Biology subjects

Pico, A. R.

Publications and source records attributed to Pico, A. R..

9 recordsLinked to original sources

bioRxiv2025

Genetic regulators of neuronal survival across metabolic environments

Cellular energy metabolism and oxygen availability shape neuronal function and vulnerability, yet the genetic regulators of these metabolic processes in human neurons remain incompletely understood. Here, we performed CRISPR interference (CRISPRi) screens in human induced pluripotent stem cell (iPSC)-derived neurons across four distinct metabolic conditions and at three physiologically relevant oxygen tensions. This combinatorial approach enabled systematic interrogation of gene-environment interactions that govern neuronal metabolic adaptation. We identified genes--including genes associated with Leigh syndrome and autism spectrum disorder--whose importance for cell survival is highly sensitive to environmental context, revealing potential mechanisms underlying metabolic specification and selective neuronal vulnerability in neurological disorders. Our screens also uncovered regulators of neuronal glycolysis, including KIAA1429 and MAPT among others, which are previously uncharacterized modulators of neuronal glucose utilization and metabolic flexibility. Our work nominates candidate metabolic interventions and gene targets for enhancing neuronal resilience under hypoxic or nutrient-limited conditions.

systems biology↗

bioRxiv2021

SARS-CoV-2 spike protein induces abnormal inflammatory blood clots neutralized by fibrin immunotherapy

Blood clots are a central feature of coronavirus disease-2019 (COVID-19) and can culminate in pulmonary embolism, stroke, and sudden death. However, it is not known how abnormal blood clots form in COVID-19 or why they occur even in asymptomatic and convalescent patients. Here we report that the Spike protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the blood coagulation factor fibrinogen and induces structurally abnormal blood clots with heightened proinflammatory activity. SARS-CoV-2 Spike virions enhanced fibrin-mediated microglia activation and induced fibrinogen-dependent lung pathology. COVID-19 patients had fibrin autoantibodies that persisted long after acute infection. Monoclonal antibody 5B8, targeting the cryptic inflammatory fibrin epitope, inhibited thromboinflammation. Our results reveal a procoagulant role for the SARS-CoV-2 Spike and propose fibrin-targeting interventions as a treatment for thromboinflammation in COVID-19. One-Sentence SummarySARS-CoV-2 spike induces structurally abnormal blood clots and thromboinflammation neutralized by a fibrin-targeting antibody.

immunology↗

bioRxiv2020

25 Years of Pathway Figures

BackgroundPathway diagrams are fundamental tools for describing biological processes in all aspects of science, including training, generating hypotheses, describing new knowledge and ultimately as communication tools in published work. Thousands of pathway diagrams are published each year as figures in papers. But as static images the pathway knowledge represented in figures is not accessible to researchers for computational queries and analyses. In this study, we aimed to identify pathway figures published in the past 25 years, to characterize the human gene content in figures by optical character recognition, and to describe their utility as a resource for pathway knowledge. ApproachTo identify pathway figures representing 25 years of published research, we trained a ma-chine learning service on manually-classified figures and applied it to 235,081 image query results from PubMed Central. Our previously described pipeline 1 was utilized to extract hu-man genes from the pathway figure images. These figures were characterized in terms of their parent papers, human gene content and enriched disease terms. Diverse use cases were explored for this newly accessible pathway resource. ResultsWe identified 64,643 pathway figures published between 1995 and 2019, depicting 1,112,551 instances of human genes (13,464 unique NCBI Genes) in various interactions and contexts. This represents more genes than found in the text of the same papers, as well as genes not found in any pathway database. We developed an interactive web tool to explore the results from the 65k set of figures, and used this tool to explore the history of scientific discovery of the Hippo Signaling pathway. We also defined a filtered set of 32k pathway figures useful for enrichment analysis.

bioinformatics↗

bioRxiv2019

Wikidata as a FAIR knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.

bioinformatics↗

bioRxiv2019

RCy3: Network Biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from igraph and graph ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

bioinformatics↗

bioRxiv2019

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and that some dedicated methods are available only as web servers with limited cell type gene expression signatures. In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

bioinformatics↗

bioRxiv2018

Identifying Genes in Published Pathway Figure Images

BACKGROUNDPathway figures are commonly found in the biomedical literature providing intuitive models of complex processes in a visually concise format. The contents of a pathway figure often reflect the key findings and relevant context of an article. Unfortunately, the vast majority of pathway figures are drawn as one-off static images despite freely available pathway tools and resources, thus rendering their contents inaccessible to search, data mining and downstream analysis.\n\nAPPROACHLeveraging advances in optical character recognition and domain expertise in pathway modeling, we devised an approach to identify genes in published pathway figures. The approach was optimized against a set of figure images obtained from PubMed Central and tested against a set of 400 curated pathways with known content from WikiPathways (F-measure 95.2%).\n\nRESULTSApplied to 3982 published pathway figures spanning a four year period, our approach identified 29,189 gene symbols representing 4159 unique gene identifiers. The gene content unlocked from just this small sample of published figures includes novel and diverse pathway associations unmatched by any pathway database. Our approach over doubled the number of genes associated with the articles containing these figures as compared to combined annotations available from PubMed and PubTator. Encouraged by these initial results, we plan to scale the approach to make the molecular contents of the continuing stream of published pathway figures more accessible.

bioinformatics↗

bioRxiv2018

ERNIE: A Data Platform for Research Assessment

Data mining coupled to network analysis has been successfully used to study relationships between basic discovery and translational applications such as drug development; and to document research collaborations and knowledge flows. Assembling relevant data for such studies in a form that supports analysis presents challenges. We have developed Enhanced Research Network Information Environment (ERNIE), an open source, scalable cloud-based platform that (i) integrates data drawn from public and commercial sources (ii) provides users with analytical workflows that incorporate expert input at critical stages. A modular design enables the addition, deletion, or substitution of data sources. To demonstrate the capabilities of ERNIE, we have conducted case studies that span drug development and pharmacogenetics. In these studies, we analyze data from regulatory documents, bibliographic and patent databases, research grant records, and clinical trials, to document collaborations and identify influential research accomplishments.

scientific communication and education↗

bioRxiv2017

Research Synergy and Drug Development: Bright Stars in Neighboring Constellations

Drug discovery and subsequent availability of a new breakthrough therapeutic or cure is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. As a proof of principle, we have conducted a case study of five anti-cancer therapeutics. We have linked the work of roughly 237,000 authors in 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. We have enriched the content of networks of these therapeutics by annotating them with information on research awards as well as peer review that preceded these awards. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks as well as awards from the National Institutes of Health that supported this research. Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

scientific communication and education↗