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In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info, a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.infocan be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12–18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose–response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2–4and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from H9 human embryonic stem cell lines, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

The lysis and extraction of soluble bacterial proteins from cells is a common practice for proteomics analyses, but insoluble bacterial biomasses are often left behind. Here, we show that with triflic acid treatment, the insoluble bacterial biomass of Gram–and Gram+bacteria can be rendered soluble. We use LC-MS/MS shotgun proteomics to show that bacterial proteins in the soluble and insoluble postlysis fractions differ significantly. Additionally, in the case of Gram–Pseudomonas aeruginosa, triflic acid treatment enables the enrichment of cell-envelope-associated proteins. Finally, we apply triflic acid to a human microbiome sample to show that this treatment is robust and enables the identification of a new, complementary subset of proteins from a complex microbial mixture.

The endocrine pancreas is one of the most inaccessible organs of the human body. Its autoimmune attack leads to type 1 diabetes (T1D) in a genetically susceptible population and a lifelong need for exogenous insulin replacement. Monitoring disease progression by sampling peripheral blood would provide key insights into T1D immune-mediated mechanisms and potentially change preclinical diagnosis and the evaluation of therapeutic interventions. This effort has been limited to the measurement of circulating anti-islet antibodies, which despite a recognized diagnostic value, remain poorly predictive at the individual level for a fundamentally CD4 T cell–dependent disease. Here, peptide–major histocompatibility complex tetramers were used to profile blood anti-insulin CD4 T cells in mice and humans. While percentages of these were not directly informative, the state of activation of anti-insulin T cells measured by RNA and protein profiling was able to distinguish the absence of autoimmunity versus disease progression. Activated anti-insulin CD4 T cell were detected not only at time of diagnosis but also in patients with established disease and in some at-risk individuals. These results support the concept that antigen-specific CD4 T cells might be used to monitor autoimmunity in real time. This advance can inform our approach to T1D diagnosis and therapeutic interventions in the preclinical phase of anti-islet autoimmunity.

Tandem mass spectrometry based shotgun proteomics of distal gut microbiomes is exceedingly difficult due to the inherent complexity and taxonomic diversity of the samples. We introduce two new methodologies to improve metaproteomic studies of microbiome samples. These methods include the stable isotope labeling in mammals to permit protein quantitation across two mouse cohorts as well as the application of activity-based probes to enrich and analyze both host and microbial proteins with specific functionalities. We used these technologies to study the microbiota from the adoptive T cell transfer mouse model of inflammatory bowel disease (IBD) and compare these samples to an isogenic control, thereby limiting genetic and environmental variables that influence microbiome composition. The data generated highlight quantitative alterations in both host and microbial proteins due to intestinal inflammation and corroborates the observed phylogenetic changes in bacteria that accompany IBD in humans and mouse models. The combination of isotope labeling with shotgun proteomics resulted in the total identification of 4434 protein clusters expressed in the microbial proteomic environment, 276 of which demonstrated differential abundance between control and IBD mice. Notably, application of a novel cysteine-reactive probe uncovered several microbial proteases and hydrolases overrepresented in the IBD mice. Implementation of these methods demonstrated that substantial insights into the identity and dysregulation of host and microbial proteins altered in IBD can be accomplished and can be used in the interrogation of other microbiome-related diseases.

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times per month. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.infoand http://myvariant.info. Both are offered free of charge to the research community.

Amidst the proteomes of human tissues lie subsets of proteins that are closely involved in conserved pathophysiological processes. Much of biomedical research concerns interrogating disease signature proteins and defining their roles in disease mechanisms. With advances in proteomics technologies, it is now feasible to develop targeted proteomics assays that can accurately quantify protein abundance as well as their post-translational modifications; however, with rapidly accumulating number of studies implicating proteins in diseases, current resources are insufficient to target every protein without judiciously prioritizing the proteins with high significance and impact for assay development. We describe here a data science method to prioritize and expedite assay development on high-impact proteins across research fields by leveraging the biomedical literature record to rank and normalize proteins that are popularly and preferentially published by biomedical researchers. We demonstrate this method by finding priority proteins across six major physiological systems (cardiovascular, cerebral, hepatic, renal, pulmonary, and intestinal). The described method is data-driven and builds upon the collective knowledge of previous publications referenced on PubMed to lend objectivity to target selection. The method and resulting popular protein lists may also be useful for exploring biological processes associated with various physiological systems and research topics, in addition to benefiting ongoing efforts to facilitate the broad translation of proteomics technologies.

Interpretation of the clinical significance of genomic alterations remains the most severe bottleneck preventing the realization of personalized medicine in cancer. We propose a knowledge commons to facilitate collaborative contributions and open discussion of clinical decision-making based on genomic events in cancer.

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novopeptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.

The unfolded protein response (UPR) maintains endoplasmic reticulum (ER) proteostasis through the activation of transcription factors such as XBP1s and ATF6. The functional consequences of these transcription factors for ER proteostasis remain poorly defined. Here, we describe methodology that enables orthogonal, small-molecule-mediated activation of the UPR-associated transcription factors XBP1s and/or ATF6 in the same cell independent of stress. We employ transcriptomics and quantitative proteomics to evaluate ER proteostasis network remodeling owing to the XBP1s and/or ATF6 transcriptional programs. Furthermore, we demonstrate that the three ER proteostasis environments accessible by activating XBP1s and/or ATF6 differentially influence the folding, trafficking, and degradation of destabilized ER client proteins without globally affecting the endogenous proteome. Our data reveal how the ER proteostasis network is remodeled by the XBP1s and/or ATF6 transcriptional programs at the molecular level and demonstrate the potential for selective restoration of aberrant ER proteostasis of pathologic, destabilized proteins through arm-selective UPR activation.

A chemical‐probe array composed of 47 off‐the‐shelf dyes was prepared in solution format (New York Tongue 1: NYT‐1) and was tested in the identification and quantitation of 47 cation analytes, including 44 metal ions, in addition to H+, NH4+, and tetrabutylammonium (TBA). The cation solutions were tested in a series of concentrations and the fold‐change in effective absorbance was analyzed by principal‐component analysis (PCA), hierarchical‐cluster analysis (HCA), and nearest‐neighbor decision to determine both identity and quantity of the analytes. Apart from alkali‐metal ions (Na+, K+, Li+, Cs+, and Rb+), which behave very similarly to each other due mainly to their low response, most of the cations were clearly distinguishable at 10 mMconcentration. The practical detection limit of each analyte was also determined by a sequential dilution and the nearest‐neighbor decision method. In the finalized working analyte concentration range (approximately 10 mMdown to 0.33 μM), by considering alkali metals as one analyte group, most of the analytes were correctly identified (99.4 %). Furthermore, the success rate at which the concentration of each analyte was correctly determined was also high (96.8 %).

Rab GTPases and SNARE fusion proteins direct cargo trafficking through the exocytic and endocytic pathways of eukaryotic cells. We have used steady state mRNA expression profiling and computational hierarchical clustering methods to generate a global overview of the distribution of Rabs, SNAREs, and coat machinery components, as well as their respective adaptors, effectors, and regulators in 79 human and 61 mouse nonredundant tissues. We now show that this systems biology approach can be used to define building blocks for membrane trafficking based on Rab-centric protein activity hubs. These Rab-regulated hubs provide a framework for an integrated coding system, the membrome network, which regulates the dynamics of the specialized membrane architecture of differentiated cells. The distribution of Rab-regulated hubs illustrates a number of facets that guides the overall organization of subcellular compartments of cells and tissues through the activity of dynamic protein interaction networks. An interactive website for exploring datasets comprising components of the Rab-regulated hubs that define the membrome of different cell and organ systems in both human and mouse is available at http://www.membrome.org/.

Salivary gland cancers comprise a heterogeneous group of neoplasms whose biological and clinical characteristics differ considerably from those of mucosal squamous cell carcinomas of the head and neck. One of the most common subtypes, adenoid cystic carcinoma (ACC), is notable for its myoepithelial differentiation, proclivity for hematogenous spread, and slow but progressive clinical course. The molecular alterations that underlie its development and progression are poorly characterized. Here we used oligonucleotide microarray analysis to survey the expression of 8920 different human genes in 15 ACCs, one ACC cell line, and five normal major salivary glands. We observed expression of genes indicative of myoepithelial differentiation, as expected, including those whose protein products are components of basement membranes and extracellular matrix. Other genes that were highly ranked for their expression in ACC were those encoding the transcription factors SOX4 and AP-2γ, the latter of which also was overexpressed in ACC relative to 175 other carcinomas from 10 anatomical sites that we had previously profiled. Additional genes, which were highly expressed in ACC compared to the other carcinomas, included casein kinase 1, epsilonand frizzled-7, both members of the Wnt/β-catenin signaling pathway. Our study documents for the first time the diverse spectrum of genes overexpressed in ACC and highlights gene products and pathways that in the future might be exploited as therapeutic targets for this cancer, which up until now, has shown limited response to chemotherapeutic approaches.

Molecular docking programs screen chemical databases for novel ligands that fit protein binding sites. When one compound fits the site well, close analogs typically do the same. Therefore, many of the compounds that are found in such screens resemble one another. This reduces the variety and novelty of the compounds suggested. In an attempt to increase the diversity of docking hit lists, the Available Chemicals Directory was grouped into families of related structures. All members of every family were docked and scored, but only the best scoring molecule of a high‐ranking family was allowed in the hit list. The identity and scores of the other members of these families were recorded as annotations to the best family member, but they were not independently ranked. This family‐based docking method was compared with molecule‐by‐molecule docking in screens against the structures of thymidylate synthase, dihydrofolate reductase (DHFR), and the cavity site of the mutant T4 lysozyme Leu99 → Ala (L99A). In each case, the diversity of the hit list increased, and more families of known ligands were found. To investigate whether the newly identified hits were sensible, we tested representative examples experimentally for binding to L99A and DHFR. Of the six compounds tested against L99A, five bound to the internal cavity. Of the seven compounds tested against DHFR, six inhibited the enzyme with apparent Ki values between 0.26 and 100 μM. The segregation of potential ligands into families of related molecules is a simple technique to increase the diversity of candidates suggested by database screens. The general approach should be applicable to most docking methods. Proteins 2001;42:279–293. © 2000 Wiley‐Liss, Inc.

Molecular docking programs screen chemical databases for novel ligands that fit protein binding sites. When one compound fits the site well, close analogs typically do the same. Therefore, many of the compounds that are found in such screens resemble one another. This reduces the variety and novelty of the compounds suggested. In an attempt to increase the diversity of docking hit lists, the Available Chemicals Directory was grouped into families of related structures. All members of every family were docked and scored, but only the best scoring molecule of a high‐ranking family was allowed in the hit list. The identity and scores of the other members of these families were recorded as annotations to the best family member, but they were not independently ranked. This family‐based docking method was compared with molecule‐by‐molecule docking in screens against the structures of thymidylate synthase, dihydrofolate reductase (DHFR), and the cavity site of the mutant T4 lysozyme Leu99 → Ala (L99A). In each case, the diversity of the hit list increased, and more families of known ligands were found. To investigate whether the newly identified hits were sensible, we tested representative examples experimentally for binding to L99A and DHFR. Of the six compounds tested against L99A, five bound to the internal cavity. Of the seven compounds tested against DHFR, six inhibited the enzyme with apparent Kivalues between 0.26 and 100 μM. The segregation of potential ligands into families of related molecules is a simple technique to increase the diversity of candidates suggested by database screens. The general approach should be applicable to most docking methods. Proteins 2001;42:279–293. © 2000 Wiley‐Liss, Inc.

The protein folding game Foldit shows that games are an effective way to recruit, engage and organize ordinary citizens to help solve difficult scientific problems.

Mohawk is enriched in meniscal tissue and promotes meniscus cell phenotype, enhances repair, and protects against cartilage destruction in mice.

FoxO play a key role in postnatal cartilage development, maturation, homeostasis, and osteoarthritis pathogenesis.

Gene annotation, as measured by links to the biomedical literature and funded grants, is governed by a power law, indicating that researchers favor the extensive study of relatively few genes. This emphasizes the need for data-driven science to accomplish genome-wide gene annotation.

Finding that nicotinic receptors containing the α6 subunit, but not the α4, inhibit chronic pain points to a new set of potential therapeutic drugs.

Online gene annotation resources are indispensable for analysis of genomics data. However, the landscape of these online resources is highly fragmented, and scientists often visit dozens of these sites for each gene in a candidate gene list. Here, we introduce BioGPS http://biogps.gnf.org, a centralized gene portal for aggregating distributed gene annotation resources. Moreover, BioGPS embraces the principle of community intelligence, enabling any user to easily and directly contribute to the BioGPS platform.