Protein Signaling Networks Research Articles

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.

Oncogenic signaling is coupled to colorectal cancer cell differentiation state

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this process, sequential mutations of APC, KRAS, TP53, and SMAD4 enable oncogenic signaling and establish the hallmarks of cancer. Here, we use mass cytometry of isogenic human colon organoids and patient-derived cancer organoids to capture oncogenic signaling, cell phenotypes, and differentiation states in a high-dimensional single-cell map. We define a differentiation axis in all tumor progression states from normal to cancer. Our data show that colorectal cancer driver mutations shape the distribution of cells along the differentiation axis. In this regard, subsequent mutations can have stem cell promoting or restricting effects. Individual nodes of the cancer cell signaling network remain coupled to the differentiation state, regardless of the presence of driver mutations. We use single-cell RNA sequencing to link the (phospho-)protein signaling network to transcriptomic states with biological and clinical relevance. Our work highlights how oncogenes gradually shape signaling and transcriptomes during tumor progression.

Abstract 875: Breast cancer bioinformatics: Untangling the roles of nucleolin and BRCA1 in dysregulated DNA repair

Abstract Proteins that enable cells to sense and repair DNA damage are essential for maintaining the stability of genomes across cell divisions; dysregulation of these processes is an established hallmark of tumorigenesis. Nucleolin (NCL), a major RNA binding protein (RBP) and a caretaker tumor suppressor protein BRCA1 (the breast & ovarian cancer susceptibility gene) colocalize in breast cancer. Both NCL and BRCA1 have defined roles in homologous recombination (HR) and non-homologous end joining (NHEJ) DNA repair pathways which are often dysregulated in breast cancer. BRCA1, along with a complex molecular network of signaling proteins gets recruited to damaged replication forks to initiate the HR pathway where NCL function is also implicated. However, how NCL collaborates with BRCA1 to orchestrate the complex mechanism of DNA repair under stress conditions remains unknown. To address this gap in knowledge, we have applied in silico approaches to uncover the key players which drive the molecular interactions of NCL and BRCA1 at the damaged sites. We utilized the NIH PPI, IntAct, STRING, BioGRID, GeneMania, PrePPI, and Mentha databases to derive an overlapping interactome. In this study we present a comprehensive interactome analysis and 47 proteins identified that interact with both NCL and BRCA1. As expected, majority of these common interactors participate in DNA damage damage response along with several that are implicated in regulating the gene expression via chromatin remodeling/RNA binding or controlling cell proliferation. We classified this interactome into 5 major categories based on their GO functional annotations: chromatin remodeling, DNA damage, DNA repair, RNA-binding proteins, and cell cycle. Previously, we have successfully used computational approaches to model the RNA-binding domains (RBD) of NCL and delineate the binding interfaces between NCL-RBD and a subset of miRNA that are specifically dysregulated in breast cancer. Using the same strategy, we have modeled the full length NCL to provide a complete structural representation of the protein. Our model fills the gap in missing NCL structural data, especially the unexplored N- and C - termini that may play important roles in NCL protein-protein interactions. Our results provide predicted interaction scenarios between NCL and the subset of the overlapping interactome of NCL and BRCA1, focused on DNA repair mechanisms. These in silico models are critical to understand the protein complexity at the interface of damaged DNA to identify candidates that can be targeted in breast carcinoma. Citation Format: Nitu Farhin, Andy Lam, Anjana D. Saxena, Shaneen Singh. Breast cancer bioinformatics: Untangling the roles of nucleolin and BRCA1 in dysregulated DNA repair [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 875.

Native Size-Exclusion Chromatography–Based Mass Spectrometry Reveals New Components of the Early Heat Shock Protein 90 Inhibition Response Among Limited Global Changes

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery—suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the early changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) for 8 h in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated HT29 cells at this relatively early time-point. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including known and unknown components of the HSP90-dependent proteome. We validate two of these—the actin-binding protein Anillin and the mitochondrial isocitrate dehydrogenase 3 complex—as novel HSP90 inhibitor-modulated proteins. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.

Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

A "multi-omics" analysis of blood-brain barrier and synaptic dysfunction in APOE4 mice.

Apolipoprotein E4 (APOE4), the main susceptibility gene for Alzheimer's disease, leads to blood-brain barrier (BBB) breakdown in humans and mice. Remarkably, BBB dysfunction predicts cognitive decline and precedes synaptic deficits in APOE4 human carriers. How APOE4 affects BBB and synaptic function at a molecular level, however, remains elusive. Using single-nucleus RNA-sequencing and phosphoproteome and proteome analysis, we show that APOE4 compared with APOE3 leads to an early disruption of the BBB transcriptome in 2-3-mo-old APOE4 knock-in mice, followed by dysregulation in protein signaling networks controlling cell junctions, cytoskeleton, clathrin-mediated transport, and translation in brain endothelium, as well as transcription and RNA splicing suggestive of DNA damage in pericytes. Changes in BBB signaling mechanisms paralleled an early, progressive BBB breakdown and loss of pericytes, which preceded postsynaptic interactome disruption and behavioral deficits that developed 2-5 mo later. Thus, dysregulated signaling mechanisms in endothelium and pericytes in APOE4 mice reflect a molecular signature of a progressive BBB failure preceding changes in synaptic function and behavior.

MAGI1 inhibits interferon signaling to promote influenza A infection

We have shown that membrane-associated guanylate kinase with inverted domain structure-1 (MAGI1), a scaffold protein with six PSD95/DiscLarge/ZO-1 (PDZ) domains, is involved in the regulation of endothelial cell (EC) activation and atherogenesis in mice. In addition to causing acute respiratory disease, influenza A virus (IAV) infection plays an important role in atherogenesis and triggers acute coronary syndromes and fatal myocardial infarction. Therefore, the aim of this study is to investigate the function and regulation of MAGI1 in IAV-induced EC activation. Whereas, EC infection by IAV increases MAGI1 expression, MAGI1 depletion suppresses IAV infection, suggesting that the induction of MAGI1 may promote IAV infection. Treatment of ECs with oxidized low-density lipoprotein (OxLDL) increases MAGI1 expression and IAV infection, suggesting that MAGI1 is part of the mechanistic link between serum lipid levels and patient prognosis following IAV infection. Our microarray studies suggest that MAGI1-depleted ECs increase protein expression and signaling networks involve in interferon (IFN) production. Specifically, infection of MAGI1-null ECs with IAV upregulates expression of signal transducer and activator of transcription 1 (STAT1), interferon b1 (IFNb1), myxovirus resistance protein 1 (MX1) and 2′-5′-oligoadenylate synthetase 2 (OAS2), and activate STAT5. By contrast, MAGI1 overexpression inhibits Ifnb1 mRNA and MX1 expression, again supporting the pro-viral response mediated by MAGI1. MAGI1 depletion induces the expression of MX1 and virus suppression. The data suggests that IAV suppression by MAGI1 depletion may, in part, be due to MX1 induction. Lastly, interferon regulatory factor 3 (IRF3) translocates to the nucleus in the absence of IRF3 phosphorylation, and IRF3 SUMOylation is abolished in MAGI1-depleted ECs. The data suggests that MAGI1 inhibits IRF3 activation by maintaining IRF3 SUMOylation. In summary, IAV infection occurs in ECs in a MAGI1 expression-dependent manner by inhibiting anti-viral responses including STATs and IRF3 activation and subsequent MX1 induction, and MAGI1 plays a role in EC activation, and in upregulating a pro-viral response. Therefore, the inhibition of MAGI1 is a potential therapeutic target for IAV-induced cardiovascular disease.

Optical regulation of endogenous RhoA reveals selection of cellular responses by signal amplitude.

Optical regulation of endogenous RhoA reveals selection of cellular responses by signal amplitude.

Time-resolved proximity labeling of protein networks associated with ligand-activated EGFR.

Time-resolved proximity labeling of protein networks associated with ligand-activated EGFR.

Proton‐gated Coincidence Detection is a Common Feature of GPCR Signaling

The evolutionary expansion of G protein‐coupled receptors (GPCRs) has produced a rich diversity of transmembrane sensors for many physical and chemical signals. In humans alone, over 800 GPCRs detect stimuli such as light, hormones, and metabolites to guide cellular decision‐making primarily using intracellular G protein signaling networks. This diversity is further enriched by GPCRs that function as molecular sensors capable of discerning multiple inputs to transduce cues encoded in complex, context‐dependent signals. Here, we show that many GPCRs are coincidence detectors that couple proton (H+) binding to GPCR signaling. Using a large panel of receptors covering hundreds of individual GPCR‐Gα coupling combinations, we show that H+ gating both positively and negatively modulates GPCR signaling. Notably, these observations extend to all modes of GPCR pharmacology including ligand efficacy, potency, and cooperativity. Additionally, we show that GPCR antagonism and constitutive activity are regulated by H+ gating and report the discovery of new GPCR H+ sensors that can be activated solely by changes in pH. Together, these findings establish a paradigm for GPCR signaling, biology, and pharmacology applicable to acidified microenvironments such as endosomes, synapses, tumors, and ischemic vasculature.

Abstract 390: Influenza A Virus Infection Increases Magi1 Expression In Endothelial Cells And Its Depletion Inhibits Virus Replication Through Increased Expression Of Mx1

When influenza virus infects cells, it changes cellular metabolism in such a way that allows virus particles to replicate efficiently. This metabolic engineering takes place soon after virus infects cells, for which the PSD95/DiscLarge/ZO-1 (PDZ) domain of certain proteins is known to play a role. Membrane-associated guanylate kinase with inverted domain structure-1 (MAGI1) is a scaffold protein with 6 PDZ domains, and we have shown that it is involved in the regulation of endothelial cell (EC) activation and atherosclerosis in mice. Since recent studies indicate that the vascular endothelium can be infected by influenza A virus (IAV) and plays a role in the influenza-induced pathogenesis and cardiovascular disease (CVD), we investigated the role of MAGI1 in IAV infection using cultured human umbilical vein endothelial cells (HUVECs) as well as human lung microvascular endothelial cells (HULECs). We found increased MAGI1 mRNA expression in IAV-infected cells. Conversely, when MAGI1 depleted ECs were infected with IAV, virus infection and replication was greatly suppressed. Our microarray studies revealed that depletion of MAGI1 in HUVECs increased the protein expression and signaling networks involved in interferon production. Specifically, we found that the MAGI1 null condition induced expression of anti-viral response genes including interferon-induced GTP-binding protein MX1, an antiviral protein, interferon beta1, a cytokine promotor STAT1 (signal transducer and activator of transcription 1), and also increased protein expression levels of STAT1, phosphorylated STAT5 and MX1. Co-transfection of HUVECs with siMX1 and siMAGI1 impaired MAGI1 depletion-induced suppression of IAV infection. Furthermore, we found nuclear localization of interferon regulatory factor 3 (IRF3) in MAGI1 depleted cells, indicating that MAGI1 depletion elicits the interferon production and signaling. Taken together, we conclude that IAV infection and replication occurs in ECs in a MAGI1 expression dependent manner. Thus, MGAI1 depletion in ECs suppresses IAV replication, and this suppression is due to increased MX1 expression, which induces IRF3 activation and interferon production. MAGI1 can be a potential therapeutic target for influenza-induced CVD.

Proton-gated coincidence detection is a common feature of GPCR signaling

Proton-gated coincidence detection is a common feature of GPCR signaling

Antimicrobial protein REG3A and signaling networks are predictive of stroke outcomes.

Regenerating Family Member 3 Alpha (REG3A) is a multifunctional protein with antimicrobial activity, and primarily secreted by the intestine and pancreas. Studies have shown an increased expression of REG3A in systemic inflammatory responses to acute injury and infection, but studies investigating REG3A during the pathogenesis of ischemic stroke are limited. The aims of this study were to examine the associations between arterial expression of REG3A and other arterial inflammatory proteins implicated in stroke pathogenesis, as well as associations between REG3A and markers of poor outcome for ischemic stroke. The University of Kentucky Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC) protocol (clinicaltrials.gov NCT03153683) utilizes thrombectomy to isolate intracranial arterial blood (i.e. distal to thrombus) and systemic arterial blood (i.e. carotid). Samples were analyzed by Olink Proteomics for N=42subjects. Statistical analyses of plasma proteins included 2-sample t-tests, spearman and biserial correlations, and robust regression models to elucidate network signaling and association to clinical outcomes. Results indicated that levels of systemic REG3A were positively correlated with inflammatory proteins interleukin IL6 (R=0.344, p=0.030) and IL17C (R=0.468, p=0.002). 2-sided t- tests examining differences of systemic REG3A within quartiles of NIHSS admission score depicted significant differences between quartiles. Those with NIHSS scores corresponding to moderate and moderate-severe neurofunctional deficits had significantly higher levels of systemic REG3A compared to those with NIHSS scores corresponding to mild and mild-moderate neurofunctional deficits (p=0.016). STRING analyses of proteins in each robust regression model demonstrated substantial networking between REG3A and other systemic proteins highly relevant to ischemic stroke. The present study provides novel data on systemic REG3A in the context of ischemic stroke. These results demonstrate the influential role of REG3A regarding surrogate functional and radiographic outcomes of stroke severity. Additionally, they provide novel insight into the role of REG3A and related proteins during the complex neuroinflammatory process of ischemic stroke. These data provide a foundation for future studies to investigate REG3A and related networking proteins as potential biomarkers with prognostic potential, as well as potential therapeutic targets.

Deconvoluting complex protein interaction networks through reductionist strategies in peptide biochemistry: Modern approaches and research questions.

Deconvoluting complex protein interaction networks through reductionist strategies in peptide biochemistry: Modern approaches and research questions.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Recent ultra-rare inherited variants implicate new autism candidate risk genes.

Autism is a highly heritable complex disorder in which de novo mutation (DNM) variation contributes significantly to risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare variants. We report and replicate a transmission disequilibrium of private, likely gene-disruptive (LGD) variants in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin-protein ligase complex, intracellular transport and Erb signaling protein networks. We estimate that these variants are approximately 2.5 generations old and significantly younger than other variants of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and appear to act on a distinct set of genes not yet associated with autism.

Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma (MM)

Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma (MM)

Proton‐gated coincidence detection is a common feature of GPCR signaling

The evolutionary expansion of G protein-coupled receptors (GPCRs) has produced a rich diversity of transmembrane sensors for many physical and chemical signals. In humans alone, over 800 GPCRs detect stimuli such as light, hormones, and metabolites to guide cellular decision making primarily using intracellular G protein signaling networks. This diversity is further enriched by GPCRs that function as molecular logic gates capable of discerning multiple inputs to transduce cues encoded in complex, context-dependent signals. Here, we show that many GPCRs are switch-like Boolean-gated coincidence detectors that couple proton (H+) binding to GPCR signaling. Using a panel of 28 receptors, covering 280 individual GPCR-Gα coupling combinations, we show that H+ gating both positively and negatively modulates GPCR signaling. Notably, these observations extend to all modes of GPCR pharmacology including ligand efficacy, potency, and cooperativity. Additionally, we show that GPCR antagonism and constitutive activity are regulated by H+ gating and report the discovery of a new acid sensor, the adenosine A2a receptor (ADORA2A), which can be activated solely by acidic pH. Together, these findings establish a new paradigm for GPCR biology and pharmacology in acidified microenvironments such as endosomes, synapses, tumors, and ischemic vasculature.

Combining Mass Spectrometry-Based Phosphoproteomics with a Network-Based Approach to Reveal FLT3-Dependent Mechanisms of Chemoresistance.

FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.

A quantitative systems pharmacological approach identified activation of JNK signaling pathway as a promising treatment strategy for refractory HER2 positive breast cancer

HER2-positive breast cancer (BC) is a rapidly growing and aggressiveBC subtype that predominantly affects younger women. Despite improvements in patient outcomes with anti-HER2 therapy, primary and/or acquired resistance remain a major clinical challenge. Here, we sought to use a quantitativesystems pharmacological (QSP) approach to evaluate the efficacy of lapatinib (LAP), abemaciclib (ABE) and 5-fluorouracil (5-FU) mono- and combination therapies in JIMT-1 cells, a HER2+ BC cell line exhibiting intrinsic resistanceto trastuzumab. Concentration-response relationships and temporal profiles of cellular viability were assessed upon exposure to single agents and theircombinations. To quantify the nature and intensity of drug-drug interactions, pharmacodynamic cellular response models were generated, to characterize single agent and combination time course data. Temporal changes in cell-cycle phase distributions, intracellular protein signaling, and JIMT-1 cellular viability were quantified, and a systems-based protein signaling network model was developed, integrating protein dynamics to drive the observed changes in cell viability. Global sensitivity analyses for each treatment arm were performed, to identify the most influential parameters governing cellular responses. Our QSP model was able to adequately characterize protein dynamic and cellular viability trends following single and combination drug exposure. Moreover, the model and subsequent sensitivity analyses suggest that the activation of the stress pathway, through pJNK, has the greatest impact over the observed declines of JIMT-1 cell viability in vitro. These findings suggest that dual HER2 and CDK 4/6 inhibition may be a promising novel treatment strategy for refractory HER2+ BC, however, proof-of-concept in vivo studies are needed to further evaluate the combined use of these therapies.