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Sympathetic activation during cold exposure increases adipocyte thermogenesis via the expression of mitochondrial protein uncoupling protein 1 (UCP1) 1 . The propensity of adipocytes to express UCP1 is under a critical influence of the adipose microenvironment and varies between sexes and among various fat depots 2-7 . Here we report that mammary gland ductal epithelial cells in the adipose niche regulate cold-induced adipocyte UCP1 expression in female mouse subcutaneous white adipose tissue (scWAT). Single-cell RNA sequencing shows that glandular luminal epithelium subtypes express transcripts that encode secretory factors controlling adipocyte UCP1 expression under cold conditions. We term these luminal epithelium secretory factors 'mammokines'. Using 3D visualization of whole-tissue immunofluorescence, we reveal sympathetic nerve-ductal contact points. We show that mammary ducts activated by sympathetic nerves limit adipocyte UCP1 expression via the mammokine lipocalin 2. In vivo and ex vivo ablation of mammary duct epithelium enhance the cold-induced adipocyte thermogenic gene programme in scWAT. Since the mammary duct network extends throughout most of the scWAT in female mice, females show markedly less scWAT UCP1 expression, fat oxidation, energy expenditure and subcutaneous fat mass loss compared with male mice, implicating sex-specific roles of mammokines in adipose thermogenesis. These results reveal a role of sympathetic nerve-activated glandular epithelium in adipocyte UCP1 expression and suggest that mammary duct luminal epithelium has an important role in controlling glandular adiposity., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Acid suppressants are widely-used classes of medications linked to increased risks of aerodigestive infections. Prior studies of these medications as potentially reversible risk factors for COVID-19 have been conflicting. We aimed to determine the impact of chronic acid suppression use on COVID-19 infection risk while simultaneously evaluating the influence of social determinants of health to validate known and discover novel risk factors. We assessed the association of chronic acid suppression with incident COVID-19 in a 1:1 case-control study of 900 patients tested across three academic medical centers in California, USA. Medical comorbidities and history of chronic acid suppression use were manually extracted from health records by physicians following a pre-specified protocol. Socio-behavioral factors by geomapping publicly-available data to patient zip codes were incorporated. We identified no evidence to support an association between chronic acid suppression and COVID-19 (adjusted odds ratio 1.04, 95% CI 0.92-1.17, P = 0.515). However, several medical and social features were positive (Latinx ethnicity, BMI ≥ 30, dementia, public transportation use, month of the pandemic) and negative (female sex, concurrent solid tumor, alcohol use disorder) predictors of new infection. These findings demonstrate the value of integrating publicly-available databases with medical data to identify critical features of communicable diseases., (© 2021. The Author(s).)

Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.

There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods., Competing Interests: Competing interestsA.J.B., B.N., and E.R. are inventors on a filed disclosure on the Philter technology at the University of California. A.J.B., B.N., and E.R. are inventors on a filed patent some of the components of which are described in this paper., (© The Author(s) 2020.)

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

The original version of the Data Descriptor contained errors in the author list and affiliations. Rita Leite's first name was misspelled as "Rite" and affiliations 4 and 5 were incorrectly swapped. In addition, members of the March of Dimes Prematurity Research Center consortium were not listed in the agreed positions within the author list. These errors have now been corrected in the HTML and PDF versions.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart., Competing Interests: Competing interestsThe authors declare no competing interests.

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components-Private Data, Shared Data, Data Analysis, and Resources-for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the Shared Data portal (www.immport.org/immport-open), which allows research data to be repurposed to accelerate the translation of new insights into discoveries.

Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

Open clinical trial data offer many opportunities for the scientific community to independently verify published results, evaluate new hypotheses and conduct meta-analyses. These data provide valuable opportunities for scientific advances in medical research. Herein we present the comparative meta-analysis of different standard of care treatments from newly available comparator arm data from several prostate cancer clinical trials. Comparison of survival rates following treatment with mitoxantrone or docetaxel in combination with prednisone as well as prednisone alone, validated the previously demonstrated superiority of treatment with docetaxel. Additionally, comparison of four testosterone suppression treatments in hormone-refractory prostate cancer revealed that subjects who had undergone surgical castration had significantly lower survival rates than those treated with LHRH, anti-androgen or LHRH plus anti-androgen, suggesting that this treatment option is less optimal. This study illustrates how the use of patient-level clinical trial data enables meta-analyses that can provide new insights into clinical outcomes of standard of care treatments and thus, once validated, has the potential to help optimize healthcare delivery.

The central dogma of molecular biology describes the translation of genetic information from mRNA to protein, but does not specify the quantitation or timing of this process across the genome. We have analyzed protein and gene expression in a diverse set of human tissues. To study concordance and discordance of gene and protein expression, we integrated mass spectrometry data from the Human Proteome Map project and RNA-Seq measurements from the Genotype-Tissue Expression project. We analyzed 16,561 genes and the corresponding proteins in 14 tissue types across nearly 200 samples. A comprehensive tissue- and gene-specific analysis revealed that across the 14 tissues, correlation between mRNA and protein expression was positive and ranged from 0.36 to 0.5. We also identified 1,012 genes whose RNA and protein expression was correlated across all the tissues and examined genes and proteins that were concordantly and discordantly expressed for each tissue of interest. We extended our analysis to look for genes and proteins that were differentially correlated in cancer compared to normal tissues, showing higher levels of correlation in normal tissues. Finally, we explored the implications of these findings in the context of biomarker and drug target discovery.

Prediction of new disease indications for approved drugs by computational methods has been based largely on the genomics signatures of drugs and diseases. We propose a method for drug repositioning that uses the clinical signatures extracted from over 13 years of electronic medical records from a tertiary hospital, including >9.4 M laboratory tests from >530,000 patients, in addition to diverse genomics signatures. Cross-validation using over 17,000 known drug-disease associations shows this approach outperforms various predictive models based on genomics signatures and a well-known "guilt-by-association" method. Interestingly, the prediction suggests that terbutaline sulfate, which is widely used for asthma, is a promising candidate for amyotrophic lateral sclerosis for which there are few therapeutic options. In vivo tests using zebrafish models found that terbutaline sulfate prevents defects in axons and neuromuscular junction degeneration in a dose-dependent manner. A therapeutic potential of terbutaline sulfate was also observed when axonal and neuromuscular junction degeneration have already occurred in zebrafish model. Cotreatment with a β2-adrenergic receptor antagonist, butoxamine, suggests that the effect of terbutaline is mediated by activation of β2-adrenergic receptors.

Advanced age is associated with an increased risk of vascular morbidity, attributable in part to impairments in new blood vessel formation. Mesenchymal stem cells (MSCs) have previously been shown to play an important role in neovascularization and deficiencies in these cells have been described in aged patients. Here we utilize single cell transcriptional analysis to determine the effect of aging on MSC population dynamics. We identify an age-related depletion of a subpopulation of MSCs characterized by a pro-vascular transcriptional profile. Supporting this finding, we demonstrate that aged MSCs are also significantly compromised in their ability to support vascular network formation in vitro and in vivo. Finally, aged MSCs are unable to rescue age-associated impairments in cutaneous wound healing. Taken together, these data suggest that age-related changes in MSC population dynamics result in impaired therapeutic potential of aged progenitor cells. These findings have critical implications for therapeutic cell source decisions (autologous versus allogeneic) and indicate the necessity of strategies to improve functionality of aged MSCs.

Deregulation of lysine methylation signalling has emerged as a common aetiological factor in cancer pathogenesis, with inhibitors of several histone lysine methyltransferases (KMTs) being developed as chemotherapeutics. The largely cytoplasmic KMT SMYD3 (SET and MYND domain containing protein 3) is overexpressed in numerous human tumours. However, the molecular mechanism by which SMYD3 regulates cancer pathways and its relationship to tumorigenesis in vivo are largely unknown. Here we show that methylation of MAP3K2 by SMYD3 increases MAP kinase signalling and promotes the formation of Ras-driven carcinomas. Using mouse models for pancreatic ductal adenocarcinoma and lung adenocarcinoma, we found that abrogating SMYD3 catalytic activity inhibits tumour development in response to oncogenic Ras. We used protein array technology to identify the MAP3K2 kinase as a target of SMYD3. In cancer cell lines, SMYD3-mediated methylation of MAP3K2 at lysine 260 potentiates activation of the Ras/Raf/MEK/ERK signalling module and SMYD3 depletion synergizes with a MEK inhibitor to block Ras-driven tumorigenesis. Finally, the PP2A phosphatase complex, a key negative regulator of the MAP kinase pathway, binds to MAP3K2 and this interaction is blocked by methylation. Together, our results elucidate a new role for lysine methylation in integrating cytoplasmic kinase-signalling cascades and establish a pivotal role for SMYD3 in the regulation of oncogenic Ras signalling.