Biology subjects

Han, N.

Publications and source records attributed to Han, N..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Evaluation of false positive and false negative errors in targeted next generation sequencing

BackgroundAlthough next generation sequencing (NGS) has been adopted as an essential diagnostic tool in various diseases, NGS errors have been the most serious problem in clinical implementation. Especially in cancers, low level mutations have not been easy to analyze, due to the contaminating normal cells and tumor heterozygosity. ResultsIn targeted NGS (T-NGS) analyses for reference-standard samples containing mixtures of homozygote H. mole DNA with blood genomic DNA at various ratios from four certified NGS service providers, large differences in the lower detection limit of variants (16.3 times, 1.51[~]24.66%) and the false positive (FP) error rate (4280 times, 5.814 x 10-4 [~]1.359 x 10-7) were found. Employment of the commercially available Dragen system for bioinformatic analyses reduced FP errors in the results from companies BB and CC, but the errors originating from the NGS raw data persisted. Bioinformatic conditional adjustment to increase sensitivity (less than 2 times) led to a much higher FP error rate (610[~]8200 times). In addition, problems such as biased preferential reference base calls during bioinformatic analysis and high-rate FN errors in HLA regions were found in the NGS analysis. ConclusionT-NGS results from certified NGS service providers can be quite various in their sensitivity and FP error rate, suggesting the necessity of further quality controls for clinical implementation of T-NGS. The present study also suggests that mixtures of homozygote and heterozygote DNAs can be easily employed as excellent reference-standard materials for quality control of T-NGS.

genomics↗

bioRxiv2024

Immunogenicity of autologous and allogeneic human primary cholangiocyte organoids

Primary human cells cultured in 3D organoid format have great promise as potential regenerative cellular therapies, but their immunogenicity has not yet been fully characterized. In this study, we use in vitro co-cultures and in vivo humanized mouse experimental models to examine the human immune response to autologous and allogeneic primary cholangiocyte organoids (PCOs). Our data demonstrate that PCOs upregulate the expression of HLA-I and HLA-II in inflammatory conditions. The immune response to allogeneic PCOs is driven by both HLA-I and HLA-II and is substantially ameliorated by donor-recipient HLA matching. Autologous PCOs induce a low-level immune infiltration into the graft site, while allogeneic cells display evolving stages of immune rejection in vivo. Our findings have important implications for the design and clinical translation of autologous and allogeneic organoid cellular therapies. ONE-SENTENCE SUMMARYThe immune response to human primary cholangiocyte organoids is ameliorated by donor-recipient HLA matching.

immunology↗

bioRxiv2023

Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes

Progressive supranuclear palsy (PSP) is a rare Parkinsonian disorder characterized by problems with movement, balance, cognition, and other symptoms. PSP differs from Alzheimers disease (AD) and other neurodegenerative diseases displaying abnormal forms of the microtubule-associated protein tau ("tauopathies") by the presence of pathology not only in neurons, but also in astrocytes and oligodendrocytes. Genetic contributors may mediate these differences, however much of PSP genetics remains unexplained. Here we conducted the largest genome-wide association study (GWAS) of PSP to date including 2,779 cases (2,595 neuropathologically-confirmed) and 5,584 controls and identified six independent PSP susceptibility loci with genome-wide significant (p < 5x10-8) associations including five known (MAPT, MOBP, STX6, RUNX2, SLCO1A2) and one novel locus (C4A). Integration with cell type-specific epigenomic annotations revealed a unique oligodendrocytic signature that distinguishes PSP from AD and Parkinsons disease. Candidate PSP risk gene prioritization using expression quantitative trait loci (eQTLs) identified oligodendrocyte-specific effects on gene expression in half of the genome-wide significant loci, as well as an association with elevated C4A expression in bulk brain tissue which may be driven by increased C4A copy number in PSP cases. Finally, histological studies demonstrated abnormal tau aggregates in oligodendrocytes that colocalize with C4 (complement) deposition. Integrating GWAS with functional studies including epigenomic and eQTL analyses, we identified potential causal roles for variation in MOBP, STX6, RUNX2, SLCO1A2, and C4A in the pathogenesis of PSP.

neuroscience↗

bioRxiv2023

Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles

Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level -diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individuals unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.

bioinformatics↗

bioRxiv2022

scRNA-Seq-based drug repurposing targeting idiopathic pulmonary fibrosis (IPF)

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease, which affects around three million people worldwide and is characterized by impaired regeneration from recurrent injury to the alveolar epithelium resulting in progressive lung scarring. In this work, we target a cell transition in the differentiation of AT2 cells into mature AT1 cells which is inhibited in IPF and contributes to the regeneration of the alveolar epithelium. We hypothesize that inducing this transition promotes lung regeneration and can ameliorate the disease. To this end, we characterized the intermediate population to AT1 cell transition signature using multiple recently generated single-cell RNA-Seq datasets on murine bleomycin injury and IPF patients. We then matched this signature to drug perturbation signatures retrieved from the LINCS database to identify the most suitable candidates for drug repurposing. Compound classes identified in this analysis include glucocorticoids, HSP90 inhibitors, HDAC inhibitors and a range of kinase inhibitors. Furthermore, we are able to interpret our findings by identifying multiple potential direct targets and downstream effectors, such as NFKB1 and HIF1A. Overall, we show how scRNA-Seq data can potentially be leveraged for drug repurposing by enabling better characterization of cell transitions which can subsequently be used for signature matching.

bioinformatics↗

bioRxiv2022

Full-length dystrophin is expressed across human tissues and DMD downregulation commonly occurring in tumours coincides with Duchenne-like molecular alterations

Mutations of the DMD gene, encoding dystrophins, cause Duchenne muscular dystrophy (DMD). Some tumors also display altered dystrophin expression and recent studies identified a developmental onset of DMD. Given that embryogenesis and carcinogenesis share many mechanisms, we analyzed a broad spectrum of tumors to establish whether dystrophin loss evokes related outcomes. Transcriptomic, proteomic, and mutation datasets from fifty tumor tissues and matching controls (10,894 samples) and 140 corresponding tumor cell lines were analyzed. Interestingly, DMD expression was widespread across healthy tissues at levels comparable to housekeeping genes. In 80% of tumors, DMD expression was reduced due to transcriptional downregulation and not somatic mutations. The full-length transcript encoding Dp427 was decreased in 68% of tumors, while Dp71 variants showed variability of expression. Hierarchical clustering analysis of DMD transcripts distinguished malignant from control tissues. Transcriptomes of primary tumors and tumor cell lines with low DMD expression showed enrichment of specific pathways in the differentially expressed genes. Pathways consistently identified: ECM-receptor interaction, calcium signaling and PI3K-Akt, are also altered in DMD muscle. Notably, low DMD expression was associated with a more advanced stage, older age of onset, and reduced survival across different tumors. Thus, DMD transcription occurs throughout a spectrum of normal tissues. The molecular signature associated with its frequent downregulation in malignancies is concordant with changes found in Duchenne muscles, even though these malignancies originate from tissues never previously associated with dystrophin expression or function. Therefore, the importance of this largest known gene extends beyond its roles identified in DMD, certainly into oncology.

genetics↗

bioRxiv2022

dialogi: Utilising NLP with chemical and disease similarities to drive the identification of Drug-Induced Liver Injury literature

Drug-Induced Liver Injury (DILI), despite its low occurrence rate, can cause severe side effects or even lead to death. Thus, it is one of the leading causes for terminating the development of new, and restricting the use of already-circulating, drugs. Moreover, its multifactorial nature, combined with a clinical presentation that often mimics other liver diseases, complicate the identification of DILI-related literature, which remains the main medium for sourcing results from the clinical practice and experimental studies. In this work- contributing to the Literature AI for DILI Challenge of the Critical Assessment of Massive Data Analysis (CAMDA) 2021- we present an automated pipeline for distinguishing between DILI-positive and negative papers. We used Natural Language Processing (NLP) to filter out the uninformative parts of a text, and identify and extract mentions of chemicals and diseases. We combined that information with small-molecule and disease embeddings, which are capable of capturing chemical and disease similarities, to improve classification performance. The former are directly sourced from the Chemical Checker (CC). For the latter, we collected data that encode different aspects of disease similarity from the National Library of Medicines (NLM) Medical Subject Headings (MeSH) thesaurus and the Comparative Toxicogenomics Database (CTD). Following a similar procedure as the one used in the CC, vector representations for diseases were learnt and evaluated. Two Neural Network (NN) classifiers were developed: one that only accepts texts as input (baseline model) and an augmented classifier that also utilises chemical and disease embeddings (extended model). We trained, validated, and tested the models through a Nested Cross-Validation (NCV) scheme with 10 outer and 5 inner folds. During this, the baseline and extended models performed virtually identically, with macro F1-scores of 95.04 {+/-} 0.61% and 94.80 {+/-} 0.41%, respectively. Upon validation on an external, withheld, dataset, representing imbalanced data, the extended model achieved an F1-score of 91.14 {+/-} 1.62%, outperforming its baseline counterpart, which got a lower score of 88.30 {+/-} 2.44%. We make further comparisons between the classifiers and discuss future improvements and directions, including utilising chemical and disease embeddings for visualisation and exploratory analysis of the DILI-positive literature.

bioinformatics↗

bioRxiv2022

Intercellular CRISPR Screens Enhance the Discovery of Cancer Immunotherapy Targets

Cancer immunotherapy works through the interplay between immune and cancer cells. Particularly, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PDCD1 (PD-1) and CD274 (PD-L1), are crucial for removing cancer cells. However, immune checkpoint inhibitors targeting these interactions are effective only to a subset of patients, requiring the development of novel immunotherapy drugs with novel targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been used to discover regulators of immune cell function as immunotherapeutic targets. However, the method has two main limitations. First, performing CRISPR screens in one cell type alone makes it difficult to identify essential intercellular interactions due to the focus on single genes instead of interactions. Second, pooled screening is associated with high noise levels. Therefore, we propose intercellular CRISPR screens, which perform genome-wide CRISPR screening in every interacting cell type to discover intercellular interactions as immunotherapeutic targets. Intercellular CRISPR screens use two individual genome-wide CRISPR screens one each in immune and cancer cells to evaluate intercellular interactions that are crucial for killing cancer cells. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to assess the effects of intercellular interactions on CTL function by incorporating both CRISPR datasets and the expression levels of ligands and receptors. Our results showed that intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable using single datasets. To quantitatively evaluate the methods performance, we used data for cytokines and costimulatory molecules because they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the F1 score of discovering these genes relative to using single CRISPR datasets by more than twice. Our results indicate that intercellular CRISPR screens can identify novel immune-oncology targets that were not obtained using individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types, such as natural killer cells, to identify important intercellular interactions as potential immunotherapeutic targets.

immunology↗

bioRxiv2022

DILIc: An AI-based classifier to search for Drug-Induced Liver Injury literature

Drug-Induced Liver Injury (DILI) is a class of Adverse Drug Reactions (ADR) which causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of western countries and is a major cause of attrition of novel drug candidates. Manual trawling of literature for is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related papers from the huge ocean of literature could be invaluable for the drug discovery community. In this project, we built an artificial intelligence (AI) model combining the power of Natural Language Processing (NLP) and Machine Learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g. stopwords) and uses customized functions to extract relevant keywords as singleton, pair, triplet and so on. These keywords are processed by apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods build a DILI classifier (DILIC) with 94.91% cross-validation and 94.14% external validation accuracy. To make DILIC as accessible as possible, including to researchers without coding experience, an R Shiny App capable of classifing single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/).

bioinformatics↗

bioRxiv2021

Predicting genes associated with RNA methylation pathways using machine learning

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function.

bioinformatics↗

bioRxiv2021

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of "first activation" as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat- dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time- concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://github.com/anikaliu/DILICascades_App), which allows users to query events of interest in more detail. Author SummaryUnderstanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time- series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.

bioinformatics↗

bioRxiv2021

Genomic evidence for divergent co-infections of SARS-CoV-2 lineages

Recently, patients co-infected by two SARS-CoV-2 lineages have been sporadically reported. Concerns are raised because previous studies have demonstrated co-infection may contribute to the recombination of RNA viruses and cause severe clinic symptoms. In this study, we have estimated the compositional lineage(s), tendentiousness, and frequency of co-infection events in population from a large-scale genomic analysis for SARS-CoV-2 patients. SARS-CoV-2 lineage(s) infected in each sample have been recognized from the assignment of within-host site variations into lineage-defined feature variations by introducing a hypergeometric distribution method. Of all the 29,993 samples, 53 (~0.18%) co-infection events have been identified. Apart from 52 co-infections with two SARS-CoV-2 lineages, one sample with co-infections of three SARS-CoV-2 lineages was firstly identified. As expected, the co-infection events mainly happened in the regions where have co-existed more than two dominant SARS-CoV-2 lineages. However, co-infection of two sub-lineages in Delta lineage were detected as well. Our results provide a useful reference framework for the high throughput detecting of SARS-CoV-2 co-infection events in the Next Generation Sequencing (NGS) data. Although low in average rate, the co-infection events showed an increasing tendency with the increased diversity of SARS-CoV-2. And considering the large base of SARS-CoV-2 infections globally, co-infected patients would be a nonnegligible population. Thus, more clinical research is urgently needed on these patients.

microbiology↗

bioRxiv2021

Rice LEAFY COTYLEDON1 hinders photosynthesis in the embryo development to promote seed dormancy

LEAFY COTYLEDON1 (LEC1) is the central regulator of seed development. During seed development, rice embryo photosynthesis is completely blocked, which is different from Arabidopsis green embryo. However, effects of LEC1 on photosynthesis in developing seeds is largely elusive. We generated OsLEC1 mutants using the CRISPR/Cas9 technique. Oslec1 mutant seeds lost the ability of dormancy and triggered photosynthesis in embryos at the early developing stage. Transcriptome analysis demonstrated that Oslec1 mutation promoted photosynthesis and altered diverse hormonal pathways and stress response contributing to seed dormancy. Further, genome-wide identification of OsLEC1 binding sites demonstrated that OsLEC1 directly bound to genes involved in photosynthesis, photomorphogenesis, as well as abscisic acid (ABA) and gibberellin (GA) pathways, in seed maturation. We illustrated an OsLEC1-controlling gene network during seed development, including the interconnection between photosynthesis and ABA/GA biosynthesis/signalling. Our findings suggested that OsLEC1 is an inhibitor of photosynthesis during embryo development to promote rice seed maturation. This study would provide new understanding for the OsLEC1 regulatory mechanisms on photosynthesis in the monocot seed development.

plant biology↗

bioRxiv2021

Genome-wide association study and functional validation implicates JADE1 in tauopathy

Primary age-related tauopathy (PART) is a neurodegenerative tauopathy with features distinct from but also overlapping with Alzheimer disease (AD). While both exhibit Alzheimer-type temporal lobe neurofibrillary degeneration alongside amnestic cognitive impairment, PART develops independently of amyloid-{beta} (A{beta}) deposition in plaques. The pathogenesis of PART is unknown, but evidence suggests it is associated with genes that promote tau pathology as well as others that protect from A{beta} toxicity. Here, we performed a genetic association study in an autopsy cohort of individuals with PART (n=647) using Braak neurofibrillary tangle stage as a quantitative trait adjusting for sex, age, genotyping platform, and principal components. We found significant associations with some candidate loci associated with AD and progressive supranuclear palsy, a primary tauopathy (SLC24A4, MS4A6A, HS3ST1, MAPT and EIF2AK3). Genome-wide association analysis revealed a novel significant association with a single nucleotide polymorphism on chromosome 4 (rs56405341) in a locus containing three genes, including JADE1 which was significantly upregulated in tangle-bearing neurons by single-soma RNA-seq. Immunohistochemical studies using antisera targeting JADE1 protein revealed localization within tau aggregates in autopsy brain from tauopathies containing isoforms with four microtubule-binding domain repeats (4R) and mixed 3R/4R, but not with 3R exclusively. Co-immunoprecipitation revealed a direct and specific binding of JADE1 protein to tau containing four (4R) and no N-terminal inserts (0N4R) in post-mortem human PART brain tissue. Finally, knockdown of the Drosophila JADE1 homolog rhinoceros (rno) enhanced tau-induced toxicity and apoptosis in vivo in a humanized 0N4R mutant tau knock-in model as quantified by rough eye phenotype and terminal deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) in the fly brain. Together, these findings indicate that PART has a genetic architecture that partially overlaps with AD and other tauopathies and suggests a novel role for JADE1 as a mediator of neurofibrillary degeneration.

neuroscience↗

bioRxiv2020

Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies

The global outbreak of SARS-CoV-2 necessitates the rapid development of new therapies against COVID-19 infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2-induced protein (SIP) network, based on disease signatures defined by COVID-19 multi-omic datasets(Bojkova et al., 2020; Gordon et al., 2020), and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2-induced pathways, 40 of which are already in COVID-19 clinical trials(Clinicaltrials.gov, 2020) testifying to the validity of the approach. Using artificial neural network analysis we classified these 200 drugs into 9 distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (130) and immune response (70). A subset of drugs implicated in viral replication were tested in cellular assays and two (proguanil and sulfasalazine) were shown to inhibit replication. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

pharmacology and toxicology↗

bioRxiv2020

Reconstitution of Alveolar Regeneration via novel DATPs by Inflammatory Niches

Tissue regeneration involves a multi-step process composed of diverse cellular hierarchies and states that are also implicated in tissue dysfunction and pathogenesis. Here, we leveraged single-cell RNA sequencing analysis in combination with in vivo lineage tracing and organoid models to fine-map trajectories of alveolar lineage cells during injury repair and regeneration. We identified Damage-Associated Transient Progenitors (DATPs) as a distinct AT2-lineaged population arising during alveolar regeneration. Specifically, we found that IL-1{beta}, secreted by interstitial macrophages, primes a subset of Il1r1+AT2 cells for conversion into DATPs, via a Hif1a-mediated glycolysis pathway, that are functional mediators for mature AT1 cell differentiation. Importantly, we show that chronic inflammation mediated by IL-1{beta} prevents differentiation into AT1 cells, leading to aberrant accumulation of DATPs and impaired alveolar differentiation. Our step-wise fine-mapping of cell fate transitions demonstrates how the inflammatory niche impedes alveolar regeneration by directing stem cell fate behavior.

cell biology↗

bioRxiv2020

Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions

COVID-19 has become a global pandemic. Immune dysregulation has been implicated, but immune responses remain poorly understood. We analyzed 71 COVID-19 patients compared to recovered and healthy subjects using high dimensional cytometry. Integrated analysis of [~]200 immune and >30 clinical features revealed activation of T cell and B cell subsets, but only in some patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses could reach >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable to uninfected subjects. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. These analyses identified three "immunotypes" associated with poor clinical trajectories versus improving health. These immunotypes may have implications for therapeutics and vaccines.

immunology↗

bioRxiv2020

Immunologic perturbations in severe COVID-19/SARS-CoV-2 infection

Although critical illness has been associated with SARS-CoV-2-induced hyperinflammation, the immune correlates of severe COVID-19 remain unclear. Here, we comprehensively analyzed peripheral blood immune perturbations in 42 SARS-CoV-2 infected and recovered individuals. We identified broad changes in neutrophils, NK cells, and monocytes during severe COVID-19, suggesting excessive mobilization of innate lineages. We found marked activation within T and B cells, highly oligoclonal B cell populations, profound plasmablast expansion, and SARS-CoV-2-specific antibodies in many, but not all, severe COVID-19 cases. Despite this heterogeneity, we found selective clustering of severe COVID-19 cases through unbiased analysis of the aggregated immunological phenotypes. Our findings demonstrate broad immune perturbations spanning both innate and adaptive leukocytes that distinguish dysregulated host responses in severe SARS-CoV-2 infection and warrant therapeutic investigation. One Sentence SummaryBroad immune perturbations in severe COVID-19

immunology↗