BCI-838, an orally active mGluR2/3 receptor antagonist pro-drug, rescues learning behavior deficits in the PS19 MAPTP301S mouse model of tauopathy.

Tauopathies are a heterogeneous group of neurodegenerative disorders that are clinically and pathologically distinct from Alzheimer's disease (AD) having tau inclusions in neurons and/or glia as their most prominent neuropathological feature. BCI-838 (MGS00210) is a group II metabotropic glutamate receptor (mGluR2/3) antagonist pro-drug. Previously, we reported that orally administered BCI-838 improved learning behavior and reduced anxiety in Dutch (APPE693Q) transgenic mice, a model of the pathological accumulation of Aß oligomers found in AD. Herein, we investigated effects of BCI-838 on PS19 male mice that express the tauopathy mutation MAPTP301S associated with human frontotemporal lobar degeneration (FTLD). These mice develop an aging-related tauopathy without amyloid accumulation. Mice were divided into three experimental groups: (1) non-transgenic wild type mice treated with vehicle, (2) PS19 mice treated with vehicle and (3) PS19 mice treated with 5 mg/kg BCI-838. Groups of 10-13 mice were utilized. Vehicle or BCI-838 was administered by oral gavage for 4 weeks. Behavioral testing consisting of a novel object recognition task was conducted after drug administration. Two studies were performed beginning treatment of mice at 3 or 7 months of age. One month of BCI-838 treatment rescued deficits in recognition memory in PS19 mice whether treatment was begun at 3 or 7 months of age. These studies extend the potential utility of BCI-838 to neurodegenerative conditions that have tauopathy as their underlying basis. They also suggest an mGluR2/3 dependent mechanism as a basis for the behavioral deficits in PS19 mice.

Quantitative In Vivo Imaging of the Androgen Receptor Axis Reveals Degree of Prostate Cancer Radiotherapy Response.

Noninvasive biomarkers for androgen receptor (AR) pathway activation are urgently needed to better monitor patient response to prostate cancer therapies. AR is a critical driver and mediator of resistance of prostate cancer but currently available noninvasive prostate cancer biomarkers to monitor AR activity are discordant with downstream AR pathway activity. External beam radiotherapy (EBRT) remains a common treatment for all stages of prostate cancer, and DNA damage induced by EBRT upregulates AR pathway activity to promote therapeutic resistance. [89Zr]11B6-PET is a novel modality targeting prostate-specific protein human kallikrein 2 (hK2), which is a surrogate biomarker for AR activity. Here, we studied whether [89Zr]11B6-PET can accurately assess EBRT-induced AR activity.Genetic and human prostate cancer mouse models received EBRT (2-50 Gy) and treatment response was monitored by [89Zr]11B6-PET/CT. Radiotracer uptake and expression of AR and AR target genes was quantified in resected tissue.EBRT increased AR pathway activity and [89Zr]11B6 uptake in LNCaP-AR and 22RV1 tumors. EBRT increased prostate-specific [89Zr]11B6 uptake in prostate cancer-bearing mice (Hi-Myc x Pb_KLK2) with no significant changes in uptake in healthy (Pb_KLK2) mice, and this correlated with hK2 protein levels. IMPLICATIONS: hK2 expression in prostate cancer tissue is a proxy of EBRT-induced AR activity that can noninvasively be detected using [89Zr]11B6-PET; further clinical evaluation of hK2-PET for monitoring response and development of resistance to EBRT in real time is warranted.

Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.

Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.

OBJECTIVES: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. BACKGROUND: Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. METHODS: A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. RESULTS: We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation. CONCLUSIONS: DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease.

The evident genetic, pathological, and clinical heterogeneity of Alzheimer's disease (AD) poses challenges for traditional drug development. We conducted a computational drug repurposing screen for drugs to treat apolipoprotein (apo) E4-related AD. We first established apoE-genotype-dependent transcriptomic signatures of AD by analyzing publicly-available human brain database. We then queried these signatures against the Connectivity Map database containing transcriptomic perturbations of >1300 drugs to identify those that best reverse apoE-genotype-specific AD signatures. Bumetanide was identified as a top drug for apoE4 AD. Bumetanide treatment of apoE4 mice without or with Aß accumulation rescued electrophysiological, pathological, or cognitive deficits. Single-nucleus RNA-sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed in apoE4-iPSC-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 in two electronic health record databases, suggesting effectiveness of bumetanide in preventing AD.

Genome-wide association study identifies novel single nucleotide polymorphisms having age-specific effect on prostate-specific antigen levels.

BACKGROUND: Testing for prostate-specific antigen (PSA) levels in blood are widely used and associated with prostate cancer risk and outcome. After puberty, PSA levels increase by age and multiple single nucleotide polymorphisms (SNPs) have been found to be associated with PSA levels. However, the relationship between the effects of SNPs and age on PSA remains unknown. METHODS: To test for SNP × age interaction, we conducted a genome-wide association study using 2394 men without prostate cancer diagnosis from Malmö, Sweden as a discovery set and 2137 men from the eMERGE study (USA) for validation. Linear regression was used to identify significant interactions between SNP and age (p < 1 × 10-4 for discovery, p < .05 for validation). RESULTS: The 15 SNPs from three different loci (8p11.22, 8p12, 3q25.31) are found to have age-specific effect on PSA levels. Expression quantitative trait loci (eQTLs) analysis shows that 12 SNPs from 3q25.31 locus affect the expression level of three genes: KCNAB1, SLC33A1, PLCH1. CONCLUSIONS: Our results suggest that SNPs may have age-specific effect on PSA levels, which provides new direction to study genetic markers for PSA.

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Genetic signature of prostate cancer mouse models resistant to optimized hK2 targeted α-particle therapy.

Hu11B6 is a monoclonal antibody that internalizes in cells expressing androgen receptor (AR)-regulated prostate-specific enzyme human kallikrein-related peptidase 2 (hK2; KLK2). In multiple rodent models, Actinium-225-labeled hu11B6-IgG1 ([225Ac]hu11B6-IgG1) has shown promising treatment efficacy. In the present study, we investigated options to enhance and optimize [225Ac]hu11B6 treatment. First, we evaluated the possibility of exploiting IgG3, the IgG subclass with superior activation of complement and ability to mediate FC-γ-receptor binding, for immunotherapeutically enhanced hK2 targeted α-radioimmunotherapy. Second, we compared the therapeutic efficacy of a single high activity vs. fractionated activity. Finally, we used RNA sequencing to analyze the genomic signatures of prostate cancer that progressed after targeted α-therapy. [225Ac]hu11B6-IgG3 was a functionally enhanced alternative to [225Ac]hu11B6-IgG1 but offered no improvement of therapeutic efficacy. Progression-free survival was slightly increased with a single high activity compared to fractionated activity. Tumor-free animals succumbing after treatment revealed no evidence of treatment-associated toxicity. In addition to up-regulation of canonical aggressive prostate cancer genes, such as MMP7, ETV1, NTS, and SCHLAP1, we also noted a significant decrease in both KLK3 (prostate-specific antigen ) and FOLH1 (prostate-specific membrane antigen) but not in AR and KLK2, demonstrating efficacy of sequential [225Ac]hu11B6 in a mouse model.

Prostate cancer risk SNP rs10993994 is a trans-eQTL for SNHG11 mediated through MSMB.

How genome-wide association studies-identified single-nucleotide polymorphisms (SNPs) affect remote genes remains unknown. Expression quantitative trait locus (eQTL) association meta-analysis on 496 prostate tumor and 602 normal prostate samples with 117 SNPs revealed novel cis-eQTLs and trans-eQTLs. Mediation testing and colocalization analysis demonstrate that MSMB is a cis-acting mediator for SNHG11 (P < 0.01). Removing rs10993994 in LNCaP cell lines by CRISPR/Cas9 editing shows that the C-allele corresponds with an over 100-fold increase in MSMB expression and 5-fold increase in SNHG11 compared with the T-allele. Colocalization analysis confirmed that the same set of SNPs associated with MSMB expression is associated with SNHG11 expression (posterior probability of shared variants is 66.6% in tumor and 91.4% in benign). These analyses further demonstrate variants driving MSMB expression differ in tumor and normal, suggesting regulatory network rewiring during tumorigenesis.

Clinicopathologic and Genomic Factors Associated With Oncologic Outcome in Patients With Stage III to IV Chromophobe Renal Cell Carcinoma.

BACKGROUND: Chromophobe renal cell carcinoma (chRCC) is known as an indolent tumor; however, mortality still occurs. We sought to determine the clinicopathologic and genomic factors associated with aggressive chRCC. PATIENTS AND METHODS: Two different datasets were used to identify patients with clinical stage III and IV chRCC. Eighteen patients from The Cancer Genome Atlas (TCGA) database and 1693 patients from the American College of Surgeons National Cancer Database (NCDB) were used for analysis. From the TCGA, RNA-Seq expression analysis of 18,745 genes was conducted between the recurrent (n = 5; 27.8%) and nonrecurrent patients (n = 13; 72.2%). Biological significance was identified via pathway enrichment and gene function analyses. From the NCDB, Cox proportion hazards regression models were used to identify variables associated with overall survival (OS) at a median follow-up of 41.4 months. RESULTS: Between the 2 groups, 2182 genes were differentially expressed. The most commonly overexpressed pathways were neuroactive ligand-receptor interactions and cytokine-cytokine receptor interactions. The most activated gene functions were cellular, metabolic, and multicellular organismal processes. In the NCDB, multivariable analysis, age (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.03-1.05; P < .001), TNM stage IV versus III (HR, 3.86; 95% CI, 2.98-5.00; P < .001), and positive surgical margin (HR, 1.68; 95% CI, 1.45-1.96; P < .001) were associated with worse OS at a median follow-up of 41.4 months. Five-year OS was significantly lower for stage IV patients compared with stage III patients (80.0% vs. 29.9%; P < .001). CONCLUSIONS: Patients with recurrent chRCC demonstrated a differential gene expression of specific biochemical pathways. Clinical parameters associated with worse OS included age, stage, and positive surgical margin.

Genome-wide Scan Identifies Role for AOX1 in Prostate Cancer Survival.

BACKGROUND: Most men diagnosed with prostate cancer have low-risk cancers. How to predict prostate cancer progression at the time of diagnosis remains challenging. OBJECTIVE: To identify single nucleotide polymorphisms (SNPs) associated with death from prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Blood samples from 11 506 men in Sweden were collected during 1991-1996. Of these, 1053 men were diagnosed with prostate cancer and 245 died from the disease. Stage and grade at diagnosis and outcome information were obtained, and DNA from all cases was genotyped. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A total of 6 126 633 SNPs were tested for association with prostate-cancer-specific survival time using a Cox proportional hazard model, adjusted for age, stage, and grade at diagnosis. A value of 1×10-6 was used as suggestive significance threshold. Positive candidate SNPs were tested for association with gene expression using expression quantitative trait locus analysis. RESULTS AND LIMITATIONS: We found 12 SNPs at seven independent loci associated with prostate-cancer-specific survival time. One of 6 126 633 SNPs tested reached genome-wide significance (p<5×10-8) and replicated in an independent cohort: rs73055188 (p=5.27×10-9, per-allele hazard ratio [HR]=2.27, 95% confidence interval [CI] 1.72-2.98) in the AOX1 gene. A second SNP reached a suggestive level of significance (p<1×10-6) and replicated in an independent cohort: rs2702185 (p=7.1×10-7, per-allele HR=2.55, 95% CI=1.76-3.69) in the SMG7 gene. The SNP rs73055188 is correlated with AOX1 expression levels, which is associated with biochemical recurrence of prostate cancer in independent cohorts. This association is yet to be validated in other ethnic groups. CONCLUSIONS: The SNP rs73055188 at the AOX1 locus is associated with prostate-cancer-specific survival time, and AOX1 gene expression level is correlated with biochemical recurrence of prostate cancer. PATIENT SUMMARY: We identify two genetic markers that are associated with prostate-cancer-specific survival time.

Marine microbial biodiversity, bioinformatics and biotechnology (M2B3) data reporting and service standards.

Contextual data collected concurrently with molecular samples are critical to the use of metagenomics in the fields of marine biodiversity, bioinformatics and biotechnology. We present here Marine Microbial Biodiversity, Bioinformatics and Biotechnology (M2B3) standards for "Reporting" and "Serving" data. The M2B3 Reporting Standard (1) describes minimal mandatory and recommended contextual information for a marine microbial sample obtained in the epipelagic zone, (2) includes meaningful information for researchers in the oceanographic, biodiversity and molecular disciplines, and (3) can easily be adopted by any marine laboratory with minimum sampling resources. The M2B3 Service Standard defines a software interface through which these data can be discovered and explored in data repositories. The M2B3 Standards were developed by the European project Micro B3, funded under 7(th) Framework Programme "Ocean of Tomorrow", and were first used with the Ocean Sampling Day initiative. We believe that these standards have value in broader marine science.

The ocean sampling day consortium.

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

The founding charter of the Genomic Observatories Network.

The co-authors of this paper hereby state their intention to work together to launch the Genomic Observatories Network (GOs Network) for which this document will serve as its Founding Charter. We define a Genomic Observatory as an ecosystem and/or site subject to long-term scientific research, including (but not limited to) the sustained study of genomic biodiversity from single-celled microbes to multicellular organisms.An international group of 64 scientists first published the call for a global network of Genomic Observatories in January 2012. The vision for such a network was expanded in a subsequent paper and developed over a series of meetings in Bremen (Germany), Shenzhen (China), Moorea (French Polynesia), Oxford (UK), Pacific Grove (California, USA), Washington (DC, USA), and London (UK). While this community-building process continues, here we express our mutual intent to establish the GOs Network formally, and to describe our shared vision for its future. The views expressed here are ours alone as individual scientists, and do not necessarily represent those of the institutions with which we are affiliated.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community.

BACKGROUND: A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. RESULTS: Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. CONCLUSIONS: Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Modelling complex biological systems using an agent-based approach.

Many of the complex systems found in biology are comprised of numerous components, where interactions between individual agents result in the emergence of structures and function, typically in a highly dynamic manner. Often these entities have limited lifetimes but their interactions both with each other and their environment can have profound biological consequences. We will demonstrate how modelling these entities, and their interactions, can lead to a new approach to experimental biology bringing new insights and a deeper understanding of biological systems.

A call for an international network of genomic observatories (GOs).

We are entering a new era in genomics-that of large-scale, place-based, highly contextualized genomic research. Here we review this emerging paradigm shift and suggest that sites of utmost scientific importance be expanded into 'Genomic Observatories' (GOs). Investment in GOs should focus on the digital characterization of whole ecosystems, from all-taxa biotic inventories to time-series 'omics studies. The foundational layer of biodiversity-genetic variation-would thus be mainstreamed into Earth Observation systems enabling predictive modelling of biodiversity dynamics and resultant impacts on ecosystem services.