyles and glucose levels using wearables and continuous glucose monitoring (NCT03919877). Gold-standard metabolic tests were conducted to phenotype metabolic characteristics. Through the lifestyle data (2,307 meals, 1,809 nights, and 2,447 days) and 231,206 CGM readings from metabolically-phenotyped individuals with normoglycemia or prediabetes, we found: 1) eating timing was associated with hyperglycemia, muscle insulin resistance (IR), and incretin dysfunction, whereas nutrient intakes were not; 2) timing of increased activity in muscle IS and IR participants was associated with differential benefits of glucose control; 3) Integrated ML models using lifestyle factors predicted distinct metabolic characteristics (muscle, adipose IR or incretin dysfunction). Our data indicate the differential impact of lifestyles on glucose regulation among individuals with different metabolic phenotypes, highlighting the value of personalized lifestyle modifications.

Challenges and recommendations for the translation of biomarkers of aging.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Interindividual Variability in Postprandial Plasma Fructose Patterns in Adults.

High fructose consumption is associated with an increased risk of cardiometabolic disease, and fructose feeding dose-dependently induces markers reflective of poor metabolic health. However, unlike glucose, surprisingly little is known about person-to-person differences in postprandial plasma fructose patterns. Herein, we performed post hoc analyses of two published studies to address this question. In the first cohort, 16 participants' all-day plasma fructose concentration patterns (08:00-23:30) were determined (8 women and 8 men) while consuming mixed meals (breakfast, lunch, and dinner) with a fructose-sweetened beverage at each meal (30% of calories). Individually plotted results demonstrate remarkably disparate fructose patterns with respect to peak concentration and timing. A secondary study confirmed substantial interindividual variability in plasma fructose patterns over 240 min in 16 adults consuming Ensure®, a commercially available mixed macronutrient drink containing a low dose of fructose. The health ramifications of interindividual variations in postprandial fructose metabolism and the underlying physiological mechanisms driving differences in post-meal blood patterns remain to be explored. Future research is warranted to determine if interindividual variability in fructose digestion, metabolism, and postprandial blood concentration patterns is associated with cardiometabolic health phenotypes and disease risk.

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework.

Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D: muscle insulin resistance, ß-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in ß-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, ß-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and ß-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and ß-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and ß-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.

Nonlinear dynamics of multi-omics profiles during human aging.

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.

Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer.

The drivers of immune evasion are not entirely clear, limiting the success of cancer immunotherapies. Here we applied single-cell spatial and perturbational transcriptomics to delineate immune evasion in high-grade serous tubo-ovarian cancer. To this end, we first mapped the spatial organization of high-grade serous tubo-ovarian cancer by profiling more than 2.5 million cells in situ in 130 tumors from 94 patients. This revealed a malignant cell state that reflects tumor genetics and is predictive of T cell and natural killer cell infiltration levels and response to immune checkpoint blockade. We then performed Perturb-seq screens and identified genetic perturbations-including knockout of PTPN1 and ACTR8-that trigger this malignant cell state. Finally, we show that these perturbations, as well as a PTPN1/PTPN2 inhibitor, sensitize ovarian cancer cells to T cell and natural killer cell cytotoxicity, as predicted. This study thus identifies ways to study and target immune evasion by linking genetic variation, cell-state regulators and spatial biology.

Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

Pilot Safety Study of a Microfabricated Device for Anterior Stromal Puncture to Treat Corneal Epithelial Disease in the Optical Axis.

PURPOSE: Anterior stromal puncture is an inexpensive and widely used technique at the slit lamp to treat recurrent corneal erosions (RCE), common sequelae of corneal abrasions, trauma, and epithelial basement membrane dystrophies (EBMDs). The purpose of this study was to determine safety of a novel anterior stromal micropuncture device (termed a "plexitome") for use in the optical axis of the cornea. METHODS: We performed an IRB-approved clinical pilot study on 45 patients with diverse corneal pathologies to determine the safety of the plexitome device and examined corneal healing responses clinically for a minimum of 30 days after treatment. Follow on efficacy data was collected for treated patients with RCE. Micropuncture treatment was performed either through loose epithelial tissue or after debridement using a cotton tip at the slit lamp. After "imprinting," patients were treated with topical medications including antibiotics, hypertonic saline/ointment, bandage contact lenses, and/or patching according to the treating physician's routine care for the condition. RESULTS: Micropuncture using the "plexitome" device did not create visible scars in the corneal stroma of the 45 patients treated and followed for at least 30 days. Photographic evidence of imprinting was seen at in 1 patient at 30 days. There were no significant adverse events associated with treatment. CONCLUSIONS: Micropuncture of the cornea using the "plexitome" device does not create optically evident anterior stromal scarring after treatment. Micropuncture using the device may be an effective way of treating RCE and other corneal manifestations of EBMD in the optical axis, which is not currently possible using standard anterior stromal puncture methods.

Dynamic Human Gut Microbiome and Immune Shifts During an Immersive Psychosocial Therapeutic Program.

Background: Depression is a leading cause of disability worldwide yet its underlying factors, particularly microbial associations, are poorly understood. Methods: We examined the longitudinal interplay between the microbiome and immune system in the context of depression during an immersive psychosocial intervention. 142 multi-omics samples were collected from 52 well-characterized participants before, during, and three months after a nine-day inquiry-based stress reduction program. Results: We found that depression was associated with both an increased presence of putatively pathogenic bacteria and reduced microbial beta-diversity. Following the intervention, we observed reductions in neuroinflammatory cytokines and improvements in several mental health indicators. Interestingly, participants with a Prevotella-dominant microbiome showed milder symptoms when depressed, along with a more resilient microbiome and more favorable inflammatory cytokine profile, including reduced levels of CXCL-1. Conclusions: Our findings reveal a protective link between the Prevotella-dominant microbiome and depression, associated with a less inflammatory environment and moderated symptoms. These insights, coupled with observed improvements in neuroinflammatory markers and mental health from the intervention, highlight potential avenues for microbiome-targeted therapies in depression management.

PRC2-AgeIndex as a universal biomarker of aging and rejuvenation.

DNA methylation (DNAm) is one of the most reliable biomarkers of aging across mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, DNAm gain is less characterized. Studies have demonstrated that CpGs which gain methylation with age are enriched in Polycomb Repressive Complex 2 (PRC2) targets. However, whole-genome examination of all PRC2 targets as well as determination of the pan-tissue or tissue-specific nature of these associations is lacking. Here, we show that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells (PRC2 LMRs) gain methylation with age in all examined somatic mitotic cells. We estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the "PRC2-AgeIndex," defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells which can distinguish the effect of different anti-aging interventions.

The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states.

Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.

Glycan clock of ageing-analytical precision and time-dependent inter- and i-individual variability.

Ageing is a complex biological process with variations among individuals, leading to the development of ageing clocks to estimate biological age. Glycans, particularly in immunoglobulin G (IgG), have emerged as potential biomarkers of ageing, with changes in glycosylation patterns correlating with chronological age.For precision analysis, three different plasma pools were analysed over 26 days in tetraplicates, 312 samples in total. In short-term variability analysis, two cohorts were analysed: AstraZeneca MFO cohort of 26 healthy individuals (median age 20) and a cohort of 70 premenopausal Chinese women (median age 22.5) cohort monitored over 3 months. Long-term variability analysis involved two adult men aged 47 and 57, monitored for 5 and 10 years, respectively. Samples were collected every 3 months and 3 weeks, respectively. IgG N-glycan analysis followed a standardized approach by isolating IgG, its subsequent denaturation and deglycosylation followed by glycan cleanup and labelling. Capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) and ultra-performance liquid chromatography analyses were employed for glycan profiling. Statistical analysis involved normalization, batch correction, and linear mixed models to assess time effects on derived glycan traits.The intermediate precision results consistently exhibited very low coefficient of variation values across all three test samples. This consistent pattern underscores the high level of precision inherent in the CGE method for analysing the glycan clock of ageing. The AstraZeneca MFO cohort did not show any statistically significant trends, whereas the menstrual cycle cohort exhibited statistically significant trends in digalactosylated (G2), agalactosylated (G0) and fucosylation (F). These trends were attributed to the effects of the menstrual cycle. Long-term stability analysis identified enduring age-related trends in both subjects, showing a positive time effect in G0 and bisected N-acetylglucosamine, as well as a negative time effect in G2 and sialylation, aligning with earlier findings. Time effects measured for monogalactosylation, and F remained substantially lower than ones observed for other traits.The study found that IgG N-glycome analysis using CGE-LIF exhibited remarkably high intermediate precision. Moreover, the study highlights the short- and long-term stability of IgG glycome composition, coupled with a notable capacity to adapt and respond to physiological changes and environmental influences such as hormonal changes, disease, and interventions. The discoveries from this study propel personalized medicine forward by deepening our understanding of how IgG glycome relates to age-related health concerns. This study underscores the reliability of glycans as a biomarker for tracking age-related changes and individual health paths.

Integrated platform for multiscale molecular imaging and phenotyping of the human brain.

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multiscale details of individual cells and their connectivity in the human organ-scale system. We developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain. The platform includes three core elements: a vibrating microtome for ultraprecision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and a computational pipeline for reconstructing three-dimensional connectivity across multiple brain slabs (UNSLICE). We applied this platform for analyzing human Alzheimer's disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases.

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.