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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.

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.

Correction to: Scientific Data, published online 24 January 2023 In this article the funding from ‘NIMH/NIH:1U24MH114827-01 - “A Community Resource for Single Cell Data in the Brain”. ’ was omitted. The original article has been corrected.

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.

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.

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.

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.

MotivationFlow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods. Existing deconvolutional analysis of bulk transcriptomics relies on predefined marker genes in the transcriptomics data, which are unavailable for novel cell types and does not utilize the FCM data that provide canonical phenotypic definitions of the cell types.ResultsWe developed a novel analytics pipeline-FastMix-for computational immunology, which integrates flow cytometry, bulk transcriptomics and clinical covariates for identifying cell type-specific gene expression signatures and biomarker genes. FastMix addresses the 'large p, small n' problem in the gene expression and flow cytometry integration analysis via a linear mixed effects model (LMER) for both cross-sectional and longitudinal studies. Its novel moment-based estimator not only reduces bias in parameter estimation but also is more efficient than iterative optimization. The FastMix pipeline also includes a cutting-edge flow cytometry data analysis method-DAFi-for identifying cell populations of interest and their characteristics. Simulation studies showed that FastMix produced smaller type I/II errors than competing methods. Validation using real data of two vaccine studies showed that FastMix identified a consistent set of signature genes as in independent single-cell RNA-seq analysis, producing additional interesting findings.Availability and implementationSource code of FastMix is publicly available at https://github.com/terrysun0302/FastMix.Supplementary informationSupplementary data are available at Bioinformatics online.

Human enterovirus D68 (EV-D68) is a globally reemerging respiratory pathogen that is associated with the development of acute flaccid myelitis (AFM) in children. Currently, there are no approved vaccines or treatments for EV-D68 infection, and there is a paucity of data related to the virus and host-specific factors that predict disease severity and progression to the neurologic syndrome. EV-D68 infection of various animal models has served as an important platform for characterization and comparison of disease pathogenesis between historic and contemporary isolates. Still, there are significant gaps in our knowledge of EV-D68 pathogenesis that constrain the development and evaluation of targeted vaccines and antiviral therapies. Continued refinement and characterization of animal models that faithfully reproduce key elements of EV-D68 infection and disease is essential for ensuring public health preparedness for future EV-D68 outbreaks.

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures.

The current outbreak of coronavirus disease-2019 (COVID-19) caused by SARS-CoV-2 poses unparalleled challenges to global public health. SARS-CoV-2 is a Betacoronavirus, one of four genera belonging to the Coronaviridae subfamily Orthocoronavirinae. Coronaviridae, in turn, are members of the order Nidovirales, a group of enveloped, positive-stranded RNA viruses. Here we present a systematic phylogenetic and evolutionary study based on protein domain architecture, encompassing the entire proteomes of all Orthocoronavirinae, as well as other Nidovirales. This analysis has revealed that the genomic evolution of Nidovirales is associated with extensive gains and losses of protein domains. In Orthocoronavirinae, the sections of the genomes that show the largest divergence in protein domains are found in the proteins encoded in the amino-terminal end of the polyprotein (PP1ab), the spike protein (S), and many of the accessory proteins. The diversity among the accessory proteins is particularly striking, as each subgenus possesses a set of accessory proteins that is almost entirely specific to that subgenus. The only notable exception to this is ORF3b, which is present and orthologous over all Alphacoronaviruses. In contrast, the membrane protein (M), envelope small membrane protein (E), nucleoprotein (N), as well as proteins encoded in the central and carboxy-terminal end of PP1ab (such as the 3C-like protease, RNA-dependent RNA polymerase, and Helicase) show stable domain architectures across all Orthocoronavirinae. This comprehensive analysis of the Coronaviridae domain architecture has important implication for efforts to develop broadly cross-protective coronavirus vaccines.

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that have mutations associated with increased transmission and antibody escape have arisen over the course of the current pandemic. Although the current vaccines have largely been effective against past variants, the number of mutations found on the Omicron (B.1.1.529) spike protein appear to diminish the protection conferred by preexisting immunity. Using vesicular stomatitis virus (VSV) pseudoparticles expressing the spike protein of several SARS-CoV-2 variants, we evaluated the magnitude and breadth of the neutralizing antibody response over time in individuals after infection and in mRNA-vaccinated individuals. We observed that boosting increases the magnitude of the antibody response to wild-type (D614), Beta, Delta, and Omicron variants; however, the Omicron variant was the most resistant to neutralization. We further observed that vaccinated healthy adults had robust and broad antibody responses, whereas responses may have been reduced in vaccinated pregnant women, underscoring the importance of learning how to maximize mRNA vaccine responses in pregnant populations. Findings from this study show substantial heterogeneity in the magnitude and breadth of responses after infection and mRNA vaccination and may support the addition of more conserved viral antigens to existing SARS-CoV-2 vaccines.

The ability to trace every cell in some model organisms has led to the fundamental understanding of development and cellular function. However, in plants the complexity of cell number, organ size, and developmental time makes this a challenge even in the diminutive model plant Arabidopsis (Arabidopsis thaliana). Duckweed, basal nongrass aquatic monocots, provide an opportunity to follow every cell of an entire plant due to their small size, reduced body plan, and fast clonal growth habit. Here we present a chromosome-resolved genome for the highly invasive Lesser Duckweed (Lemna minuta) and generate a preliminary cell atlas leveraging low cell coverage single-nuclei sequencing. We resolved the 360 megabase genome into 21 chromosomes, revealing a core nonredundant gene set with only the ancient tau whole-genome duplication shared with all monocots, and paralog expansion as a result of tandem duplications related to phytoremediation. Leveraging SMARTseq2 single-nuclei sequencing, which provided higher gene coverage yet lower cell count, we profiled 269 nuclei covering 36.9% (8,457) of the L. minuta transcriptome. Since molecular validation was not possible in this nonmodel plant, we leveraged gene orthology with model organism single-cell expression datasets, gene ontology, and cell trajectory analysis to define putative cell types. We found that the tissue that we computationally defined as mesophyll expressed high levels of elemental transport genes consistent with this tissue playing a role in L. minuta wastewater detoxification. The L. minuta genome and preliminary cell map provide a paradigm to decipher developmental genes and pathways for an entire plant.

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.

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.

COVID-19 caused, as of September, 1rst, 2022, 599,825,400 confirmed cases, including 6,469,458 deaths. Currently used vaccines reduced severity and mortality but not virus transmission or reinfection by different strains. They are based on the Spike protein of the Wuhan reference virus, which although highly antigenic suffered many mutations in SARS-CoV-2 variants, escaping vaccine-generated immune responses. Multiepitope vaccines based on 100% conserved epitopes of multiple proteins of all SARS-CoV-2 variants, rather than a single highly mutating antigen, could offer more long-lasting protection. In this study, a multiepitope multivariant vaccine was designed using immunoinformatics and in silico approaches. It is composed of highly promiscuous and strong HLA binding CD4+ and CD8+ T cell epitopes of the S, M, N, E, ORF1ab, ORF 6 and ORF8 proteins. Based on the analysis of one genome per WHO clade, the epitopes were 100% conserved among the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. An extended epitope-conservancy analysis performed using GISAID metadata of 3,630,666 SARS-CoV-2 genomes of these variants and the additional genomes of the Epsilon, Lota, Theta, Eta, Kappa and GH490 R clades, confirmed the high conservancy of the epitopes. All but one of the CD4 peptides showed a level of conservation greater than 97% among all genomes. All but one of the CD8 epitopes showed a level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. A multiepitope and multivariant recombinant vaccine was designed and it was stable, mildly hydrophobic and non-toxic. The vaccine has good molecular docking with TLR4 and promoted, without adjuvant, strong B and Th1 memory immune responses and secretion of high levels of IL-2, IFN-γ, lower levels of IL-12, TGF-β and IL-10, and no IL-6. Experimental in vivo studies should validate the vaccine's further use as preventive tool with cross-protective properties.

Reference cell atlases powered by single cell and spatial transcriptomics technologies are becoming available to study healthy and diseased tissue at single cell resolution. One important use of these data resources is to compare cell types from new dataset with cell types in the reference atlases to evaluate their phenotypic similarities and differences, for example, for identifying novel cell types under disease conditions. For this purpose, rigorously-validated computational algorithms are needed to perform these cell type matching tasks that can compare datasets from different experiment platforms and sample types. Here, we present significant enhancements to FR-Match (v2.0)-a multivariate nonparametric statistical testing approach for matching cell types in query datasets to reference atlases. FR-Match v2.0 includes a normalization procedure to facilitate cross-platform cluster-level comparisons (e.g., plate-based SMART-seq and droplet-based 10X Chromium single cell and single nucleus RNA-seq and spatial transcriptomics) and extends the pipeline to also allow cell-level matching. In the use cases evaluated, FR-Match showed robust and accurate performance for identifying common and novel cell types across tissue regions, for discovering sub-optimally clustered cell types, and for cross-platform and cross-sample cell type matching.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.

As part of the US National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID) SARS-CoV-2 Assessment of Viral Evolution (SAVE) effort to combat the SARS-CoV-2 pandemic, [...]

Single cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method-FR-Match-that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

The Ontology for Biomedical Investigations (OBI) underwent a focused review of assay term annotations, logic and hierarchy with a goal to improve and standardize these terms. As a result, inconsistencies in W3C Web Ontology Language (OWL) expressions were identified and corrected, and additionally, standardized design patterns and a formalized template to maintain them were developed. We describe here this informative and productive process to describe the specific benefits and obstacles for OBI and the universal lessons for similar projects.

The emergence of SARS-CoV-2 variants with evidence of antibody escape highlight the importance of addressing whether the total CD4+ and CD8+ T cell recognition is also affected. Here, we compare SARS-CoV-2-specific CD4+ and CD8+ T cells against the B.1.1.7, B.1.351, P.1, and CAL.20C lineages in COVID-19 convalescents and in recipients of the Moderna (mRNA-1273) or Pfizer/BioNTech (BNT162b2) COVID-19 vaccines. The total reactivity against SARS-CoV-2 variants is similar in terms of magnitude and frequency of response, with decreases in the 10%-22% range observed in some assay/VOC combinations. A total of 7% and 3% of previously identified CD4+ and CD8+ T cell epitopes, respectively, are affected by mutations in the various VOCs. Thus, the SARS-CoV-2 variants analyzed here do not significantly disrupt the total SARS-CoV-2 T cell reactivity; however, the decreases observed highlight the importance for active monitoring of T cell reactivity in the context of SARS-CoV-2 evolution.

Despite signs of infection-including taste loss, dry mouth and mucosal lesions such as ulcerations, enanthema and macules-the involvement of the oral cavity in coronavirus disease 2019 (COVID-19) is poorly understood. To address this, we generated and analyzed two single-cell RNA sequencing datasets of the human minor salivary glands and gingiva (9 samples, 13,824 cells), identifying 50 cell clusters. Using integrated cell normalization and annotation, we classified 34 unique cell subpopulations between glands and gingiva. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry factors such as ACE2 and TMPRSS members were broadly enriched in epithelial cells of the glands and oral mucosae. Using orthogonal RNA and protein expression assessments, we confirmed SARS-CoV-2 infection in the glands and mucosae. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 and TMPRSS expression and sustained SARS-CoV-2 infection. Acellular and cellular salivary fractions from asymptomatic individuals were found to transmit SARS-CoV-2 ex vivo. Matched nasopharyngeal and saliva samples displayed distinct viral shedding dynamics, and salivary viral burden correlated with COVID-19 symptoms, including taste loss. Upon recovery, this asymptomatic cohort exhibited sustained salivary IgG antibodies against SARS-CoV-2. Collectively, these data show that the oral cavity is an important site for SARS-CoV-2 infection and implicate saliva as a potential route of SARS-CoV-2 transmission.

The increased incidence of whooping cough worldwide suggests that current vaccination against Bordetella pertussis infection has limitations in quality and duration of protection. The resurgence of infection has been linked to the introduction of acellular vaccines (aP), which have an improved safety profile compared with the previously used whole-cell (wP) vaccines. To determine immunological differences between aP and wP priming in infancy, we performed a systems approach of the immune response to booster vaccination. Transcriptomic, proteomic, cytometric, and serologic profiling revealed multiple shared immune responses with different kinetics across cohorts, including an increase of blood monocyte frequencies and strong antigen-specific IgG responses. Additionally, we found a prominent subset of aP-primed individuals (30%) with a strong differential signature, including higher levels of expression for CCL3, NFKBIA, and ICAM1. Contrary to the wP individuals, this subset displayed increased PT-specific IgE responses after boost and higher antigen-specific IgG4 and IgG3 antibodies against FHA and FIM2/3 at baseline and after boost. Overall, the results show that, while broad immune response patterns to Tdap boost overlap between aP- and wP-primed individuals, a subset of aP-primed individuals present a divergent response. These findings provide candidate targets to study the causes and correlates of waning immunity after aP vaccination.

Acute flaccid myelitis (AFM) is a disabling, polio-like illness mainly affecting children. Outbreaks of AFM have occurred across multiple global regions since 2012, and the disease appears to be caused by non-polio enterovirus infection, posing a major public health challenge. The clinical presentation of flaccid and often profound muscle weakness (which can invoke respiratory failure and other critical complications) can mimic several other acute neurological illnesses. There is no single sensitive and specific test for AFM, and the diagnosis relies on identification of several important clinical, neuroimaging, and cerebrospinal fluid characteristics. Following the acute phase of AFM, patients typically have substantial residual disability and unique long-term rehabilitation needs. In this Review we describe the epidemiology, clinical features, course, and outcomes of AFM to help to guide diagnosis, management, and rehabilitation. Future research directions include further studies evaluating host and pathogen factors, including investigations into genetic, viral, and immunological features of affected patients, host-virus interactions, and investigations of targeted therapeutic approaches to improve the long-term outcomes in this population.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests that a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. We have previously shown that protective antibody levels could be elicited in a subset of recipients with only a single dose of the hepatitis B virus (HBV) vaccine and that a wide range of antibody levels were elicited after three doses. The immune mechanisms responsible for this vaccine response variability is unclear. Using single cell RNA sequencing of sorted innate immune cell subsets, we identified two distinct myeloid dendritic cell subsets (NDRG1-expressing mDC2 and CDKN1C-expressing mDC4), the ratio of which at baseline (pre-vaccination) correlated with the immune response to a single dose of HBV vaccine. Our results suggest that the participants in our vaccine study were in one of two different dendritic cell dispositional states at baseline - an NDRG2-mDC2 state in which the vaccine elicited an antibody response after a single immunization or a CDKN1C-mDC4 state in which the vaccine required two or three doses for induction of antibody responses. To explore this correlation further, genes expressed in these mDC subsets were used for feature selection prior to the construction of predictive models using supervised canonical correlation machine learning. The resulting models showed an improved correlation with serum antibody titers in response to full vaccination. Taken together, these results suggest that the propensity of circulating dendritic cells toward either activation or suppression, their "dispositional endotype" at pre-vaccination baseline, could dictate response to vaccination.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000-126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children. Flow cytometry analysis of cellular responses using "conventional" anti-viral markers (IL2, IFN-γ, TNF, IL10 and IL4) upon RSV-peptide stimulation detected an overall low RSV response in peripheral blood. Therefore we sought an unbiased approach to identify RSV-specific immune markers using RNA-sequencing upon stimulation of infant PBMCs with overlapping peptides representing RSV antigens. To understand the cellular response using transcriptional signatures, transcription factors and cell-type specific signatures were used to investigate breadth of response across peptides. Unexpected from the ICS data, M peptide induced a response equivalent to the F-peptide and was characterized by activation of GATA2, 3, STAT3 and IRF1. This along with upregulation of several unconventional T cell signatures was only observed upon M-peptide stimulation. Moreover, signatures of natural RSV infections were identified from the data available in the public domain to investigate similarities between transcriptional signatures from PBMCs and upon peptide stimulation. This analysis also suggested activation of T cell response upon M-peptide stimulation. Hence, based on transcriptional response, markers were chosen to validate the role of M-peptide in activation of T cells. Indeed, CD4+CXCL9+ cells were identified upon M-peptide stimulation by flow cytometry. Future work using additional markers identified in this study could reveal additional unconventional T cells responding to RSV infections in infants. In conclusion, T cell responses to RSV in infants may not follow the canonical Th1/Th2 patterns of effector responses but include additional functions that may be unique to the neonatal period and correlate with clinical outcomes.

Effective countermeasures against the recent emergence and rapid expansion of the 2019 novel coronavirus (SARS-CoV-2) require the development of data and tools to understand and monitor its spread and immune responses to it. However, little information is available about the targets of immune responses to SARS-CoV-2. We used the Immune Epitope Database and Analysis Resource (IEDB) to catalog available data related to other coronaviruses. This includes SARS-CoV, which has high sequence similarity to SARS-CoV-2 and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in SARS-CoV-2 that have high homology to the SARS-CoV virus. Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2. The independent identification of the same regions using two approaches reflects the high probability that these regions are promising targets for immune recognition of SARS-CoV-2. These predictions can facilitate effective vaccine design against this virus of high priority.

High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally-optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

BackgroundVaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts.MethodsWe applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres.ResultsUsing both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.ConclusionThis study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

Enteroviruses (EVs) are positive-sense RNA viruses, with over 50,000 nucleotide sequences publicly available. While most human infections are typically associated with mild respiratory symptoms, several different EV types have also been associated with severe human disease, especially acute flaccid paralysis (AFP), particularly with endemic members of the EV-B species and two pandemic types-EV-A71 and EV-D68-that appear to be responsible for recent widespread outbreaks. Here we review the recent literature on the prevalence, characteristics, and circulation dynamics of different enterovirus types and combine this with an analysis of the sequence coverage of different EV types in public databases (e.g., the Virus Pathogen Resource). This evaluation reveals temporal and geographic differences in EV circulation and sequence distribution, highlighting recent EV outbreaks and revealing gaps in sequence coverage. Phylogenetic analysis of the EV genus shows the relatedness of different EV types. Recombination analysis of the EV-A species provides evidence for recombination as a mechanism of genomic diversification. The absence of broadly protective vaccines and effective antivirals makes human enteroviruses important pathogens of public health concern.

Enteroviruses are potentially linked to the emergence of Acute Flaccid Myelitis (AFM), a rare but very serious condition that affects the nervous system. AFM has been associated with coxsackievirus A16, enterovirus A71 (EVA71) and enterovirus D68 (EVD68). Little is known about host-pathogen interactions for these viruses, and whether immune responses may have a protective or immunopathological role in disease presentations. Towards addressing this issue, we used the Immune Epitope Database to assess the known inventory of B and T cell epitopes from enteroviruses, focusing on data related to human hosts. The extent of conservation in areas that are targets of B and T cell immune responses were examined. This analysis sheds light on regions of the enterovirus polypeptide that can be probed to induce a specific or cross-reactive B or T cell the immune response to enteroviruses, with a particular focus on coxsackievirus A16, EVA71 and EVD68. In addition, these analyses reveal the current gap-of-knowledge in the T and B cell immune responses that future studies should aim to address.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

We developed a computational approach called Domain-architecture Aware Inference of Orthologs (DAIO) for the analysis of protein orthology by combining phylogenetic and protein domain-architecture information. Using DAIO, we performed a systematic study of the proteomes of all human Herpesviridae species to define Strict Ortholog Groups (SOGs). In addition to assessing the taxonomic distribution for each protein based on sequence similarity, we performed a protein domain-architecture analysis for every protein family and computationally inferred gene duplication events. While many herpesvirus proteins have evolved without any detectable gene duplications or domain rearrangements, numerous herpesvirus protein families do exhibit complex evolutionary histories. Some proteins acquired additional domains (e.g., DNA polymerase), whereas others show a combination of domain acquisition and gene duplication (e.g., betaherpesvirus US22 family), with possible functional implications. This novel classification system of SOGs for human Herpesviridae proteins is available through the Virus Pathogen Resource (ViPR, www.viprbrc.org).

Enterovirus D68 (EV-D68) has historically been associated with respiratory illnesses. However, in the summers of 2014 and 2016, EV-D68 outbreaks coincided with a spike in polio-like acute flaccid myelitis/paralysis (AFM/AFP) cases. This raised concerns that EV-D68 could be the causative agent of AFM during these recent outbreaks. To assess the potential neurotropism of EV-D68, we utilized the neuroblastoma-derived neuronal cell line SH-SY5Y as a cell culture model to determine if differential infection is observed for different EV-D68 strains. In contrast to HeLa and A549 cells, which support viral infection of all EV-D68 strains tested, SH-SY5Y cells only supported infection by a subset of contemporary EV-D68 strains, including isolates from the 2014 outbreak. Viral replication and infectivity in SH-SY5Y were assessed using multiple assays: virus production, cytopathic effects, cellular ATP release, and VP1 capsid protein production. Similar differential neurotropism was also observed in differentiated SH-SY5Y cells, primary human neuron cultures, and a mouse paralysis model. Using the SH-SY5Y cell culture model, we determined that barriers to viral binding and entry were at least partly responsible for the differential infectivity phenotype. Transfection of genomic RNA into SH-SY5Y generated virions for all EV-D68 isolates, but only a single round of replication was observed from strains that could not directly infect SH-SY5Y. In addition to supporting virus replication and other functional studies, this cell culture model may help identify the signatures of virulence to confirm epidemiological associations between EV-D68 strains and AFM and allow for the rapid identification and characterization of emerging neurotropic strains.IMPORTANCE Since the EV-D68 outbreak during the summer of 2014, evidence of a causal link to a type of limb paralysis (AFM) has been mounting. In this article, we describe a neuronal cell culture model (SH-SY5Y cells) in which a subset of contemporary 2014 outbreak strains of EV-D68 show infectivity in neuronal cells, or neurotropism. We confirmed the difference in neurotropism in vitro using primary human neuron cell cultures and in vivo with a mouse paralysis model. Using the SH-SY5Y cell model, we determined that a barrier to viral entry is at least partly responsible for neurotropism. SH-SY5Y cells may be useful in determining if specific EV-D68 genetic determinants are associated with neuropathogenesis, and replication in this cell line could be used as rapid screening tool for identification of neurotropic EV-D68 strains. This may assist with better understanding of pathogenesis and epidemiology and with the development of potential therapies.

Allogeneic hematopoietic stem cell transplantation (allo-HCT) is a curative option for blood cancers, but the coupled effects of graft-versus-tumor and graft-versus-host disease (GVHD) limit its broader application. Outcomes improve with matching at HLAs, but other factors are required to explain residual risk of GVHD. In an effort to identify genetic associations outside the major histocompatibility complex, we conducted a genome-wide clinical outcomes study on 205 acute myeloid leukemia patients and their fully HLA-A-, HLA-B-, HLA-C-, HLA-DRB1-, and HLA-DQB1-matched (10/10) unrelated donors. HLA-DPB1 T-cell epitope permissibility mismatches were observed in less than half (45%) of acute GVHD cases, motivating a broader search for genetic factors affecting clinical outcomes. A novel bioinformatics workflow adapted from neoantigen discovery found no associations between acute GVHD and known, HLA-restricted minor histocompatibility antigens (MiHAs). These results were confirmed with microarray data from an additional 988 samples. On the other hand, Y-chromosome-encoded single-nucleotide polymorphisms in 4 genes (PCDH11Y, USP9Y, UTY, and NLGN4Y) did associate with acute GVHD in male patients with female donors. Males in this category with acute GVHD had more Y-encoded variant peptides per patient with higher predicted HLA-binding affinity than males without GVHD who matched X-paralogous alleles in their female donors. Methods and results described here have an immediate impact for allo-HCT, warranting further development and larger genomic studies where MiHAs are clinically relevant, including cancer immunotherapy, solid organ transplant, and pregnancy.

We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large 'rosehip'-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1-SST-CALB2-PVALB-) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.