Predictive Model of Lymphocyte-Specific Protein Tyrosine Kinase (LCK) Autoregulation

Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer

Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.

Development and Validation of a Prognostic Risk Model Based on Nature Killer Cells for Serous Ovarian Cancer

Nature killer (NK) cells are increasingly considered important in tumor microenvironment, but their role in predicting the prognosis of ovarian cancer has not been revealed. This study aimed to develop a prognostic risk model for ovarian cancer based on NK cells. Firstly, differentially expressed genes (DEGs) of NK cells were found by single-cell RNA-sequencing dataset analysis. Based on six NK-cell DEGs identified by univariable, Lasso and multivariable Cox regression analyses, a prognostic risk model for serous ovarian cancer was developed in the TCGA cohort. This model was then validated in three external cohorts, and evaluated as an independent prognostic factor by multivariable Cox regression analysis together with clinical characteristics. With the investigation of the underlying mechanism, a relation between a higher risk score of this model and more immune activities in tumor microenvironment was revealed. Furthermore, a detailed inspection of infiltrated immunocytes indicated that not only quantity, but also the functional state of these immunocytes might affect prognostic risk. Additionally, the potential of this model to predict immunotherapeutic response was exhibited by evaluating the functional state of cytotoxic T lymphocytes. To conclude, this study introduced a novel prognostic risk model based on NK-cell DEGs, which might provide assistance for the personalized management of serous ovarian cancer patients.

Spatially resolved in silico modeling of NKG2D signaling kinetics suggests a key role of NKG2D and Vav1 Co-clustering in generating natural killer cell activation

Natural Killer (NK) cells provide key resistance against viral infections and tumors. A diverse set of activating and inhibitory NK cell receptors (NKRs) interact with cognate ligands presented by target host cells, where integration of dueling signals initiated by the ligand-NKR interactions determines NK cell activation or tolerance. Imaging experiments over decades have shown micron and sub-micron scale spatial clustering of activating and inhibitory NKRs. The mechanistic roles of these clusters in affecting downstream signaling and activation are often unclear. To this end, we developed a predictive in silico framework by combining spatially resolved mechanistic agent based modeling, published TIRF imaging data, and parameter estimation to determine mechanisms by which formation and spatial movements of activating NKG2D microclusters affect early time NKG2D signaling kinetics in a human cell line NKL. We show co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in NKG2D microclusters plays a dominant role over ligand (ULBP3) rebinding in increasing production of phospho-Vav1(pVav1), an activation marker of early NKG2D signaling. The in silico model successfully predicts several scenarios of inhibition of NKG2D signaling and time course of NKG2D spatial clustering over a short (~3 min) interval. Modeling shows the presence of a spatial positive feedback relating formation and centripetal movements of NKG2D microclusters, and pVav1 production offers flexibility towards suppression of activating signals by inhibitory KIR ligands organized in inhomogeneous spatial patterns (e.g., a ring). Our in silico framework marks a major improvement in developing spatiotemporal signaling models with quantitatively estimated model parameters using imaging data.

Natural Killer cells are lymphocytes of our innate immunity and provide important resistance against viral infections and tumors. NK cells scan the local environment with diverse activating and inhibitory NK cell receptors (NKRs) and remain tolerized or lyse target cells expressing cognate ligands to NKRs. NKRs have been found to form micron sized clusters (or microclusters) as they interact with cognate ligands, and mechanisms regarding how the formation and movements of these microclusters influence NK cell signaling and activation, specifically related to activating NKRs, are often unclear. To this end, we develop a predictive spatially resolved early-time NK cell signaling model to study the interplay between membrane-proximal biochemical signaling events and the kinetics of microclusters of activating NKG2D and inhibitory KIR2DL2 receptors. We used published TIRF imaging data to validate our in silico models and estimate model parameters. Predictions from multiple in silico models are tested against a variety of data obtained from published imaging experiments and immunoassays. Our analysis suggests co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in the microclusters plays a major role in enhancing downstream activating signals. The developed framework can be extended to describe spatiotemporal signaling for other activating NKRs including CD16.

Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy

BACKGROUND

Systemic inflammation and immune response are involved in the pathogenesis of diabetic nephropathy (DN). However, the specific immune-associated signature during DN development is unclear. Our study aimed to reveal the roles of immune-related genes during DN progression.

METHODS

The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration.

RESULTS

92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells.

CONCLUSIONS

We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.

New horizons in drug discovery of lymphocyte-specific protein tyrosine kinase (Lck) inhibitors: a decade review (2011-2021) focussing on structure-activity relationship (SAR) and docking insights

Lymphocyte-specific protein tyrosine kinase (Lck), a non-receptor Src family kinase, has a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation, and differentiation. Lck is reported as a key factor regulating the functions of T-cell including the initiation of TCR signalling, T-cell development, in addition to T-cell homeostasis. Alteration in expression and activity of Lck results in numerous disorders such as cancer, asthma, diabetes, rheumatoid arthritis, atherosclerosis, and neuronal diseases. Accordingly, Lck has emerged as a novel target against different diseases. Herein, we amass the research efforts in literature and pharmaceutical patents during the last decade to develop new Lck inhibitors. Additionally, structure-activity relationship studies (SAR) and docking models of these new inhibitors within the active site of Lck were demonstrated offering deep insights into their different binding modes in a step towards the identification of more potent, selective, and safe Lck inhibitors.

Autophosphorylation and the Dynamics of the Activation of Lck

Lck (lymphocyte-specific protein tyrosine kinase) is an enzyme which plays a number of important roles in the function of immune cells. It belongs to the Src family of kinases which are known to undergo autophosphorylation. It turns out that this leads to a remarkable variety of dynamical behaviour which can occur during their activation. We prove that in the presence of autophosphorylation one phenomenon, bistability, already occurs in a mathematical model for a protein with a single phosphorylation site. We further show that a certain model of Lck exhibits oscillations. Finally, we discuss the relations of these results to models in the literature which involve Lck and describe specific biological processes, such as the early stages of T cell activation and the stimulation of T cell responses resulting from the suppression of PD-1 signalling which is important in immune checkpoint therapy for cancer.

Transcriptome analysis reveals potential immune function-related regulatory genes/pathways of female Lubo goat submandibular glands at different developmental stages

Background

The submandibular glands, as major salivary glands, participate in rumen digestion in goats. Sialic acid, lysozyme, immunoglobulin A (IgA), lactoferrin and other biologically active substances secreted in the submandibular glands were reported in succession, which suggests that the submandibular gland may have immune functions in addition to participating in digestion. The aim of this study was to map the expression profile of differentially expressed genes (DEGs) at three different stages by transcriptome sequencing, screen immune-related genes and pathways by bioinformatics methods, and predict the immune function of submandibular glands at different developmental stages.

Methods

Nine submandibular gland tissue samples were collected from groups of 1-month-old kids, 12-month-old adolescent goats and 24-month-old adult goats (3 samples from each group), and high-throughput transcriptome sequencing was conducted on these samples. The DEGs among the three stages were screened and analysed. Key genes and signalling pathways were selected via protein-protein interaction (PPI) network analysis.

Results

The results revealed 2,706, 2,525 and 52 DEGs between 1-month-old and 12-month-old goats, between 1-month-old and 24-month-old goats, and between 12-month-old and 24-month-old goats, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that most of the DEGs were enriched in immune- related GO terms and pathways. Based on functional enrichment analysis and network analysis, 10 genes (PTPRC, CD28, SELL, LCP2, MYC, LCK, ZAP70, ITGB2, SYK and CCR7), two signalling pathways (the T cell receptor signalling pathway and the NF-κβ signalling pathway) and eight GO terms (T cell receptor signalling pathway, neutrophil mediated immunity, B cell mediated immunity, regulation of alpha-beta T cell activation, positive regulation of T cell proliferation, regulation of leukocyte differentiation, positive regulation of antigen receptor-mediated signalling pathway, positive regulation of lymphocyte proliferation) that may play key roles in the immune functions of the goat submandibular glands at different developmental stages were identified. Moreover, we found that eight antibacterial peptide-encoding genes were downregulated in the tuberculosis and salivary secretion pathways, while all immunoglobulins were upregulated in 10 immune system pathways. These findings indicate that the submandibular glands may be important immunological organs during the growth process of goats and that the immune function of these glands gradually weakens with age up to 12 months but remains relatively stable after 12 months of age. Overall, this study will improve our understanding of transcriptional regulation related to goat submandibular gland immune function.

Integrative Approaches to Cancer Immunotherapy

Cancer immunotherapy aims to arm patients with cancer-fighting immunity. Many new cancer-specific immunotherapeutic drugs have gained approval in the past several years, demonstrating immunotherapy's efficacy and promise as an anticancer modality. Despite these successes, several outstanding questions remain for cancer immunotherapy, including how to make immunotherapy more efficacious in a broader range of cancer types and patients, and how to predict which patients will respond or not respond to therapy. We present a case for integrative systems approaches that will answer these questions. This involves applying mechanistic and statistical modeling, establishing consistent and widely adopted experimental tools to generate systems-level data, and creating sustained mechanisms of support. If implemented, these approaches will lead to major advances in cancer treatment.

Understanding the Dynamics of T-Cell Activation in Health and Disease Through the Lens of Computational Modeling

T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation–based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modeling can be used to enable the rational design and optimization of immunotherapies.

Enhancing network activation in natural killer cells: predictions from in silico modeling

Natural killer (NK) cells are part of the innate immune system and are capable of killing diseased cells. As a result, NK cells are being used for adoptive cell therapies for cancer patients. The activation of NK cell stimulatory receptors leads to a cascade of intracellular phosphorylation reactions, which activates key signaling species that facilitate the secretion of cytolytic molecules required for cell killing. Strategies that maximize the activation of such intracellular species can increase the likelihood of NK cell killing upon contact with a cancer cell and thereby improve efficacy of NK cell-based therapies. However, due to the complexity of intracellular signaling, it is difficult to deduce a priori which strategies can enhance species activation. Therefore, we constructed a mechanistic model of the CD16, 2B4 and NKG2D signaling pathways in NK cells to simulate strategies that enhance signaling. The model predictions were fit to published data and validated with a separate dataset. Model simulations demonstrate strong network activation when the CD16 pathway is stimulated. The magnitude of species activation is most sensitive to the receptor's initial concentration and the rate at which the receptor is activated. Co-stimulation of CD16 and NKG2D in silico required fewer ligands to achieve half-maximal activation than other combinations, suggesting co-stimulating these pathways is most effective in activating the species. We applied the model to predict the effects of perturbing the signaling network and found two strategies that can potently enhance network activation. When the availability of ligands is low, it is more influential to engineer NK cell receptors that are resistant to proteolytic cleavage. In contrast, for high ligand concentrations, inhibiting phosphatase activity leads to sustained species activation. The work presented here establishes a framework for understanding the complex, nonlinear aspects of NK cell signaling and provides detailed strategies for enhancing NK cell activation.

ERK Activation in CAR T Cells Is Amplified by CD28-Mediated Increase in CD3ζ Phosphorylation

Chimeric antigen receptors (CARs) are engineered receptors that mediate T cell activation. CARs are comprised of activating and co-stimulatory intracellular signaling domains derived from endogenous T cells that initiate signaling required for T cell activation, including ERK activation through the MAPK pathway. Understanding the mechanisms by which co-stimulatory domains influence signaling can help guide the design of next-generation CARs. Therefore, we constructed an experimentally validated computational model of anti-CD19 CARs in T cells bearing the CD3ζ domain alone or in combination with CD28. We performed a systematic analysis to explore the different mechanisms of CD28 co-stimulation on the ERK response time. Comparing these model simulations with experimental data indicates that CD28 primarily influences ERK activation by enhancing the phosphorylation kinetics of CD3ζ. Overall, we present a mechanistic mathematical modeling framework that can be used to gain insights into the mechanism of CAR T cell activation and produce new testable hypotheses.

More from less - bottom-up reconstitution of cell biology

The ultimate goal of bottom-up synthetic biology is recreating life in its simplest form. However, in its quest to find the minimal functional units of life, this field contributes more than its main aim by also offering a range of tools for asking, and experimentally approaching, biological questions. This Review focusses on how bottom-up reconstitution has furthered our understanding of cell biology. Studying cell biological processes in vitro has a long tradition, but only recent technological advances have enabled researchers to reconstitute increasingly complex biomolecular systems by controlling their multi-component composition and their spatiotemporal arrangements. We illustrate this progress using the example of cytoskeletal processes. Our understanding of these has been greatly enhanced by reconstitution experiments, from the first in vitro experiments 70 years ago to recent work on minimal cytoskeleton systems (including this Special Issue of Journal of Cell Science). Importantly, reconstitution approaches are not limited to the cytoskeleton field. Thus, we also discuss progress in other areas, such as the shaping of biomembranes and cellular signalling, and prompt the reader to add their subfield of cell biology to this list in the future.

Mathematical modeling identifies Lck as a potential mediator for PD-1 induced inhibition of early TCR signaling

Programmed cell death-1 (PD-1) is an inhibitory immune checkpoint receptor that negatively regulates the functioning of T cell. Although the direct targets of PD-1 were not identified, its inhibitory action on the TCR signaling pathway was known much earlier. Recent experiments suggest that the PD-1 inhibits the TCR and CD28 signaling pathways at a very early stage ─ at the level of phosphorylation of the cytoplasmic domain of TCR and CD28 receptors. Here, we develop a mathematical model to investigate the influence of inhibitory effect of PD-1 on the activation of early TCR and CD28 signaling molecules. Proposed model recaptures several quantitative experimental observations of PD-1 mediated inhibition. Model simulations show that PD-1 imposes a net inhibitory effect on the Lck kinase. Further, the inhibitory effect of PD-1 on the activation of TCR signaling molecules such as Zap70 and SLP76 is significantly enhanced by the PD-1 mediated inhibition of Lck. These results suggest a critical role for Lck as a mediator for PD-1 induced inhibition of TCR signaling network. Multi parametric sensitivity analysis explores the effect of parameter uncertainty on model simulations.

Exploration of the therapeutic aspects of Lck: A kinase target in inflammatory mediated pathological conditions

Lck, a non-receptor src family kinase, plays a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation and differentiation. As a 56 KDa protein, Lck phosphorylates tyrosine residues of various proteins such as ZAP-70, ITK and protein kinase C. The structure of Lck is comprised of three domains, one SH3 in tandem with a SH2 domain at the amino terminal and the kinase domain at the carboxy terminal. Physiologically, Lck is involved in the development, function and differentiation of T-cells. Additionally, Lck regulates neurite outgrowth and maintains long-term synaptic plasticity in neurons. Given a major role of Lck in cytokine production and T cell signaling, alteration in expression and activity of Lck may result in various diseased conditions like cancer, asthma, diabetes, rheumatoid arthritis, psoriasis, inflammatory bowel diseases such as Crohn's disease and ulcerative colitis, atherosclerosis etc. This article provides evidence and information establishing Lck as one of the therapeutic targets in various inflammation mediated pathophysiological conditions.

Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.