Prey Shifts Drive Venom Evolution in Cone Snails.

Venom systems are complex traits that have independently emerged multiple times in diverse plant and animal phyla. Within each venomous lineage there typically exists interspecific variation in venom composition where several factors have been proposed as drivers of variation, including phylogeny and diet. Understanding these factors is of broad biological interest and has implications for the development of anti-venom therapies and venom-based drug discovery. Because of their high species richness and the presence of several major evolutionary prey shifts, venomous marine cone snails (genus Conus) provide an ideal system to investigate drivers of interspecific venom variation. Here, by analyzing the venom gland expression profiles of ∼3,000 toxin genes from 42 species of cone snail, we elucidate the role of prey-specific selection pressures in shaping venom variation. By analyzing overall venom composition and individual toxin structures, we demonstrate that the shifts from vermivory to piscivory in Conus are complemented by distinct changes in venom composition independent of phylogeny. In vivo injections of venom from piscivorous cone snails in fish further showed a higher potency compared to venom of non-piscivores demonstrating a selective advantage. Together, our findings provide compelling evidence for the role of prey shifts in directing the venom composition of cone snails and expand our understanding of the mechanisms of venom variation and diversification.

Genome Sequencing is Critical for Forecasting Outcomes following Congenital Cardiac Surgery.

While genome sequencing has transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we used artificial intelligence (AI) technologies to explore the predictive value of genome sequencing in forecasting clinical outcomes following surgery for congenital heart defects (CHD). We report results for a cohort of 2,253 CHD patients from the Pediatric Cardiac Genomics Consortium with a broad range of complex heart defects, pre- and post-operative clinical variables and exome sequencing. Damaging genotypes in chromatin-modifying and cilia-related genes were associated with an elevated risk of adverse post-operative outcomes, including mortality, cardiac arrest and prolonged mechanical ventilation. The impact of damaging genotypes was further amplified in the context of specific CHD phenotypes, surgical complexity and extra-cardiac anomalies. The absence of a damaging genotype in chromatin-modifying and cilia-related genes was also informative, reducing the risk for adverse postoperative outcomes. Thus, genome sequencing enriches the ability to forecast outcomes following congenital cardiac surgery.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

BACKGROUND: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing. METHODS: The SDY Case Registry is a National Institutes of Health/Centers for Disease Control and Prevention surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases < 20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015 to 2019. The cohort included 211 children (median age 0.33 year; range 0-20 years), determined to have died suddenly and unexpectedly and from whom DNA biospecimens for DNA extractions and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex- and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy, and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, pathogenic and likely pathogenic genetic variation was identified using a Bayesian-based artificial intelligence (AI) tool. RESULTS: The SDY cohort was 43% European, 29% African, 3% Asian, 16% Hispanic, and 9% with mixed ancestries and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy, or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, potentially damaging variants in epilepsy, cardiomyopathy, and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death. CONCLUSIONS: While damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.

Assembly and annotation of 2 high-quality columbid reference genomes from sequencing of a Columba livia × Columba guinea F1 hybrid.

Pigeons and doves (family Columbidae) are one of the most diverse extant avian lineages, and many species have served as key models for evolutionary genomics, developmental biology, physiology, and behavioral studies. Building genomic resources for columbids is essential to further many of these studies. Here, we present high-quality genome assemblies and annotations for 2 columbid species, Columba livia and Columba guinea. We simultaneously assembled C. livia and C. guinea genomes from long-read sequencing of a single F1 hybrid individual. The new C. livia genome assembly (Cliv_3) shows improved completeness and contiguity relative to Cliv_2.1, with an annotation incorporating long-read IsoSeq data for more accurate gene models. Intensive selective breeding of C. livia has given rise to hundreds of breeds with diverse morphological and behavioral characteristics, and Cliv_3 offers improved tools for mapping the genomic architecture of interesting traits. The C. guinea genome assembly is the first for this species and is a new resource for avian comparative genomics. Together, these assemblies and annotations provide improved resources for functional studies of columbids and avian comparative genomics in general.

Assembly and annotation of two high-quality columbid reference genomes from sequencing of a Columba livia x Columba guinea F1 hybrid.

Pigeons and doves (family Columbidae) are one of the most diverse extant avian lineages, and many species have served as key models for evolutionary genomics, developmental biology, physiology, and behavioral studies. Building genomic resources for colubids is essential to further many of these studies. Here, we present high-quality genome assemblies and annotations for two columbid species, Columba livia and C. guinea. We simultaneously assembled C. livia and C. guinea genomes from long-read sequencing of a single F1 hybrid individual. The new C. livia genome assembly (Cliv_3) shows improved completeness and contiguity relative to Cliv_2.1, with an annotation incorporating long-read IsoSeq data for more accurate gene models. Intensive selective breeding of C. livia has given rise to hundreds of breeds with diverse morphological and behavioral characteristics, and Cliv_3 offers improved tools for mapping the genomic architecture of interesting traits. The C. guinea genome assembly is the first for this species and is a new resource for avian comparative genomics. Together, these assemblies and annotations provide improved resources for functional studies of columbids and avian comparative genomics in general.

The status of the human gene catalogue.

Scientists have been trying to identify every gene in the human genome since the initial draft was published in 2001. In the years since, much progress has been made in identifying protein-coding genes, currently estimated to number fewer than 20,000, with an ever-expanding number of distinct protein-coding isoforms. Here we review the status of the human gene catalogue and the efforts to complete it in recent years. Beside the ongoing annotation of protein-coding genes, their isoforms and pseudogenes, the invention of high-throughput RNA sequencing and other technological breakthroughs have led to a rapid growth in the number of reported non-coding RNA genes. For most of these non-coding RNAs, the functional relevance is currently unclear; we look at recent advances that offer paths forward to identifying their functions and towards eventually completing the human gene catalogue. Finally, we examine the need for a universal annotation standard that includes all medically significant genes and maintains their relationships with different reference genomes for the use of the human gene catalogue in clinical settings.

Identification of sodium channel toxins from marine cone snails of the subgenera Textilia and Afonsoconus.

Voltage-gated sodium (NaV) channels are transmembrane proteins that play a critical role in electrical signaling in the nervous system and other excitable tissues. µ-Conotoxins are peptide toxins from the venoms of marine cone snails (genus Conus) that block NaV channels with nanomolar potency. Most species of the subgenera Textilia and Afonsoconus are difficult to acquire; therefore, their venoms have yet to be comprehensively interrogated for µ-conotoxins. The goal of this study was to find new µ-conotoxins from species of the subgenera Textilia and Afonsoconus and investigate their selectivity at human NaV channels. Using RNA-seq of the venom gland of Conus (Textilia) bullatus, we identified 12 µ-conotoxin (or µ-conotoxin-like) sequences. Based on these sequences we designed primers which we used to identify additional µ-conotoxin sequences from DNA extracted from historical specimens of species from Textilia and Afonsoconus. We synthesized six of these µ-conotoxins and tested their activity on human NaV1.1-NaV1.8. Five of the six synthetic peptides were potent blockers of human NaV channels. Of these, two peptides (BuIIIB and BuIIIE) were potent blockers of hNaV1.3. Three of the peptides (BuIIIB, BuIIIE and AdIIIA) had submicromolar activity at hNaV1.7. This study serves as an example of the identification of new peptide toxins from historical DNA and provides new insights into structure-activity relationships of µ-conotoxins with activity at hNaV1.3 and hNaV1.7.

Genetic and clinical variables act synergistically to impact neurodevelopmental outcomes in children with single ventricle heart disease.

BACKGROUND: Recent large-scale sequencing efforts have shed light on the genetic contribution to the etiology of congenital heart defects (CHD); however, the relative impact of genetics on clinical outcomes remains less understood. Outcomes analyses using genetics are complicated by the intrinsic severity of the CHD lesion and interactions with conditionally dependent clinical variables. METHODS: Bayesian Networks were applied to describe the intertwined relationships between clinical variables, demography, and genetics in a cohort of children with single ventricle CHD. RESULTS: As isolated variables, a damaging genetic variant in a gene related to abnormal heart morphology and prolonged ventilator support following stage I palliative surgery increase the probability of having a low Mental Developmental Index (MDI) score at 14 months of age by 1.9- and 5.8-fold, respectively. However, in combination, these variables act synergistically to further increase the probability of a low MDI score by 10-fold. The absence of a damaging variant in a known syndromic CHD gene and a shorter post-operative ventilator support increase the probability of a normal MDI score 1.7- and 2.4-fold, respectively, but in combination increase the probability of a good outcome by 59-fold. CONCLUSIONS: Our analyses suggest a modest genetic contribution to neurodevelopmental outcomes as isolated variables, similar to known clinical predictors. By contrast, genetic, demographic, and clinical variables interact synergistically to markedly impact clinical outcomes. These findings underscore the importance of capturing and quantifying the impact of damaging genomic variants in the context of multiple, conditionally dependent variables, such as pre- and post-operative factors, and demography.

Single ventricle congenital heart disease is a birth defect. In these children, the heart has only one effective blood-pumping chamber instead of two. Surgery can reroute the blood to use only one chamber, but multiple risk factors influence how well a child develops afterwards. Studying these risk factors can be challenging because they are interconnected, i.e. children with a genetic birth defect may be more likely to have a lower birthweight, and hence more likely to spend longer in hospital after surgery. Here, we used a statistical approach not commonly applied to study congenital heart disease and describe that whether a genetic variant (a small difference in a child's DNA) is important for how a child with single ventricle heart disease develops and grows after surgery depends on the presence of other risk factors.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

Background: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing. Methods: The SDY Case Registry is a National Institutes of Health/Centers for Disease Control surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases <20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015-2019. The cohort included 211 children (mean age 1 year; range 0-20 years), determined to have died suddenly and unexpectedly and in whom DNA biospecimens and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex-and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, genetic variation predicted to be damaging was identified using a Bayesian-based artificial intelligence (AI) tool. Results: The SDY cohort was 42% European, 30% African, 17% Hispanic, and 11% with mixed ancestries, and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, damaging variants in epilepsy, cardiomyopathy and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death. Conclusions: While damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort, and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.

Neurite outgrowth deficits caused by rare PLXNB1 mutation in pediatric bipolar disorder.

Pediatric bipolar disorder (PBD) is a severe mood dysregulation condition that affects 0.5-1% of children and teens in the United States. It is associated with recurrent episodes of mania and depression and an increased risk of suicidality. However, the genetics and neuropathology of PBD are largely unknown. Here, we used a combinatorial family-based approach to characterize cellular, molecular, genetic, and network-level deficits associated with PBD. We recruited a PBD patient and three unaffected family members from a family with a history of psychiatric illnesses. Using resting-state functional magnetic resonance imaging (rs-fMRI), we detected altered resting-state functional connectivity in the patient as compared to an unaffected sibling. Using transcriptomic profiling of patient and control induced pluripotent stem cell (iPSC)-derived telencephalic organoids, we found aberrant signaling in the molecular pathways related to neurite outgrowth. We corroborated the presence of neurite outgrowth deficits in patient iPSC-derived cortical neurons and identified a rare homozygous loss-of-function PLXNB1 variant (c.1360C>C; p.Ser454Arg) responsible for the deficits in the patient. Expression of wild-type PLXNB1, but not the variant, rescued neurite outgrowth in patient neurons, and expression of the variant caused the neurite outgrowth deficits in cortical neurons from PlxnB1 knockout mice. These results indicate that dysregulated PLXNB1 signaling may contribute to an increased risk of PBD and other mood dysregulation-related disorders by disrupting neurite outgrowth and functional brain connectivity. Overall, this study established and validated a novel family-based combinatorial approach for studying cellular and molecular deficits in psychiatric disorders and identified dysfunctional PLXNB1 signaling and neurite outgrowth as potential risk factors for PBD.

DIS3 Variants are Associated With Primary Ovarian Insufficiency: Importance of Transcription/Translation in Oogenesis.

CONTEXT: A genetic etiology accounts for the majority of unexplained primary ovarian insufficiency (POI). OBJECTIVE: We hypothesized a genetic cause of POI for a sister pair with primary amenorrhea. DESIGN: The study was an observational study. Subjects were recruited at an academic institution. SUBJECTS: Subjects were sisters with primary amenorrhea caused by POI and their parents. Additional subjects included women with POI analyzed previously (n = 291). Controls were recruited for health in old age or were from the 1000 Genomes Project (total n = 233). INTERVENTION: We performed whole exome sequencing, and data were analyzed using the Pedigree Variant Annotation, Analysis and Search Tool, which identifies genes harboring pathogenic variants in families. We performed functional studies in a Drosophila melanogaster model. MAIN OUTCOME: Genes with rare pathogenic variants were identified. RESULTS: The sisters carried compound heterozygous variants in DIS3. The sisters did not carry additional rare variants that were absent in publicly available datasets. DIS3 knockdown in the ovary of D. melanogaster resulted in lack of oocyte production and severe infertility. CONCLUSIONS: Compound heterozygous variants in highly conserved amino acids in DIS3 and failure of oocyte production in a functional model suggest that mutations in DIS3 cause POI. DIS3 is a 3' to 5' exoribonuclease that is the catalytic subunit of the exosome involved in RNA degradation and metabolism in the nucleus. The findings provide further evidence that mutations in genes important for transcription and translation are associated with POI.

An improved germline genome assembly for the sea lamprey Petromyzon marinus illuminates the evolution of germline-specific chromosomes.

Programmed DNA loss is a gene silencing mechanism that is employed by several vertebrate and nonvertebrate lineages, including all living jawless vertebrates and songbirds. Reconstructing the evolution of somatically eliminated (germline-specific) sequences in these species has proven challenging due to a high content of repeats and gene duplications in eliminated sequences and a corresponding lack of highly accurate and contiguous assemblies for these regions. Here, we present an improved assembly of the sea lamprey (Petromyzon marinus) genome that was generated using recently standardized methods that increase the contiguity and accuracy of vertebrate genome assemblies. This assembly resolves highly contiguous, somatically retained chromosomes and at least one germline-specific chromosome, permitting new analyses that reconstruct the timing, mode, and repercussions of recruitment of genes to the germline-specific fraction. These analyses reveal major roles of interchromosomal segmental duplication, intrachromosomal duplication, and positive selection for germline functions in the long-term evolution of germline-specific chromosomes.

The status of the human gene catalogue.

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants.

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS. METHODS: Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search Engine (MPSE). RESULTS: MPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children's Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients. CONCLUSIONS: Our results indicate that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.

Genomic landscape of advanced prostate cancer patients with BRCA1 versus BRCA2 mutations as detected by comprehensive genomic profiling of cell-free DNA.

BRCA1-mutated prostate cancer has been shown to be less responsive to poly (ADP-ribose) polymerase (PARP) inhibitors as compared to BRCA2-mutated prostate cancer. The reason for this differential response is not clear. We hypothesized this differential sensitivity to PARP inhibitors may be explained by distinct genomic landscapes of BRCA1 versus BRCA2 co-segregating genes. In a large dataset of 7,707 men with advanced prostate cancer undergoing comprehensive genomic profiling (CGP) of cell-free DNA (cfDNA), 614 men harbored BRCA1 and/or BRCA2 alterations. Differences in the genomic landscape of co-segregating genes was investigated by Fisher's exact test and probabilistic graphical models (PGMs). Results demonstrated that BRCA1 was significantly associated with six other genes, while BRCA2 was not significantly associated with any gene. These findings suggest BRCA2 may be the main driver mutation, while BRCA1 mutations tend to co-segregate with mutations in other molecular pathways contributing to prostate cancer progression. These hypothesis-generating data may explain the differential response to PARP inhibition and guide towards the development of combinatorial drug regimens in those with BRCA1 mutation.

Comprehensive Genomic Profiling of Cell-Free DNA in Men With Advanced Prostate Cancer: Differences in Genomic Landscape Based on Race.

Advanced prostate cancer (aPC) in Black men was reported to present with aggressive features and to be associated with poor prognosis. Herein, we compared the cell-free DNA (cfDNA) genomic landscape of aPC in Black vs White men. Patients (pts) with aPC from 6 academic institutions and available cfDNA comprehensive genomic profiling (CGP) were included. Association between mutated genes and race was evaluated using Barnard's test and a Probabilistic Graphical Model (PGM) machine learning approach. Analysis included 743 aPC pts (217 Black, 526 White) with available cfDNA CGP. The frequency of alterations in the androgen receptor gene was significantly higher in Black vs White men (55.3% vs 35% respectively, P < .001). Additionally, alterations in EGFR, MYC, FGFR1, and CTNNB1 were present at higher frequencies in Black men. PGM analysis and Barnard's test were concordant. Findings from the largest cohort of Black men with aPC undergoing cfDNA CGP may guide further drug development in these men.

A dominant negative ADIPOQ mutation in a diabetic family with renal disease, hypoadiponectinemia, and hyperceramidemia.

Adiponectin, encoded by ADIPOQ, is an insulin-sensitizing, anti-inflammatory, and renoprotective adipokine that activates receptors with intrinsic ceramidase activity. We identified a family harboring a 10-nucleotide deletion mutation in ADIPOQ that cosegregates with diabetes and end-stage renal disease. This mutation introduces a frameshift in exon 3, resulting in a premature termination codon that disrupts translation of adiponectin's globular domain. Subjects with the mutation had dramatically reduced circulating adiponectin and increased long-chain ceramides levels. Functional studies suggest that the mutated protein acts as a dominant negative through its interaction with non-mutated adiponectin, decreasing circulating adiponectin levels, and correlating with metabolic disease.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.