Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity

EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning

MOTIVATION

Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epitope recognition is well recognized, methods for profiling their interactions in association to a certain disease or phenotype remains less studied. We developed EpicPred to identify phenotype-specific TCR-epitope interactions. EpicPred first predicts and removes unlikely TCR-epitope interactions to reduce false positives using the Open-set Recognition (OSR). Subsequently, multiple instance learning was used to identify TCR-epitope interactions specific to a cancer type or severity levels of COVID-19 infected patients.

RESULTS

From six public TCR databases, 244 552 TCR sequences and 105 unique epitopes were used to predict epitope-binding TCRs and to filter out non-epitope-binding TCRs using the OSR method. The predicted interactions were used to further predict the phenotype groups in two cancer and four COVID-19 TCR-seq datasets of both bulk and single-cell resolution. EpicPred outperformed the competing methods in predicting the phenotypes, achieving an average AUROC of 0.80 ± 0.07.

AVAILABILITY AND IMPLEMENTATION

The EpicPred Software is available at https://github.com/jaeminjj/EpicPred.

Ag-driven CD8 + T cell clonal expansion is a prominent feature of MASH in humans and mice

BACKGROUND AND AIMS

Chronic liver disease due to metabolic dysfunction-associated steatohepatitis (MASH) is a rapidly increasing global epidemic. MASH progression is a consequence of the complex interplay between inflammatory insults and dysregulated hepatic immune responses. T lymphocytes have been shown to accumulate in the liver during MASH, but the cause and consequence of T cell accumulation in the liver remain unclear. Our study aimed to define the phenotype and T cell receptor diversity of T cells from human cirrhotic livers and an animal model of MASH to begin resolving their function in disease.

APPROACH AND RESULTS

In these studies, we evaluated differences in T cell phenotype in the context of liver disease. Accordingly, we isolated liver resident T cell populations from humans with cirrhosis and from mice with diet-induced MASH. Using both 5' single-cell sequencing and flow cytometry, we defined the phenotype and T cell receptor repertoire of liver resident T cells during health and disease.

CONCLUSIONS

MASH-induced human cirrhosis and diet-induced MASH in mice resulted in the accumulation of activated and clonally expanded T cells in the liver. The clonally expanded T cells in the liver expressed markers of chronic antigenic stimulation, including PD1 , TIGIT , and TOX . Overall, this study establishes for the first time that T cells undergo Ag-dependent clonal expansion and functional differentiation during the progression of MASH. These studies could lead to the identification of antigenic targets that drive T cell activation, clonal expansion, and recruitment to the liver during MASH.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

The characteristics of TCR CDR3 repertoire in COVID-19 patients and SARS-CoV-2 vaccine recipients

The COVID-19 pandemic and large-scale administration of multiple SARS-CoV-2 vaccines have attracted global attention to the short-term and long-term effects on the human immune system. An analysis of the "traces" left by the body's T-cell immune response is needed, especially for the prevention and treatment of breakthrough infections and long COVID-19 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant infections. T-cell receptor complementarity determining region 3 (TCR CDR3) repertoire serves as a target molecule for monitoring the effects, mechanisms, and memory of the T-cell response. Furthermore, it has been extensively applied in the elucidation of the infectious mechanism and vaccine refinement of hepatitis B virus (HBV), influenza virus, human immunodeficiency virus (HIV), and SARS-CoV. Laboratories worldwide have utilized high-throughput sequencing (HTS) and scTCR-seq to characterize, share, and apply the TCR CDR3 repertoire in COVID-19 patients and SARS-CoV-2 vaccine recipients. This article focuses on the comparative analysis of the diversity, clonality, V&J gene usage and pairing, CDR3 length, shared CDR3 sequences or motifs, and other characteristics of TCR CDR3 repertoire. These findings provide molecular targets for evaluating T-cell response effects and short-term and long-term impacts on the adaptive immune system following SARS-CoV-2 infection or vaccination and establish a comparative archive of T-cell response "traces."

Investigation of Long-Term CD4+ T Cell Receptor Repertoire Changes Following SARS-CoV-2 Infection in Patients with Different Severities of Disease

BACKGROUND

The difference in the immune response to severe acute respiratory syndrome coro-navirus 2 (SARS-CoV-2) in patients with mild versus severe disease remains poorly understood. Recent scientific advances have recognised the vital role of both B cells and T cells; however, many questions remain unanswered, particularly for T cell responses. T cells are essential for helping the generation of SARS-CoV-2 antibody responses but have also been recognised in their own right as a major factor influencing COVID-19 disease outcomes. The examination of T cell receptor (TCR) family differences over a 12-month period in patients with varying COVID-19 disease severity is crucial for understanding T cell responses to SARS-CoV-2.

METHODS

We applied a machine learning approach to analyse TCR vb family responses in COVID-19 patients (n = 151) across multiple timepoints and disease severities alongside SARS-CoV-2 infection-naïve (healthy control) individ-uals (n = 62).

RESULTS

Blood samples from hospital in-patients with moderate, severe, or critical disease could be classified with an accuracy of 94%. Furthermore, we identified significant variances in TCR vb family specificities between disease and control subgroups.

CONCLUSIONS

Our findings suggest advantageous and disadvantageous TCR repertoire patterns in relation to disease severity. Following validation in larger cohorts, our methodology may be useful in detecting protective immunity and the assessment of long-term outcomes, particularly as we begin to unravel the immunological mechanisms leading to post-COVID complications.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

The T-cell repertoire of Spanish patients with COVID-19 as a strategy to link T-cell characteristics to the severity of the disease

BACKGROUND

The architecture and dynamics of T cell populations are critical in orchestrating the immune response to SARS-CoV-2. In our study, we used T Cell Receptor sequencing (TCRseq) to investigate TCR repertoires in 173 post-infection COVID-19 patients.

METHODS

The cohort included 98 mild and 75 severe cases with a median age of 53. We amplified and sequenced the TCR β chain Complementary Determining Region 3 (CDR3b) and performed bioinformatic analyses to assess repertoire diversity, clonality, and V/J allelic usage between age, sex and severity groups. CDR3b amino acid sequence inference was performed by clustering structural motifs and filtering validated reactive CDR3b to COVID-19.

RESULTS

Our results revealed a pronounced decrease in diversity and an increase in clonal expansion in the TCR repertoires of severe COVID-19 patients younger than 55 years old. These results reflect the observed trends in patients older than 55 years old (both mild and severe). In addition, we identified a significant reduction in the usage of key V alleles (TRBV14, TRBV19, TRBV15 and TRBV6-4) associated with disease severity. Notably, severe patients under 55 years old had allelic patterns that resemble those over 55 years old, accompanied by a skewed frequency of COVID-19-related motifs.

CONCLUSIONS

Present results suggest that severe patients younger than 55 may have a compromised TCR repertoire contributing to a worse disease outcome.

Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.

Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection

Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.

Antigen-driven CD8 + T cell clonal expansion is a prominent feature of MASH in humans and mice

Background and Aims

Chronic liver disease due to metabolic dysfunction-associated steatohepatitis (MASH) is a rapidly increasing global epidemic. MASH progression is a consequence of the complex interplay between inflammatory insults and dysregulated hepatic immune responses. T lymphocytes have been shown to accumulate in the liver during MASH, but the cause and consequence of T cell accumulation in the liver remain unclear. Our study aimed to define the phenotype and T cell receptor diversity of T cells from human cirrhotic livers and an animal model of MASH to begin resolving their function in disease.

Approach and Results

In these studies, we evaluated differences in T cell phenotype in the context of liver disease we isolated liver resident T cell populations from individuals with cirrhosis and a murine model of MASH. Using both 5' single cell sequencing and flow cytometry we defined the phenotype and T cell receptor repertoire of liver resident T cells during health and disease.

Conclusions

MASH-induced cirrhosis and diet-induced MASH in mice resulted in the accumulation of activated and clonally expanded T cells in the liver. The clonally expanded T cells in the liver expressed markers of chronic antigenic stimulation, including PD1 , TIGIT and TOX . Overall, this study establishes for the first time that T cells undergo antigen-dependent clonal expansion and functional differentiation during the progression of MASH. These studies could lead to the identification of potential antigenic targets that drive T cell activation, clonal expansion, and recruitment to the liver during MASH.

Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

Immunohematologic Biomarkers in COVID-19: Insights into Pathogenesis, Prognosis, and Prevention

Coronavirus disease 2019 (COVID-19) has had profound effects on the health of individuals and on healthcare systems worldwide. While healthcare workers on the frontlines have fought to quell multiple waves of infection, the efforts of the larger research community have changed the arch of this pandemic as well. This review will focus on biomarker discovery and other efforts to identify features that predict outcomes, and in so doing, identify possible effector and passenger mechanisms of adverse outcomes. Identifying measurable soluble factors, cell-types, and clinical parameters that predict a patient's disease course will have a legacy for the study of immunologic responses, especially stimuli, which induce an overactive, yet ineffectual immune system. As prognostic biomarkers were identified, some have served to represent pathways of therapeutic interest in clinical trials. The pandemic conditions have created urgency for accelerated target identification and validation. Collectively, these COVID-19 studies of biomarkers, disease outcomes, and therapeutic efficacy have revealed that immunologic systems and responses to stimuli are more heterogeneous than previously assumed. Understanding the genetic and acquired features that mediate divergent immunologic outcomes in response to this global exposure is ongoing and will ultimately improve our preparedness for future pandemics, as well as impact preventive approaches to other immunologic diseases.

Exploring the potential common denominator pathogenesis of system lupus erythematosus with COVID-19 based on comprehensive bioinformatics analysis

Objective

Evidences show that there may be a link between SLE and COVID-19. The purpose of this study is to screen out the diagnostic biomarkers of systemic lupus erythematosus (SLE) with COVID-19 and explore the possible related mechanisms by the bioinformatics approach.

Methods

SLE and COVID-19 datasets were extracted separately from the NCBI Gene Expression Omnibus (GEO) database. The limma package in R was used to obtain the differential genes (DEGs). The protein interaction network information (PPI) and core functional modules were constructed in the STRING database using Cytoscape software. The hub genes were identified by the Cytohubba plugin, and TF-gene together with TF-miRNA regulatory networks were constructed via utilizing the Networkanalyst platform. Subsequently, we generated subject operating characteristic curves (ROC) to verify the diagnostic capabilities of these hub genes to predict the risk of SLE with COVID-19 infection. Finally, a single-sample gene set enrichment (ssGSEA) algorithm was used to analyze immune cell infiltration.

Results

A total of 6 common hub genes (CDC6, PLCG1, KIF15, LCK, CDC25C, and RASGRP1) were identified with high diagnostic validity. These gene functional enrichments were mainly involved in cell cycle, and inflammation-related pathways. Compared to the healthy controls, abnormal infiltration of immune cells was found in SLE and COVID-19, and the proportion of immune cells linked to the 6 hub genes.

Conclusion

Our research logically identified 6 candidate hub genes that could predict SLE complicated with COVID-19. This work provides a foothold for further study of potential pathogenesis in SLE and COVID-19.

Architecture of the SARS-CoV-2-specific T cell repertoire

The T cell response plays an indispensable role in the early control and successful clearance of SARS-CoV-2 infection. However, several important questions remain about the role of cellular immunity in COVID-19, including the shape and composition of disease-specific T cell repertoires across convalescent patients and vaccinated individuals, and how pre-existing T cell responses to other pathogens—in particular, common cold coronaviruses—impact susceptibility to SARS-CoV-2 infection and the subsequent course of disease. This review focuses on how the repertoire of T cell receptors (TCR) is shaped by natural infection and vaccination over time. We also summarize current knowledge regarding cross-reactive T cell responses and their protective role, and examine the implications of TCR repertoire diversity and cross-reactivity with regard to the design of vaccines that confer broader protection against SARS-CoV-2 variants.