Protein Signaling Networks Research Articles

MTORC2: A neglected player in aging regulation.

Mammalian target of rapamycin (mTOR) is a serine/threonine kinase that plays a pivotal role in various biological processes, through integrating external and internal signals, facilitating gene transcription and protein translation, as well as by regulating mitochondria and autophagy functions. mTOR kinase operates within two distinct protein complexes known as mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), which engage separate downstream signaling pathways impacting diverse cellular processes. Although mTORC1 has been extensively studied as a pro-proliferative factor and a pro-aging hub if activated aberrantly, mTORC2 received less attention, particularly regarding its implication in aging regulation. However, recent studies brought increasing evidence or clues for us, which implies the associations of mTORC2 with aging, as the genetic elimination of unique subunits of mTORC2, such as RICTOR, has been shown to alleviate aging progression in comparison to mTORC1 inhibition. In this review, we first summarized the basic characteristics of mTORC2, including its protein architecture and signaling network. We then focused on reviewing the molecular signaling regulation of mTORC2 in cellular senescence and organismal aging, and proposed the multifaceted regulatory characteristics under senescent and nonsenescent contexts. Next, we outlined the research progress of mTOR inhibitors in the field of antiaging and discussed future prospects and challenges. It is our pleasure if this review article could provide meaningful information for our readers and call forth more investigations working on this topic.

Neomorphic Gαo mutations gain interaction with Ric8 proteins in GNAO1 encephalopathies.

GNAO1 mutated in pediatric encephalopathies encodes the major neuronal G protein Gαo. Of the more than 80 pathogenic mutations, most are single amino acid substitutions spreading across the Gαo sequence. We performed extensive characterization of Gαo mutants, showing abnormal GTP uptake and hydrolysis and deficiencies in binding Gβγ and RGS19. Plasma membrane localization of Gαo was decreased for a subset of mutations that leads to epilepsy; dominant interactions with GPCRs also emerged for the more severe mutants. Pathogenic mutants massively gained interaction with Ric8A and, surprisingly, Ric8B proteins, relocalizing them from cytoplasm to Golgi. Of these 2 mandatory Gα-subunit chaperones, Ric8A is normally responsible for the Gαi/Gαo, Gαq, and Gα12/Gα13 subfamilies, and Ric8B solely responsible for Gαs/Gαolf. Ric8 mediates the disease dominance when engaging in neomorphic interactions with pathogenic Gαo through imbalance of the neuronal G protein signaling networks. As the strength of Gαo-Ric8B interactions correlates with disease severity, our study further identifies an efficient biomarker and predictor for clinical manifestations in GNAO1 encephalopathies. Our work uncovers the neomorphic molecular mechanism of mutations underlying pediatric encephalopathies and offers insights into other maladies caused by G protein malfunctioning and further genetic diseases.

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

Abstract 3490: Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks

Abstract Sex differences in lung adenocarcinoma (LUAD) are evident in incidence rates, prognostic outcomes, and therapy responses, yet the underlying molecular mechanisms driving these disparities remain underexplored. In this study, we conducted a comprehensive proteogenomic analysis encompassing 38 females and 73 males with LUAD from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Employing Transcription Inference using Gene Expression and Regulatory data (TIGER), we inferred sex-differentially activated transcription factors (TFs) from The Cancer Genome Atlas (TCGA) LUAD gene expression data and identified sex-differentially activated kinases using CPTAC protein phosphorylation data. We further constructed a comprehensive kinase-TF signaling network by integrating these sex-differentially activated kinases with TFs, identifying all paths shorter than 3 in the protein interaction networks to highlight druggable pathways. Our analyses revealed that many proteins exhibit not only sex-biased abundance but also sex-biased phosphorylation and acetylation. Furthermore, these sex-biased proteins were associated with critical biological pathways including cell proliferation, immune response, and metabolism. Using kinase-TF signaling networks, we found substantial sex bias in the activities of clinically actionable TFs and kinases, including the glucocorticoid receptor (NR3C1), AR, AURKA, CDK6, and MAPK14. Leveraging the PRISM cancer cell line screening database, we identified several small-molecule drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors, potentially exhibiting sex-specific efficacy as LUAD therapeutics. Our findings showed that the activity of some clinically relevant TFs and kinases differ by sex in LUAD, underscoring the need to consider sex as a biological variable and the utility of multi-omics integrative protein signaling networks in advancing our understanding of cancer biology and the development of sex-aware therapeutics. Citation Format: Chen Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush, Camila M. Lopes-Ramos. Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks [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 3490.

DephosNet: A Novel Transfer Learning Approach for Dephosphorylation Site Prediction

Protein dephosphorylation is the process of removing phosphate groups from protein molecules, which plays a vital role in regulating various cellular processes and intricate protein signaling networks. The identification and prediction of dephosphorylation sites are crucial for this process. Previously, there was a lack of effective deep learning models for predicting these sites, often resulting in suboptimal outcomes. In this study, we introduce a deep learning framework known as “DephosNet”, which leverages transfer learning to enhance dephosphorylation site prediction. DephosNet employs dual-window sequential inputs that are embedded and subsequently processed through a series of network architectures, including ResBlock, Multi-Head Attention, and BiGRU layers. It generates predictions for both dephosphorylation and phosphorylation site probabilities. DephosNet is pre-trained on a phosphorylation dataset and then fine-tuned on the parameters with a dephosphorylation dataset. Notably, transfer learning significantly enhances DephosNet’s performance on the same dataset. Experimental results demonstrate that, when compared with other state-of-the-art models, DephosNet outperforms them on both the independent test sets for phosphorylation and dephosphorylation.

Control of intracellular pH and bicarbonate by CO2 diffusion into human sperm

The reaction of CO2 with H2O to form bicarbonate (HCO3−) and H+ controls sperm motility and fertilization via HCO3−-stimulated cAMP synthesis. A complex network of signaling proteins participates in this reaction. Here, we identify key players that regulate intracellular pH (pHi) and HCO3− in human sperm by quantitative mass spectrometry (MS) and kinetic patch-clamp fluorometry. The resting pHi is set by amiloride-sensitive Na+/H+ exchange. The sperm-specific putative Na+/H+ exchanger SLC9C1, unlike its sea urchin homologue, is not gated by voltage or cAMP. Transporters and channels implied in HCO3− transport are not detected, and may be present at copy numbers < 10 molecules/sperm cell. Instead, HCO3− is produced by diffusion of CO2 into cells and readjustment of the CO2/HCO3−/H+ equilibrium. The proton channel Hv1 may serve as a unidirectional valve that blunts the acidification ensuing from HCO3− synthesis. This work provides a new framework for the study of male infertility.

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 &amp; 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.

How protein signaling networks respond to different input strengths is an important but poorly understood problem in cell biology. For example, RhoA can promote focal adhesion (FA) growth or disassembly, but how RhoA activity mediates these opposite outcomes is not clear. Here, we develop a photoswitchable RhoA guanine nucleotide exchange factor (GEF), psRhoGEF, to precisely control endogenous RhoA activity. Using this optical tool, we discover that peak FA disassembly selectively occurs upon activation of RhoA to submaximal levels. We also find that Src activation at FAs selectively occurs upon submaximal RhoA activation, identifying Src as an amplitude-dependent RhoA effector. Finally, a pharmacological Src inhibitor reverses the direction of the FA response to RhoA activation from disassembly to growth, demonstrating that Src functions to suppress FA growth upon RhoA activation. Thus, rheostatic control of RhoA activation by psRhoGEF reveals that cells can use signal amplitude to produce multiple responses to a single biochemical signal.

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

SUMMARYLigand binding to the EGF receptor (EGFR) triggers multiple signal-transduction processes and promotes endocytosis of the receptor. The mechanisms of EGFR endocytosis and its cross-talk with signaling are poorly understood. Here, we combine peroxidase-catalyzed proximity labeling, isobaric peptide tagging, and quantitative mass spectrometry to define the dynamics of the proximity proteome of ligand-activated EGFR. Using this approach, we identify a network of signaling proteins, which remain associated with the receptor during its internalization and trafficking through the endosomal system. We show that Trk-fused gene (TFG), a protein known to function at the endoplasmic reticulum exit sites, is enriched in the proximity proteome of EGFR in early/sorting endosomes and localized in these endosomes and demonstrate that TFG regulates endosomal sorting of EGFR. This study provides a comprehensive resource of time-dependent nanoscale environment of EGFR, thus opening avenues to discovering new regulatory mechanisms of signaling and intracellular trafficking of receptor tyrosine kinases.

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.

The Interplay between Hydrogen Sulfide and Phytohormone Signaling Pathways under Challenging Environments.

Hydrogen sulfide (H2S) serves as an important gaseous signaling molecule that is involved in intra- and intercellular signal transduction in plant–environment interactions. In plants, H2S is formed in sulfate/cysteine reduction pathways. The activation of endogenous H2S and its exogenous application has been found to be highly effective in ameliorating a wide variety of stress conditions in plants. The H2S interferes with the cellular redox regulatory network and prevents the degradation of proteins from oxidative stress via post-translational modifications (PTMs). H2S-mediated persulfidation allows the rapid response of proteins in signaling networks to environmental stimuli. In addition, regulatory crosstalk of H2S with other gaseous signals and plant growth regulators enable the activation of multiple signaling cascades that drive cellular adaptation. In this review, we summarize and discuss the current understanding of the molecular mechanisms of H2S-induced cellular adjustments and the interactions between H2S and various signaling pathways in plants, emphasizing the recent progress in our understanding of the effects of H2S on the PTMs of proteins. We also discuss future directions that would advance our understanding of H2S interactions to ultimately mitigate the impacts of environmental stresses in the plants.

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.

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.