Common PIEZO1 Allele in African Populations Causes RBC Dehydration and Attenuates Plasmodium Infection.

Hereditary xerocytosis is thought to be a rare genetic condition characterized by red blood cell (RBC) dehydration with mild hemolysis. RBC dehydration is linked to reduced Plasmodium infection in vitro; however, the role of RBC dehydration in protection against malaria in vivo is unknown. Most cases of hereditary xerocytosis are associated with gain-of-function mutations in PIEZO1, a mechanically activated ion channel. We engineered a mouse model of hereditary xerocytosis and show that Plasmodium infection fails to cause experimental cerebral malaria in these mice due to the action of Piezo1 in RBCs and in T cells. Remarkably, we identified a novel human gain-of-function PIEZO1 allele, E756del, present in a third of the African population. RBCs from individuals carrying this allele are dehydrated and display reduced Plasmodium infection in vitro. The existence of a gain-of-function PIEZO1 at such high frequencies is surprising and suggests an association with malaria resistance.

Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources.

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.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

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.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Repositioning the Early Pathology of Type 1 Diabetes to the Extraislet Vasculature.

Type 1 diabetes (T1D) is a prototypic T cell-mediated autoimmune disease. Because the islets of Langerhans are insulated from blood vessels by a double basement membrane and lack detectable lymphatic drainage, interactions between endocrine and circulating T cells are not permitted. Thus, we hypothesized that initiation and progression of anti-islet immunity required islet neolymphangiogenesis to allow T cell access to the islet. Combining microscopy and single cell approaches, the timing of this phenomenon in mice was situated between 5 and 8 wk of age when activated anti-insulin CD4 T cells became detectable in peripheral blood while peri-islet pathology developed. This "peri-insulitis," dominated by CD4 T cells, respected the islet basement membrane and was limited on the outside by lymphatic endothelial cells that gave it the attributes of a tertiary lymphoid structure. As in most tissues, lymphangiogenesis seemed to be secondary to local segmental endothelial inflammation at the collecting postcapillary venule. In addition to classic markers of inflammation such as CD29, V-CAM, and NOS, MHC class II molecules were expressed by nonhematopoietic cells in the same location both in mouse and human islets. This CD45- MHC class II+ cell population was capable of spontaneously presenting islet Ags to CD4 T cells. Altogether, these observations favor an alternative model for the initiation of T1D, outside of the islet, in which a vascular-associated cell appears to be an important MHC class II-expressing and -presenting cell.

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

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 apilimod2-4 and 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 induced pluripotent stem cells, 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.

Short-term learning of the vestibulo-ocular reflex induced by a custom interactive computer game.

Retinal image slip during head rotation drives motor learning in the rotational vestibulo-ocular reflex (VOR) and forms the basis of gaze-stability exercises that treat vestibular dysfunction. Clinical exercises, however, are unengaging, cannot easily be titrated to the level of impairment, and provide neither direct feedback nor tracking of the patient's adherence, performance, and progress. To address this, we have developed a custom application for VOR training based on an interactive computer game. In this study, we tested the ability of this game to induce VOR learning in individuals with normal vestibular function, and we compared the efficacy of single-step and incremental learning protocols. Eighteen participants played the game twice on different days. All participants tolerated the game and were able to complete both sessions. The game scenario incorporated a series of brief head rotations, similar to active head impulses, that were paired with a dynamic acuity task and with a visual-vestibular mismatch (VVM) intended to increase VOR gain (single-step: 300 successful trials at ×1.5 viewing; incremental: 100 trials each of ×1.13, ×1.33, and ×1.5 viewing). Overall, VOR gain increased by 15 ± 4.7% (mean ± 95% CI, P < 0.001). Gains increased similarly for active and passive head rotations, and, contrary to our hypothesis, there was little effect of the learning strategy. This study shows that an interactive computer game provides robust VOR training and has the potential to deliver effective, engaging, and trackable gaze-stability exercises to patients with a range of vestibular dysfunctions.NEW & NOTEWORTHY This study demonstrates the feasibility and efficacy of a customized computer game to induce motor learning in the high-frequency rotational vestibulo-ocular reflex. It provides a physiological basis for the deployment of this technology to clinical vestibular rehabilitation.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

SUMMARY: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.

A genome-wide RNAi screen for modifiers of the circadian clock in human cells.

Two decades of research identified more than a dozen clock genes and defined a biochemical feedback mechanism of circadian oscillator function. To identify additional clock genes and modifiers, we conducted a genome-wide small interfering RNA screen in a human cellular clock model. Knockdown of nearly 1000 genes reduced rhythm amplitude. Potent effects on period length or increased amplitude were less frequent; we found hundreds of these and confirmed them in secondary screens. Characterization of a subset of these genes demonstrated a dosage-dependent effect on oscillator function. Protein interaction network analysis showed that dozens of gene products directly or indirectly associate with known clock components. Pathway analysis revealed these genes are overrepresented for components of insulin and hedgehog signaling, the cell cycle, and the folate metabolism. Coupled with data showing many of these pathways are clock regulated, we conclude the clock is interconnected with many aspects of cellular function.

Quantitative Metaproteomics and Activity-based Protein Profiling of Patient Fecal Microbiome Identifies Host and Microbial Serine-type Endopeptidase Activity Associated With Ulcerative Colitis.

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 novo peptide 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.

Schema Playground: a tool for authoring, extending, and using metadata schemas to improve FAIRness of biomedical data.

BACKGROUND: Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. RESULTS: Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema-a large multi-class schema for harmonizing various COVID-19 related resources. CONCLUSIONS: We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Design and application of a knowledge network for automatic prioritization of drug mechanisms.

MOTIVATION: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. RESULTS: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides. AVAILABILITY AND IMPLEMENTATION: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BioThings SDK: a toolkit for building high-performance data APIs in biomedical research.

SUMMARY: To meet the increased need of making biomedical resources more accessible and reusable, Web Application Programming Interfaces (APIs) or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info and MyChem.info for integrating annotations on genes, variants and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. AVAILABILITY AND IMPLEMENTATION: The BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Oxidative stress diverts tRNA synthetase to nucleus for protection against DNA damage.

Tyrosyl-tRNA synthetase (TyrRS) is known for its essential aminoacylation function in protein synthesis. Here we report a function for TyrRS in DNA damage protection. We found that oxidative stress, which often downregulates protein synthesis, induces TyrRS to rapidly translocate from the cytosol to the nucleus. We also found that angiogenin mediates or potentiates this stress-induced translocalization. The nuclear-localized TyrRS activates transcription factor E2F1 to upregulate the expression of DNA damage repair genes such as BRCA1 and RAD51. The activation is achieved through direct interaction of TyrRS with TRIM28 to sequester this vertebrate-specific epigenetic repressor and its associated HDAC1 from deacetylating and suppressing E2F1. Remarkably, overexpression of TyrRS strongly protects against UV-induced DNA double-strand breaks in zebrafish, whereas restricting TyrRS nuclear entry completely abolishes the protection. Therefore, oxidative stress triggers an essential cytoplasmic enzyme used for protein synthesis to translocate to the nucleus to protect against DNA damage.

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses.

BACKGROUND: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. RESULTS: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. CONCLUSIONS: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells.

MOTIVATION: Spatial transcriptomics (ST) technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for ST data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology. RESULTS: We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy. AVAILABILITY AND IMPLEMENTATION: The SpaCell package is open source under an MIT licence and it is available at https://github.com/BiomedicalMachineLearning/SpaCell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.

MOTIVATION: Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depend on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction (RE). RESULTS: In this article, we introduce the Relationship Extraction Module of the web-based application Mark2Cure (M2C) and demonstrate that citizen scientists can perform RE. We confirm the importance of accurate named entity recognition on user performance of RE and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the M2C Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration and natural language processing. AVAILABILITY AND IMPLEMENTATION: Mark2Cure platform: https://mark2cure.org; Mark2Cure source code: https://github.com/sulab/mark2cure; and data and analysis code for this article: https://github.com/gtsueng/M2C_rel_nb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The ReFRAME library as a comprehensive drug repurposing library and its application to the treatment of cryptosporidiosis.

The chemical diversity and known safety profiles of drugs previously tested in humans make them a valuable set of compounds to explore potential therapeutic utility in indications outside those originally targeted, especially neglected tropical diseases. This practice of "drug repurposing" has become commonplace in academic and other nonprofit drug-discovery efforts, with the appeal that significantly less time and resources are required to advance a candidate into the clinic. Here, we report a comprehensive open-access, drug repositioning screening set of 12,000 compounds (termed ReFRAME; Repurposing, Focused Rescue, and Accelerated Medchem) that was assembled by combining three widely used commercial drug competitive intelligence databases (Clarivate Integrity, GVK Excelra GoStar, and Citeline Pharmaprojects), together with extensive patent mining of small molecules that have been dosed in humans. To date, 12,000 compounds (∼80% of compounds identified from data mining) have been purchased or synthesized and subsequently plated for screening. To exemplify its utility, this collection was screened against Cryptosporidium spp., a major cause of childhood diarrhea in the developing world, and two active compounds previously tested in humans for other therapeutic indications were identified. Both compounds, VB-201 and a structurally related analog of ASP-7962, were subsequently shown to be efficacious in animal models of Cryptosporidium infection at clinically relevant doses, based on available human doses. In addition, an open-access data portal (https://reframedb.org) has been developed to share ReFRAME screen hits to encourage additional follow-up and maximize the impact of the ReFRAME screening collection.

Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.

BACKGROUND: Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these advances, the number of compounds successfully repositioned via computational screening remains low. New strategies for algorithm evaluation that more accurately reflect the repositioning potential of a compound could provide a better target for future optimizations. RESULTS: Using a text-mined database, we applied a previously described network-based computational repositioning algorithm, yielding strong results via cross-validation, averaging 0.95 AUROC on test-set indications. However, to better approximate a real-world scenario, we built a time-resolved evaluation framework. At various time points, we built networks corresponding to prior knowledge for use as a training set, and then predicted on a test set comprised of indications that were subsequently described. This framework showed a marked reduction in performance, peaking in performance metrics with the 1985 network at an AUROC of .797. Examining performance reductions due to removal of specific types of relationships highlighted the importance of drug-drug and disease-disease similarity metrics. Using data from future timepoints, we demonstrate that further acquisition of these kinds of data may help improve computational results. CONCLUSIONS: Evaluating a repositioning algorithm using indications unknown to input network better tunes its ability to find emerging drug indications, rather than finding those which have been randomly withheld. Focusing efforts on improving algorithmic performance in a time-resolved paradigm may further improve computational repositioning predictions.

Correcting the F508del-CFTR variant by modulating eukaryotic translation initiation factor 3-mediated translation initiation.

Inherited and somatic rare diseases result from >200,000 genetic variants leading to loss- or gain-of-toxic function, often caused by protein misfolding. Many of these misfolded variants fail to properly interact with other proteins. Understanding the link between factors mediating the transcription, translation, and protein folding of these disease-associated variants remains a major challenge in cell biology. Herein, we utilized the cystic fibrosis transmembrane conductance regulator (CFTR) protein as a model and performed a proteomics-based high-throughput screen (HTS) to identify pathways and components affecting the folding and function of the most common cystic fibrosis-associated mutation, the F508del variant of CFTR. Using a shortest-path algorithm we developed, we mapped HTS hits to the CFTR interactome to provide functional context to the targets and identified the eukaryotic translation initiation factor 3a (eIF3a) as a central hub for the biogenesis of CFTR. Of note, siRNA-mediated silencing of eIF3a reduced the polysome-to-monosome ratio in F508del-expressing cells, which, in turn, decreased the translation of CFTR variants, leading to increased CFTR stability, trafficking, and function at the cell surface. This finding suggested that eIF3a is involved in mediating the impact of genetic variations in CFTR on the folding of this protein. We posit that the number of ribosomes on a CFTR mRNA transcript is inversely correlated with the stability of the translated polypeptide. Polysome-based translation challenges the capacity of the proteostasis environment to balance message fidelity with protein folding, leading to disease. We suggest that this deficit can be corrected through control of translation initiation.