Preschool-age children maintain a distinct memory CD4+ T cell and memory B cell response after SARS-CoV-2 infection.

The development of the human immune system lasts for several years after birth. The impact of this maturation phase on the quality of adaptive immunity and the acquisition of immunological memory after infection at a young age remains incompletely defined. Here, using an antigen-reactive T cell (ARTE) assay and multidimensional flow cytometry, we profiled circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-reactive CD3+CD4+CD154+ T cells in children and adults before infection, during infection, and 11 months after infection, stratifying children into separate age groups and adults according to disease severity. During SARS-CoV-2 infection, children younger than 5 years old displayed a lower antiviral CD4+ T cell response, whereas children older than 5 years and adults with mild disease had, quantitatively and phenotypically, comparable virus-reactive CD4+ T cell responses. Adults with severe disease mounted a response characterized by higher frequencies of virus-reactive proinflammatory and cytotoxic T cells. After SARS-CoV-2 infection, preschool-age children not only maintained neutralizing SARS-CoV-2-reactive antibodies postinfection comparable to adults but also had phenotypically distinct memory T cells displaying high inflammatory features and properties associated with migration toward inflamed sites. Moreover, preschool-age children had markedly fewer circulating virus-reactive memory B cells compared with the other cohorts. Collectively, our results reveal unique facets of antiviral immunity in humans at a young age and indicate that the maturation of adaptive responses against SARS-CoV-2 toward an adult-like profile occurs in a progressive manner.

Combining mutation and recombination statistics to infer clonal families in antibody repertoires.

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B-cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution, and dynamics. We present HILARy (high-precision inference of lineages in antibody repertoires), an efficient, fast, and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

Computational detection of antigen-specific B cell receptors following immunization.

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes.

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

Physical activity as a mediator in the relationship between health locus of control and health-related quality of life in multiple sclerosis patients with different disease duration.

BACKGROUND: The importance of undertaking physical activity for functioning of patients with multiple sclerosis (MS) has been repeatedly highlighted. However, the research on the role of physical activity in shaping the quality of life of patients with different disease duration is scarce. OBJECTIVE: The aim of this study was to identify the mediating role of physical activity in the relationship between health locus of control (HLoC) and health-related quality of life in MS patients with varying disease duration. METHODS: The study included 339 patients with MS from rehabilitation centers. The Multiple Sclerosis Impact Scale (MSIS-29) was used to measure health-related quality of life, physical activity was assessed by the Godin Leisure Time Exercise Questionnaire (GLTEQ) and the Health Locus of Control Questionnaire was used to measure HLoC. RESULTS: Physical activity has been shown to be a mediator in the relationship between intrinsic HLoC and health-related quality of life particularly in patients with longer disease duration. Intrinsic HLoC was positively associated with engaging in physical activity, which in turn was positively associated with the physical component of quality of life in patients with longer (indirect effect: ß = -0.077, p < 0.05) and moderate (ß = -0.040, p < 0.05) duration of illness. CONCLUSION: The results highlight the importance of psychological resources for undertaking quality-of-life-related physical activity by MS patients with long disease duration. Particularly important here is the internal HLoC, which promotes physical activity that increases the chance of a high quality of life.

Bayesian estimation of the Kullback-Leibler divergence for categorical systems using mixtures of Dirichlet priors.

In many applications in biology, engineering, and economics, identifying similarities and differences between distributions of data from complex processes requires comparing finite categorical samples of discrete counts. Statistical divergences quantify the difference between two distributions. However, their estimation is very difficult and empirical methods often fail, especially when the samples are small. We develop a Bayesian estimator of the Kullback-Leibler divergence between two probability distributions that makes use of a mixture of Dirichlet priors on the distributions being compared. We study the properties of the estimator on two examples: probabilities drawn from Dirichlet distributions and random strings of letters drawn from Markov chains. We extend the approach to the squared Hellinger divergence. Both estimators outperform other estimation techniques, with better results for data with a large number of categories and for higher values of divergences.

Innate-like T cell subset commitment in the murine thymus is independent of TCR characteristics and occurs during proliferation.

How T-cell receptor (TCR) characteristics determine subset commitment during T-cell development is still unclear. Here, we addressed this question for innate-like T cells, mucosal-associated invariant T (MAIT) cells, and invariant natural killer T (iNKT) cells. MAIT and iNKT cells have similar developmental paths, leading in mice to two effector subsets, cytotoxic (MAIT1/iNKT1) and IL17-secreting (MAIT17/iNKT17). For iNKT1 vs iNKT17 fate choice, an instructive role for TCR affinity was proposed but recent data argue against this model. Herein, we examined TCR role in MAIT and iNKT subset commitment through scRNAseq and TCR repertoire analysis. In our dataset of thymic MAIT cells, we found pairs of T-cell clones with identical amino acid TCR sequences originating from distinct precursors, one of which committed to MAIT1 and the other to MAIT17 fates. Quantitative in silico simulations indicated that the number of such cases is best explained by lineage choice being independent of TCR characteristics. Comparison of TCR features of MAIT1 and MAIT17 clonotypes demonstrated that the subsets cannot be distinguished based on TCR sequence. To pinpoint the developmental stage associated with MAIT sublineage choice, we demonstrated that proliferation takes place both before and after MAIT fate commitment. Altogether, we propose a model of MAIT cell development in which noncommitted, intermediate-stage MAIT cells undergo a first round of proliferation, followed by TCR characteristics-independent commitment to MAIT1 or MAIT17 lineage, followed by an additional round of proliferation. Reanalyzing a published iNKT TCR dataset, we showed that this model is also relevant for iNKT cell development.

Learning predictive signatures of HLA type from T-cell repertoires.

T cells recognize a wide range of pathogens using surface receptors that interact directly with pep-tides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

Is the 2016 ESC diagnostic algorithm useful for assessing the prevalence of chronic heart failure in population-based studies?

BACKGROUND: Chronic heart failure (CHF) is a major healthcare problem. However, there are no epidemiological studies assessing the prevalence of CHF in the general population with diagnosis based on algorithms recommended for clinical practice. AIM: The aim of the HF-Pomorskie survey was to assess the prevalence of three basic components of the 2016 ESC diagnostic algorithm for CHF (symptoms, N-terminal pro B-type natriuretic peptide [NT-proBNP], and abnormalities on echocardiography) and to determine whether this algorithm may be applicable to studies in general population samples. METHODS: The study was performed in a representative sample of 313 adults (170 women and 143 men) aged between 20 and 90 years (mean 55.2 years [15.3]) in Northern Poland. A questionnaire to determine New York Heart Association [NYHA] class, laboratory tests including NT-proBNP, as well as transthoracic echocardiography and spirometry examinations were performed in all subjects. RESULTS: Dyspnea (NYHA class II-IV) was reported by 13.7% of recruited participants. Dyspnea and elevated levels of NT-proBNP (>125 pg/ml) were found in 7.7% of all examined subjects, while dyspnea, elevated NT-proBNP levels accompanied by systolic or diastolic abnormalities on echocardiography occurred in 4.8%. In the group without dyspnea (86.3% of all examined subjects), every sixth subject had an elevated level of NT-proBNP. On the other hand, 5.8% of studied subjects reported a previous diagnosis of CHF, which was confirmed using the current ESC algorithm in 78% of them. CONCLUSIONS: The prevalence of CHF assessed by the 2016 ESC diagnostic algorithm in the representative sample of adults was equal to 4.8%. The clinical algorithm for the diagnosis of CHF is fully applicable to the representative surveys in the general population. However, due to logistic and economic factors, echocardiography examination and NT-proBNP determination can be limited to patients reporting dyspnea or previous diagnosis of CHF.

21-Carba-23-selenaporphyrinoid Dyads-An Azepine Unit as a Merging Motif.

The oxidation of 10,15-diaryl-21-carba-23-selenaporphyrinoids resulted in the creation of dyads. The dimerization process follows a [5+2] cycloaddition path with the formation of an azepine unit. The arrays display two direct bonds between the peripheral carbocyclic carbon atoms of one carbaselenaporphyrinic subunit and the central carbon and nitrogen atoms of the second subunit. This results in a unique canted arrangement of two carbaporphyrinoid planes resembling an open seashell-like motif.

Probabilities of developing HIV-1 bNAb sequence features in uninfected and chronically infected individuals.

HIV-1 broadly neutralizing antibodies (bNAbs) are able to suppress viremia and prevent infection. Their induction by vaccination is therefore a major goal. However, in contrast to antibodies that neutralize other pathogens, HIV-1-specific bNAbs frequently carry uncommon molecular characteristics that might prevent their induction. Here, we perform unbiased sequence analyses of B cell receptor repertoires from 57 uninfected and 46 chronically HIV-1- or HCV-infected individuals and learn probabilistic models to predict the likelihood of bNAb development. We formally show that lower probabilities for bNAbs are predictive of higher HIV-1 neutralization activity. Moreover, ranking bNAbs by their probabilities allows to identify highly potent antibodies with superior generation probabilities as preferential targets for vaccination approaches. Importantly, we find equal bNAb probabilities across infected and uninfected individuals. This implies that chronic infection is not a prerequisite for the generation of bNAbs, fostering the hope that HIV-1 vaccines can induce bNAb development in uninfected people.

Dynamical Information Synergy in Biochemical Signaling Networks.

Biological cells encode information about their environment through biochemical signaling networks that control their internal state and response. This information is often encoded in the dynamical patterns of the signaling molecules, rather than just their instantaneous concentrations. Here, we analytically calculate the information contained in these dynamics for a number of paradigmatic cases in the linear regime, for both static and time-dependent input signals. When considering oscillatory output dynamics, we report on the emergence of synergy between successive measurements, meaning that the joint information in two measurements exceeds the sum of the individual information. We extend our analysis numerically beyond the scope of linear input encoding to reveal synergetic effects in the cases of frequency or damping modulation, both of which are relevant to classical biochemical signaling systems.

Evolutionary stability of antigenically escaping viruses.

Antigenic variation is the main immune escape mechanism for RNA viruses like influenza or SARS-CoV-2. While high mutation rates promote antigenic escape, they also induce large mutational loads and reduced fitness. It remains unclear how this cost-benefit trade-off selects the mutation rate of viruses. Using a traveling wave model for the coevolution of viruses and host immune systems in a finite population, we investigate how immunity affects the evolution of the mutation rate and other nonantigenic traits, such as virulence. We first show that the nature of the wave depends on how cross-reactive immune systems are, reconciling previous approaches. The immune-virus system behaves like a Fisher wave at low cross-reactivities, and like a fitness wave at high cross-reactivities. These regimes predict different outcomes for the evolution of nonantigenic traits. At low cross-reactivities, the evolutionarily stable strategy is to maximize the speed of the wave, implying a higher mutation rate and increased virulence. At large cross-reactivities, where our estimates place H3N2 influenza, the stable strategy is to increase the basic reproductive number, keeping the mutation rate to a minimum and virulence low.

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Towards a quantitative theory of tolerance.

A cornerstone of the classical view of tolerance is the elimination of self-reactive T cells via negative selection in the thymus. However, high-throughput T cell receptor (TCR) sequencing data have so far failed to detect substantial signatures of negative selection in the observed repertoires. In addition, quantitative estimates as well as recent experiments suggest that the elimination of self-reactive T cells is at best incomplete. We discuss several recent theoretical ideas that might explain tolerance while being consistent with these observations, including collective decision-making through quorum sensing, and sensitivity to change through dynamic tuning and adaptation. We propose that a unified quantitative theory of tolerance should combine these elements to help to explain the plasticity of the immune system and its robustness to autoimmunity.

Parallel SPR and QCM-D Quantitative Analysis of CD9, CD63, and CD81 Tetraspanins: A Simple and Sensitive Way to Determine the Concentration of Extracellular Vesicles Isolated from Human Lung Cancer Cells.

Tetraspanins, including CD9, CD63, and CD81, are transmembrane biomarkers that play a crucial role in regulating cancer cell proliferation, invasion, and metastasis, as well as plasma membrane dynamics and protein trafficking. In this study, we developed simple, fast, and sensitive immunosensors to determine the concentration of extracellular vesicles (EVs) isolated from human lung cancer cells using tetraspanins as biomarkers. We employed surface plasmon resonance (SPR) and quartz crystal microbalance with dissipation (QCM-D) as detectors. The monoclonal antibodies targeting CD9, CD63, and CD81 were oriented vertically in the receptor layer using either a protein A sensor chip (SPR) or a cysteamine layer that modified the gold crystal (QCM-D) without the use of amplifiers. The SPR studies demonstrated that the interaction of EVs with antibodies could be described by the two-state reaction model. Furthermore, the EVs' affinity to monoclonal antibodies against tetraspanins decreased in the following order: CD9, CD63, and CD81, as confirmed by the QCM-D studies. The results indicated that the developed immunosensors were characterized by high stability, a wide analytical range from 6.1 × 104 particles·mL-1 to 6.1 × 107 particles·mL-1, and a low detection limit (0.6-1.8) × 104 particles·mL-1. A very good agreement between the results obtained using the SPR and QCM-D detectors and nanoparticle tracking analysis demonstrated that the developed immunosensors could be successfully applied to clinical samples.

New, fast and cheap prediction tests for BRCA1 gene mutations identification in clinical samples.

Despite significant progress in cancer therapy, cancer is still the second cause of mortality in the world. The necessity to make quick therapeutic decisions forces the development of procedures allowing to obtain a reliable result in a quick and unambiguous manner. Currently, detecting predictive mutations, including BRCA1, is the basis for effectively treating advanced breast cancer. Here, we present new insight on gene mutation detection. We propose a cheap BRCA1 mutation detection tests based on the surface plasmon resonance (SPR) or quartz crystal microbalance with energy dissipation (QCM-D) response changes recorded during a hybridization process of an oligonucleotide molecular probe with DNA fragments, with and without the BRCA1 mutation. The changes in the morphology of the formed DNA layer caused by the presence of the mutation were confirmed by atomic force microscopy. The unique property of the developed SPR and QCM tests is really short time of analysis: ca. 6 min for SPR and ca. 25 min for QCM. The proposed tests have been verified on 22 different DNA extracted from blood leukocytes collected from cancer patients: 17 samples from patients with various BRCA1 gene mutation variants including deletion, insertion and missense single-nucleotide and 5 samples from patients without any BRCA1 mutation. Our test is a response to the need of medical diagnostics for a quick, unambiguous test to identify mutations of the BRCA1 gene, including missense single-nucleotide (SNPs).

Inspecting the interaction between human immunodeficiency virus and the immune system through genetic turnover.

Chronic infections of the human immunodeficiency virus (HIV) create a very complex coevolutionary process, where the virus tries to escape the continuously adapting host immune system. Quantitative details of this process are largely unknown and could help in disease treatment and vaccine development. Here we study a longitudinal dataset of ten HIV-infected people, where both the B-cell receptors and the virus are deeply sequenced. We focus on simple measures of turnover, which quantify how much the composition of the viral strains and the immune repertoire change between time points. At the single-patient level, the viral-host turnover rates do not show any statistically significant correlation, however, they correlate if one increases the amount of statistics by aggregating the information across patients. We identify an anti-correlation: large changes in the viral pool composition come with small changes in the B-cell receptor repertoire. This result seems to contradict the naïve expectation that when the virus mutates quickly, the immune repertoire needs to change to keep up. However, a simple model of antagonistically evolving populations can explain this signal. If it is sampled at intervals comparable with the sweep time, one population has had time to sweep while the second cannot start a counter-sweep, leading to the observed anti-correlation. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

DRAG in situ barcoding reveals an increased number of HSPCs contributing to myelopoiesis with age.

Ageing is associated with changes in the cellular composition of the immune system. During ageing, hematopoietic stem and progenitor cells (HSPCs) that produce immune cells are thought to decline in their regenerative capacity. However, HSPC function has been mostly assessed using transplantation assays, and it remains unclear how HSPCs age in the native bone marrow niche. To address this issue, we present an in situ single cell lineage tracing technology to quantify the clonal composition and cell production of single cells in their native niche. Our results demonstrate that a pool of HSPCs with unequal output maintains myelopoiesis through overlapping waves of cell production throughout adult life. During ageing, the increased frequency of myeloid cells is explained by greater numbers of HSPCs contributing to myelopoiesis rather than the increased myeloid output of individual HSPCs. Strikingly, the myeloid output of HSPCs remains constant over time despite accumulating significant transcriptomic changes throughout adulthood. Together, these results show that, unlike emergency myelopoiesis post-transplantation, aged HSPCs in their native microenvironment do not functionally decline in their regenerative capacity.