The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machinereadable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. INTRODUCTIONIn light of the vast scientific resources made available through genomics, the science of deciphering molecular mechanisms is expanding rapidly. Scientists who once hunted for disease genes or sought to distinguish key concepts in evolution are now turning their attention to the details of molecular assembly and mechanism to further understand medicine and the key concepts underlying biology. The Biomolecular Interaction Network Database (BIND) was designed to store complete information about molecular assembly through a database structure in order to archive interactions and reactions arising from biopolymers (protein, RNA and DNA), as well as small molecules, lipids and carbohydrates. Detailed information about molecular mechanism, such as the chemical product(s) of an enzymatic reaction, can be encoded in BIND. The underlying ontology of the BIND database is chemistry, and as such, BIND is capable of storing information about molecular interactions to atomic resolution. The taxonomic scope of BIND is

Significance Macrolide antibiotics inhibit translation by binding in the ribosomal nascent peptide exit tunnel. It was believed that macrolides interfere with protein synthesis by obstructing the egress of nascent proteins. In contrast to this view, the results of ribosome profiling analysis suggest that the main mode of macrolide action is context-specific inhibition of peptide bond formation. The ribosome with a macrolide molecule bound in the tunnel is impaired in catalysis of peptide bond formation between specific combinations of the peptidyl donors and aminoacyl acceptors, leading to interruption of translation when such problematic substrates are encountered. These findings underscore the existence of a link between the ribosomal tunnel and the peptidyl transferase center and pave the way for development of superior antibiotics.

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%

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.

Chronic glial activation and neuroinflammation induced by the amyloid-b peptide (Ab) contribute to Alzheimer's disease (AD) pathology. APOE4 is the greatest AD-genetic risk factor; increasing risk up to 12-fold compared to APOE3, with APOE4-specific neuroinflammation an important component of this risk. This editorial review discusses the role of APOE in inflammation and AD, via a literature review, presentation of novel data on Ab-induced neuroinflammation, and discussion of future research directions. The complexity of chronic neuroinflammation, including multiple detrimental and beneficial effects occurring in a temporal and cell-specific manner, has resulted in conflicting functional data for virtually every inflammatory mediator. Defining a neuroinflammatory phenotype (NIP) is one way to address this issue, focusing on profiling the changes in inflammatory mediator expression during disease progression. Although many studies have shown that APOE4 induces a detrimental NIP in peripheral inflammation and Ab-independent neuroinflammation, data for APOE-modulated Ab-induced neuroinflammation are surprisingly limited. We present data supporting the hypothesis that impaired apoE4 function modulates Ab-induced effects on inflammatory receptor signaling, including amplification of detrimental (toll-like receptor 4-p38a) and suppression of beneficial (IL-4R-nuclear receptor) pathways. To ultimately develop APOE genotype-specific therapeutics, it is critical that future studies define the dynamic NIP profile and pathways that underlie APOE-modulated chronic neuroinflammation.

Summary Transcriptional regulation during CD4+ T cell fate decisions enables their differentiation into distinct states, guiding immune responses towards antibody production via Tfh cells or inflammation by Teff cells. Tfh–Teff fate commitment is regulated by mutual antagonism between the transcription factors Bcl6 and Blimp-1. Here we examined how T cell receptor (TCR) signals establish and arbitrate Bcl6–Blimp-1 counter-antagonism. We found that the TCR-signal induced transcription factor Irf4 is essential for the differentiation of Bcl6-expressing Tfh and Blimp-1-expressing Teff cells. Increased TCR signaling raised Irf4 amounts and promoted Teff fates at the expense of Tfh ones. Importantly, orthogonal induction of Irf4 expression redirected Tfh fate trajectories towards those of Teff. Mechanistically, we linked greater Irf4 abundance with its recruitment towards low affinity binding sites within Teff cis-regulatory elements, including those of Prdm1. We propose that the Irf4 locus functions as the “reader” of TCR signal strength, in turn, concentration-dependent activity of Irf4 “writes” T helper fate choice.

ObjectiveApplying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties.Materials and MethodsDatasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty.ResultsWe identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%.DiscussionPrevious qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables.ConclusionsPost-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition.

B lymphopoiesis requires that immunoglobulin genes be accessible to the RAG1-RAG2 recombinase. However, the RAG proteins bind widely to open chromatin suggesting that additional mechanisms must restrict RAG-mediated DNA cleavage. Here, we demonstrate developmental downregulation of interleukin 7 (IL-7) receptor signaling in small pre-B cells induced expression of the bromodomain family member BRWD1, which was recruited to a specific epigenetic landscape at Igk dictated by pre-BCR-dependent Erk activation. BRWD1 enhanced RAG recruitment, increased gene accessibility and positioned nucleosomes 5′ to each Jκ recombination signal sequence. BRWD1 thus targets recombination to Igk and places recombination within the context of signaling cascades that control B cell development. Our findings provide a paradigm in which, at any particular antigen receptor locus, specialized mechanisms enforce lineage and stage specific recombination.