Bcl11b sustains multipotency and restricts effector programs of intestinal-resident memory CD8+ T cells

Regulation of intestinal tissue‑resident memory T cells: a potential target for inflammatory bowel disease

Tissue-resident memory T (TRM) cells are populations which settle down in non-lymphoid tissues instead of returning to secondary lymph organs after the antigen presentation. These cells can provide rapid on-site immune protection as well as long-term tissue damage. It is reported that TRM cells from small intestine and colon exhibited distinctive patterns of cytokine and granzyme expression along with substantial transcriptional and functional heterogeneity. In this review, we focus on the reason why they lodge in intestinal tract, their developmental plasticity of going back to to circulation, as well as their regulators associated with retention, maintenance, exhaustion and metabolism. We also elaborate their role in the inflammatory bowel disease (IBD) and discuss the potential therapeutic strategies targeting TRM cells.

BCL11b interacts with RNA and proteins involved in RNA processing and developmental diseases

BCL11b is a transcription regulator and a tumor suppressor involved in lymphomagenesis, central nervous system (CNS) and immune system developments. BCL11b favors persistence of HIV latency and contributes to control cell cycle, differentiation and apoptosis in multiple organisms and cell models. Although BCL11b recruits the non-coding RNA 7SK and epigenetic enzymes to regulate gene expression, BCL11b-associated ribonucleoprotein complexes are unknown. Thanks to CLIP-seq and quantitative LC-MS/MS mass spectrometry approaches complemented with systems biology validations, we show that BCL11b interacts with RNA splicing and non-sense-mediated decay proteins, including FUS, SMN1, UPF1 and Drosha, which may contribute in isoform selection of protein-coding RNA isoforms from noncoding-RNAs isoforms (retained introns or nonsense mediated RNA). Interestingly, BCL11b binds to RNA transcripts and proteins encoded by the same genes (FUS, ESWR1, CHD and Tubulin). Our study highlights that BCL11b targets RNA processing and splicing proteins, and RNAs that implicate cell cycle, development, neurodegenerative, and cancer pathways. These findings will help future mechanistic understanding of developmental disorders. IMPORTANCE: BCL11b-protein and RNA interactomes reveal BLC11b association with specific nucleoprotein complexes involved in the regulation of genes expression. BCL11b interacts with RNA processing and splicing proteins.

The IL-21/IL-21R signaling axis regulates CD4+ T-cell responsiveness to IL-12 to promote bacterial-induced colitis

IL-21/IL-21R signaling dysregulation is linked to multiple chronic intestinal inflammatory disorders in humans and animal models of human diseases. In addition to its critical requirement for the generation and development of germinal center B cells, IL-21/IL-21R signaling can also regulate the effector functions of a variety of T-cell subsets. The antibody-mediated abrogation of IL-21/IL-21R signaling led to the impaired expression of IFN-γ by mucosal CD4+ T cells from human subjects with colitis, suggesting an IL-21/IL-21R-triggered positive feedback loop of the TH1 immune response in the colon. Despite recent advances in our understanding of the mechanisms underpinning the regulation of proinflammatory immune responses by the IL-21/IL-21R signaling axis, it remains unclear how this pathway or its downstream molecules contribute to inflammation during bacterial-induced colitis. This study found that IL-21 enhances the surface expression of IL-12Rβ2, but not IL-12Rβ1, in CD4+ T cells, leading to TH1 differentiation and stability. Consistently, these findings also point to an indispensable role of the IL-12Rβ2 signaling axis in promoting proinflammatory immune responses during Citrobacter rodentium-induced colitis. Genetic deletion of the IL-12Rβ2 signaling pathway led to the attenuation of C. rodentium-induced colitis in vivo. The genetic deletion of the IL-12Rβ2 signaling pathway did not alter the host's ability to respond adequately to C. rodentium infection or the ability of Il12rb2-/- mice to express antigen-specific cytokines (IFN-γ, IL-17A). IL-21 is a pleiotropic cytokine exerting a wide range of immunomodulatory functions in multiple tissues, and its direct targeting may result in undesirable off-target consequences. These findings highlight the possibility for targeted manipulations of signaling cascades downstream of main regulators of proinflammatory responses to control invading pathogens while preserving the integrity of host immune responses. A better understanding of the novel mechanisms by which IL-21/IL-21R signaling regulates bacterial-induced colitis will provide insights into the development of new therapeutic and preventive strategies to harness IL-21/IL-21R signaling or its downstream molecules to treat infectious colitis.

MAdCAM-1 co-stimulation combined with retinoic acid and TGF-β induces blood CD8+ T cells to adopt a gut CD101+ TRM phenotype

Resident memory T cells (TRMs) help control local immune homeostasis and contribute to tissue-protective immune responses. The local cues that guide their differentiation and localization are poorly defined. We demonstrate that mucosal vascular addressin cell adhesion molecule 1, a ligand for the gut-homing receptor α4β7 integrin, in the presence of retinoic acid and transforming growth factor-β (TGF-β) provides a co-stimulatory signal that induces blood cluster of differentiation (CD8+ T cells to adopt a TRM-like phenotype. These cells express CD103 (integrin αE) and CD69, the two major TRM cell-surface markers, along with CD101. They also express C-C motif chemokine receptors 5 (CCR5) , C-C motif chemokine receptors 9 (CCR9), and α4β7, three receptors associated with gut homing. A subset also expresses E-cadherin, a ligand for αEβ7. Fluorescent lifetime imaging indicated an αEβ7 and E-cadherin cis interaction on the plasma membrane. This report advances our understanding of the signals that drive the differentiation of CD8+ T cells into resident memory T cells and provides a means to expand these cells in vitro, thereby affording an avenue to generate more effective tissue-specific immunotherapies.

TCF1-positive and TCF1-negative TRM CD8 T cell subsets and cDC1s orchestrate melanoma protection and immunotherapy response

BACKGROUND

Melanoma, the most lethal form of skin cancer, has undergone a transformative treatment shift with the advent of checkpoint blockade immunotherapy (CBI). Understanding the intricate network of immune cells infiltrating the tumor and orchestrating the control of melanoma cells and the response to CBI is currently of utmost importance. There is evidence underscoring the significance of tissue-resident memory (TRM) CD8 T cells and classic dendritic cell type 1 (cDC1) in cancer protection. Transcriptomic studies also support the existence of a TCF7+ (encoding TCF1) T cell as the most important for immunotherapy response, although uncertainty exists about whether there is a TCF1+TRM T cell due to evidence indicating TCF1 downregulation for tissue residency activation.

METHODS

We used multiplexed immunofluorescence and spectral flow cytometry to evaluate TRM CD8 T cells and cDC1 in two melanoma patient cohorts: one immunotherapy-naive and the other receiving immunotherapy. The first cohort was divided between patients free of disease or with metastasis 2 years postdiagnosis while the second between CBI responders and non-responders.

RESULTS

Our study identifies two CD8+TRM subsets, TCF1+ and TCF1-, correlating with melanoma protection. TCF1+TRM cells show heightened expression of IFN-γ and Ki67 while TCF1- TRM cells exhibit increased expression of cytotoxic molecules. In metastatic patients, TRM subsets undergo a shift in marker expression, with the TCF1- subset displaying increased expression of exhaustion markers. We observed a close spatial correlation between cDC1s and TRMs, with TCF1+TRM/cDC1 pairs enriched in the stroma and TCF1- TRM/cDC1 pairs in tumor areas. Notably, these TCF1- TRMs express cytotoxic molecules and are associated with apoptotic melanoma cells. Both TCF1+ and TCF1- TRM subsets, alongside cDC1, prove relevant to CBI response.

CONCLUSIONS

Our study supports the importance of TRM CD8 T cells and cDC1 in melanoma protection while also highlighting the existence of functionally distinctive TCF1+ and TCF1- TRM subsets, both crucial for melanoma control and CBI response.

The emerging role of effector functions exerted by tissue-resident memory T cells

The magnitude of the effector functions of memory T cells determines the consequences of the protection against invading pathogens and tumor development or the pathogenesis of autoimmune and allergic diseases. Tissue-resident memory T cells (TRM cells) are unique T-cell populations that persist in tissues for long periods awaiting re-encounter with their cognate antigen. Although TRM cell reactivation primarily requires the presentation of cognate antigens, recent evidence has shown that, in addition to the conventional concept, TRM cells can be reactivated without the presentation of cognate antigens. Non-cognate TRM cell activation is triggered by cross-reactive antigens or by several combinations of cytokines, including interleukin (IL)-2, IL-7, IL-12, IL-15 and IL-18. The activation mode of TRM cells reinforces their cytotoxic activity and promotes the secretion of effector cytokines (such as interferon-gamma and tumor necrosis factor-alpha). This review highlights the key features of TRM cell maintenance and reactivation and discusses the importance of effector functions that TRM cells exert upon being presented with cognate and/or non-cognate antigens, as well as cytokines secreted by TRM and non-TRM cells within the tissue microenvironment.

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis

BACKGROUND

Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

RESULTS

To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.

CONCLUSION

MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis

Background

Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

Results

To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.

Conclusion

MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.