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Neurofibromatosis 2 (NF2) is a dominantly inherited disorder characterized by the occurrence of bilateral vestibular schwannomas and other central nervous system tumors including multiple meningiomas. Genetic linkage studies and investigations of both sporadic and familial tumors suggest that NF2 is caused by inactivation of a tumor suppressor gene in chromosome 22q12. We have identified a candidate gene for the NF2 tumor suppressor that has suffered nonoverlapping deletions in DNA from two independent NF2 families and alterations in meningiomas from two unrelated NF2 patients. The candidate gene encodes a 587 amino acid protein with striking similarity to several members of a family of proteins proposed to link cytoskeletal components with proteins in the cell membrane. The NF2 gene may therefore constitute a novel class of tumor suppressor gene.

Tobacco use is a leading contributor to disability and death worldwide, and genetic factors contribute in part to the development of nicotine dependence. To identify novel genes for which natural variation contributes to the development of nicotine dependence, we performed a comprehensive genome wide association study using nicotine dependent smokers as cases and non-dependent smokers as controls. To allow the efficient, rapid, and cost effective screen of the genome, the study was carried out using a two-stage design. In the first stage, genotyping of over 2.4 million single nucleotide polymorphisms (SNPs) was completed in case and control pools. In the second stage, we selected SNPs for individual genotyping based on the most significant allele frequency differences between cases and controls from the pooled results. Individual genotyping was performed in 1050 cases and 879 controls using 31 960 selected SNPs. The primary analysis, a logistic regression model with covariates of age, gender, genotype and gender by genotype interaction, identified 35 SNPs with P-values less than 10(-4) (minimum P-value 1.53 x 10(-6)). Although none of the individual findings is statistically significant after correcting for multiple tests, additional statistical analyses support the existence of true findings in this group. Our study nominates several novel genes, such as Neurexin 1 (NRXN1), in the development of nicotine dependence while also identifying a known candidate gene, the beta3 nicotinic cholinergic receptor. This work anticipates the future directions of large-scale genome wide association studies with state-of-the-art methodological approaches and sharing of data with the scientific community.

Many tumour types have been reported to have deletion of 9p21 (refs 1-6). A candidate target suppressor gene, p16 (p16INK4a/MTS-1/CDKN2), was recently identified within the commonly deleted region in tumour cell lines. An increasing and sometimes conflicting body of data has accumulated regarding the frequency of homozygous deletion and the importance of p16 in primary tumours. We tested 545 primary tumours by microsatellite analysis with existing and newly cloned markers around the p16 locus. We have now found that small homozygous deletions represent the predominant mechanism of inactivation at 9p21 in bladder tumours and are present in other tumour types, including breast and prostate cancer. Moreover, fine mapping of these deletions implicates a 170 kb minimal region that includes p16 and excludes p15.

Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Modifications to exon amplification have been instituted that increase its speed, efficiency and reliability. Exons were isolated from target human or mouse genomic DNA sources ranging from 30 kilobases (kb) to 3 megabases (Mb) in complexity. The efficiency was dependent upon the amount of input DNA, and ranged from isolation of an exon for every 20 kb to an exon for every 80 kb of target genomic DNA. In these studies, several novel genes and a smaller number of genes isolated previously that reside on human chromosome 9 have been identified. These results indicate that exon amplification is presently adaptable to large scale isolation of exons from complex sources of genomic DNA.

Manganese-superoxide dismutase (Sod2) removes mitochondrially derived superoxide (O 2 . ) at near-diffusion limiting rates and is the only antioxidant enzyme whose expression is regulated by numerous stimuli. Here it is shown that Sod2 also serves as a source of the intracellular signaling molecule H 2 O 2 . Sod2-dependent increases in the steady-state levels of H 2 O 2 led to ERK1/2 activation and subsequent downstream transcriptional increases in matrix metalloproteinase-1 (MMP-1) expression, which were reversed by expression of the H 2 O 2 -detoxifying enzyme, catalase. In addition, a single nucleotide polymorphism has recently been identified (1G/2G) at base pair ؊1607 that creates an Ets site adjacent to an AP-1 site at base pair ؊1602 and has been shown to dramatically enhance transcription of the MMP-1 promoter. Luciferase promoter constructs containing either the 1G or 2G variation were 25-or 1000-fold more active when transiently transfected into Sod2-overexpressing cell lines, respectively. The levels of MMP-2, -3, and -7 were also increased in the Sod2-overexpressing cell lines, suggesting that Sod2 may function as a "global" redox regulator of MMP expression. In addition, Sod2 ؊/؉ mouse embryonic fibroblasts failed to respond to the cytokine-mediated induction of the murine functional analog of MMP-1, MMP-13. This study provides evidence that the modulation of Sod2 activity by a wide array of pathogenic and inflammatory stimuli may be utilized by the cell as a primary signaling mechanism leading to matrix metalloproteinase expression.