Biology subjects

Affaticati, F.

Publications and source records attributed to Affaticati, F..

5 recordsLinked to original sources

bioRxiv2026

The T cell receptor repertoire captures healthy aging and CMV independently from epigenetic clocks

Human aging is the process through which numerous biological changes occur during life, affecting various processes. In some elderly individuals, this functional decline becomes more pronounced, leading to frailty, a condition characterised by reduced physiological reserves and increased vulnerability to stress. With the global rise of life expectancy, identification of biomarkers for healthspan and frailty are becoming more important. In this study, we directly compare two molecular readouts, namely the T cell receptor (TCR) repertoire and epigenetic clocks, on their ability to discern healthy aging. On blood samples from sixteen individuals, across age-matched healthy elderly and frail individuals, both TCR sequencing and epigenetic profiling were performed. A significantly higher TCR repertoire diversity in the CD4+ T cells differentiated the healthy elderly individuals from the frailty ones. Epigenetic clock signatures of biological relative to chronological ageing rate, did not show a clear difference between both groups. However, when taking into account the CMV-serostatus, a significant increase in epigenetic aging could be observed in the CMV-seropositive individuals. Our results support a clear hypothesis on the role of CMV infection in the healthy aging of the immune system. In healthy elderly, CMV is typically controlled by CD4+ T cells, however, in the frail elderly, the burden of managing the infection shifts to the CD8+ T cells. This change is marked by two key changes: a decrease in TCR diversity for seropositive individuals compared to seronegative individuals, as well as an increase in the fraction of CMV-associated TCRs within the CD8+ T cells. These findings contribute to our understanding of aging and provide insight into how CMV-infection may affect healthy aging and frailty. They also underline the crucial role of the immune system in healthy aging and the value of further investigating ageing-related health/disease patterns in the TCR repertoire to determine healthspan/lifespan.

immunology↗

bioRxiv2025

T cell-microbiome associations captured through T cell receptor convergence analysis

The gut microbiome modulates mucosal immunity, yet how specific bacterial taxa shape the diversity and specificity of T cell receptor (TCR) repertoires remains poorly understood. Existing approaches emphasize single-species effects or broad immune features, without pinpointing which microbes drive specific T cell clonotypes. We present AIRRWAS, a computational framework that integrates TCR-microbiome interaction analysis with targeted in vitro validation to detect genus-level TCR convergence. Applied to three independent cohorts, AIRRWAS identified reproducible associations between convergent TCR clusters and 21 bacterial genera spanning core commensals, probiotics and taxa with immunomodulatory roles. Predicted clonotypes were enriched within the TCR-microbiome interaction network and preferentially activated by genus-matched stimuli, eliciting different functional T cell responses. These findings demonstrate that distinct repertoires can share genus-specific TCR motifs, enabling detection of shared immune signatures. AIRRWAS can map these TCR- microbiome interactions, laying the groundwork for biomarker discovery immune monitoring and the development of microbiome-targeted therapies.

bioinformatics↗

bioRxiv2024

Bridging immunotypes and enterotypes using a systems immunology approach

Unveiling the systemic effects of disease and health requires an holistic approach that has mainly revolved around well established, directly determinable molecular relationships such as the protein synthesis cascade and epigenetic mechanisms. In this study, involving 394 individuals, we found direct linkage of branches spanning human biological functions often not studied in conjunction, using clinical data, gut microbial abundances, blood immune cell repertoires, blood transcriptomic and blood T cell receptor data. Contrary to current paradigms, we demonstrate that immunotypes and enterotypes are orthogonal, likely fulfilling distinct roles in maintaining homeostasis, only bridged via the blood transcriptome. We also identified two distinct inflammatory profiles: the first driven by interferon signalling and the other characterised by non-viral, NF-kB and IL-6 markers. Lastly, we present compelling data showing strong associations of the Ruminococcaceae and Christensenellaceae bacteria with a healthy immunotype and transcriptomic pattern, highlighting their potential role in immune health. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC="FIGDIR/small/625344v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@c6b5eorg.highwire.dtl.DTLVardef@15d0c3forg.highwire.dtl.DTLVardef@1cf29eeorg.highwire.dtl.DTLVardef@1c9ff8d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Multi-view learning to unravel the different levels underlying hepatitis B vaccine response

The immune system acts as the intricate apparatus dedicated to mounting a defence that ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are the critical tool developed. However, vaccine responses are governed by levels that when interrogated separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modelling can aid in gaining actionable insights on response markers shared across populations, capture the immune system diversity, and disentangle confounders. We thus sought to assess this multi-view modelling capacity on the responsiveness to Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine induced antibodies against HBV surface antigen (anti-HBs) in early-converters (n=21; <2 month) and late-converters (n=9; <6 months), was defined based on the anti-HBs titres (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modelling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction out-performed single-view methods in terms of area under curve and balanced accuracy, confirming an increase in predictive power to be gained. The interpretation of the findings showed that age, gender, inflammation-related gene sets and pre-existing vaccine specific T-cells were associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements the clinical seroconversion and all single modalities. Importantly, this modelling could identify what features predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify key features regulating responsiveness.

systems biology↗

bioRxiv2022

Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions

The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR-epitope pair is still a challenging task, especially for novel epitopes, because the underlying patterns are largely unknown to domain experts and machine learning models. To achieve a deeper understanding of TCR-epitope interactions, we have used interpretable deep learning techniques to gain insights into the performance of TCR-epitope binding machine learning models. We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. Additionally, our results show the importance of using interpretability techniques to verify the predictions of machine learning models for challenging molecular biology problems where small hard-to-detect problems can accumulate to inaccurate results.

bioinformatics↗