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Supplementary Table 1. Crystallographic Data and Refinement statistics. Supplementary Table 2. Comparing Target Inhibition by Quizartinib and PLX3397 in Engineered Ba/F3 cells Expressing FLT3-ITD, FLT3-ITD/F691L and FLT3-ITD/D835Y. Supplementary Table 3. Inhibitory Concentration (IC50) for Proliferation of Human Leukemia Cell Lines in PLX3397. Supplementary Table 4. 48 Hour Inhibitory Concentration (IC50) for Proliferation of Ba/F3 Cells Expressing Quizartinib and PLX3397-Resistant FLT3-ITD Mutant Isoforms. Supplementary Table 5. Patient Characteristics. Supplementary Table 6. Low Frequency FLT3 Kinase Domain Mutations of Uncertain Significance Observed at the Time of Resistance in Patient 1.14. Supplementary Figure 1. Composite omit map contoured at 1sigma level for the bound quizartinib and a structural water molecule. Supplementary Figure 2. Comparing the actual and previously predicted binding modes of quizartinib. Supplementary Figure 3. Structural model showing that D835 induces the DFG-in conformation of the activation loop, resulting in an orientation of F830 that precludes quizartinib binding. Supplementary Figure 4. Structural superposition of quizartinib (green) and PLX3397 (orange) highlighting the key structural difference responsible for their different susceptibilities to L691. Supplementary Figure 5. PLX3397 Inhibits FLT3 Signaling In Vitro. Supplementary Figure 6. Molm14 F691L Cells Demonstrate Resistance to Quizartinib. Supplementary Figure 7. Activity of PLX3397Against Quizartinib Resistance-Causing FLT3-ITD Kinase Domain Mutations.

Summary An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications.

Reduced NREM sleep in humans is associated with AD neuropathology. Recent work has demonstrated a reduction in NREM sleep in preclinical AD, pointing to its potential utility as an early marker of dementia. We test the hypothesis that reduced NREM delta power and increased tauopathy are associated with shared underlying cortical molecular networks in preclinical AD. We integrate multi-omics data from two extensive public resources, a human Alzheimer’s disease cohort from the Mount Sinai Brain Bank (N = 125) reflecting AD progression and a (C57BL/6J × 129S1/SvImJ) F2 mouse population in which NREM delta power was measured (N = 98). Two cortical gene networks, including a CLOCK-dependent circadian network, are associated with NREM delta power and AD tauopathy progression. These networks were validated in independent mouse and human cohorts. Identifying gene networks related to preclinical AD elucidate possible mechanisms associated with the early disease phase and potential targets to alter the disease course.

Background Inflammatory bowel disease (IBD) is a complex disease with variable presentation, progression, and response to therapies. Current disease classification is based on subjective clinical phenotypes. The peripheral blood immunophenome can reflect local inflammation, and thus we measured 39 circulating immune cell types in a large cohort of IBD and control subjects and performed immunotype:phenotype associations. Methods We performed fluorescence-activated cell sorting or CyTOF analysis on blood from 728 Crohn’s disease, 464 ulcerative colitis, and 334 non-IBD patients, with available demographics, endoscopic and clinical examinations and medication use. Results We observed few immune cell types commonly affected in IBD (lowered natural killer cells, B cells, and CD45RA– CD8 T cells). Generally, the immunophenome was distinct between ulcerative colitis and Crohn’s disease. Within disease subtype, there were further distinctions, with specific immune cell types associating with disease duration, behavior, and location. Thiopurine monotherapy altered abundance of many cell types, often in the same direction as disease association, while anti-tumor necrosis factor (anti-TNF) monotherapy demonstrated an opposing pattern. Concomitant use of an anti-TNF and thiopurine was not synergistic, but rather was additive. For example, thiopurine monotherapy use alone or in combination with anti-TNF was associated with a dramatic reduction in major subclasses of B cells. Conclusions We present a peripheral map of immune cell changes in IBD related to disease entity and therapies as a resource for hypothesis generation. We propose the changes in B cell subsets could affect antibody formation and potentially explain the mechanism behind the superiority of combination therapy through the impact of thiopurines on pharmacokinetics of anti-TNFs., Graphical abstract

ObjectiveIBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments.DesignTranscriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn’s disease, n=421 UC and 243 controls) in the Mount Sinai Crohn’s and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts.ResultsbMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time.ConclusionTranscriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.

[Image: see text] One-third of the world’s population carries Mycobacterium tuberculosis (Mtb), the infectious agent that causes tuberculosis (TB), and every 17 s someone dies of TB. After infection, Mtb can live dormant for decades in a granuloma structure arising from the host immune response, and cholesterol is important for this persistence of Mtb. Current treatments require long-duration drug regimens with many associated toxicities, which are compounded by the high doses required. We phenotypically screened 35 6-azasteroid analogues against Mtb and found that, at low micromolar concentrations, a subset of the analogues sensitized Mtb to multiple TB drugs. Two analogues were selected for further study to characterize the bactericidal activity of bedaquiline and isoniazid under normoxic and low-oxygen conditions. These two 6-azasteroids showed strong synergy with bedaquiline (fractional inhibitory concentration index = 0.21, bedaquiline minimal inhibitory concentration = 16 nM at 1 μM 6-azasteroid). The rate at which spontaneous resistance to one of the 6-azasteroids arose in the presence of bedaquiline was approximately 10(–9), and the 6-azasteroid-resistant mutants retained their isoniazid and bedaquiline sensitivity. Genes in the cholesterol-regulated Mce3R regulon were required for 6-azasteroid activity, whereas genes in the cholesterol catabolism pathway were not. Expression of a subset of Mce3R genes was down-regulated upon 6-azasteroid treatment. The Mce3R regulon is implicated in stress resistance and is absent in saprophytic mycobacteria. This regulon encodes a cholesterol-regulated stress-resistance pathway that we conclude is important for pathogenesis and contributes to drug tolerance, and this pathway is vulnerable to small-molecule targeting in live mycobacteria.

Background Preschool-based interventions offer promise to instill healthy behaviors in children, which can be a strategy to reduce the burden of cardiovascular disease later. However, their efficacy in underserved communities is not well established. Objectives The purpose of this study was to assess the impact of a preschool-based health promotion educational intervention in an underserved community. Methods This cluster-randomized controlled study involved 15 Head Start preschools in Harlem, New York. Schools and their children were randomized 3:2 to receive either a 4-month (50 h) educational intervention to instill healthy behaviors in relation to diet, physical activity, body/heart awareness, and emotion management; or their standard curriculum (control). The primary outcome was the change from baseline in the overall knowledge, attitudes, and habits (KAH) score of the children at 5 months. As secondary outcomes, we evaluated the changes in KAH subcomponents and emotion comprehension. Linear mixed-effects models were used to test for intervention effects. Results The authors enrolled 562 preschool children age 3 to 5 years, 51% female, 54% Hispanic/Latino, and 37% African-American. Compared with the control group, the mean relative change from baseline in the overall KAH score was ∼2.2 fold higher in the intervention group (average absolute difference of 2.86 points; 95% confidence interval: 0.58 to 5.14; p = 0.014). The maximal effect was observed in children who received >75% of the curriculum. Physical activity and body/heart awareness components, and knowledge and attitudes domains, were the main drivers of the effect (p values Conclusions This multidimensional school-based educational intervention may be an effective strategy for establishing healthy behaviors among preschoolers from a diverse and socioeconomically disadvantaged community. Early primordial prevention strategies may contribute to reducing the global burden of cardiovascular disease. (Family-Based Approach in a Minority Community Integrating Systems-Biology for Promotion of Health [FAMILIA]; NCT02343341)

e13563 Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with multiple phenotypes and endotypes and associated with lung cancer development. In this study, we analyzed the clinical features of COPD including various phenotypic and endotypic features from the electronic health records (EHR) as the potential risk factors for lung cancer development in a large cohort of COPD patients. Methods: We identified a COPD cohort based on electronic Medical Records and Genomics (eMERGE) network COPD cohort identification algorithms with minor modifications from the Mount Sinai Data Warehouse (2000-2020) and followed the patients on the first diagnosis of lung cancer. The development of lung cancer in COPD patients was confirmed by manual chart review. We retrieved the clinical features from EHRs and conducted Kaplan Meier (KM) analysis and multivariable Cox-regression modeling for hazard ratio (HR) analysis. Results: We found that 3.8 % of COPD patients (824 out of 21,658) developed lung cancer. While COPD patients with emphysema and smoking history (former or current) showed an increased risk of lung cancer onset, patients with concurrent asthma and corticosteroid (ICS) inhalation history showed a reduced risk of lung cancer onset (adjusted HR in Table). Interestingly, COPD patients with higher eosinophil counts showed late onset of lung cancer (>300 cells/ul, cancer rate at 5y=2.4%, p=< 0.002; 150-300 cells/ul, cancer rate at 5y=2.9%, p=0.003) when compared to patients with low eosinophils count (300 cells/ul; HR:0.72, 95% CI: 0.57-0.89, p=0.003) when compared to those with low eosinophils count (

SUMMARYAn important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of ulcerative colitis (UC) patients require colectomy within five years of diagnosis, but polygenic risk scores (PRS) utilizing findings from GWAS are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn’s disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a Transcriptional Risk Score (TRS). Here we demonstrate that both measured (TRS) and polygenic predicted gene expression (PPTRS) identify UC patients at 5-fold elevated risk of colectomy with data from the PROTECT clinical trial and UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with transcriptome-wide association studies to stratify risk of disease complications.

IntroThe molecular mechanisms of chronic fatigue syndrome (CFS, or Myalgic encephalomyelitis), a disease defined by extreme, long-term fatigue, remain largely uncharacterized, and presently no molecular diagnostic test and no specific treatments exist to diagnose and treat CFS patients. While CFS has historically had an estimated prevalence of 0.1-0.5% [1], concerns of a “long hauler” version of Coronavirus disease 2019 (COVID-19) that symptomatically overlaps CFS to a significant degree(Supplemental Table-1)and appears to occur in 10% of COVID-19 patients[2], has raised concerns of a larger spike in CFS [3]. Here, we established molecular signatures of CFS and a corresponding network-based disease context from RNA-sequencing data generated on whole blood and FACs sorted specific peripheral blood mononuclear cells (PBMCs) isolated from CFS cases and non-CFS controls. The immune cell type specific molecular signatures of CFS we identified, overlapped molecular signatures from other fatiguing illnesses, demonstrating a common molecular etiology. Further, after constructing a probabilistic causal model of the CFS gene expression data, we identified master regulator genes modulating network states associated with CFS, suggesting potential therapeutic targets for CFS.

Background And Aims Disease extent varies in ulcerative colitis (UC) from proctitis to left-sided colitis to pancolitis and is a major prognostic factor. When the extent of UC is limited there is often a sharp demarcation between macroscopically involved and uninvolved areas and what defines this or subsequent extension is unknown. We characterized the demarcation site molecularly and determined genes associated with subsequent disease extension. Methods We performed RNA sequence analysis of biopsy specimens from UC patients with endoscopically and histologically confirmed limited disease, of which a subset later extended. Biopsy specimens were obtained from the endoscopically inflamed upper (proximal) limit of disease, immediately adjacent to the uninvolved colon, as well as at more proximal, endoscopically uninflamed colonic segments. Results Differentially expressed genes were identified in the endoscopically inflamed biopsy specimens taken at each patient's most proximal diseased site relative to healthy controls. Expression of these genes in the more proximal biopsy specimens transitioned back to control levels abruptly or gradually, the latter pattern supporting the concept that disease exists beyond the endoscopic disease demarcation site. The gradually transitioning genes were associated with inflammation, angiogenesis, glucuronidation, and homeodomain pathways. A subset of these genes in inflamed biopsy specimens was found to predict disease extension better than clinical features and were responsive to biologic therapies. Network analysis revealed critical roles for interferon signaling in UC inflammation and poly(ADP-ribose) polymerase 14 (PARP14) was a predicted key driver gene of extension. Higher PARP14 protein levels were found in inflamed biopsy specimens of patients with limited UC that subsequently extended. Conclusion Molecular predictors of disease extension reveal novel strategies for disease prognostication and potential therapeutic targeting.

Background Whole-genome sequencing (WGS) is increasingly used to map the spread of bacterial and viral pathogens in nosocomial settings. A limiting factor for more widespread adoption of WGS for hospital infection prevention practices is the availability of standardized tools for genomic epidemiology. Methods We developed the Pathogen Sequencing Phylogenomic Outbreak Toolkit (PathoSPOT) to automate integration of genomic and medical record data for rapid detection and tracing of nosocomial outbreaks. To demonstrate its capabilities, we applied PathoSPOT to complete genome surveillance data of 197 MRSA bacteremia cases from two hospitals during a 2-year period. Results PathoSPOT identified 8 clonal clusters encompassing 33 patients (16.8% of cases), none of which had been recognized by standard practices. The largest cluster corresponded to a prolonged outbreak of a hospital-associated MRSA clone among 16 adults, spanning 9 wards over a period of 21 months. Analysis of precise timeline and location data with our toolkit suggested that an initial exposure event in a single ward led to infection and long-term colonization of multiple patients, followed by transmissions to other patients during recurrent hospitalizations. Conclusions We demonstrate that PathoSPOT genomic surveillance enables the detection of complex transmission chains that are not readily apparent from epidemiological data and that contribute significantly to morbidity and mortality, enabling more effective intervention strategies.

Polygenic and environmental factors are underlying causes of inflammatory bowel disease (IBD). We hypothesized that integration of the genetic loci controlling a metabolite's abundance, with known IBD genetic susceptibility loci, may help resolve metabolic drivers of IBD.We measured the levels of 1300 metabolites in the serum of 484 patients with ulcerative colitis (UC) and 464 patients with Crohn's disease (CD) and 365 controls. Differential metabolite abundance was determined for disease status, subtype, clinical and endoscopic disease activity, as well as IBD phenotype including disease behavior, location, and extent. To inform on the genetic basis underlying metabolic diversity, we integrated metabolite and genomic data. Genetic colocalization and Mendelian randomization analyses were performed using known IBD risk loci to explore whether any metabolite was causally associated with IBD.We found 173 genetically controlled metabolites (metabolite quantitative trait loci, 9 novel) within 63 non-overlapping loci (7 novel). Furthermore, several metabolites significantly associated with IBD disease status and activity as defined using clinical and endoscopic indexes. This constitutes a resource for biomarker discovery and IBD biology insights. Using this resource, we show that a novel metabolite quantitative trait locus for serum butyrate levels containing ACADS was not supported as causal for IBD; replicate the association of serum omega-6 containing lipids with the fatty acid desaturase 1/2 locus and identify these metabolites as causal for CD through Mendelian randomization; and validate a novel association of serum plasmalogen and TMEM229B, which was predicted as causal for CD.An exploratory analysis combining genetics and unbiased serum metabolome surveys can reveal novel biomarkers of disease activity and potential mediators of pathology in IBD.

Epidemiological studies have long recognized risky behaviors as potentially modifiable factors for the onset and flares of inflammatory bowel disease (IBD); yet, the underlying mechanisms are largely unknown. Recently, the genetic susceptibilities to cigarette smoking, alcohol and cannabis use [i.e. substance use (SU)] have been characterized by well-powered genome-wide association studies (GWASs). We aimed to assess the impact of genetic determinants of SU on IBD risk. Using Mount Sinai Crohn’s and Colitis Registry (MSCCR) cohort of 1058 IBD cases and 188 healthy controls, we computed the polygenic risk score (PRS) for SU and correlated them with the observed IBD diagnoses, while adjusting for genetic ancestry, PRS for IBD and SU behavior at enrollment. The results were validated in a pediatric cohort with no SU exposure. PRS of alcohol consumption (DrnkWk), smoking cessation and age of smoking initiation, were associated with IBD risk in MSCCR even after adjustment for PRSIBD and actual smoking status. One interquartile range decrease in PRSDrnkWk was significantly associated to higher IBD risk (i.e. inverse association) (with odds ratio = 1.65 and 95% confidence interval: 1.32, 2.06). The association was replicated in a pediatric Crohn’s disease cohort. Colocalization analysis identified a locus on chromosome 16 with polymorphisms in IL27, SULT1A2 and SH2B1, which reached genome-wide statistical significance in GWAS (P

To further explore genetic links between complex traits, we developed a comprehensive framework to harmonize and integrate extensive genotype and phenotype data from the four well-characterized cohorts with the focus on cardiometabolic diseases deposited to the database of Genotypes and Phenotypes (dbGaP). We generated a series of polygenic risk scores (PRS) to investigate pleiotropic effects of loci that confer genetic risk for 19 common diseases and traits on body height, type 2 diabetes (T2D), and myocardial infarction (MI). In a meta-analysis of 20,021 subjects, we identified shared genetic determinants of Crohn’s Disease (CD), a type of inflammatory bowel disease, and body height (p = 5.5 × 10–5). The association of PRS-CD with height was replicated in UK Biobank (p = 1.1 × 10–5) and an independent cohort of 510 CD cases and controls (1.57 cm shorter height per PRS-CD interquartile increase, p = 5.0 × 10−3 and a 28% reduction in CD risk per interquartile increase in PRS-height, p = 1.1 × 10–3, with the effect independent of CD diagnosis). A pathway analysis of the variants overlapping between PRS-height and PRS-CD detected significant enrichment of genes from the inflammatory, immune-mediated and growth factor regulation pathways. This finding supports the clinical observation of growth failure in patients with childhood-onset CD and demonstrates the value of using individual-level data from dbGaP in searching for shared genetic determinants. This information can help provide a refined insight into disease pathogenesis and may have major implications for novel therapies and drug repurposing.

Background COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C., Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.

BACKGROUND COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Background and Aims The presence of gastrointestinal symptoms and high levels of viral RNA in the stool suggest active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication within enterocytes. Methods Here, in multiple, large cohorts of patients with inflammatory bowel disease (IBD), we have studied the intersections between Coronavirus Disease 2019 (COVID-19), intestinal inflammation, and IBD treatment. Results A striking expression of ACE2 on the small bowel enterocyte brush border supports intestinal infectivity by SARS-CoV-2. Commonly used IBD medications, both biologic and nonbiologic, do not significantly impact ACE2 and TMPRSS2 receptor expression in the uninflamed intestines. In addition, we have defined molecular responses to COVID-19 infection that are also enriched in IBD, pointing to shared molecular networks between COVID-19 and IBD. Conclusions These data generate a novel appreciation of the confluence of COVID-19– and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19. Preprint doi: https://doi.org/10.1101/2020.05.21.109124

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions.One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

Blighted Gotham Deaths caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in New York City (NYC) during the spring of 2020 have vastly exceeded those reported in China and many other countries. What were the early events that led to such a severe outbreak? Gonzalez-Reiche et al. sampled some of the early patients seeking assistance in February and March of 2020 at the Mount Sinai Health System. Phylogenetic analysis of virus sequences in these people, who were drawn from across NYC, showed that the virus had been independently introduced many times from Europe and elsewhere in the United States. Subsequent clusters of community transmission occurred. The focus of infection in NYC is a marker of the role this city plays as a two-way hub for human movement. Science this issue p. 297

Summary: Zika virus (ZIKV) is an emerging, mosquito-borne flavivirus responsible for recent epidemics across the Americas, and it is closely related to dengue virus (DENV). Here, we study samples from 46 DENV-naive and 43 DENV-immune patients with RT-PCR-confirmed ZIKV infection at early-acute, late-acute, and convalescent time points from our pediatric cohort study in Nicaragua. We analyze the samples via RNA sequencing (RNA-seq), CyTOF, and multiplex cytokine/chemokine Luminex to generate a comprehensive, innate immune profile during ZIKV infection. Immunophenotyping and analysis of cytokines/chemokines reveal that CD14+ monocytes play a key role during ZIKV infection. Further, we identify CD169 (Siglec-1) on CD14+ monocytes as a potential biomarker of acute ZIKV infection. Strikingly distinct transcriptomic and immunophenotypic signatures are observed at all three time points. Interestingly, pre-existing dengue immunity has minimal impact on the innate immune response to Zika. Finally, this comprehensive immune profiling and network analysis of ZIKV infection in children serves as a valuable resource. : At three time points after Zika virus infection, Michlmayr et al. perform comprehensive immunoprofiling of pediatric cohort samples via RNA-seq, CyTOF, and Luminex cytokine/chemokine array, resulting in distinct temporal patterns of gene expression, cell profiles, and cytokines/chemokines. They show CD14+ monocytes play a central role, identify CD169 as a potential biomarker of acute ZIKV infection along with upregulation of CXCL10, and find no impact of prior dengue virus infection on the innate immune response to Zika. Keywords: Zika virus, monocytes, dengue virus, innate immunity, immune profiling, RNA-seq, CyTOF, network model, biomarker, Luminex

Whole-genome sequencing (WGS) of Staphylococcus aureus is increasingly used as part of infection prevention practices. In this study, we established a long-read technology-based WGS screening program of all first-episode methicillin-resistant Staphylococcus aureus (MRSA) blood infections at a major urban hospital., Whole-genome sequencing (WGS) of Staphylococcus aureus is increasingly used as part of infection prevention practices. In this study, we established a long-read technology-based WGS screening program of all first-episode methicillin-resistant Staphylococcus aureus (MRSA) blood infections at a major urban hospital. A survey of 132 MRSA genomes assembled from long reads enabled detailed characterization of an outbreak lasting several months of a CC5/ST105/USA100 clone among 18 infants in a neonatal intensive care unit (NICU). Available hospital-wide genome surveillance data traced the origins of the outbreak to three patients admitted to adult wards during a 4-month period preceding the NICU outbreak. The pattern of changes among complete outbreak genomes provided full spatiotemporal resolution of its progression, which was characterized by multiple subtransmissions and likely precipitated by equipment sharing between adults and infants. Compared to other hospital strains, the outbreak strain carried distinct mutations and accessory genetic elements that impacted genes with roles in metabolism, resistance, and persistence. This included a DNA recognition domain recombination in the hsdS gene of a type I restriction modification system that altered DNA methylation. Transcriptome sequencing (RNA-Seq) profiling showed that the (epi)genetic changes in the outbreak clone attenuated agr gene expression and upregulated genes involved in stress response and biofilm formation. Overall, our findings demonstrate the utility of long-read sequencing for hospital surveillance and for characterizing accessory genomic elements that may impact MRSA virulence and persistence.