Capturing cell-fate decisions from the molecular signatures of a receptor-dependent signaling response

Quantitative modeling of signaling in aggressive B cell lymphoma unveils conserved core network

B cell receptor (BCR) signaling is required for the survival and maturation of B cells and is deregulated in B cell lymphomas. While proximal BCR signaling is well studied, little is known about the crosstalk of downstream effector pathways, and a comprehensive quantitative network analysis of BCR signaling is missing. Here, we semi-quantitatively modelled BCR signaling in Burkitt lymphoma (BL) cells using systematically perturbed phosphorylation data of BL-2 and BL-41 cells. The models unveiled feedback and crosstalk structures in the BCR signaling network, including a negative crosstalk from p38 to MEK/ERK. The relevance of the crosstalk was verified for BCR and CD40 signaling in different BL cells and confirmed by global phosphoproteomics on ERK itself and known ERK target sites. Compared to the starting network, the trained network for BL-2 cells was better transferable to BL-41 cells. Moreover, the BL-2 network was also suited to model BCR signaling in Diffuse large B cell lymphoma cells lines with aberrant BCR signaling (HBL-1, OCI-LY3), indicating that BCR aberration does not cause a major downstream rewiring.

Understanding noise in cell signalling in the prospect of drug-targets

The introduction of noise to signals can alter central regulatory switches of cellular processes leading to diseases. Noise is inherently present in the cellular signalling system and plays a decisive role in the input-output (I/O) relation. The current study aims to understand the noise tolerance of motif structures in the cell signalling processes. The vulnerability of a node to noise could be a significant factor in causing signalling error and need to be controlled. We developed stochastic differential equation (SDE) based mathematical models for different network motifs with two nodes and studied the association between motif structure and signal-noise relation. A two-dimensional parameter space analysis on motif sensitivity with noise and input signal variation was performed to classify and rank the motifs. Identifying sensitive motifs and their high druggability infers their significance in screening potential drug-target candidates. Finally, we proposed a theoretical framework to identify nodes from a network as potential drug targets. We applied this mathematical formalism to three cancer networks to identify drug-targets and validated them with existing databases.

NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells' survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.

Defining Proteomic Signatures to Predict Multidrug Persistence in Pseudomonas aeruginosa

Bacterial persisters are difficult to eradicate because of their ability to survive prolonged exposure to a range of different antibiotics. Because they often represent small subpopulations of otherwise drug-sensitive bacterial populations, studying their physiological state and antibiotic stress response remains challenging. Sorting and enrichment procedures of persister fractions introduce experimental biases limiting the significance of follow-up molecular analyses. In contrast, proteome analysis of entire bacterial populations is highly sensitive and reproducible and can be employed to explore the persistence potential of a given strain or isolate. Here, we summarize methodology to generate proteomic signatures of persistent Pseudomonas aeruginosa isolates with variable fractions of persisters. This includes proteome sample preparation, mass spectrometry analysis, and an adaptable machine learning regression pipeline. We show that this generic method can determine a common proteomic signature of persistence among different P. aeruginosa hyper-persister mutants. We propose that this approach can be used as diagnostic tool to gauge antimicrobial persistence of clinical isolates.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Integrative meta-modeling identifies endocytic vesicles, late endosome and the nucleus as the cellular compartments primarily directing RTK signaling

Recently, intracellular receptor signaling has been identified as a key component mediating cell responses for various receptor tyrosine kinases (RTKs). However, the extent each endocytic compartment (endocytic vesicle, early endosome, recycling endosome, late endosome, lysosome and nucleus) contributes to receptor signaling has not been quantified. Furthermore, our understanding of endocytosis and receptor signaling is complicated by cell- or receptor-specific endocytosis mechanisms. Therefore, towards understanding the differential endocytic compartment signaling roles, and identifying how to achieve signal transduction control for RTKs, we delineate how endocytosis regulates RTK signaling. We achieve this via a meta-analysis across eight RTKs, integrating computational modeling with experimentally derived cell (compartment volume, trafficking kinetics and pH) and ligand-receptor (ligand/receptor concentration and interaction kinetics) physiology. Our simulations predict the abundance of signaling from eight RTKs, identifying the following hierarchy in RTK signaling: PDGFRβ > IGFR1 > EGFR > PDGFRα > VEGFR1 > VEGFR2 > Tie2 > FGFR1. We find that endocytic vesicles are the primary cell signaling compartment; over 43% of total receptor signaling occurs within the endocytic vesicle compartment for these eight RTKs. Mechanistically, we found that high RTK signaling within endocytic vesicles may be attributed to their low volume (5.3 × 10^-19 L) which facilitates an enriched ligand concentration (3.2 μM per ligand molecule within the endocytic vesicle). Under the analyzed physiological conditions, we identified extracellular ligand concentration as the most sensitive parameter to change; hence the most significant one to modify when regulating absolute compartment signaling. We also found that the late endosome and nucleus compartments are important contributors to receptor signaling, where 26% and 18%, respectively, of average receptor signaling occurs across the eight RTKs. Conversely, we found very low membrane-based receptor signaling, exhibiting <1% of the total receptor signaling for these eight RTKs. Moreover, we found that nuclear translocation, mechanistically, requires late endosomal transport; when we blocked receptor trafficking from late endosomes to the nucleus we found a 57% reduction in nuclear translocation. In summary, our research has elucidated the significance of endocytic vesicles, late endosomes and the nucleus in RTK signal propagation.

Effective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model

The complexity of cancer signaling networks limits the efficacy of most single-agent treatments and brings about challenges in identifying effective combinatorial therapies. In this study, we used chronic active B-cell receptor (BCR) signaling in diffuse large B-cell lymphoma as a model system to establish a computational framework to optimize combinatorial therapy in silico We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex cross-talk between the NFκB, ERK, and AKT pathways and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model, we predicted viability responses of many single drug and drug combinations in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Ultimately, our work establishes a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations. Cancer Res; 77(8); 1818-30. ©2017 AACR.

The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells

The intricate dynamic control and plasticity of RAS to ERK mitogenic, survival and apoptotic signalling has mystified researches for more than 30 years. Therapeutics targeting the oncogenic aberrations within this pathway often yield unsatisfactory, even undesired results, as in the case of paradoxical ERK activation in response to RAF inhibition. A direct approach of inhibiting single oncogenic proteins misses the dynamic network context governing the network signal processing. In this review, we discuss the signalling behaviour of RAS and RAF proteins in normal and in cancer cells, and the emerging systems-level properties of the RAS-to-ERK signalling network. We argue that to understand the dynamic complexities of this control system, mathematical models including mechanistic detail are required. Looking into the future, these dynamic models will build the foundation upon which more effective, rational approaches to cancer therapy will be developed.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Forecasting cell death dose-response from early signal transduction responses in vitro

The rapid pharmacodynamic response of cells to toxic xenobiotics is primarily coordinated by signal transduction networks, which follow a simple framework: the phosphorylation/dephosphorylation cycle mediated by kinases and phosphatases. However, the time course from initial pharmacodynamic response(s) to cell death following exposure can have a vast range. Viewing this time lag between early signaling events and the ultimate cellular response as an opportunity, we hypothesize that monitoring the phosphorylation of proteins related to cell death and survival pathways at key, early time points may be used to forecast a cell's eventual fate, provided that we can measure and accurately interpret the protein responses. In this paper, we focused on a three-phased approach to forecast cell death after exposure: (1) determine time points relevant to important signaling events (protein phosphorylation) by using estimations of adenosine triphosphate production to reflect the relationship between mitochondrial-driven energy metabolism and kinase response, (2) experimentally determine phosphorylation values for proteins related to cell death and/or survival pathways at these significant time points, and (3) use cluster analysis to predict the dose-response relationship between cellular exposure to a xenobiotic and plasma membrane degradation at 24 h post-exposure. To test this approach, we exposed HepG2 cells to two disparate treatments: a GSK-3β inhibitor and a MEK inhibitor. After using our three-phased approach, we were able to accurately forecast the 24 h HepG2 plasma membrane degradation dose-response from protein phosphorylation values as early as 20 min post-MEK inhibitor exposure and 40 min post-GSK-3β exposure.

Negative interactions and feedback regulations are required for transient cellular response

Signal transduction is a process required to conduct information from a receptor to the nucleus. This process is vital for the control of cellular function and fate. The dynamics of signaling activation and inhibition determine processes such as apoptosis, proliferation, and differentiation. Thus, it is important to understand the factors modulating transient and sustained response. To address this question, by applying mathematical approach we have studied the factors which can alter the activation nature of downstream signaling molecules. The factors which we have investigated are loops (feed forward and feedback loops), cross-talk of signal transduction pathways, and the change in the concentration of the signaling molecules. Based on our results we conclude that among these factors feedback loop and the cross-talks which directly inhibit the target protein dominantly controls the transient cellular response.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Using partial least squares regression to analyze cellular response data

This Teaching Resource provides lecture notes, slides, and a problem set for a lecture introducing the mathematical concepts and interpretation of partial least squares regression (PLSR) that were part of a course entitled "Systems Biology: Mammalian Signaling Networks." PLSR is a multivariate regression technique commonly applied to analyze relationships between signaling or transcriptional data and cellular behavior.

Modeling the latent dimensions of multivariate signaling datasets

Cellular signal transduction is coordinated by modifications of many proteins within cells. Protein modifications are not independent, because some are connected through shared signaling cascades and others jointly converge upon common cellular functions. This coupling creates a hidden structure within a signaling network that can point to higher level organizing principles of interest to systems biology. One can identify important covariations within large-scale datasets by using mathematical models that extract latent dimensions-the key structural elements of a measurement set. In this paper, we introduce two principal component-based methods for identifying and interpreting latent dimensions. Principal component analysis provides a starting point for unbiased inspection of the major sources of variation within a dataset. Partial least-squares regression reorients these dimensions toward a specific hypothesis of interest. Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

Unraveling the design principle for motif organization in signaling networks

Cellular signaling networks display complex architecture. Defining the design principle of this architecture is crucial for our understanding of various biological processes. Using a mathematical model for three-node feed-forward loops, we identify that the organization of motifs in specific manner within the network serves as an important regulator of signal processing. Further, incorporating a systemic stochastic perturbation to the model we could propose a possible design principle, for higher-order organization of motifs into larger networks in order to achieve specific biological output. The design principle was then verified in a large, complex human cancer signaling network. Further analysis permitted us to classify signaling nodes of the network into robust and vulnerable nodes as a result of higher order motif organization. We show that distribution of these nodes within the network at strategic locations then provides for the range of features displayed by the signaling network.

Defining the antigen receptor-dependent regulatory network that induces arrest of cycling immature B-lymphocytes

Background

Engagement of the antigen receptor on immature B-lymphocytes leads to cell cycle arrest, and subsequent apoptosis. This is an essential process for eliminating self reactive B cells during its different stages of development. However, the mechanism by which it is achieved is not completely understood.

Results

Here we employed a systems biology approach that combined extensive experimentation with in silico methodologies to chart the network of receptor-activated pathways that mediated the arrest of immature B cells in the G1 phase of the cell cycle. Interestingly, we found that only a sparse network of signaling intermediates was recruited upon engagement of the antigen receptor. This then led to the activation of a restricted subset of transcription factors, with the consequent induction of genes primarily involved in the cell death pathway. Subsequent experiments revealed that the weak initiation of intracellular signaling pathways derived from desensitization of the receptor-proximal protein tyrosine kinase Lyn, to receptor-dependent activation. Intriguingly, the desensitization was a result of the constitutive activation of this kinase in unstimulated cells, which was likely maintained through a regulatory feedback loop involving the p38 MAP kinase. The high basal activity then attenuated the ability of the antigen receptor to recruit Lyn, and thereby also the downstream signaling intermediates. Finally, integration of these results into a mathematical model provided further substantiation to the novel finding that the ground state of the intracellular signaling machinery constitutes an important determinant of the outcome of receptor-induced cellular responses.

Conclusions

Our results identify the global events leading to the G1 arrest and subsequent apoptosis in immature B cells upon receptor activation.

Integration of multiple signaling pathway activities resolves K-RAS/N-RAS mutation paradox in colon epithelial cell response to inflammatory cytokine stimulation

Colon tumors frequently harbor mutation in K-RAS and/or N-RAS, members of a GTPase family operating as a central hub for multiple key signaling pathways. While these proteins are strongly homologous, they exhibit diverse downstream effects on cell behavior. Utilizing an isogenic panel of human colon carcinoma cells bearing oncogenic mutations in K-RAS and/or N-RAS, we observed that K-RAS and double mutants similarly yield elevated apoptosis in response to treatment with TNFalpha compared to N-RAS mutants. Regardless, and in surprising contrast, key phospho-protein signals were more similar between N-RAS and dual mutants. This apparent contradiction could not be explained by any of the key signals individually, but a multi-pathway model constructed from the single-mutant cell line data was able to predict the behavior of the dual-mutant cell line. This success arises from a quantitative integration of multiple pro-apoptotic (pIkappaBalpha, pERK2) and pro-survival (pJNK, pHSP27) signals in manner not easily discerned from intuitive inspection.

Integration of a phosphatase cascade with the mitogen-activated protein kinase pathway provides for a novel signal processing function

We mathematically modeled the receptor-dependent mitogen-activated protein kinase (MAPK) signaling by incorporating the regulation through cellular phosphatases. Activation induced the alignment of a phosphatase cascade in parallel with the MAPK pathway. A novel regulatory motif was, thus, generated, providing for the combinatorial control of each MAPK intermediate. This ensured a non-linear mode of signal transmission with the output being shaped by the balance between the strength of input signal and the activity gradient along the phosphatase axis. Shifts in this balance yielded modulations in topology of the motif, thereby expanding the repertoire of output responses. Thus, we identify an added dimension to signal processing wherein the output response to an external stimulus is additionally filtered through indicators that define the phenotypic status of the cell.

Integration of signals from the B-cell antigen receptor and the IL-4 receptor leads to a cooperative shift in the cellular response axis

Although intracellular signaling events activated through individual cell surface receptors have been characterized in detail, cells are often exposed to multiple stimuli simultaneously in physiological situations. The response elicited then is defined through the cooperative interactions between signals activated by these multiple stimuli. Examples of such instances include cooperativity between individual isoforms of G-protein-coupled receptors, between different growth factor receptors, or between growth factor and integrin receptors. Mechanisms by which the integration of signals emanating from independent receptors influences cellular responses, however, are unknown. In this report, we studied interactions between the antigen and the IL-4 receptors in immature B cells. While stimulation through the B-cell antigen receptor alone causes cell cycle arrest and subsequent apoptosis, the inclusion of IL-4 during stimulation provides a protective effect. We therefore sought to obtain a systems view on how crosstalk between the two respective cell surface receptors modulates the cellular response. We found that, in comparison to the effects of B-cell receptor activation alone, combined stimulation through both receptors enforced a marked reorientation in the 'survival vs. apoptosis' axis of the signaling machinery. The consequent modulation of transcription factor activities yielded an integrated network, spanning the signaling and the transcriptional regulatory components, that was now biased towards the recruitment of molecules with a pro-survival function. This alteration in network properties influenced early-induced gene expression, in a manner that could rationalize the antagonistic effect of the IL-4 receptor on B-cell receptor signaling. Importantly, this antagonism was achieved through an expansion in the repertoire of the genes expressed, wherein the newly generated products counteracted the effects of the B-cell receptor-specific subset. Thus the plasticity of the regulatory networks is also experienced at the level of gene expression, and is the resultant pattern obtained that then modulates cell-fate decisions.

Inductive and suppressive networks regulate TLR9-dependent gene expression in vivo

Bacterial DNA expressing unmethylated CpG motifs binds to TLR9, thereby stimulating a broadly protective, innate immune response. Although CpG-mediated signal transduction has been studied, the scope of TLR9-dependent gene expression is incompletely understood. To resolve these issues, mice were treated with immunostimulatory CpG oligonucleotides (ODN) and splenic mRNA levels monitored from 30 min through 3 days by microarray. Through the unique application of bioinformatic analysis to these experimental data, this study is the first to describe the complex regulatory networks responsible for TLR9-mediated gene expression. Current results are the first to establish that CpG-induced stimulation of the innate immune system proceeds in multiple waves over time, and gene up-regulation is mediated by a small number of temporally activated "major inducers" and "minor inducers". An additional study of TNF knockout mice supports the conclusion that the regulatory networks identified by our bioinformatic analysis accurately identified CpG ODN-driven gene-gene interactions in vivo. Equally important, this work identifies the counter-regulatory mechanisms embedded within the signaling cascade that suppresses the proinflammatory response triggered in vivo by CpG DNA stimulation. Identifying these network interactions provides novel and global insights into the regulation of TLR9-mediated gene activation, improves our understanding of TLR-mediated host defense, and facilitates the development of interventions designed to optimize the nature and duration of the ensuing response.

The systematic production of cells for cell therapies

Stem cells have emerged as the starting material of choice for bioprocesses to produce cells and tissues to treat degenerative, genetic, and immunological disease. Translating the biological properties and potential of stem cells into therapies will require overcoming significant cell-manufacturing and regulatory challenges. Bioprocess engineering fundamentals, including bioreactor design and process control, need to be combined with cellular systems biology principles to guide the development of next-generation technologies capable of producing cell-based products in a safe, robust, and cost-effective manner. The step-wise implementation of these bioengineering strategies will enhance cell therapy product quality and safety, expediting clinical development.

Cellular phosphatases facilitate combinatorial processing of receptor-activated signals

BACKGROUND

Although reciprocal regulation of protein phosphorylation represents a key aspect of signal transduction, a larger perspective on how these various interactions integrate to contribute towards signal processing is presently unclear. For example, a key unanswered question is that of how phosphatase-mediated regulation of phosphorylation at the individual nodes of the signaling network translates into modulation of the net signal output and, thereby, the cellular phenotypic response.

RESULTS

To address the above question we, in the present study, examined the dynamics of signaling from the B cell antigen receptor (BCR) under conditions where individual cellular phosphatases were selectively depleted by siRNA. Results from such experiments revealed a highly enmeshed structure for the signaling network where each signaling node was linked to multiple phosphatases on the one hand, and each phosphatase to several nodes on the other. This resulted in a configuration where individual signaling intermediates could be influenced by a spectrum of regulatory phosphatases, but with the composition of the spectrum differing from one intermediate to another. Consequently, each node differentially experienced perturbations in phosphatase activity, yielding a unique fingerprint of nodal signals characteristic to that perturbation. This heterogeneity in nodal experiences, to a given perturbation, led to combinatorial manipulation of the corresponding signaling axes for the downstream transcription factors.

CONCLUSION

Our cumulative results reveal that it is the tight integration of phosphatases into the signaling network that provides the plasticity by which perturbation-specific information can be transmitted in the form of a multivariate output to the downstream transcription factor network. This output in turn specifies a context-defined response, when translated into the resulting gene expression profile.

An insight into molecular mechanisms of human T helper cell differentiation

Selective activation of T helper (Th) cell subsets plays an important role in immune response to pathogens as well as in the pathogenesis of human allergy and inflammatory diseases. Th1 cells along with the recently discovered Th17 cells play a role in the pathogenesis of autoimmune diseases. Th2 cytokines lead to series of inflammatory processes characteristic for asthma and other atopic diseases. To understand the pathogenesis of immune-mediated diseases it is crucial to dissect pathways and regulatory networks leading to the development of distinct Th subsets. Such knowledge may lead to better strategies for developing diagnostics and therapies for these diseases. The differentiation of Th1, Th2, and Th17 effector cells is driven by signals originating from T cell and costimulatory receptors as well as cytokines in the surroundings of activated naive T helper cells. There are several proteins involved in the regulation of this differentiation process. Most of the data on T helper cell differentiation have been acquired using mouse. In this review, we have summarized what is known about human T helper differentiation. In addition, selected differences between human and mouse will be discussed.