Sergio Abrignani

Regulatory T cells (Tregs) are central mediators of immune evasion in cancer, but the mechanisms that shape their functional diversity within tumours remain poorly understood. While transcriptional heterogeneity among tumour-infiltrating Tregs has been described, the contribution of post-transcriptional mechanisms, including mRNA translation, to their functional identity remains largely unexplored. In this study, we introduce CITePuro-seq, a novel method that links single-cell transcriptomes with active protein synthesis rates, enabling simultaneous measurement of the transcriptional and mRNA translational state in individual cells. Using this approach together with ex vivo puromycin incorporation assays, spatial proteomics and functional cytokine assays, we characterized Tregs isolated from pleural mesothelioma (PM) and non-small cell lung cancer (NSCLC), two malignancies with distinct responses to immunotherapy. Mitochondrial density, cytokine secretion profiles and transcriptional dynamics were examined in relation to mRNA translational activity in order to dissect the metabolic and functional determinants of Treg heterogeneity. We found that Tregs from PM and NSCLC were transcriptionally distinct, yet equally suppressive. PM Tregs exhibited markedly higher translational activity compared to NSCLC Tregs. Mechanistically, mitochondrial density emerged as a key determinant of translational activation, linking respiratory capacity to protein synthesis and defining distinct metabolic niches within the tumour microenvironment. Higher translational activity directly determined cytokine secretion patterns, and in turn drove transcriptional reshaping associated with enhanced suppressive function. In particular, translationally active PM Tregs appeared to produce higher levels of cytokines, including IL-10, potentially establishing a self-reinforcing immunosuppressive network. Taken together, these findings identify protein synthesis as an important contributor to Treg heterogeneity in cancer. We suggest that mRNA translational activity is an important parameter in predicting immunotherapy outcomes, and position translational regulation as a potential therapeutic target in cancer immunology.

The incidence of early-onset colorectal cancer (EO-CRC, diagnosed earlier than age 50) is rising worldwide. Despite distinctive clinicopathological features, whether EO-CRC represents a biologically distinct entity from standard-onset CRC (SO-CRC) remains unclear. To investigate molecular underpinnings of EO-CRC, we applied high-resolution label-free mass spectrometry coupled with transcriptomic approaches on primary tumours, healthy mucosae, and metastases of EO-CRC and SO-CRC patients. Most EO-CRC displayed reactivation of placental-like programs and HERVH reactivation, a family of retrotransposons maintaining pluripotency. These features were retained in patient-derived organoids (PDOs) showing sensitivity to pharmacological ATR (Ataxia Telangiectasia and Rad3-related) inhibition. While these findings point to specific EO-CRC vulnerabilities, they require further validation in larger geographically distinct series. These findings distinguish most EO-CRC from SO-CRC as they possess specific placental mimicry and HERVH reactivation. The placental mimicry and HERVH reactivation observed may provide a molecular rationale for EO-CRC aggressive behaviour and suggest potential avenues for therapeutic targeting.

The molecular mechanisms responsible for the heightened reactivity of quiescent T cells in human early life remain largely elusive. Our previous research identified that quiescent adult naïve CD4 ⁺ T cells express LINE1 (long interspersed nuclear elements 1) spliced in previously unknown isoforms, and their down-regulation marks the transition to activation. Here, we unveil that neonatal naïve T cell quiescence is characterized by enhanced energy production and protein synthesis. This phenotype is associated with the absence of LINE1 expression attributed to tonic T cell receptor/mTOR complex 1 (mTORC1) signaling and (polypyrimidine tract-binding protein 1 (PTBP1)–mediated LINE1 splicing suppression. The absence of LINE1 expression primes these cells for rapid execution of the activation program by directly regulating protein synthesis. LINE1 expression progressively increases in childhood and adults, peaking in elderly individuals, and, by decreasing protein synthesis, contributes to immune senescence in aging. Our study proposes LINE1 as a critical player of human T cell function across the human life span.

Arthropod‐borne viruses, such as dengue virus (DENV), pose significant global health threats, with DENV alone infecting around 400 million people annually and causing outbreaks beyond endemic regions. This study aimed to enhance serological diagnosis and discover new drugs by identifying immunogenic protein regions of DENV. Utilizing a comprehensive approach, the study focused on peptides capable of distinguishing DENV from other flavivirus infections through serological analyses. Over 200 patients with confirmed arbovirus infection were profiled using high‐density pan flavivirus peptide arrays comprising 6253 peptides and the computational method matrix of local coupling energy (MLCE). Twenty‐four peptides from nonstructural and structural viral proteins were identified as specifically recognized by individuals with DENV infection. Six peptides were confirmed to distinguish DENV from Zika virus (ZIKV), West Nile virus (WNV), Yellow Fever virus (YFV), Usutu virus (USUV), and Chikungunya virus (CHIKV) infections, as well as healthy controls. Moreover, the combination of two immunogenic peptides emerged as a potential serum biomarker for DENV infection. These peptides, mapping to highly accessible regions on protein structures, show promise for diagnostic and prophylactic strategies against flavivirus infections. The described methodology holds broader applicability in the serodiagnosis of infectious diseases.

Background: Autosomal Dominant-Hyper-IgE Syndrome (AD-HIES) is caused by dominant-negative (DN) STAT3 mutations and characterized by high IgE levels, a lack of Th17-cells and recurrent infections with extracellular pathogens. We previously identified an enigmatic population of IL-10 producing CCR6 ⁺ B-helper T-cells and investigated here their relationship to Th17-cells and STAT3 signalling requirements. Methods: Human blood lymphocytes were analysed by multiparametric flow cytometry in healthy donors and AD-HIES patients. Analysis was performed by conventional gating or with bioinformatic tools. FACS-purified T-cell subsets were activated in vitro and Th17 differentiation assessed. T-cell antigen specificities were assessed by activation with heat-killed pathogens or antigenic peptide pools. B helper capacities were determined according to antibody secretion in B-T co-cultures by ELISA. Results: CCR6 ⁺ Th-cells that lacked subset-defining differentiation markers were mostly non-polarised central memory T-cells (T _CM ) that produced IL-10 and expressed RORγt. They were pre-committed to a Th17 fate, since TCR stimulation in the absence of polarising cytokines induced efficient Th17 differentiation. The latter was promoted by an autocrine loop of STAT3-activating cytokines. CCR6 ⁺ Th-cells were reduced in patients with DN-STAT3 mutations but contained activated CCR6 ⁺ T _CM that produced IL-10 and responded vigorously to AD-HIES-associated pathogens. These residual CCR6 ⁺ Th-cells provided B-cell help for IgG and IgE production. Conclusions: Th17 differentiation in AD-HIES patients was not completely impaired but arrested at an intermediate stage of IL-10 producing “pre-Th17”-cells. Surprisingly, DN-STAT3 mutations did not inhibit IL-10 production by CD4 ⁺ T-cells. Pre-Th17-cells were activated by AD-HIES-associated pathogens and possessed B-helper functions, suggesting that they are not protective but promote aberrant IgE production.

Regulatory T-cells (Tregs) maintain immune homeostasis, but antigens activating adaptive Tregs in human pathologies are ill-defined. EOMES ⁺ type-1 regulatory (Tr1)-like T-cells had a dysregulated homeostasis in multiple sclerosis (MS), which was related to their activation in the central nervous system (CNS). EOMES ⁺ Tr1-like cells were enriched and clonally expanded in patient’s cerebrospinal fluid (CSF) and were the major IL-10-producing T-cell subset in the CNS. Regulatory T-cells from PwMS produced IL-10 and IFN-γ with antigens derived from Epstein-Barr Virus (EBV), but not from myelin. EOMES ⁺ Tr1-like cells responded selectively to the latency-associated antigen EBNA1, whereas FOXP3 ⁺ Tregs and Th1-cells responded also to lytic EBV antigens. EBNA1-specific EOMES ⁺ Tr1-like cells were present in patients carrying the HLA-DRB1*15:01 risk allele, were associated with anti-EBNA1 IgG and disappeared upon therapeutic B-cell depletion. IL-10 ⁺ EOMES ⁺ Tr1-like were present in MS brain lesions, and some were in the vicinity of EBV-infected B-cells and CD8 ⁺ T-cells. Notably, EOMES ⁺ Tr1-like cells suppressed CD8+T-cell activation by EBV-infected B-cells. We propose that the insufficient protective functions of Tregs in MS are due to their aberrant anti-viral specificities that promote immune escape of a disease-associated virus.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of the transcriptional landscape of complex tissues, enabling the discovery of novel cell types and biological functions. However, the identification and classification of cells from scRNA-seq datasets remain significant challenges. To address this, we developed a new computational tool called CIA (Cluster Independent Annotation), which accurately identifies cell types across different datasets without requiring a fully annotated reference dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to functional annotation of single cells. Our results demonstrate that CIA outperforms other state-of-the-art approaches, while also having significantly lower computational running time. Overall, CIA simplifies the process of obtaining reproducible signature-based cell assignments that can be easily interpreted through graphical summaries providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells. The CIA framework is implemented in both the Python and R programming languages, making it applicable to all main single-cell analysis frameworks, and it is available under the MIT license with its documentation at the following links: Python package: https://pypi.org/project/cia-python/ Python tutorial: https://cia-python.readthedocs.io/en/latest/tutorial/Cluster_Independent_Annotation.html R package and tutorial: https://github.com/ingmbioinfo/CIA_R