Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases.

Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.

Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection.

BACKGROUND: Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning-based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts. METHODS: Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence. RESULTS: We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88-0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79-0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90-0.96 and 0.90-0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram). CONCLUSIONS: Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors.

BACKGROUND: Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events. METHODS: We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models. RESULTS: Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models. CONCLUSIONS: We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

PIEZO1 mediates a mechanothrombotic pathway in diabetes.

Thrombosis is the leading complication of common human disorders including diabetes, coronary heart disease, and infection and remains a global health burden. Current anticoagulant therapies that target the general clotting cascade are associated with unpredictable adverse bleeding effects, because understanding of hemostasis remains incomplete. Here, using perturbational screening of patient peripheral blood samples for latent phenotypes, we identified dysregulation of the major mechanosensory ion channel Piezo1 in multiple blood lineages in patients with type 2 diabetes mellitus (T2DM). Hyperglycemia activated PIEZO1 transcription in mature blood cells and selected high Piezo1­expressing hematopoietic stem cell clones. Elevated Piezo1 activity in platelets, red blood cells, and neutrophils in T2DM triggered discrete prothrombotic cellular responses. Inhibition of Piezo1 protected against thrombosis both in human blood and in zebrafish genetic models, particularly in hyperglycemia. Our findings identify a candidate target to precisely modulate mechanically induced thrombosis in T2DM and a potential screening method to predict patient-specific risk. Ongoing remodeling of cell lineages in hematopoiesis is an integral component of thrombotic risk in T2DM, and related mechanisms may have a broader role in chronic disease.

FNTM: a server for predicting functional networks of tissues in mouse.

Functional Networks of Tissues in Mouse (FNTM) provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function. FNTM is an ideal starting point for clinical and translational researchers considering a mouse model for their disease of interest, researchers already working with mouse models who are interested in discovering new genes related to their pathways or phenotypes of interest, and biologists working with other organisms to explore the functional relationships of their genes of interest in specific mouse tissue contexts. FNTM predicts tissue-specific functional relationships in 200 tissues, does not require any registration or installation and is freely available for use at http://fntm.princeton.edu.

ANAT: a tool for constructing and analyzing functional protein networks.

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of "anchor" proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

Cocos: Constructing multi-domain protein phylogenies.

Phylogenies of multi-domain proteins have to incorporate macro-evolutionary events, which dramatically increases the complexity of their construction.We present an application to infer ancestral multi-domain proteins given a species tree and domain phylogenies. As the individual domain phylogenies are often incongruent, we provide diagnostics for the identification and reconciliation of implausible topologies. We implement and extend a suggested algorithmic approach by Behzadi and Vingron (2006).