Cellular Signaling Networks Research Articles

Applications of single-cell RNA sequencing in rheumatoid arthritis

Single cell RNA sequencing (scRNA-seq) is a relatively new technology that provides an unprecedented, detailed view of cellular heterogeneity and function by delineating the transcriptomic difference among individual cells. This will allow for mapping of cell-type-specific signaling during physiological and pathological processes, to build highly specific models of cellular signaling networks between the many discrete clusters that are present. This technology therefore provides a powerful approach to dissecting the cellular and molecular mechanisms that contribute to autoimmune diseases, including rheumatoid arthritis (RA). scRNA-seq can offer valuable insights into RA unique cellular states and transitions, potentially enabling development of novel drug targets. However, some challenges that still limit its mainstream utilization and include higher costs, a lower sensitivity for low-abundance transcripts, and a relatively complex data analysis workflow relative to bulk or traditional RNA-seq. This minireview explores the emerging application of scRNA-seq in RA research, highlighting its role in producing important insights that can help pave the way for innovative and more effective therapeutic strategies.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Insight of the molecular mechanism of inhibitors located at different allosteric sites regulating the activity of wild type and mutant KRAS (G12)

As the important hub of many cellular signaling networks, KRAS (Kirsten rat sarcoma viral oncogene homologue) has been identified as a tumor biomarker. It is the frequently mutated oncogene in human cancers, and KRAS protein activation caused by mutations, such as G12D, has been found in many human tumors tissues. Although, there are two specific allosteric sites (AS1 and AS2) on the KRAS protein that can be used as the targets for inhibitor development, the difference of regulatory mechanisms between two individual allosteric sites still not be reported. Here, using molecular dynamics simulations combined with molecular mechanics generalized born surface area (MM/GBSA) analysis, we found that both of the inhibitors, located at AS1 and AS2, were able to reduce the binding free energy between wild type, mutant KRAS (G12/D/V/S/C) and GTP remarkably, however the effect of inhibitors on the binding free energy between wild type, mutant KRAS and GDP was limited. In addition, the degree of decrease of binding free energy between KRAS and GTP caused by inhibitors at AS2 was significantly greater than that caused by inhibitors at AS1. Further analysis revealed that both inhibitors at AS1 and AS2 were able to regulate the fluctuation of Switch Ⅰ and Switch Ⅱ to expand the pocket of the orthosteric site (GTP binding site), thereby reducing the binding of KRAS to GTP. Noteworthy there was significant differences in the regulatory preferences on Switch Ⅰ and Switch Ⅱ between two type inhibitor. The inhibitor at AS2 mainly regulated Switch Ⅱ to affect the pocket of the orthosteric site, while the inhibitor at AS1 mainly expand the pocket of the orthosteric site by regulating the fluctuation of Switch Ⅰ. Our study compared the differences between two type inhibitors in regulating the KRAS protein activity and revealed the advantages of the AS2 as the small molecule drug target, aiming to provide theoretical guidance for the research of novel KRAS protein inhibitors.

Building RNA-Mediated Artificial Signaling Pathways between Endogenous Genes.

ConspectusSophisticated genetic networks play a pivotal role in orchestrating cellular responses through intricate signaling pathways across diverse environmental conditions. Beyond the inherent complexity of natural cellular signaling networks, the construction of artificial signaling pathways (ASPs) introduces a vast array of possibilities for reshaping cellular responses, enabling programmable control of living organisms. ASPs can be integrated with existing cellular networks and redirect output responses as desired, allowing seamless communication and coordination with other cellular processes, thereby achieving designable transduction within cells. Among diversified ASPs, establishing connections between originally independent endogenous genes is of particular significance in modifying the genetic networks, so that cells can be endowed with new capabilities to sense and deal with abnormal factors related to differentiated gene expression (i.e., solve the issues of the aberrant gene expression induced by either external or internal stimuli). In a typical scenario, the two genes X and Y in the cell are originally expressed independently. After the introduction of an ASP, changes in the expression of gene X may exert a designed impact on gene Y, subsequently inducing the cellular response related to gene Y. If X represents a disease signal and Y serves as a therapeutic module, the introduction of the ASP empowers cells with a new spontaneous defense system to handle potential risks, which holds great potential for both fundamental and translational studies.In this Account, we primarily review our endeavors in the construction of RNA-mediated ASPs between endogenous genes that can respond to differentiated RNA expression. In contrast to other molecules that may be restricted to specific pathways, synthetic RNA circuits can be easily utilized and expanded as a general platform for constructing ASPs with a high degree of programmability and tunability for diversified functionalities through predictable Watson-Crick base pairing. We first provide an overview of recent advancements in RNA-based genetic circuits, encompassing but not limited to utilization of RNA toehold switches, siRNA and CRISPR systems. Despite notable progress, most reported RNA circuits have to contain at least one exogenous RNA X as input or one engineered RNA Y as a target, which is not suitable for establishing endogenous gene connections. While exogenous RNAs can be engineered and controlled as desired, constructing a general and efficient platform for manipulation of naturally occurring RNAs poses a formidable challenge, especially for the mammalian system. With a focus on this goal, we are devoted to developing efficient strategies to manipulate cell responses by establishing RNA-mediated ASPs between endogenous genes, particularly in mammalian cells. Our step-by-step progress in engineering customized cell signaling circuits, from bacterial cells to mammalian cells, from gene expression regulation to phenotype control, and from small RNA to long mRNA of low abundance and more complex secondary structures, is systematically described. Finally, future perspectives and potential applications of these RNA-mediated ASPs between endogenous genes are also discussed.

Mitochondrial Oxidative Stress Regulates FOXP3+ T-Cell Activity and CD4-Mediated Inflammation in Older Adults with Frailty.

In healthy older adults, the immune system generally preserves its response and contributes to a long, healthy lifespan. However, rapid deterioration in immune regulation can lead to chronic inflammation, termed inflammaging, which accelerates pathological aging and diminishes the quality of life in older adults with frailty. A significant limitation in current aging research is the predominant focus on comparisons between young and older populations, often overlooking the differences between healthy older adults and those experiencing pathological aging. Our study elucidates the intricate immunological dynamics of the CD4/Treg axis in frail older adults compared to comparable age-matched healthy older adults. By utilizing publicly available RNA sequencing and single-cell RNA sequencing (scRNAseq) data from peripheral blood mononuclear cells (PBMCs), we identified a specific Treg cell subset and transcriptional landscape contributing to the dysregulation of CD4+ T-cell responses. We explored the molecular mechanisms underpinning Treg dysfunction, revealing that Tregs from frail older adults exhibit reduced mitochondrial protein levels, impairing mitochondrial oxidative phosphorylation. This impairment is driven by the TNF/NF-kappa B pathway, leading to cumulative inflammation. Further, we gained a deeper understanding of the CD4/Treg axis by predicting the effects of gene perturbations on cellular signaling networks. Collectively, these findings highlight the age-related relationship between mitochondrial dysfunction in the CD4/Treg axis and its role in accelerating aging and frailty in older adults. Targeting Treg dysfunction offers a critical basis for developing tailored therapeutic strategies aimed at improving the quality of life in older adults.

Deciphering the dual roles of PHD finger proteins from oncogenic drivers to tumor suppressors.

PHD (plant homeodomain) finger proteins emerge as central epigenetic readers and modulators in cancer biology, orchestrating a broad spectrum of cellular processes pivotal to oncogenesis and tumor suppression. This review delineates the dualistic roles of PHD fingers in cancer, highlighting their involvement in chromatin remodeling, gene expression regulation, and interactions with cellular signaling networks. PHD fingers' ability to interpret specific histone modifications underscores their influence on gene expression patterns, impacting crucial cancer-related processes such as cell proliferation, DNA repair, and apoptosis. The review delves into the oncogenic potential of certain PHD finger proteins, exemplified by PHF1 and PHF8, which promote tumor progression through epigenetic dysregulation and modulation of signaling pathways like Wnt and TGFβ. Conversely, it discusses the tumor-suppressive functions of PHD finger proteins, such as PHF2 and members of the ING family, which uphold genomic stability and inhibit tumor growth through their interactions with chromatin and transcriptional regulators. Additionally, the review explores the therapeutic potential of targeting PHD finger proteins in cancer treatment, considering their pivotal roles in regulating cancer stem cells and influencing the immune response to cancer therapy. Through a comprehensive synthesis of current insights, this review underscores the complex but promising landscape of PHD finger proteins in cancer biology, advocating for further research to unlock novel therapeutic avenues that leverage their unique cellular roles.

Phosphoproteomics Reveals Selective Regulation of Signaling Pathways by Lysophosphatidic Acid Species in Macrophages.

Lysophosphatidic acid (LPA) species, prevalent in the tumor microenvironment (TME), adversely impact various cancers. In ovarian cancer, the 18:0 and 20:4 LPA species are selectively associated with shorter relapse-free survival, indicating distinct effects on cellular signaling networks. Macrophages represent a cell type of high relevance in the TME, but the impact of LPA on these cells remains obscure. Here, we uncovered distinct LPA-species-specific responses in human monocyte-derived macrophages through unbiased phosphoproteomics, with 87 and 161 phosphosites upregulated by 20:4 and 18:0 LPA, respectively, and only 24 shared sites. Specificity was even more pronounced for downregulated phosphosites (163 versus 5 sites). Considering the high levels 20:4 LPA in the TME and its selective association with poor survival, this finding may hold significant implications. Pathway analysis pinpointed RHO/RAC1 GTPase signaling as the predominantly impacted target, including AHRGEF and DOCK guanine exchange factors, ARHGAP GTPase activating proteins, and regulatory protein kinases. Consistent with these findings, exposure to 20:4 resulted in strong alterations to the actin filament network and a consequent enhancement of macrophage migration. Moreover, 20:4 LPA induced p38 phosphorylation, a response not mirrored by 18:0 LPA, whereas the pattern for AKT was reversed. Furthermore, RNA profiling identified genes involved in cholesterol/lipid metabolism as selective targets of 20:4 LPA. These findings imply that the two LPA species cooperatively regulate different pathways to support functions essential for pro-tumorigenic macrophages within the TME. These include cellular survival via AKT activation and migration through RHO/RAC1 and p38 signaling.

Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study

BackgroundN-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene. ResultsOut of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer. ConclusionThis in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2-induced stomatal closure. In this work, we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the model's potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.

Phosphoproteomic analysis of the adaption of epididymal epithelial cells to corticosterone challenge.

The epididymis has long been of interest owing to its role in promoting the functional maturation of the male germline. More recent evidence has also implicated the epididymis as an important sensory tissue responsible for remodeling of the sperm epigenome, both under physiological conditions and in response to diverse forms of environmental stress. Despite this knowledge, the intricacies of the molecular pathways involved in regulating the adaptation of epididymal tissue to paternal stressors remains to be fully resolved. The overall objective of this study was to investigate the direct impact of corticosterone challenge on a tractable epididymal epithelial cell line (i.e., mECap18 cells), in terms of driving adaptation of the cellular proteome and phosphoproteome signaling networks. The newly developed phosphoproteomic platform EasyPhos coupled with sequencing via an Orbitrap Exploris 480 mass spectrometer, was applied to survey global changes in the mECap18 cell (phospho)proteome resulting from sub-chronic (10-day) corticosterone challenge. The imposed corticosterone exposure regimen elicited relatively subtle modifications of the global mECap18 proteome (i.e., only 73 out of 4171 [∼1.8%] proteins displayed altered abundance). By contrast, ∼15% of the mECap18 phosphoproteome was substantially altered following corticosterone challenge. In silico analysis of the corresponding parent proteins revealed an activation of pathways linked to DNA damage repair and oxidative stress responses as well as a reciprocal inhibition of pathways associated with organismal death. Corticosterone challenge also induced the phosphorylation of several proteins linked to the biogenesis of microRNAs. Accordingly, orthogonal validation strategies confirmed an increase in DNA damage, which was ameliorated upon selective kinase inhibition, and an altered abundance profile of a subset of microRNAs in corticosterone-treated cells. Together, these data confirm that epididymal epithelial cells are reactive to corticosterone challenge, and that their response is tightly coupled to the opposing action of cellular kinases and phosphatases.

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma.

Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients. Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA). HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients' prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells. This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.

Functions of HP1 in preventing chromosomal instability.

Chromosomal instability (CIN), caused by errors in the segregation of chromosomes during mitosis, is a hallmark of many types of cancer. The fidelity of chromosome segregation is governed by a sophisticated cellular signaling network, one crucial orchestrator of which is Heterochromatin protein 1 (HP1). HP1 dynamically localizes to distinct sites at various stages of mitosis, where it regulates key mitotic events ranging from chromosome-microtubule attachment to sister chromatid cohesion to cytokinesis. Our evolving comprehension of HP1's multifaceted role has positioned it as a central protein in the orchestration of mitotic processes.

Abstract 6212: A differential ligand-receptor network inference method to identify alterations in communication between myeloid cells and CD8 T cells in response to PancVAX neo-epitope peptide vaccine, anti-CTLA-4 and anti-PD-1 antibodies in a murine model of pancreatic ductal adenocarcinoma

Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is a deadly cancer with a low tumor mutational burden that leads to the generation of few neo-epitopes derived from mutated genes that can be recognized by cytotoxic T cells. The immunosuppressive microenvironment of PDAC comprises suppressive cell populations that include myeloid cells, cancer associated fibroblast subpopulations, and regulatory T cells that reduce T cell infiltration and cytotoxic function. We have developed a murine personalized cancer vaccine to enhance cytotoxic T cell function against PDAC, PancVAX (Kinkead et al, JCI Insight 2018). Survival benefit with PancVAX was dependent on the addition of immune checkpoint inhibitor (ICI) therapy to increase the number of cytotoxic CD8 T cells infiltrating tumors and maintain their cytotoxic function (Huff et al, JCI Insight 2023). Yet, the impact of cell-cell communication on effector T cell function arising from the immunosuppressive microenvironment of PDAC remains unknown. We previously developed a ligand-receptor inference method called Domino that infers inter- and intra-cellular signaling networks in single-cell RNA-seq data (Cherry et al, Nat Biomed Eng 2021). However, this technique is limited to data from single conditions. Understanding how this combination immunotherapy alters cellular signaling networks requires new computational tools that identify changes in regulatory networks between treatment conditions. We developed capabilities for assessment of differential signaling with Domino founded on a basis of differential signal receipt. Active receptors are identified by receptor expression in recipient cells that is correlated with downstream transcription factors in the intracellular signaling cascade. Identification of causative ligands for receptor activation is assessed by a probability of interaction for ligand-receptor interactions between cell types. This probability metric considers mean expression of ligand and receptor genes as well as the number of sender cells for comparison across treatment groups. Comparing communication networks in PancVAX alone to PancVAX with ICI, PancVAX with ICI enhances cell-cell adhesion and interferon gamma signaling in cytotoxic CD8 T cells towards exhausted CD8 T cells. Macrophages in PancVAX with ICI enhance expression of immune checkpoint ligands towards CD8 T cells, despite the presence of blocking ICI antibodies. Intracellular signaling downstream of PD-1 in exhausted T cells shows diminished numbers of transcription factors linked to PD-1 signaling. This software informs prioritization of inhibitory signals for modulation in future immunotherapy regimens and can be applied generally to other tumor subtypes and treatment contexts. Citation Format: Jacob T. Mitchell, Atul Deshpande, Amanda L. Huff, Chris Cherry, Sushma Nagaraj, Kavita Krishnan, Dmitrijs Lvovs, Jennifer H. Elisseeff, Elizabeth M. Jaffee, Neeha Zaidi, Elana J. Fertig. A differential ligand-receptor network inference method to identify alterations in communication between myeloid cells and CD8 T cells in response to PancVAX neo-epitope peptide vaccine, anti-CTLA-4 and anti-PD-1 antibodies in a murine model of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6212.

Advances in Analytical Techniques and Therapeutic Applications of Phytochemicals

The attentiveness towards phytochemicals, which represents the group of biologically active compounds derived from plants, also referred to as phytocomponents, and has sprung up due to their therapeutics implications. This review article encourages researchers to appreciate the most recent innovations in analytical approaches to the determination and measurement of phytochemicals, methods such as spectroscopy, chromatography and mass spectrometry are some of the techniques examined here. Moreover, it covers the bioactivity and mechanisms of action of phytochemicals, the effect of which is their being directing to molecular targets and leading to a modulation of cellular signaling network. The way that the plant-derived compounds are absorbed, distributed, metabolized and excreted (pharmacokinetics) and the way that they act on the body (pharmacodynamics) are examined in details and they are found to be efficient for treating many diseases. Drug discovery and development also include a range of interdisciplinary fields such as chemistry, pharmacology, and other disciplines. In these areas, research includes also industry considerations and ethical practices. This overview covers the range of achievements based on phytochemical research in the current stage and the expectations on its prospective influence on health care. Keywords: Phytochemicals, Analytical Techniques, Bioactivity, Pharmacokinetics and Pharmacodynamics

Cellular and clinical impact of protein phosphatase enzyme epigenetic silencing in multiple cancer tissues

BackgroundProtein Phosphatase Enzymes (PPE) and protein kinases simultaneously control phosphorylation mechanisms that tightly regulate intracellular signalling pathways and stimulate cellular responses. In human malignancies, PPE and protein kinases are frequently mutated resulting in uncontrolled kinase activity and PPE suppression, leading to cell proliferation, migration and resistance to anti-cancer therapies. Cancer associated DNA hypermethylation at PPE promoters gives rise to transcriptional silencing (epimutations) and is a hallmark of cancer. Despite recent advances in sequencing technologies, data availability and computational capabilities, only a fraction of PPE have been reported as transcriptionally inactive as a consequence of epimutations.MethodsIn this study, we examined promoter-associated DNA methylation profiles in Protein Phosphatase Enzymes and their Interacting Proteins (PPEIP) in a cohort of 705 cancer patients in five tissues (Large intestine, Oesophagus, Lung, Pancreas and Stomach) in three cell models (primary tumours, cancer cell lines and 3D embedded cancer cell cultures). As a subset of PPEIP are known tumour suppressor genes, we analysed the impact of PPEIP promoter hypermethylation marks on gene expression, cellular networks and in a clinical setting.ResultsHere, we report epimutations in PPEIP are a frequent occurrence in the cancer genome and manifest independent of transcriptional activity. We observed that different tumours have varying susceptibility to epimutations and identify specific cellular signalling networks that are primarily affected by epimutations. Additionally, RNA-seq analysis showed the negative impact of epimutations on most (not all) Protein Tyrosine Phosphatase transcription. Finally, we detected novel clinical biomarkers that inform on patient mortality and anti-cancer treatment sensitivity.ConclusionsWe propose that DNA hypermethylation marks at PPEIP frequently contribute to the pathogenesis of malignancies and within the precision medicine space, hold promise as biomarkers to inform on clinical features such as patient survival and therapeutic response.

Deciphering the Receptor-Mediated Signaling Pathways of Interleukin-19 and Interleukin-20.

Interleukin-19 (IL-19) and Interleukin-20 (IL-20) are inflammatory cytokines belonging to the IL-10 family with immunoregulatory properties. Emerging evidence highlights the importance of association of these cytokines with both immunological and inflammatory disorders, including chronic inflammation, cardiac dysfunction, and cancer. IL-19 and IL-20 bind to the heterodimeric receptor complex and induce multiple downstream signaling cascades by activating the signal transducer and activator of transcription 3 (STAT3), Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), AKT serine/threonine kinase 1 (AKT1), and NFKB inhibitor alpha (NFKBIA), leading to proinflammatory and anti-inflammatory reactions in cancer, inflammation, tumor microenvironment, and infectious diseases. Considering the significant role of these cytokines, we integrated its cellular signaling network by combining multiomics molecular events associated with 56 molecules of induced by IL-19 and 156 molecules of by IL-20. The reactions of these signaling events are classified into enzyme catalysis/post-translational modifications, activation/inhibition events, molecular associations, gene regulations at the mRNA and protein level, and the protein translocation events. We believe that this signaling pathway map would serve as a knowledge base, that aid researchers and clinicians to understand and explore the intricate mechanisms and identify novel signaling components and therapeutic targets for diseases associated with dysregulated IL-19 and IL-20 signaling.

Fast and scalable querying of eukaryotic linear motifs with gget elm.

Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. The manual and source code are available at https://github.com/pachterlab/gget.

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Potential prognosis and immunotherapy predictor TFAP2A in pan-cancer.

TFAP2A is critical in regulating the expression of various genes, affecting various biological processes and driving tumorigenesis and tumor development. However, the significance of TFAP2A in carcinogenesis processes remains obscure. In our study, we explored multiple databases including TCGA, GTEx, HPA, cBioPortal, TCIA, and other well-established databases for further analysis to expound TFAP2A expression, genetic alternations, and their relationship with the prognosis and cellular signaling network alternations. GO term and KEGG pathway enrichment analysis as well as GSEA were conducted to examine the common functions of TFAP2A. RT-qPCR, Western Blot and Dual Luciferase Reporter assay were employed to perform experimental validation. TFAP2A mRNA expression level was upregulated and its genetic alternations were frequently present in most cancer types. The enrichment analysis results prompted us to investigate the changes in the tumor immune microenvironment further. We discovered that the expression of TFAP2A was significantly associated with the expression of immune checkpoint genes, immune subtypes, ESTIMATE scores, tumor-infiltrating immune cells, and the possible role of TFAP2A in predicting immunotherapy efficacy. In addition, high TFAP2A expression significantly correlated with several ICP genes, and promoted the expression of PD-L1 on mRNA and protein levels through regulating its expression at the transcriptional level. TFAP2A protein level was upregulated in fresh colon tumor tissue samples compared to that in the adjacent normal tissues, which essentially positively correlated with the expression of PD-L1. Our study suggests that targeting TFAP2A may provide a novel and effective strategy for cancer treatment.

An End-to-end Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances have witnessed the achievements of artificial intelligence (AI) methods aimed at predicting PPIs. However, these approaches often handle the intricate web of relationships and mechanisms among proteins, drugs, diseases, ribonucleic acid (RNA), and protein structures in a fragmented or superficial manner. This is typically due to the limitations of non-end-to-end learning frameworks, which can lead to sub-optimal feature extraction and fusion, thereby compromising the prediction accuracy. To address these deficiencies, this paper introduces a novel end-to-end learning model, the Knowledge Graph Fused Graph Neural Network (KGF-GNN). This model comprises three integral components: (1) Protein Associated Network (PAN) Construction: We begin by constructing a PAN that extensively captures the diverse relationships and mechanisms linking proteins with drugs, diseases, RNA, and protein structures. (2) Graph Neural Network for Feature Extraction: A Graph Neural Network (GNN) is then employed to distill both topological and semantic features from the PAN, alongside another GNN designed to extract topological features directly from observed PPI networks. (3) Multi-layer Perceptron for Feature Fusion: Finally, a multi-layer perceptron integrates these varied features through end-to-end learning, ensuring that the feature extraction and fusion processes are both comprehensive and optimized for PPI prediction. Extensive experiments conducted on real-world PPI datasets validate the effectiveness of our proposed KGF-GNN approach, which not only achieves high accuracy in predicting PPIs but also significantly surpasses existing state-of-the-art models. This work not only enhances our ability to predict PPIs with a higher precision but also contributes to the broader application of AI in Bioinformatics, offering profound implications for biological research and therapeutic development.