Dissecting the dynamic transcriptional landscape of early T helper cell differentiation into Th1, Th2, and Th1/2 hybrid cells

Multistability and predominant hybrid phenotypes in a four node mutually repressive network of Th1/Th2/Th17/Treg differentiation

Elucidating the emergent dynamics of cellular differentiation networks is crucial to understanding cell-fate decisions. Toggle switch - a network of mutually repressive lineage-specific transcription factors A and B - enables two phenotypes from a common progenitor: (high A, low B) and (low A, high B). However, the dynamics of networks enabling differentiation of more than two phenotypes from a progenitor cell has not been well-studied. Here, we investigate the dynamics of a four-node network A, B, C, and D inhibiting each other, forming a toggle tetrahedron. Our simulations show that this network is multistable and predominantly allows for the co-existence of six hybrid phenotypes where two of the nodes are expressed relatively high as compared to the remaining two, for instance (high A, high B, low C, low D). Finally, we apply our results to understand naïve CD4+ T cell differentiation into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process.

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model

Background

Osteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA.

Methods

We screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set by combining weighted coexpression analysis (WGCNA). Functional enrichment of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to explore the correlation of Imr-DEGs. A random forest machine learning model was constructed to analyze the characteristic genes of OA, and the diagnostic significance was determined by the Receiver Operating Characteristic Curve (ROC) curve, with external datasets used to verify the diagnostic ability. Different immune subtypes of OA were identified by unsupervised clustering, and the function of these subtypes was analyzed by gene set enrichment analysis (GSVA). The Drug-Gene Interaction Database was used to explore the relationship between characteristic genes and drugs.

Results

Single sample gene set enrichment analysis (ssGSEA) revealed that 16 of 28 immune cell subsets in the dataset significantly differed between OA and normal groups. There were 26 Imr-DEGs identified by WGCNA, showing that functional enrichment was related to immune response. Using the random forest machine learning model algorithm, nine characteristic genes were obtained: BLNK (AUC = 0.809), CCL18 (AUC = 0.692), CD74 (AUC = 0.794), CSF1R (AUC = 0.835), RAC2 (AUC = 0.792), INSR (AUC = 0.765), IL11 (AUC = 0.662), IL18 (AUC = 0.699), and TLR7 (AUC = 0.807). A nomogram was constructed to predict the occurrence and development of OA, and the calibration curve confirmed the accuracy of these 9 genes in OA diagnosis.

Conclusion

This study identified characteristic genes related to the immune microenvironment in OA, providing new insight into the risk factors of OA.

A review of CD4+ T cell differentiation and diversity in dogs

CD4+ T cells are an integral component of the adaptive immune response, carrying out many functions to combat a diverse range of pathogenic challenges. These cells exhibit remarkable plasticity, differentiating into specialized subsets such as T helper type 1 (TH1), TH2, TH9, TH17, TH22, regulatory T cells (Tregs), and follicular T helper (TFH) cells. Each subset is capable of addressing a distinct immunological need ranging from pathogen eradication to regulation of immune homeostasis. As the immune response subsides, CD4+ T cells rest down into long-lived memory phenotypes-including central memory (TCM), effector memory (TEM), resident memory (TRM), and terminally differentiated effector memory cells (TEMRA) that are localized to facilitate a swift and potent response upon antigen re-encounter. This capacity for long-term immunological memory and rapid reactivation upon secondary exposure highlights the role CD4+ T cells play in sustaining both adaptive defense mechanisms and maintenance. Decades of mouse, human, and to a lesser extent, pig T cell research has provided the framework for understanding the role of CD4+ T cells in immune responses, but these model systems do not always mimic each other. Although our understanding of pig immunology is not as extensive as mouse or human research, we have gained valuable insight by studying this model. More akin to pigs, our understanding of CD4+ T cells in dogs is much less complete. This disparity exists in part because canine immunologists depend on paradigms from mouse and human studies to characterize CD4+ T cells in dogs, with a fraction of available lineage-defining antibody markers. Despite this, every major CD4+ T cell subset has been described to some extent in dogs. These subsets have been studied in various contexts, including in vitro stimulation, homeostatic conditions, and across a range of disease states. Canine CD4+ T cells have been categorized according to lineage-defining characteristics, trafficking patterns, and what cytokines they produce upon stimulation. This review addresses our current understanding of canine CD4+ T cells from a comparative perspective by highlighting both the similarities and differences from mouse, human, and pig CD4+ T cell biology. We also discuss knowledge gaps in our current understanding of CD4+ T cells in dogs that could provide direction for future studies in the field.

Immunophenotypic classification regarding prognosis in peripheral T cell lymphoma, NOS, and nodal T follicular helper T cell lymphoma, angioimmunoblastic-type

This study aimed to determine the clinicopathological predictive factors of peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS), and nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (nTFH, AI-type). In this single-centered, retrospective study, medical records of 59 patients who were diagnosed with PTCL, NOS, or nTFH, AI-type from March 2007 to September 2022 were reviewed. The clinicopathological variables, including immunohistochemistry(IHC) subgroups, distinguishing TBX21 from the GATA3 subgroups were analyzed. Overall, 28 patients (75.7%) in the TBX21 group were PTCL, NOS. There were 9 (24.3%) patients in the GATA3 group. In univariable analyses, lymphoma subtype, age, and performance status were associated with progression-free survival (PFS), and overall survival (OS). In multivariable analyses, lymphoma subtype, and performance status were related to PFS and OS (P = 0.012, P < 0.001, P = 0.006, and P < 0.001, respectively). The GATA3 subgroup tended to have a worse prognosis in univariable analyses; however, it became more insignificant in multivariable when lymphoma subtype and performance status were adjusted (P = 0.065, P = 0.180, P = 0.972, and P = 0.265, respectively). The double-positive group showed variable prognoses of better PFS and worse OS. PD-1 and PD-L1 were associated with the EBV in situ hybridization (P = 0.027, and P = 0.005), and PD-1 was associated with CD30 expression (P = 0.043). This study demonstrated the potential of IHC classification to predict prognosis for PTCL, NOS, as well as nTFH AI-type, although further validation is necessary. Treatments targeting CD30, PD-1, and PD-L1 appear promising for lymphoma treatment.

Clinical and CSF single-cell profiling of post-COVID-19 cognitive impairment

Natural history and mechanisms for persistent cognitive symptoms ("brain fog") following acute and often mild COVID-19 are unknown. In a large prospective cohort of people who underwent testing a median of 9 months after acute COVID-19 in the New York City/New Jersey area, we found that cognitive dysfunction is common; is not influenced by mood, fatigue, or sleepiness; and is correlated with MRI changes in very few people. In a subgroup that underwent cerebrospinal fluid analysis, there are no changes related to Alzheimer's disease or neurodegeneration. Single-cell gene expression analysis in the cerebrospinal fluid shows findings consistent with monocyte recruitment, chemokine signaling, cellular stress, and suppressed interferon response-especially in myeloid cells. Longitudinal analysis shows slow recovery accompanied by key alterations in inflammatory genes and increased protein levels of CXCL8, CCL3L1, and sTREM2. These findings suggest that the prognosis for brain fog following COVID-19 correlates with myeloid-related chemokine and interferon-responsive genes.

Differentiation and Regulation of Bovine Th2 Cells In Vitro

Bovine Th2 cells have usually been characterized by IL4 mRNA expression, but it is unclear whether their IL4 protein expression corresponds to transcription. We found that grass-fed healthy beef cattle, which had been regularly exposed to parasites on the grass, had a low frequency of IL4+ Th2 cells during flow cytometry, similar to animals grown in feedlots. To assess the distribution of IL4+ CD4+ T cells across tissues, samples from the blood, spleen, abomasal (draining), and inguinal lymph nodes were examined, which revealed limited IL4 protein detection in the CD4+ T cells across the examined tissues. To determine if bovine CD4+ T cells may develop into Th2 cells, naïve cells were stimulated with anti-bovine CD3 under a Th2 differentiation kit in vitro. The cells produced primarily IFNγ proteins, with only a small fraction (<10%) co-expressing IL4 proteins. Quantitative PCR confirmed elevated IFNγ transcription but no significant change in IL4 transcription. Surprisingly, GATA3, the master regulator of IL4, was highest in naïve CD4+ T cells but was considerably reduced following differentiation. To determine if the differentiated cells were true Th2 cells, an unbiased proteomic assay was carried out. The assay identified 4212 proteins, 422 of which were differently expressed compared to those in naïve cells. Based on these differential proteins, Th2-related upstream components were predicted, including CD3, CD28, IL4, and IL33, demonstrating typical Th2 differentiation. To boost IL4 expression, T cell receptor (TCR) stimulation strength was reduced by lowering anti-CD3 concentrations. Consequently, weak TCR stimulation essentially abolished Th2 expansion and survival. In addition, extra recombinant bovine IL4 (rbIL4) was added during Th2 differentiation, but, despite enhanced expansion, the IL4 level remained unaltered. These findings suggest that, while bovine CD4+ T cells can respond to Th2 differentiation stimuli, the bovine IL4 pathway is not regulated in the same way as in mice and humans. Furthermore, Ostertagia ostertagi (OO) extract, a gastrointestinal nematode in cattle, inhibited signaling via CD3, CD28, IL4, and TLRs/MYD88, indicating that external pathogens can influence bovine Th2 differentiation. In conclusion, though bovine CD4+ T cells can respond to IL4-driven differentiation, IL4 expression is not a defining feature of differentiated bovine Th2 cells.

Hybrid lineages of CD4+ T cells: a handbook update

CD4+ T lymphocytes have been classified into several lineages, according to their gene expression profiles and their effector responses. Interestingly, recent evidence is showing that many lineages could yield hybrid phenotypes with unique properties and functions. It has been reported that such hybrid lineages might underlie pathologies or may function as effector cells with protection capacities against molecular threats. In this work, we reviewed the characteristics of the hybrid lineages reported in the literature, in order to identify the expression profiles that characterize them and the markers that could be used to identify them. We also review the differentiation cues that elicit their hybrid origin and what is known about their physiological roles.

Transcriptional profiling upon T cell stimulation reveals down-regulation of inflammatory pathways in T and B cells in SLE versus Sjögren's syndrome

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share clinical as well as pathogenic similarities. Although previous studies suggest various abnormalities in different immune cell compartments, dedicated cell-type specific transcriptomic signatures are often masked by patient heterogeneity. Here, we performed transcriptional profiling of isolated CD4, CD8, CD16 and CD19 lymphocytes from pSS and SLE patients upon T cell stimulation, in addition to a steady-state condition directly after blood drawing, in total comprising 581 sequencing samples. T cell stimulation, which induced a pronounced inflammatory response in all four cell types, gave rise to substantial re-modulation of lymphocyte subsets in the two autoimmune diseases compared to healthy controls, far exceeding the transcriptomic differences detected at steady-state. In particular, we detected cell-type and disease-specific down-regulation of a range of pro-inflammatory cytokine and chemokine pathways. Such differences between SLE and pSS patients are instrumental for selective immune targeting by future therapies.

CD4 levels and NSCLC metastasis: the benefits of maintaining moderate levels

OBJECTIVES

Prior researches indicate that peripheral blood CD4 levels have an inverse correlation with distant tumor metastasis in non-small cell lung cancer (NSCLC). However, the linear relationship between CD4 and distant metastasis lacks clarity. Hence, the objective of this study was to ascertain the linear relationship between CD4 and distant metastasis in NSCLC patients.

METHODS

This retrospective study analyzed clinical and laboratory data of NSCLC patients between March 2016 and July 2022 at the Cancer Hospital of Anhui University of Technology. The study first applied a generalized summation model and smoothing curve fitting to determine if there was a linear relationship between CD4 and NSCLC metastasis. Secondarily, univariate logistic analysis and multiple linear regression were used to analyze the odds ratio (OR) of CD4 as a continuous variable, dichotomous variable, and trichotomous variable when predicting NSCLC metastasis. In addition, stratified and subgroup analyses were conducted to assess the reliability of CD4 in different NSCLC patient populations.

RESULTS

The study included a total of 213 NSCLC patients, among which 122 had distant metastasis and 91 had no metastasis. The smoothing curve fitting analysis revealed a U-shaped relationship between CD4 and NSCLC metastasis with a threshold effect. The univariate logistic analysis indicated that continuous CD4 expression was not significantly associated with NSCLC metastasis (P = 0.051); however, high levels of CD4 expression (≥ 35.06%) were found to be a protective factor against NSCLC metastasis when CD4+ T was a dichotomous variable (OR = 0.49, P = 0.010). Furthermore, multivariate linear regression models showed that low (< 32%) or high levels (> 44%) of CD4 significantly increased the risk of NSCLC metastasis compared to medium levels (32-44%) when CD4+ T was trichotomized. The significance was maintained in stratified analysis in relation to age, sex, type of pathology, smoke, PS, and T stage. CD4 levels were U-shaped in relation to different sites of distant metastases (bone, brain, liver), but not with lung metastases.

CONCLUSIONS

A threshold effect is shown to exist between the peripheral blood CD4 and distant metastasis in NSCLC patients. It was revealed that the risk of distant metastasis is lower when CD4 is maintained between 32 and 44%, whereas low (< 32%) or high (> 44) levels of CD4 are associated with an increased risk of distant metastasis in NSCLC patients.

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Transcriptional and Epigenetic Regulation of Context-Dependent Plasticity in T-Helper Lineages

Th cell lineage determination and functional specialization are tightly linked to the activation of lineage-determining transcription factors (TFs) that bind cis-regulatory elements. These lineage-determining TFs act in concert with multiple layers of transcriptional regulators to alter the epigenetic landscape, including DNA methylation, histone modification and three-dimensional chromosome architecture, in order to facilitate the specific Th gene expression programs that allow for phenotypic diversification. Accumulating evidence indicates that Th cell differentiation is not as rigid as classically held; rather, extensive phenotypic plasticity is an inherent feature of T cell lineages. Recent studies have begun to uncover the epigenetic programs that mechanistically govern T cell subset specification and immunological memory. Advances in next generation sequencing technologies have allowed global transcriptomic and epigenomic interrogation of CD4+ Th cells that extends previous findings focusing on individual loci. In this review, we provide an overview of recent genome-wide insights into the transcriptional and epigenetic regulation of CD4+ T cell-mediated adaptive immunity and discuss the implications for disease as well as immunotherapies.