p53 is a central regulator driving neurodegeneration caused by C9orf72 poly(PR).

The most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is a GGGGCC repeat expansion in the C9orf72 gene. We developed a platform to interrogate the chromatin accessibility landscape and transcriptional program within neurons during degeneration. We provide evidence that neurons expressing the dipeptide repeat protein poly(proline-arginine), translated from the C9orf72 repeat expansion, activate a highly specific transcriptional program, exemplified by a single transcription factor, p53. Ablating p53 in mice completely rescued neurons from degeneration and markedly increased survival in a C9orf72 mouse model. p53 reduction also rescued axonal degeneration caused by poly(glycine-arginine), increased survival of C9orf72 ALS/FTD-patient-induced pluripotent stem cell (iPSC)-derived motor neurons, and mitigated neurodegeneration in a C9orf72 fly model. We show that p53 activates a downstream transcriptional program, including Puma, which drives neurodegeneration. These data demonstrate a neurodegenerative mechanism dynamically regulated through transcription-factor-binding events and provide a framework to apply chromatin accessibility and transcription program profiles to neurodegeneration.

A MicroRNA Linking Human Positive Selection and Metabolic Disorders.

Positive selection in Europeans at the 2q21.3 locus harboring the lactase gene has been attributed to selection for the ability of adults to digest milk to survive famine in ancient times. However, the 2q21.3 locus is also associated with obesity and type 2 diabetes in humans, raising the possibility that additional genetic elements in the locus may have contributed to evolutionary adaptation to famine by promoting energy storage, but which now confer susceptibility to metabolic diseases. We show here that the miR-128-1 microRNA, located at the center of the positively selected locus, represents a crucial metabolic regulator in mammals. Antisense targeting and genetic ablation of miR-128-1 in mouse metabolic disease models result in increased energy expenditure and amelioration of high-fat-diet-induced obesity and markedly improved glucose tolerance. A thrifty phenotype connected to miR-128-1-dependent energy storage may link ancient adaptation to famine and modern metabolic maladaptation associated with nutritional overabundance.

Landscape of cohesin-mediated chromatin loops in the human genome.

Physical interactions between distal regulatory elements have a key role in regulating gene expression, but the extent to which these interactions vary between cell types and contribute to cell-type-specific gene expression remains unclear. Here, to address these questions as part of phase III of the Encyclopedia of DNA Elements (ENCODE), we mapped cohesin-mediated chromatin loops, using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), and analysed gene expression in 24 diverse human cell types, including core ENCODE cell lines. Twenty-eight per cent of all chromatin loops vary across cell types; these variations modestly correlate with changes in gene expression and are effective at grouping cell types according to their tissue of origin. The connectivity of genes corresponds to different functional classes, with housekeeping genes having few contacts, and dosage-sensitive genes being more connected to enhancer elements. This atlas of chromatin loops complements the diverse maps of regulatory architecture that comprise the ENCODE Encyclopedia, and will help to support emerging analyses of genome structure and function.

Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits.

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Multiomic blood correlates of genetic risk identify presymptomatic disease alterations.

Transitions from health to disease are characterized by dysregulation of biological networks under the influence of genetic and environmental factors, often over the course of years to decades before clinical symptoms appear. Understanding these dynamics has important implications for preventive medicine. However, progress has been hindered both by the difficulty of identifying individuals who will eventually go on to develop a particular disease and by the inaccessibility of most disease-relevant tissues in living individuals. Here we developed an alternative approach using polygenic risk scores (PRSs) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and found that these PRSs were associated with 766 detectable alterations in proteomic, metabolomic, and standard clinical laboratory measurements (clinical labs) from blood plasma across several thousand mostly healthy individuals. We recapitulated a variety of known relationships (e.g., glutamatergic neurotransmission and inflammation with depression, IL-33 with asthma) and found associations directly suggesting therapeutic strategies (e.g., Ω-6 supplementation and IL-13 inhibition for amyotrophic lateral sclerosis) and influences on longevity (leukemia inhibitory factor, ceramides). Analytes altered in high-genetic-risk individuals showed concordant changes in disease cases, supporting the notion that PRS-associated analytes represent presymptomatic disease alterations. Our results provide insights into the molecular pathophysiology of a range of traits and suggest avenues for the prevention of health-to-disease transitions.

The origins of specialized pottery and diverse alcohol fermentation techniques in Early Neolithic China.

In China, pottery containers first appeared about 20000 cal. BP, and became diverse in form during the Early Neolithic (9000-7000 cal. BP), signaling the emergence of functionally specialized vessels. China is also well-known for its early development of alcohol production. However, few studies have focused on the connections between the two technologies. Based on the analysis of residues (starch, phytolith, and fungus) adhering to pottery from two Early Neolithic sites in north China, here we demonstrate that three material changes occurring in the Early Neolithic signal innovation of specialized alcoholic making known in north China: (i) the spread of cereal domestication (millet and rice), (ii) the emergence of dedicated pottery types, particularly globular jars as liquid storage vessels, and (iii) the development of cereal-based alcohol production with at least two fermentation methods: the use of cereal malts and the use of moldy grain and herbs (qu and caoqu) as starters. The latter method was arguably a unique invention initiated in China, and our findings account for the earliest known examples of this technique. The major ingredients include broomcorn millet, Triticeae grasses, Job's tears, rice, beans, snake gourd root, ginger, possible yam and lily, and other plants, some probably with medicinal properties (e.g., ginger). Alcoholic beverages made with these methods were named li, jiu, and chang in ancient texts, first recorded in the Shang oracle-bone inscriptions (ca. 3200 cal. BP); our findings have revealed a much deeper history of these diverse fermentation technologies in China.

Performance of Nucleic Acid Amplification Tests for Detection of Severe Acute Respiratory Syndrome Coronavirus 2 in Prospectively Pooled Specimens.

Pooled nucleic acid amplification tests for severe acute respiratory syndrome coronavirus 2 could increase availability of testing at decreased cost. However, the effect of dilution on analytical sensitivity through sample pooling has not been well characterized. We tested 1,648 prospectively pooled specimens by using 3 nucleic acid amplification tests for severe acute respiratory syndrome coronavirus 2: a laboratory-developed real-time reverse transcription PCR targeting the envelope gene, and 2 commercially available Panther System assays targeting open reading frame 1ab. Positive percent agreement (PPA) of pooled versus individual testing ranged from 71.7% to 82.6% for pools of 8 and from 82.9% to 100.0% for pools of 4. We developed and validated an independent stochastic simulation model to estimate effects of dilution on PPA and efficiency of a 2-stage pooled real-time reverse transcription PCR testing algorithm. PPA was dependent on the proportion of tests with positive results, cycle threshold distribution, and assay limit of detection.

SARS-CoV-2 RNA in Wastewater Settled Solids Is Associated with COVID-19 Cases in a Large Urban Sewershed.

Wastewater-based epidemiology may be useful for informing public health response to viral diseases like COVID-19 caused by SARS-CoV-2. We quantified SARS-CoV-2 RNA in wastewater influent and primary settled solids in two wastewater treatment plants to inform the preanalytical and analytical approaches and to assess whether influent or solids harbored more viral targets. The primary settled solids samples resulted in higher SARS-CoV-2 detection frequencies than the corresponding influent samples. Likewise, SARS-CoV-2 RNA was more readily detected in solids using one-step digital droplet (dd)RT-PCR than with two-step RT-QPCR and two-step ddRT-PCR, likely owing to reduced inhibition with the one-step ddRT-PCR assay. We subsequently analyzed a longitudinal time series of 89 settled solids samples from a single plant for SARS-CoV-2 RNA as well as coronavirus recovery (bovine coronavirus) and fecal strength (pepper mild mottle virus) controls. SARS-CoV-2 RNA targets N1 and N2 concentrations correlated positively and significantly with COVID-19 clinically confirmed case counts in the sewershed. Together, the results demonstrate that measuring SARS-CoV-2 RNA concentrations in settled solids may be a more sensitive approach than measuring SARS-CoV-2 in influent.

Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study.

BACKGROUND: Lifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk. METHODS AND FINDINGS: We performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m2 BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m2 BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12-1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m2) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m2) with a family history (1.21-fold reduction, 95% CI 1.13-1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual's entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life. CONCLUSIONS: In a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.

Addressing 6 challenges in generative AI for digital health: A scoping review.

Generative artificial intelligence (AI) can exhibit biases, compromise data privacy, misinterpret prompts that are adversarial attacks, and produce hallucinations. Despite the potential of generative AI for many applications in digital health, practitioners must understand these tools and their limitations. This scoping review pays particular attention to the challenges with generative AI technologies in medical settings and surveys potential solutions. Using PubMed, we identified a total of 120 articles published by March 2024, which reference and evaluate generative AI in medicine, from which we synthesized themes and suggestions for future work. After first discussing general background on generative AI, we focus on collecting and presenting 6 challenges key for digital health practitioners and specific measures that can be taken to mitigate these challenges. Overall, bias, privacy, hallucination, and regulatory compliance were frequently considered, while other concerns around generative AI, such as overreliance on text models, adversarial misprompting, and jailbreaking, are not commonly evaluated in the current literature.

Nightmares share genetic risk factors with sleep and psychiatric traits.

Nightmares are vivid, extended, and emotionally negative or negative dreams that awaken the dreamer. While sporadic nightmares and bad dreams are common and generally harmless, frequent nightmares often reflect underlying pathologies of emotional regulation. Indeed, insomnia, depression, anxiety, or alcohol use have been associated with nightmares in epidemiological and clinical studies. However, the connection between nightmares and their comorbidities are poorly understood. Our goal was to examine the genetic risk factors for nightmares and estimate correlation or causality between nightmares and comorbidities. We performed a genome-wide association study (GWAS) in 45,255 individuals using a questionnaire-based assessment on the frequency of nightmares during the past month and genome-wide genotyping data. While the GWAS did not reveal individual risk variants, heritability was estimated at 5%. In addition, the genetic correlation analysis showed a robust correlation (rg > 0.4) of nightmares with anxiety (rg = 0.671, p = 7.507e-06), depressive (rg = 0.562, p = 1.282e-07) and posttraumatic stress disorders (rg = 0.4083, p = 0.0152), and personality trait neuroticism (rg = 0.667, p = 4.516e-07). Furthermore, Mendelian randomization suggested causality from insomnia to nightmares (beta = 0.027, p = 0.0002). Our findings suggest that nightmares share genetic background with psychiatric traits and that insomnia may increase an individual's liability to experience frequent nightmares. Given the significant correlations with psychiatric and psychological traits, it is essential to grow awareness of how nightmares affect health and disease and systematically collect information about nightmares, especially from clinical samples and larger cohorts.

SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease.

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.

Diversity of ribosomes at the level of rRNA variation associated with human health and disease.

With hundreds of copies of ribosomal DNA (rDNA) it is unknown whether they possess sequence variations that ultimately form different types of ribosomes. Here, we developed an algorithm for variant-calling between paralog genes (termed RGA) and compared rDNA variations with rRNA variations from long-read sequencing of translating ribosomes (RIBO-RT). Our analyses identified dozens of highly abundant rRNA variants, largely indels, that are incorporated into translationally active ribosomes and assemble into distinct ribosome subtypes encoded on different chromosomes. We developed an in-situ rRNA sequencing method (SWITCH-seq) revealing that variants are co-expressed within individual cells and found that they possess different structures. Lastly, we observed tissue-specific rRNA-subtype expression and linked specific rRNA variants to cancer. This study therefore reveals the variation landscape of translating ribosomes within human cells.

An epidemiological introduction to human metabolomic investigations.

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Metabolomic epidemiology offers insights into disease aetiology.

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

SIRPα controls CD47-dependent platelet clearance in mice and humans.

Over the last decade, more data has revealed that increased surface expression of the "don't eat me" CD47 protein on cancer cells plays a role in immune evasion and tumor progression, with CD47 blockade emerging as a new therapy in immuno-oncology. CD47 is critical in regulating cell homeostasis and clearance, as binding of CD47 to the inhibitory receptor SIRPα can prevent phagocytosis and macrophage-mediated cell clearance. The purpose of this study was to examine the role of the CD47-SIRPα signal in platelet homeostasis and clearance. Therapeutic reagents targeting the CD47-SIRPα axis are very promising for treatment of hematologic malignancies and solid tumors, but lead to transient anemia or thrombocytopenia in a subset of patients. We found that platelet homeostatic clearance is regulated through the CD47-SIRPα axis and that therapeutic blockade to disrupt this interaction in mice and in humans has a significant impact on platelet levels. Furthermore, we identified genetic variations at the SIRPA locus that impact platelet levels in humans such that higher SIRPA gene expression is associated with higher platelet levels. SIRPA expression at either end of the normal range may affect clinical outcomes of treatment with anti-CD47 therapy.

A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes.

Recent large-scale genomic association studies found evidence for a genetic link between increased risk of type 2 diabetes and decreased risk for adiposity-related traits, reminiscent of metabolically obese normal weight (MONW) association signatures. However, the target genes and cellular mechanisms driving such MONW associations remain to be identified. Here, we systematically identify the cellular programmes of one of the top-scoring MONW risk loci, the 2q24.3 risk locus, in subcutaneous adipocytes. We identify a causal genetic variant, rs6712203, an intronic single-nucleotide polymorphism in the COBLL1 gene, which changes the conserved transcription factor motif of POU domain, class 2, transcription factor 2, and leads to differential COBLL1 gene expression by altering the enhancer activity at the locus in subcutaneous adipocytes. We then establish the cellular programme under the genetic control of the 2q24.3 MONW risk locus and the effector gene COBLL1, which is characterized by impaired actin cytoskeleton remodelling in differentiating subcutaneous adipocytes and subsequent failure of these cells to accumulate lipids and develop into metabolically active and insulin-sensitive adipocytes. Finally, we show that perturbations of the effector gene Cobll1 in a mouse model result in organismal phenotypes matching the MONW association signature, including decreased subcutaneous body fat mass and body weight along with impaired glucose tolerance. Taken together, our results provide a mechanistic link between the genetic risk for insulin resistance and low adiposity, providing a potential therapeutic hypothesis and a framework for future identification of causal relationships between genome associations and cellular programmes in other disorders.

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.

A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy.

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation.

Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.