T cells are an important part of our immune system. T helper (Th) cells support and coordinate central functions of the adaptive immune response and thus contribute significantly to the defense against various pathogens and to the control of the body’s own cells. Various specialized Th cell populations, including Th1, Th2 and Th17 cells, have developed to cover the enormous variety of potential pathogens and to be able to react optimally to different conditions. The focus of this article is on a closer look at the Th1 cell population, highlighting differences to other Th cell types.
T cells are an important part of our immune system. T helper (Th) cells support and coordinate central functions of the adaptive immune response and thus contribute significantly to the defense against various pathogens and to the control of the body’s own cells. Various specialized Th cell populations, including Th1, Th2 and Th17 cells, have developed to cover the enormous variety of potential pathogens and to be able to react optimally to different conditions. These all differentiate from naive CD4+ T cells after initial contact with pathogens or antigen-presenting cells (APCs). Depending on the Th cell population, certain cytokines are secreted that act on other cells as part of the external environment and thus trigger various immunological processes. The subdivision of Th cells into different populations is primarily based on the expression of certain transcription factors, the presentation of surface molecules and the production of specific cytokines. Transcription factors essentially determine the differentiation of naive CD4+ T cells into the respective Th cell type and control the gene expression for the fulfillment of the respective effector functions. For Th1 cells, it is primarily the transcription factor T-BET that induces the production of the Th1 cytokine IFNγ. In contrast, Th2 cells are characterized by the expression of GATA3 and the secretion of IL-4, IL-5 and IL-13, while Th17 cells exhibit the transcription factor RORγt and the production of IL-17 and IL-22. The focus of this article is on a closer look at the Th1 cell population, highlighting differences to other Th cell types.
Th1 cells between effective immune response and autoimmune diseases
After the maturation of T cells in the thymus, naive CD4+ T cells circulate in the body and encounter APCs in secondary lymphoid organs. When APCs present an epitope on their MHCII receptors that a T cell recognizes via its T cell receptor (TCR), the T cell is activated upon successful costimulation. The metabolism of the T cell then changes drastically in order to cover the increased energy requirements and prepare the cell for the upcoming clonal expansion. In addition, cytokines in the environment, such as IL-12, which is secreted by dendritic cells or macrophages, activate specific differentiation programs to polarize the cell towards Th1. The induced transcription factors TBET, STAT1 and STAT4 activate the production of IFNγ, which in turn amplifies and maintains the activity of TBET via a positive feedback loop. The expression of Th1-specific transcription factors in a Th1 cell population inhibits the differentiation of other subpopulations to a certain extent, which leads to a targeted immune response. TBET, for example, inhibits the transcription of GATA3 and leads to epigenetic changes that make the IFNγ locus more accessible, while the locus encoding the Th2 cytokines IL-4 and IL-13 is transcriptionally repressed. The proteins CXCR3 and CCR5 expressed on the surface of activated Th1 cells enable, among other things, the migration of activated Th1 cells from secondary lymphoid organs to the site of infection. Th1 cells continue to secrete IFNγ there and activate macrophages, which then eliminate intracellular pathogens (Fig. 1). The reactive oxygen molecules and activated enzymes formed in the process can also affect the surrounding tissue. Th1 cells also produce TNF and induce the production of TNF, IL-1 and chemokines in other cells, such as macrophages. As a result, they promote a pro-inflammatory environment and lead to the recruitment of further immune cells. Without adequate control of the immune response, chronic inflammation can occur. However, Th1 cells are also able to produce IL-10, which counteracts this development.
Th1 cells play a particularly important role in the defense against intracellular pathogens such as various bacteria and viruses, while Th2 cells are generally important for the humoral defense against parasites such as worms. The ratio of the amounts of different cytokines can provide information about the course of an infection. For example, intracellular parasites such as Leishmania usually induce a Th1-dominated response. Patients with higher levels of Th1 cytokines such as IFNγ show better courses of cutaneous leishmaniasis than patients with higher levels of Th2 cytokines such as IL-4. A balanced Th1-Th2 response seems to play a role especially in early stages of infection [1]. Disruptions in the regular Th1 immune response lead to increased susceptibility to certain bacteria and viruses; patients with mutations in the IFNγ receptor are susceptible to infections with mycobacteria or salmonella.
The identification of genes associated with immunodeficiencies has contributed to the elucidation of signaling pathways and immune cell functions. Targeted knock-downs or knock-outs of genes in animal models are also helpful in deciphering gene functions and differentiating the contributions of individual cells and their tasks. A balanced, efficient and controlled immune response is not only crucial in the defense against pathogens, but also for the prevention of autoimmune diseases. Th1 cells play a central role in autoimmune diseases such as rheumatoid arthritis, type 1 diabetes and multiple sclerosis. Th17 cells are also frequently involved in the pathogenesis of autoimmune diseases. In general, the better we understand the processes of differentiation and activation of T cells as a whole and of subpopulations in particular, the better we can understand how they are altered in various diseases and find ways to influence them in the long term.
Gene expression and regulatory mechanisms
Although in molecular research changes in the abundance of RNAs are often regarded as a proxy for cellular processes, gene expression is a complex process with diverse regulatory mechanisms (Fig. 2) . Ultimately, it is primarily proteins, their activities, modifications, subcellular localization and interactions that determine the state of a cell or an entire organism. One of the mechanisms for regulating protein levels is translational control, which means that mRNAs are translated more or less depending on the conditions. This mainly affects systems in which cells have to adapt quickly to new conditions, such as in the activation and differentiation of T cells. Other regulatory mechanisms concern proteins themselves, i.e. that they can be modified after synthesis and folding by post-translational modifications (PTMs) or proteolytic cleavage or removed again by targeted protein degradation. In addition to phosphorylation, lesser-known PTMs such as prenylation and palmitoylation, in which various lipid molecules are attached to proteins, also play a decisive role in the differentiation and functionality of Th cells. This shows that the analysis of different molecules and regulatory mechanisms is essential for a comprehensive understanding of cellular processes.
Omics analyses
Omics technologies represent a comprehensive group of analytical approaches that allow cells to be studied in detail at different molecular levels. These levels mainly include the genome, the transcriptome, the proteome and the metabolome, each of which comprises the entirety of the DNA, transcribed RNAs, proteins and metabolites present in the cell. The applications for their analyses include, for example, whole genome sequencing for genomics, whole transcriptome sequencing for transcriptomics and mass spectrometry for proteomics and metabolomics. In each area, there are a variety of additional techniques and applications that allow further nuances and aim to analyze different cellular processes. An additional application that plays a major role in our own research into Th1 cells is ribosome profiling to determine the translatome. Here, the actually translated sections in RNAs are examined. After disruption of the cells, the extract is treated with RNases, which degrade all RNA except those sections that are located within elongating ribosomes and are thus protected. In further experimental steps, these RNA pieces are purified, processed and finally sequenced. This provides an exact picture of the translation at a specific point in time. At the protein level, various methods can be used to analyze turnover, modification with PTMs and even their conformation. The application of various omics methods, the development of ever better and more sensitive technologies and the integration of the collected data are gradually opening up the regulated processes of the cell.
In the following sections we will present, without claiming to be exhaustive, some interesting methodological innovations and results from the literature and our own research that have been obtained using various omics technologies in the field of T cell research.
Translatomics
RNA translation data obtained by ribosome profiling can be combined with corresponding RNA sequencing data to determine the translational efficiency of specific transcripts. This provides information on how efficiently the various RNAs are translated. There are currently only a few studies that use this method in T cells. Meyers and colleagues [2] use ribosome profiling to investigate the influence of a mutation in the mTOR signaling pathway on naive CD4+ T cells. In a study by Manfrini and colleagues [3], the translational control of metabolic processes was investigated in a sample of Th1 cells over one week after stimulation of naive CD4+ T cells. In our own research, we used ribosome profiling in combination with RNA sequencing and proteomics to investigate changes during the differentiation of naïve CD4+ T cells into Th1 cells. Interestingly, the analysis of changes in translational efficiency at different time points has shown that several proteins are translationally regulated in the biological process of protein prenylation.
Ribosome profiling can also be used to discover previously unknown translation events and thus potential new targets for influencing T cell differentiation. As described in the review by Della Bella, Koch and Baerenfaller [4], many functional microRNAs and long ncRNAs that influence the activation and differentiation of T cells are hidden in so-called non-coding RNAs (ncRNAs). In addition, they often also contain short open reading frames (sORF), i.e. RNA sequence segments that code for less than 100 amino acids. It has already been shown in several organisms and cell systems that the translation of these sORFs can lead to functional sORF-encoded polypeptides (SEP) in addition to regulatory tasks. Based on our ribosome profiling datafor Th1 cell differentiation, we were able to identify several such SEPs. Since many of the regulatory processes are conserved, it can be assumed that these SEPs play a role in the differentiation of Th1 cells. Exactly what role, however, still needs to be investigated in more detail.
Proteomics
As shown for example by Wolf and colleagues [5], proteomics in combination with other omics technologies can provide deeper insights into the cellular processes involved in CD4+ T cell activation. In their study, they activated human, naive CD4+ T cells non-specifically in vitro and analyzed the proteome of the cells at different time points using mass spectrometry. The proteomics data were then combined with RNA sequencing data, transcription factor binding data and epigenetic data on DNA accessibility. This enabled them to show how naive CD4+ T cells are outwardly dormant but ready to quickly adapt to their new tasks when activated. This is due to the fact that they continuously synthesize and degrade a small group of proteins, including many transcription factors such as ETS1, LEF-1, FOXP1, KLF-2 or TCF-1. Furthermore, it has been shown that naive CD4+ T cells contain many inactive ribosomes in the cytoplasm, which translate RNAs after activation whose translation was suppressed up to this point. CD69 is one of the proteins whose translation is inhibited, but is quickly found on the cell surface after activation. While it was previously known that naïve CD4+ T cells rapidly adapt the expression of RNA and proteins upon activation and drastically remodel their metabolism, these results impressively illustrate that translational regulation and targeted protein degradation contribute to regulating these processes.
In our research, we have determined proteins that are regulated during the differentiation and activation of Th1 cells. In addition, we have enriched and identified prenylated proteins to collect information about this post-translational modification during the processes. The comparison of the differentially prenylated proteins with the measurements of the whole proteome allowed us to identify proteins that are differentially prenylated during the activation of Th1 cells. Of the proteins identified, many were already known to be prenylated, such as members of the G protein family, including RAS and RAB proteins. These have an influence on the differentiation and activation of T cells, but of course they also fulfill important functions in other cell types. The blockade of activity, especially of RAS by various pharmacological interventions such as the inhibition of prenylation, has been studied extensively, especially in the field of oncology, but with limited success so far. On the other hand, lonafarnib, which prevents a subtype of prenylation, has been approved in the EU for the treatment of progeria since 2022. Interestingly, the prenylated proteins identified in our data include proteins that do not contain the typical prenylation motif at the protein C-terminus.
Bioinformatic sequence and structural analyses revealed new prenylation motifs at the protein C-terminus on the one hand and prenylation at cysteines within the protein sequence on the other. Since the prenylation of proteins is closely linked to their localization and function, this provides novel insights into the various regulatory mechanisms during the activation of Th1 cells. In general, the methods just described enable the comprehensive influence of prenylation blockade on the proteome and the prenylome to be recorded. This allows both intended and undesired effects of therapies on cells to be recognized and controlled. For the targeted inhibition of proteins, knowledge of their 3D structure is a great advantage. Thanks to enormous progress in the field of structural analysis, the structure of a protein can now be predicted on the basis of protein sequences. One example of this is CXCR3, a G-protein coupled receptor (GPCR), which is also known as a surface marker of Th1 cells and whose signal transduction is mediated by newly identified and known prenylated proteins, among others. The structure of CXCR3 has not yet been deciphered using conventional experimental methods, but is now available as a prediction (Fig. 3). The structure of many other membrane proteins has also been made accessible in this way. This also facilitates the design of molecules in order to influence them specifically. In the case of CXCR3, this could have some therapeutic potential.
Single cell analysis
The above-mentioned characteristics of different Th cell populations and results of different omics technologies are mostly based on the analysis of many different cells of the same population. This blurs the differences between individual cells, even though they are not homogeneous. For example, it was established relatively early on using methods such as flow cytometry that there are Th cells that express both the cytokine IFNγ, which is typical for Th1, and IL-17, which is typical for Th17, which blurs the originally defined populations. In addition, there is a certain plasticity between different Th cell populations such as Th1 and Th17 cells. This means that Th cells can change their functional identity depending on changes in external conditions. Other factors such as the signal strength of the TCR upon activation, the presentation of the epitope or epigenetic conditions also influence the differentiation of Th cells and thus their behavior. The further development of various omics methods, particularly in single-cell RNA sequencing (scRNA-Seq) and mass cytometry, has enabled the differentiated analysis of more complex cell populations.
Mass cytometry
Mass cytometry, also known as CyTOF, is a technology that detects multiple, predefined proteins in a large number of individual cells and has a high sensitivity and precision in quantification. For this purpose, a predefined panel of proteins is labeled using heavy metal-coupled antibodies. Tortola and colleagues [6] used mass cytometry to analyze the expression of cytokines in different Th cells to analyze the diversity of Th cell responses generated in vitro and in animal models. They observed a large heterogeneity of cytokine responses within Th cell subpopulations. For example, in a mouse model for influenza A, as expected, Th cells with a Th1 phenotype were detected with expression of IFNγ expression. However, within this population there were differences in the expression of TNF, GM-CSF, IL-10 and IL-2 and some IFNγ positive cells also produced IL-13.
Since even in vitro differentiated cells did not show uniform cytokine expression patterns, the question arises as to which expression patterns we recognize as cell populations as opposed to cell states. In vivo, the circumstances of T cell differentiation are even less uniform, as humans do not grow up in isolated and sterile conditions and are exposed to microorganisms and various infectious diseases throughout their lives, which shapes the immune response. Therefore, it is extremely relevant to analyze Th cell functions in more complex systems. A study in the group of Joller [7] investigated the effects of sequential, heterologous infections on the immune response in a mouse model. Mass cytometry has shown that Th1 memory cells generated after a viral infection also have a protective effect in subsequent bacterial infections and that memory cells can thus respond rapidly to heterologous challenge.
Single-cell RNA sequencing
ScRNA-Seq followed by clustering analyses and the assignment of individual cell populations to specific clusters has become an enormously popular method in recent years, enabling comprehensive and increasingly sophisticated analyses and constantly proclaiming new cell populations. In order to identify relevant differences in cell populations in scRNA-Seq data, the different cell types should be included in a reference that is used for comparison. This can be limiting when analyzing patient samples, which may have disease-specific cell populations. Andreatta and colleagues [8] have therefore created a reference atlas for T cells based on scRNA-Seq data. In addition, they have developed a method to compare newly generated scRNA-Seq data with the reference atlas. This allows relevant differences to be identified, even if not all cell states are included in the reference.
Single-cell RNA sequencing combined with sequencing of T-cell receptors
Particularly noteworthy here is the method for the combined analysis of scRNA-Seq data with single-cell sequencing of the TCR of the same cell. This enables the tracking of the clonal expansion of certain T cells together with the determination of their associated transcription profile. This has been used, for example, to track the development of human thymocytes and discover trends in VDJ recombination of T cells [9]. In their study, Cano-Gamez and colleagues [10] analyzed the differentiation of human Th1 cells with scRNA-Seq and proteomics and integrated it with a published TCR sequencing dataset. The focus was on the effect of cytokines on naive CD4+ and memory T cells. Analysis of cell type-specific expression patterns and cell states revealed a transcriptional gradient from naïve CD4+ T cells to effector T memory cells.
Data Science
As shown several times before, interesting results are often achieved by integrating different types of data generated using different methods. An important aspect in the planning of such multi-omics experiments is the focus on the subsequent data analysis, which often makes the adaptation and development of new bioinformatics methods indispensable. When integrating different data types or data sets from different laboratories, there are also protocol-related deviations that can have an influence on the subsequent results. It is therefore of great importance to apply standardization to laboratory protocols and data analysis, to normalize data and to use appropriate methods to fill in missing data and to integrate data. A clever experimental design with multiple samples from the same biological replicate under different conditions or at different time points can help to reduce bias and variance so that real differences between groups can be uncovered.
On the other hand, the wealth of data and the increasing number of samples also make it possible to model complex processes and apply machine learning. This allows biomarkers or other relevant patterns to be recognized in large and complex data sets. Rade and colleagues [11] analyzed transcriptome data from 224 samples from publicly available datasets from different CD4+ Th cell populations with the aim of discovering stable and consistent gene signatures across different time points in all T cell populations. They identified biomarker signatures of Th cell activation with time-resolved gene expression profiles of 521 relevant genes. In another study, Puniya and colleagues [12] used a mechanistic model to predict T cell differentiation under various environmental conditions that have not yet been investigated experimentally. The model predicted both classical and mixed T cell phenotypes that co-express transcription factors from different differentiated T cell populations. These are just a few examples of how bioinformatics analyses can provide new insights into the differentiation and activation of T cells.
Perspectives
With the advent of omics technologies, it has become possible to study the differentiation and activation of Th1 cells and other T cell populations in more detail, contributing to a more comprehensive understanding of these cells and the immune response. Further insights into the differentiation and activation of T cells will certainly follow in the future as the methods are improved and further developed. This opens up the possibility of identifying biomarkers for the diagnosis and therapy control of immunological diseases and finding new therapeutic approaches to influence them.
Literature:
- Carneiro MB, Lopes ME, Hohman LS, et al.: Th1-Th2 Cross-Regulation Controls Early Leishmania Infection in the Skin by Modulating the Size of the Permissive Monocytic Host Cell Reservoir. Cell Host Microbe 2020; 27: 752-768; doi: 10.1016/j.chom.2020.03.011.
- Myers DR, Norlin E, Vercoulen Y, Roose JP: Active Tonic mTORC1 Signals Shape Baseline Translation in Naive T Cells. Cell Rep 2019; 27: 1858–1874.
- Manfrini N, Ricciardi S, Alfieri R, et al.: Ribosome profiling unveils translational regulation of metabolic enzymes in primary CD4+ Th1 cells. Developmental & Comparative Immunology 2020, 109: 103697; doi: 10.1016/j.dci.2020.103697.
- Della Bella E, Koch J, Baerenfaller K: Translation and emerging functions of non-coding RNAs in inflammation and immunity. Allergy 2022; 77: 2025–2037.
- Wolf T, et al.: Dynamics in protein translation sustaining T cell preparedness. Nat Immunol 2020; 21: 927–937.
- Tortola L, et al.: High-Dimensional T Helper Cell Profiling Reveals a Broad Diversity of Stably Committed Effector States and Uncovers Interlineage Relationships. Immunity 2020; 53: 597–613.
- Rakebrandt N, Yassini N, Kolz A, et al.: Memory Th1 cells modulate heterologous diseases through innate function. bioRxiv 2023;
doi: 10.1101/2023.03.22.533799. - Andreatta M, et al.: Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat Commun 2021; 12: 2965.
- Park JE, et al.: A cell atlas of human thymic development defines T cell repertoire formation. Science 2020; 367: eaay3224.
- Cano-Gamez E, et al.: Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines. Nat Commun 2020; 11: 1801.
- Rade M, et al.: A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation. Genome Biology 2023; 24: 287.
- Puniya BL, et al.: A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage. Frontiers in Physiology 2018; 9.
Further reading:
- Abbas AK, Lichtman AH, Pillai S, Baker DL: Cellular and Molecular Immunology. (Elsevier, Philadelphia, Pennsylvania, 2022).
- Taheri M, et al.: Emerging Role of Non-Coding RNAs in Regulation of T-Lymphocyte Function. Frontiers in Immunology 2021; 12.
InFo NEUROLOGIE & PSYCHIATRIE 2024; 22(3): 11–17
