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1.
PLoS One ; 19(1): e0285093, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38236918

RESUMEN

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.


Asunto(s)
Ontologías Biológicas , COVID-19 , Enfermedades Transmisibles , Virosis , Humanos , Pandemias , Vocabulario Controlado , COVID-19/epidemiología
2.
J Invest Dermatol ; 144(2): 252-262.e4, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-37598867

RESUMEN

Tissue transcriptomics is used to uncover molecular dysregulations underlying diseases. However, the majority of transcriptomics studies focus on single diseases with limited relevance for understanding the molecular relationship between diseases or for identifying disease-specific markers. In this study, we used a normalization approach to compare gene expression across nine inflammatory skin diseases. The normalized datasets were found to retain differential expression signals that allowed unsupervised disease clustering and identification of disease-specific gene signatures. Using the NS-Forest algorithm, we identified a minimal set of biomarkers and validated their use as diagnostic disease classifier. Among them, PTEN was identified as being a specific marker for cutaneous lupus erythematosus and found to be strongly expressed by lesional keratinocytes in association with pathogenic type I IFNs. In fact, PTEN facilitated the expression of IFN-ß and IFN-κ in keratinocytes by promoting activation and nuclear translocation of IRF3. Thus, cross-comparison of tissue transcriptomics is a valid strategy to establish a molecular disease classification and to identify pathogenic disease biomarkers.


Asunto(s)
Dermatitis , Lupus Eritematoso Cutáneo , Lupus Eritematoso Sistémico , Humanos , Biomarcadores/metabolismo , Dermatitis/patología , Perfilación de la Expresión Génica , Queratinocitos/metabolismo , Lupus Eritematoso Cutáneo/diagnóstico , Lupus Eritematoso Cutáneo/genética , Lupus Eritematoso Cutáneo/metabolismo , Lupus Eritematoso Sistémico/genética , Fosfohidrolasa PTEN/genética , Piel/patología
3.
Front Cell Neurosci ; 17: 1256619, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-38094513

RESUMEN

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types, and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach-first by linking to expression of known marker genes, then using the NSForest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website, and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes within the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

4.
Nat Genet ; 55(12): 2189-2199, 2023 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-37945900

RESUMEN

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.


Asunto(s)
Neoplasias Cerebelosas , Meduloblastoma , Neoplasias , Humanos , ADN Circular , Meduloblastoma/genética , Estudios Retrospectivos , Neoplasias/genética , Oncogenes , Neoplasias Cerebelosas/genética
5.
Cell ; 186(22): 4818-4833.e25, 2023 10 26.
Artículo en Inglés | MEDLINE | ID: mdl-37804831

RESUMEN

MXRA8 is a receptor for chikungunya (CHIKV) and other arthritogenic alphaviruses with mammalian hosts. However, mammalian MXRA8 does not bind to alphaviruses that infect humans and have avian reservoirs. Here, we show that avian, but not mammalian, MXRA8 can act as a receptor for Sindbis, western equine encephalitis (WEEV), and related alphaviruses with avian reservoirs. Structural analysis of duck MXRA8 complexed with WEEV reveals an inverted binding mode compared with mammalian MXRA8 bound to CHIKV. Whereas both domains of mammalian MXRA8 bind CHIKV E1 and E2, only domain 1 of avian MXRA8 engages WEEV E1, and no appreciable contacts are made with WEEV E2. Using these results, we generated a chimeric avian-mammalian MXRA8 decoy-receptor that neutralizes infection of multiple alphaviruses from distinct antigenic groups in vitro and in vivo. Thus, different alphaviruses can bind MXRA8 encoded by different vertebrate classes with distinct engagement modes, which enables development of broad-spectrum inhibitors.


Asunto(s)
Alphavirus , Animales , Humanos , Fiebre Chikungunya , Virus Chikungunya/química , Mamíferos , Receptores Virales/metabolismo
6.
Nat Immunol ; 24(10): 1616-1627, 2023 10.
Artículo en Inglés | MEDLINE | ID: mdl-37667052

RESUMEN

Millions of people are suffering from Long COVID or post-acute sequelae of COVID-19 (PASC). Several biological factors have emerged as potential drivers of PASC pathology. Some individuals with PASC may not fully clear the coronavirus SARS-CoV-2 after acute infection. Instead, replicating virus and/or viral RNA-potentially capable of being translated to produce viral proteins-persist in tissue as a 'reservoir'. This reservoir could modulate host immune responses or release viral proteins into the circulation. Here we review studies that have identified SARS-CoV-2 RNA/protein or immune responses indicative of a SARS-CoV-2 reservoir in PASC samples. Mechanisms by which a SARS-CoV-2 reservoir may contribute to PASC pathology, including coagulation, microbiome and neuroimmune abnormalities, are delineated. We identify research priorities to guide the further study of a SARS-CoV-2 reservoir in PASC, with the goal that clinical trials of antivirals or other therapeutics with potential to clear a SARS-CoV-2 reservoir are accelerated.


Asunto(s)
COVID-19 , Humanos , Síndrome Post Agudo de COVID-19 , ARN Viral/genética , SARS-CoV-2 , Antivirales , Progresión de la Enfermedad
8.
Bioinformatics ; 39(10)2023 10 03.
Artículo en Inglés | MEDLINE | ID: mdl-37756695

RESUMEN

MOTIVATION: Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. RESULTS: We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. AVAILABILITY AND IMPLEMENTATION: The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).


Asunto(s)
Neoplasias Hematológicas , Neoplasias , Humanos , Redes Neurales de la Computación , Neoplasias/diagnóstico , Citometría de Flujo/métodos , Programas Informáticos
9.
Cell Rep Med ; 4(6): 101088, 2023 06 20.
Artículo en Inglés | MEDLINE | ID: mdl-37295422

RESUMEN

The coronavirus (CoV) family includes several viruses infecting humans, highlighting the importance of exploring pan-CoV vaccine strategies to provide broad adaptive immune protection. We analyze T cell reactivity against representative Alpha (NL63) and Beta (OC43) common cold CoVs (CCCs) in pre-pandemic samples. S, N, M, and nsp3 antigens are immunodominant, as shown for severe acute respiratory syndrome 2 (SARS2), while nsp2 and nsp12 are Alpha or Beta specific. We further identify 78 OC43- and 87 NL63-specific epitopes, and, for a subset of those, we assess the T cell capability to cross-recognize sequences from representative viruses belonging to AlphaCoV, sarbecoCoV, and Beta-non-sarbecoCoV groups. We find T cell cross-reactivity within the Alpha and Beta groups, in 89% of the instances associated with sequence conservation >67%. However, despite conservation, limited cross-reactivity is observed for sarbecoCoV, indicating that previous CoV exposure is a contributing factor in determining cross-reactivity. Overall, these results provide critical insights in developing future pan-CoV vaccines.


Asunto(s)
COVID-19 , Resfriado Común , Humanos , Linfocitos T , SARS-CoV-2 , Reacciones Cruzadas
10.
PLoS Biol ; 21(6): e3002133, 2023 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-37390046

RESUMEN

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.


Asunto(s)
Encéfalo , Neurociencias , Animales , Humanos , Ratones , Ecosistema , Neuronas
11.
Sci Rep ; 13(1): 9567, 2023 06 13.
Artículo en Inglés | MEDLINE | ID: mdl-37311768

RESUMEN

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.


Asunto(s)
Corteza Visual Primaria , Transcriptoma , Animales , Ratones , Hibridación Fluorescente in Situ , Perfilación de la Expresión Génica , Algoritmos
13.
Vaccine ; 41(20): 3171-3177, 2023 05 11.
Artículo en Inglés | MEDLINE | ID: mdl-37088603

RESUMEN

The widespread outbreak of the monkeypox virus (MPXV) recognized in 2022 poses new challenges for public healthcare systems worldwide. With more than 86,000 people infected, there is concern that MPXV may become endemic outside of its original geographical area leading to repeated human spillover infections or continue to be spread person-to-person. Fortunately, classical public health measures (e.g., isolation, contact tracing and quarantine) and vaccination have blunted the spread of the virus, but cases are continuing to be reported in 28 countries in March 2023. We describe here the vaccines and drugs available for the prevention and treatment of MPXV infections. However, although their efficacy against monkeypox (mpox) has been established in animal models, little is known about their efficacy in the current outbreak setting. The continuing opportunity for transmission raises concerns about the potential for evolution of the virus and for expansion beyond the current risk groups. The priorities for action are clear: 1) more data on the efficacy of vaccines and drugs in infected humans must be gathered; 2) global collaborations are necessary to ensure that government authorities work with the private sector in developed and low and middle income countries (LMICs) to provide the availability of treatments and vaccines, especially in historically endemic/enzootic areas; 3) diagnostic and surveillance capacity must be increased to identify areas and populations where the virus is present and may seed resurgence; 4) those at high risk of severe outcomes (e.g., immunocompromised, untreated HIV, pregnant women, and inflammatory skin conditions) must be informed of the risk of infection and be protected from community transmission of MPXV; 5) engagement with the hardest hit communities in a non-stigmatizing way is needed to increase the understanding and acceptance of public health measures; and 6) repositories of monkeypox clinical samples, including blood, fluids, tissues and lesion material must be established for researchers. This MPXV outbreak is a warning that pandemic preparedness plans need additional coordination and resources. We must prepare for continuing transmission, resurgence, and repeated spillovers of MPXV.


Asunto(s)
Vacunas , Embarazo , Animales , Humanos , Femenino , /prevención & control , Virus de la Viruela de los Monos , Vacunación , Brotes de Enfermedades/prevención & control
14.
medRxiv ; 2023 Feb 10.
Artículo en Inglés | MEDLINE | ID: mdl-36798344

RESUMEN

Motivation: Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. Results: We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes the available sample-level training data and predicts both the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. Availability: The source code of CSNN and datasets used in the experiments are publicly available on GitHub and FlowRepository. Contact: Edgar E. Robles: roblesee@uci.edu and Yu Qian: mqian@jcvi.org. Supplementary information: Supplementary data are available on GitHub and at Bioinformatics online.

15.
bioRxiv ; 2023 Jun 10.
Artículo en Inglés | MEDLINE | ID: mdl-36824745

RESUMEN

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach - first by linking to expression of known marker genes, then using the NS-Forest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website (https://umgear.org/), and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes withing the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

16.
bioRxiv ; 2023 Jan 05.
Artículo en Inglés | MEDLINE | ID: mdl-36656777

RESUMEN

The Coronavirus (CoV) family includes a variety of viruses able to infect humans. Endemic CoVs that can cause common cold belong to the alphaCoV and betaCoV genera, with the betaCoV genus also containing subgenera with zoonotic and pandemic concern, including sarbecoCoV (SARS-CoV and SARS-CoV-2) and merbecoCoV (MERS-CoV). It is therefore warranted to explore pan-CoV vaccine concepts, to provide adaptive immune protection against new potential CoV outbreaks, particularly in the context of betaCoV sub lineages. To explore the feasibility of eliciting CD4 + T cell responses widely cross-recognizing different CoVs, we utilized samples collected pre-pandemic to systematically analyze T cell reactivity against representative alpha (NL63) and beta (OC43) common cold CoVs (CCC). Similar to previous findings on SARS-CoV-2, the S, N, M, and nsp3 antigens were immunodominant for both viruses while nsp2 and nsp12 were immunodominant for NL63 and OC43, respectively. We next performed a comprehensive T cell epitope screen, identifying 78 OC43 and 87 NL63-specific epitopes. For a selected subset of 18 epitopes, we experimentally assessed the T cell capability to cross-recognize sequences from representative viruses belonging to alphaCoV, sarbecoCoV, and beta-non-sarbecoCoV groups. We found general conservation within the alpha and beta groups, with cross-reactivity experimentally detected in 89% of the instances associated with sequence conservation of >67%. However, despite sequence conservation, limited cross-reactivity was observed in the case of sarbecoCoV (50% of instances), indicating that previous CoV exposure to viruses phylogenetically closer to this subgenera is a contributing factor in determining cross-reactivity. Overall, these results provided critical insights in the development of future pan-CoV vaccines.

17.
Sci Data ; 10(1): 50, 2023 01 24.
Artículo en Inglés | MEDLINE | ID: mdl-36693887

RESUMEN

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.


Asunto(s)
Ontologías Biológicas , Encéfalo , Animales , Humanos , Ratones , Callithrix , Recolección de Datos/normas
18.
Nucleic Acids Res ; 51(D1): D678-D689, 2023 01 06.
Artículo en Inglés | MEDLINE | ID: mdl-36350631

RESUMEN

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.


Asunto(s)
Genómica , Programas Informáticos , Virus , Humanos , Bacterias/genética , Biología Computacional , Bases de Datos Genéticas , Gripe Humana , Virus/genética
19.
Cell Host Microbe ; 30(12): 1662-1670.e4, 2022 12 14.
Artículo en Inglés | MEDLINE | ID: mdl-36463861

RESUMEN

The monkeypox virus (MPXV) outbreak confirmed in May 2022 in non-endemic countries is raising concern about the pandemic potential of novel orthopoxviruses. Little is known regarding MPXV immunity in the context of MPXV infection or vaccination with vaccinia-based vaccines (VACV). As with vaccinia, T cells are likely to provide an important contribution to overall immunity to MPXV. Here, we leveraged the epitope information available in the Immune Epitope Database (IEDB) on VACV to predict potential MPXV targets recognized by CD4+ and CD8+ T cell responses. We found a high degree of conservation between VACV epitopes and MPXV and defined T cell immunodominant targets. These analyses enabled the design of peptide pools able to experimentally detect VACV-specific T cell responses and MPXV cross-reactive T cells in a cohort of vaccinated individuals. Our findings will facilitate the monitoring of cellular immunity following MPXV infection and vaccination.


Asunto(s)
Vaccinia , Humanos , Virus Vaccinia , Virus de la Viruela de los Monos/fisiología , Epítopos
20.
Front Bioinform ; 2: 1020189, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-36353215

RESUMEN

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has demonstrated its ability to rapidly and continuously evolve, leading to the emergence of thousands of different sequence variants, many with distinctive phenotypic properties. Fortunately, the broad application of next generation sequencing (NGS) across the globe has produced a wealth of SARS-CoV-2 genome sequences, offering a comprehensive picture of how this virus is evolving so that accurate diagnostics, reliable therapeutics, and prophylactic vaccines against COVID-19 can be developed and maintained. The millions of SARS-CoV-2 sequences deposited into genomic sequencing databases, including GenBank, BV-BRC, and GISAID, are annotated with the dates and geographic locations of sample collection, and can be aligned to and compared with the Wuhan-Hu-1 reference genome to extract their constellation of nucleotide and amino acid substitutions. By aggregating these data into concise datasets, the spread of variants through space and time can be assessed. Variant tracking efforts have initially focused on the Spike protein due to its critical role in viral tropism and antibody neutralization. To identify emerging variants of concern as early as possible, we developed a computational pipeline to process the genomic data and assign risk scores based on both epidemiological and functional parameters. Epidemiological dynamics are used to identify variants exhibiting substantial growth over time and spread across geographical regions. Experimental data that quantify Spike protein regions targeted by adaptive immunity and critical for other virus characteristics are used to predict variants with consequential immunogenic and pathogenic impacts. The growth assessment and functional impact scores are combined to produce a Composite Score for any set of Spike substitutions detected. With this systematic method to routinely score and rank emerging variants, we have established an approach to identify threatening variants early and prioritize them for experimental evaluation.

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