Epigenetic memory of coronavirus infection in innate immune cells and their progenitors.

Inflammation can trigger lasting phenotypes in immune and non-immune cells. Whether and how human infections and associated inflammation can form innate immune memory in hematopoietic stem and progenitor cells (HSPC) has remained unclear. We found that circulating HSPC, enriched from peripheral blood, captured the diversity of bone marrow HSPC, enabling investigation of their epigenomic reprogramming following coronavirus disease 2019 (COVID-19). Alterations in innate immune phenotypes and epigenetic programs of HSPC persisted for months to 1 year following severe COVID-19 and were associated with distinct transcription factor (TF) activities, altered regulation of inflammatory programs, and durable increases in myelopoiesis. HSPC epigenomic alterations were conveyed, through differentiation, to progeny innate immune cells. Early activity of IL-6 contributed to these persistent phenotypes in human COVID-19 and a mouse coronavirus infection model. Epigenetic reprogramming of HSPC may underlie altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors.

Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4+ T Cells in COVID-19.

The contribution of CD4+ T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present single-cell transcriptomic analysis of >100,000 viral antigen-reactive CD4+ T cells from 40 COVID-19 patients. In hospitalized patients compared to non-hospitalized patients, we found increased proportions of cytotoxic follicular helper cells and cytotoxic T helper (TH) cells (CD4-CTLs) responding to SARS-CoV-2 and reduced proportion of SARS-CoV-2-reactive regulatory T cells (TREG). Importantly, in hospitalized COVID-19 patients, a strong cytotoxic TFH response was observed early in the illness, which correlated negatively with antibody levels to SARS-CoV-2 spike protein. Polyfunctional TH1 and TH17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4+ T cells compared to influenza-reactive CD4+ T cells. Together, our analyses provide insights into the gene expression patterns of SARS-CoV-2-reactive CD4+ T cells in distinct disease severities.

A Species-Wide Inventory of NLR Genes and Alleles in Arabidopsis thaliana.

Infectious disease is both a major force of selection in nature and a prime cause of yield loss in agriculture. In plants, disease resistance is often conferred by nucleotide-binding leucine-rich repeat (NLR) proteins, intracellular immune receptors that recognize pathogen proteins and their effects on the host. Consistent with extensive balancing and positive selection, NLRs are encoded by one of the most variable gene families in plants, but the true extent of intraspecific NLR diversity has been unclear. Here, we define a nearly complete species-wide pan-NLRome in Arabidopsis thaliana based on sequence enrichment and long-read sequencing. The pan-NLRome largely saturates with approximately 40 well-chosen wild strains, with half of the pan-NLRome being present in most accessions. We chart NLR architectural diversity, identify new architectures, and quantify selective forces that act on specific NLRs and NLR domains. Our study provides a blueprint for defining pan-NLRomes.

Human Anti-fungal Th17 Immunity and Pathology Rely on Cross-Reactivity against Candida albicans.

Th17 cells provide protection at barrier tissues but may also contribute to immune pathology. The relevance and induction mechanisms of pathologic Th17 responses in humans are poorly understood. Here, we identify the mucocutaneous pathobiont Candida albicans as the major direct inducer of human anti-fungal Th17 cells. Th17 cells directed against other fungi are induced by cross-reactivity to C. albicans. Intestinal inflammation expands total C. albicans and cross-reactive Th17 cells. Strikingly, Th17 cells cross-reactive to the airborne fungus Aspergillus fumigatus are selectively activated and expanded in patients with airway inflammation, especially during acute allergic bronchopulmonary aspergillosis. This indicates a direct link between protective intestinal Th17 responses against C. albicans and lung inflammation caused by airborne fungi. We identify heterologous immunity to a single, ubiquitous member of the microbiota as a central mechanism for systemic induction of human anti-fungal Th17 responses and as a potential risk factor for pulmonary inflammatory diseases.

Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases.

Pro-inflammatory autoantigen-specific CD4+ T helper (auto-Th) cells are central orchestrators of autoimmune diseases (AIDs). We aimed to characterize these cells in human AIDs with defined autoantigens by combining human leukocyte antigen (HLA)-tetramer-based and activation-based multidimensional ex vivo analyses. In aquaporin4-antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD) patients, auto-Th cells expressed CD154, but proliferative capacity and pro-inflammatory cytokines were strongly reduced. Instead, exhaustion-associated co-inhibitory receptors were expressed together with FOXP3, the canonical regulatory T cell (Treg) transcription factor. Auto-Th cells responded in vitro to checkpoint inhibition and provided potent B cell help. Cells with the same exhaustion-like (ThEx) phenotype were identified in soluble liver antigen (SLA)-antibody-autoimmune hepatitis and BP180-antibody-positive bullous pemphigoid, AIDs of the liver and skin, respectively. While originally described in cancer and chronic infection, our data point to T cell exhaustion as a common mechanism of adaptation to chronic (self-)stimulation across AID types and link exhausted CD4+ T cells to humoral autoimmune responses, with implications for therapeutic targeting.

The pre-exposure SARS-CoV-2-specific T cell repertoire determines the quality of the immune response to vaccination.

SARS-CoV-2 infection and vaccination generates enormous host-response heterogeneity and an age-dependent loss of immune-response quality. How the pre-exposure T cell repertoire contributes to this heterogeneity is poorly understood. We combined analysis of SARS-CoV-2-specific CD4+ T cells pre- and post-vaccination with longitudinal T cell receptor tracking. We identified strong pre-exposure T cell variability that correlated with subsequent immune-response quality and age. High-quality responses, defined by strong expansion of high-avidity spike-specific T cells, high interleukin-21 production, and specific immunoglobulin G, depended on an intact naive repertoire and exclusion of pre-existing memory T cells. In the elderly, T cell expansion from both compartments was severely compromised. Our results reveal that an intrinsic defect of the CD4+ T cell repertoire causes the age-dependent decline of immune-response quality against SARS-CoV-2 and highlight the need for alternative strategies to induce high-quality T cell responses against newly arising pathogens in the elderly.

Low-Avidity CD4+ T Cell Responses to SARS-CoV-2 in Unexposed Individuals and Humans with Severe COVID-19.

CD4+ T cells reactive against SARS-CoV-2 can be found in unexposed individuals, and these are suggested to arise in response to common cold coronavirus (CCCoV) infection. Here, we utilized SARS-CoV-2-reactive CD4+ T cell enrichment to examine the antigen avidity and clonality of these cells, as well as the relative contribution of CCCoV cross-reactivity. SARS-CoV-2-reactive CD4+ memory T cells were present in virtually all unexposed individuals examined, displaying low functional avidity and multiple, highly variable cross-reactivities that were not restricted to CCCoVs. SARS-CoV-2-reactive CD4+ T cells from COVID-19 patients lacked cross-reactivity to CCCoVs, irrespective of strong memory T cell responses against CCCoV in all donors analyzed. In severe but not mild COVID-19, SARS-CoV-2-specific T cells displayed low functional avidity and clonality, despite increased frequencies. Our findings identify low-avidity CD4+ T cell responses as a hallmark of severe COVID-19 and argue against a protective role for CCCoV-reactive T cells in SARS-CoV-2 infection.

Advances and challenges in identifying and characterizing G-quadruplex-protein interactions.

G-quadruplexes (G4s) are peculiar nucleic acid secondary structures formed by DNA or RNA and are considered as fundamental features of the genome. Many proteins can specifically bind to G4 structures. There is increasing evidence that G4-protein interactions involve in the regulation of important cellular processes, such as DNA replication, transcription, RNA splicing, and translation. Additionally, G4-protein interactions have been demonstrated to be potential targets for disease treatment. In order to unravel the detailed regulatory mechanisms of G4-binding proteins (G4BPs), biochemical methods for detecting G4-protein interactions with high specificity and sensitivity are highly demanded. Here, we review recent advances in screening and validation of new G4BPs and highlight both their features and limitations.

Interpreting omics data with pathway enrichment analysis.

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Benchmarking enrichment analysis methods with the disease pathway network.

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Deqformer: high-definition and scalable deep learning probe design method.

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.

irGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

From G1 to M: a comparative study of methods for identifying cell cycle phases.

Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.

Generalized reporter score-based enrichment analysis for omics data.

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics.

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 µg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

Multilevel analysis between Physcomitrium patens and Mortierellaceae endophytes explores potential long-standing interaction among land plants and fungi.

The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.

Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries.

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Leveraging gene co-regulation to identify gene sets enriched for disease heritability.

Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWASs) detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by predicted expression. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; a gene set is enriched for heritability if genes with high co-regulation to the set have higher TWAS chi-square statistics than genes with low co-regulation to the set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well calibrated and well powered. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched sets, recapitulating known biology. For Alzheimer disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify enriched gene sets.