Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus

Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells

The latent reservoir of HIV persists for decades in people living with HIV (PWH) on antiretroviral therapy (ART). To determine if persistence arises from the natural dynamics of memory CD4+ T cells harboring HIV, we compared the clonal dynamics of HIV proviruses to that of memory CD4+ T cell receptors (TCRβ) from the same PWH and from HIV-seronegative people. We show that clonal dominance of HIV proviruses and antigen-specific CD4+ T cells are similar but that the field's understanding of the persistence of the less clonally dominant reservoir is significantly limited by undersampling. We demonstrate that increasing reservoir clonality over time and differential decay of intact and defective proviruses cannot be explained by mCD4+ T cell kinetics alone. Finally, we develop a stochastic model of TCRβ and proviruses that recapitulates experimental observations and suggests that HIV-specific negative selection mediates approximately 6% of intact and 2% of defective proviral clearance. Thus, HIV persistence is mostly, but not entirely, driven by natural mCD4+ T cell kinetics.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

BACKGROUND

Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.

RESULTS

To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences.

CONCLUSIONS

We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.

Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon hepatitis B vaccination

Antigen recognition through the T cell receptor (TCR) αβ heterodimer is one of the primary determinants of the adaptive immune response. Vaccines activate naïve T cells with high specificity to expand and differentiate into memory T cells. However, antigen-specific memory CD4 T cells exist in unexposed antigen-naïve hosts. In this study, we use high-throughput sequencing of memory CD4 TCRβ repertoire and machine learning to show that individuals with preexisting vaccine-reactive memory CD4 T cell clonotypes elicited earlier and higher antibody titers and mounted a more robust CD4 T cell response to hepatitis B vaccine. In addition, integration of TCRβ sequence patterns into a hepatitis B epitope-specific annotation model can predict which individuals will have an early and more vigorous vaccine-elicited immunity. Thus, the presence of preexisting memory T cell clonotypes has a significant impact on immunity and can be used to predict immune responses to vaccination.

Immune cells called CD4 T cells help the body build immunity to infections caused by bacteria and viruses, or after vaccination. Receptor proteins on the outside of the cells recognize pathogens, foreign molecules called antigens, or vaccine antigens. Vaccine antigens are usually inactivated bacteria or viruses, or fragments of these pathogens. After recognizing an antigen, CD4 T cells develop into memory CD4 T cells ready to defend against future infections with the pathogen.

People who have never been exposed to a pathogen, or have never been vaccinated against it, may nevertheless have preexisting memory cells ready to defend against it. This happens because CD4 T cells can recognize multiple targets, which enables the immune system to be ready to defend against both new and familiar pathogens.

Elias, Meysman, Bartholomeus et al. wanted to find out whether having preexisting memory CD4 T cells confers an advantage for vaccine-induced immunity. Thirty-four people who were never exposed to hepatitis B or vaccinated against it participated in the study. These individuals provided blood samples before vaccination, with 2 doses of the hepatitis B vaccine, and at 3 time points afterward. Using next generation immune sequencing and machine learning techniques, Elias et al. analyzed the individuals’ memory CD4 T cells before and after vaccination.

The experiments showed that preexisting memory CD4 T cells may determine vaccination outcomes, and people with more preexisting memory cells develop quicker and stronger immunity after vaccination against hepatitis B. This information may help scientists to better understand how people develop immunity to pathogens. It may guide them develop better vaccines or predict who will develop immunity after vaccination.

Generative models of T-cell receptor sequences

T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free variational autoencoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable, at a lower computational cost.

Single-cell analysis shows that adipose tissue of persons with both HIV and diabetes is enriched for clonal, cytotoxic, and CMV-specific CD4+ T cells

Persons with HIV are at increased risk for diabetes mellitus compared with individuals without HIV. Adipose tissue is an important regulator of glucose and lipid metabolism, and adipose tissue T cells modulate local inflammatory responses and, by extension, adipocyte function. Persons with HIV and diabetes have a high proportion of CX3CR1⁺ GPR56⁺ CD57⁺ (C-G-C⁺) CD4⁺ T cells in adipose tissue, a subset of which are cytomegalovirus specific, whereas individuals with diabetes but without HIV have predominantly CD69⁺ CD4⁺ T cells. Adipose tissue CD69⁺ and C-G-C⁺ CD4⁺ T cell subsets demonstrate higher receptor clonality compared with the same cells in blood, potentially reflecting antigen-driven expansion, but C-G-C⁺ CD4⁺ T cells have a more inflammatory and cytotoxic RNA transcriptome. Future studies will explore whether viral antigens have a role in recruitment and proliferation of pro-inflammatory C-G-C⁺ CD4⁺ T cells in adipose tissue of persons with HIV.

Longitudinal analysis of T-cell receptor repertoires reveals persistence of antigen-driven CD4+ and CD8+ T-cell clusters in systemic sclerosis

The T-cell receptor (TCR) is a highly polymorphic surface receptor that allows T-cells to recognize antigenic peptides presented on the major histocompatibility complex (MHC). Changes in the TCR repertoire have been observed in several autoimmune conditions, and these changes are suggested to predispose autoimmunity. Multiple lines of evidence have implied an important role for T-cells in the pathogenesis of Systemic Sclerosis (SSc), a complex autoimmune disease. One of the major questions regarding the roles of T-cells is whether expansion and activation of T-cells observed in the diseases pathogenesis is antigen driven. To investigate the temporal TCR repertoire dynamics in SSc, we performed high-throughput sequencing of CD4⁺ and CD8⁺ TCRβ chains on longitudinal samples obtained from four SSc patients collected over a minimum of two years. Repertoire overlap analysis revealed that samples taken from the same individual over time shared a high number of TCRβ sequences, indicating a clear temporal persistence of the TCRβ repertoire in CD4⁺ as well as CD8⁺ T-cells. Moreover, the TCRβs that were found with a high frequency at one time point were also found with a high frequency at the other time points (even after almost four years), showing that frequencies of dominant TCRβs are largely consistent over time. We also show that TCRβ generation probability and observed TCR frequency are not related in SSc samples, showing that clonal expansion and persistence of TCRβs is caused by antigenic selection rather than convergent recombination. Moreover, we demonstrate that TCRβ diversity is lower in CD4⁺ and CD8⁺ T-cells from SSc patients compared with memory T-cells from healthy individuals, as SSc TCRβ repertoires are largely dominated by clonally expanded persistent TCRβ sequences. Lastly, using "Grouping of Lymphocyte Interactions by Paratope Hotspots" (GLIPH2), we identify clusters of TCRβ sequences with homologous sequences that potentially recognize the same antigens and contain TCRβs that are persist in SSc patients. In conclusion, our results show that CD4⁺ and CD8⁺ T-cells are highly persistent in SSc patients over time, and this persistence is likely a result from antigenic selection. Moreover, persistent TCRs form high similarity clusters with other (non-)persistent sequences that potentially recognize the same epitopes. These data provide evidence for an antigen driven expansion of CD4⁺/CD8⁺ T-cells in SSc.

High-throughput sequencing of CD4+ T cell repertoire reveals disease-specific signatures in IgG4-related disease

Background

CD4⁺ T cells play critical roles in the pathogenesis of IgG4-related disease (IgG4-RD). The aim of this study was to investigate the TCR repertoire of peripheral blood CD4⁺ T cells in IgG4-RD.

Methods

The peripheral blood was collected from six healthy controls and eight IgG4-RD patients. TCR β-chain libraries of CD4⁺ T cells were constructed by 5′-rapid amplification of cDNA ends (5′-RACE) and sequenced by Illumina Miseq platform. The relative similarity of TCR repertoires between samples was evaluated according to the total frequencies of shared clonotypes (metric F), correlation of frequencies of shared clonotypes (metric R), and total number of shared clonotypes (metric D).

Results

The clonal expansion and diversity of CD4⁺ T cell repertoire were comparable between healthy controls and IgG4-RD patients, while the proportion of expanded and coding degenerated clones, as an indicator of antigen-driven clonal expansion, was significantly higher in IgG4-RD patients. There was no significant difference in TRBV and TRBJ gene usage between healthy controls and IgG4-RD patients. The complementarity determining region 3 (CDR3) length distribution was skewed towards longer fragments in IgG4-RD. Visualization of relative similarity of TCR repertoires by multi-dimensional scaling analysis showed that TCR repertoires of IgG4-RD patients were separated from that of healthy controls in F and D metrics. We identified 11 IgG4-RD-specific CDR3 amino acid sequences that were expanded in at least 2 IgG4-RD patients, while not detected in healthy controls. According to TCR clonotype networks constructed by connecting all the CDR3 sequences with a Levenshtein distance of 1, 3 IgG4-RD-specific clusters were identified. We annotated the TCR sequences with known antigen specificity according to McPAS-TCR database and found that the frequencies of TCR sequences associated with each disease or immune function were comparable between healthy controls and IgG4-RD patients.

Conclusion

According to our study of CD4⁺ T cells from eight IgG4-RD patients, TCR repertoires of IgG4-RD patients were different from that of healthy controls in the proportion of expanded and coding degenerated clones and CDR3 length distribution. In addition, IgG4-RD-specific TCR sequences and clusters were identified in our study.

Deep generative models for T cell receptor protein sequences

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.