Prog Neuropsychopharmacol Biol Psychiatry. 2023 Jul 12:110826. doi: 10.1016/j.pnpbp.2023.110826. Online ahead of print.

ABSTRACT

Cognitive dysfunction contributes significantly to the burden caused by Major Depressive Disorder (MDD). Yet, while compelling evidence suggests that different biological processes play a part in both MDD aetiology and the development of cognitive decline more generally, we only begin to understand the molecular underpinnings of depression-related cognitive impairment. Developments in psychometric assessments, molecular high-throughput methods and systems biology derived analysis strategies advance this endeavour. Here, we aim to identify gene expression signatures associated with cognitive dysfunction and cognitive improvement following therapy using RNA sequencing to analyze the whole blood-derived transcriptome of altogether 101 MDD patients who enrolled in the CERT-D study. The mRNA(Nova)Seq based transcriptome was analysed from whole blood taken at baseline assessment, and patients' cognitive performance was measured twice at baseline and following eight weeks of therapy by means of the THINC integrated tool. Thirty-six patients showed comparatively low cognitive performance at baseline assessment, and 32 patients showed comparatively strong cognitive improvement following therapy. Differential gene expression analysis was performed using limma to a significance threshold of 0.05 and a logFC cutoff of |1.2|. Although we observed some indications for expression differences related to low cognitive performance and cognitive therapy response, signals did not withstand adjustment for multiple testing. Applying WGCNA, we retrieved altogether 25 modules of co-expressed genes and we used a combination of correlational and linear analyses to identify modules related to baseline cognitive performance and cognitive improvement following therapy. Three immune modules reflected distinct but interrelated immune processes (the yellow module: neutrophil-mediated immunity, the darkorange module: interferon signaling, the tan module: platelet activation), and higher expression of the yellow (r = -0.21, p < .05), the dark orange (r = 0.2, p < .05), and the tan (r = -0.23, p < .05) module correlated significantly negatively with patients' cognitive baseline performance. Patients' cognitive baseline performance was a significant predictor of the darkorange module (b = -0.039, p < .05) and the tan module's expression (b = 0.02, p < .05) and was close to becoming a significant predictor of the yellow module's expression (b = -0.02, p = .05). Furthermore, patients characterized by comparatively low cognitive performance at baseline showed significantly higher expression of the tan module when compared to all other patients F(1,97) = 4.32, p < .05, eta = 0.04. Following eight weeks of treatment, we observed altogether significant improvement in patients' cognitive performance (b = 0.30, p < .001), and patients with comparatively high cognitive gain showed noticeably lower, but not significantly lower F(1,98) = 3.76, p = .058, expression of a dark turquoise module, which reflects complement and B-cell-associated immune processes. Noteworthy, the relation between cognitive performance and module expression remained observable after controlling for symptom severity and BMI, which partly accounted for variance in module expression. As such, our findings provide further evidence for the involvement of immune processes in MDD related cognitive dysfunction and they suggest that different immune processes contribute to the development and long-term persistence of cognitive dysfunction in the context of depression.

PMID:37451594 | DOI:10.1016/j.pnpbp.2023.110826

J Biol Chem. 2023 Jul 12:105051. doi: 10.1016/j.jbc.2023.105051. Online ahead of print.

ABSTRACT

Sialylation is a terminal glycosylated modification of glycoproteins that regulates critical biological events such as cell adhesion and immune response. Our previous study showed that integrin α3β1 plays a crucial role in regulating the sialylation of N-glycans. However, the underlying mechanism for the regulation remains unclear. This study investigated how sialylation is affected by focal adhesion kinase (FAK), which is a critical downstream signal molecule of integrin β1. We established a stable focal adhesion kinase (FAK) knockout (KO) cell line using the CRISPR/Cas9 system in HeLa cells. The results obtained from lectin blot, flow cytometric analysis, and mass spectrometry (MS) showed that the sialylation levels were significantly decreased in the KO cells compared with that in wild-type (WT) cells. Moreover, phosphatidylinositol 4-phosphate (PI4P) expression levels were also reduced in the KO cells due to a decrease in the stability of phosphatidylinositol 4-kinase-IIα (PI4KIIα). Notably, the decreased levels of sialylation, PI4P, and the complex formation between GOLPH3 and ST3GAL4 or ST6GAL1, which are the main sialyltransferases for modification of N-glycans, were significantly restored by the re-expression of FAK. Furthermore, the decreased sialylation and phosphorylation of Akt and cell migration caused by FAK deficiency all were restored by overexpressing PI4KIIα, which suggests that PI4KIIα is one of the downstream molecules of FAK. These findings indicate that FAK regulates sialylation via the PI4P synthesis pathway and a novel mechanism is suggested for the integrin-FAK-PI4KIIα-GOLPH3-ST axis modulation of sialylation in N-glycans.

PMID:37451482 | DOI:10.1016/j.jbc.2023.105051

Mol Cell. 2023 Jul 12:S1097-2765(23)00468-9. doi: 10.1016/j.molcel.2023.06.021. Online ahead of print.

ABSTRACT

For cells to perform their biological functions, they need to adopt specific shapes and form functionally distinct subcellular compartments. This is achieved in part via an asymmetric distribution of mRNAs within cells. Currently, the main model of mRNA localization involves specific sequences called "zipcodes" that direct mRNAs to their proper locations. However, while thousands of mRNAs localize within cells, only a few zipcodes have been identified, suggesting that additional mechanisms contribute to localization. Here, we assess the role of mRNA stability in localization by combining the isolation of the soma and neurites of mouse primary cortical and mESC-derived neurons, SLAM-seq, m6A-RIP-seq, the perturbation of mRNA destabilization mechanisms, and the analysis of multiple mRNA localization datasets. We show that depletion of mRNA destabilization elements, such as m6A, AU-rich elements, and suboptimal codons, functions as a mechanism that mediates the localization of mRNAs associated with housekeeping functions to neurites in several types of neurons.

PMID:37451262 | DOI:10.1016/j.molcel.2023.06.021

Sci Immunol. 2023 Jul 21;8(85):eadf4312. doi: 10.1126/sciimmunol.adf4312. Epub 2023 Jul 14.

ABSTRACT

Celiac disease (CD) is an autoimmune disease in which intestinal inflammation is induced by dietary gluten. The means through which gluten-specific CD4+ T cell activation culminates in intraepithelial T cell (T-IEL)-mediated intestinal damage remain unclear. Here, we performed multiplexed single-cell analysis of intestinal and gluten-induced peripheral blood T cells from patients in different CD states and healthy controls. Untreated, active, and potential CD were associated with an enrichment of activated intestinal T cell populations, including CD4+ follicular T helper (TFH) cells, regulatory T cells (Tregs), and natural CD8+ αβ and γδ T-IELs. Natural CD8+ αβ and γδ T-IELs expressing activating natural killer cell receptors (NKRs) exhibited a distinct TCR repertoire in CD and persisted in patients on a gluten-free diet without intestinal inflammation. Our data further show that NKR-expressing cytotoxic cells, which appear to mediate intestinal damage in CD, arise from a distinct NKR-expressing memory population of T-IELs. After gluten ingestion, both αβ and γδ T cell clones from this memory population of T-IELs circulated systemically along with gluten-specific CD4+ T cells and assumed a cytotoxic and activating NKR-expressing phenotype. Collectively, these findings suggest that cytotoxic T cells in CD are rapidly mobilized in parallel with gluten-specific CD4+ T cells after gluten ingestion.

PMID:37450575 | DOI:10.1126/sciimmunol.adf4312

J Chem Theory Comput. 2023 Jul 14. doi: 10.1021/acs.jctc.3c00333. Online ahead of print.

ABSTRACT

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

PMID:37450482 | DOI:10.1021/acs.jctc.3c00333

Genes Genomics. 2023 Jul 14. doi: 10.1007/s13258-023-01429-y. Online ahead of print.

NO ABSTRACT

PMID:37450237 | DOI:10.1007/s13258-023-01429-y

Methods Mol Biol. 2023;2690:457-467. doi: 10.1007/978-1-0716-3327-4_35.

ABSTRACT

In recent years, extracting information from biological data has become a particularly valuable way of gaining knowledge. Molecular interaction networks provide a framework for visualizing cellular processes, but their complexity frequently makes their interpretation difficult. Proteins are one of the primary determinants of biological function. Indeed, most biological activities in the living cells are functionally regulated by protein-protein interactions (PPIs). Thus, studying protein interactions is critical for understanding their roles within the cell. Exploring the PPI networks can open new avenues for future experimental studies and offer interspecies predictions for effective interaction mapping. In this chapter we will demonstrate how to construct, visualize, and analyze a protein-protein interaction network using NetworkX.

PMID:37450166 | DOI:10.1007/978-1-0716-3327-4_35

Methods Mol Biol. 2023;2690:419-427. doi: 10.1007/978-1-0716-3327-4_32.

ABSTRACT

As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.

PMID:37450163 | DOI:10.1007/978-1-0716-3327-4_32

Methods Mol Biol. 2023;2690:385-399. doi: 10.1007/978-1-0716-3327-4_30.

ABSTRACT

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

PMID:37450161 | DOI:10.1007/978-1-0716-3327-4_30

Methods Mol Biol. 2023;2690:311-334. doi: 10.1007/978-1-0716-3327-4_26.

ABSTRACT

Mapping protein-protein interactions is crucial to understand protein function. Recent advances in proximity-dependent biotinylation (BioID) coupled to mass spectrometry (MS) allow the characterization of protein complexes in diverse plant models. Here, we describe the use of BioID in hairy root cultures of tomato and provide detailed information on how to analyze the data obtained by MS.

PMID:37450157 | DOI:10.1007/978-1-0716-3327-4_26

Methods Mol Biol. 2023;2690:223-239. doi: 10.1007/978-1-0716-3327-4_20.

ABSTRACT

Yeast two-hybrid next-generation interaction screening (Y2H-NGIS) uses the output of next-generation sequencing to mine for novel protein-protein interactions. Here, we outline the analytics underlying Y2H-NGIS datasets. Different systems, libraries, and experimental designs comprise Y2H-NGIS methodologies. We summarize the analysis in several layers that comprise the characterization of baits and preys, quantification, and identification of true interactions for subsequent secondary validation. We present two software designed for this purpose, NGPINT and Y2H-SCORES, which are used as front-end and back-end tools in the analysis. Y2H-SCORES software can be used and adapted to analyze different datasets not only from Y2H-NGIS but from other techniques ruled by similar biological principles.

PMID:37450151 | DOI:10.1007/978-1-0716-3327-4_20

Clin Cancer Res. 2023 Jul 14:CCR-23-0604. doi: 10.1158/1078-0432.CCR-23-0604. Online ahead of print.

ABSTRACT

PURPOSE: PD-1 blockade plus chemotherapy has become the new standard of care in patients with untreated advanced non-small-cell lung cancer (NSCLC), whereas predictive biomarkers remain undetermined.

PATIENTS AND METHODS: We integrated clinical, genomic and survival data of 427 NSCLC patients treated with first-line PD-1 blockade plus chemotherapy or chemotherapy from two phase 3 trials (CameL and CameL-sq) and investigated the predictive and prognostic value of HLA class I evolutionary divergence (HED).

RESULTS: High HED could predict significantly improved objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) in those received PD-1 blockade plus chemotherapy (In the CameL trial, ORR: 81.8% vs. 53.2%; P = 0.032; PFS: hazard ratio [HR], 0.47; P = 0.012; OS: HR, 0.40; P = 0.014; In the CameL-sq trial, ORR: 89.2% vs 62.3%; P = 0.007; PFS: HR, 0.49; P = 0.005; OS: HR, 0.38; P = 0.002), but not chemotherapy. In multivariate analysis adjusted for PD-L1 expression and tumor mutation burden, high HED was independently associated with markedly better ORR, PFS and OS in both two trials. Moreover, joint utility of HED and PD-L1 expression showed better performance than either alone in predicting treatment benefit from PD-1 blockade plus chemotherapy. Single-cell RNA sequencing of 58,977 cells collected from 11 patients revealed that tumors with high HED had improved antigen presentation and T cell mediated antitumor immunity, indicating an inflamed tumor microenvironment phenotype.

CONCLUSION: These findings suggest that high HED could portend survival benefit in advanced NSCLC treated with first-line PD-1 blockade plus chemotherapy.

PMID:37449971 | DOI:10.1158/1078-0432.CCR-23-0604

Bioinformatics. 2023 Jul 14:btad437. doi: 10.1093/bioinformatics/btad437. Online ahead of print.

ABSTRACT

MOTIVATION: The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics.

RESULTS: Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks.

AVAILABILITY AND IMPLEMENTATION: SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37449910 | DOI:10.1093/bioinformatics/btad437

Integr Biol (Camb). 2023 Apr 11;15:zyad008. doi: 10.1093/intbio/zyad008.

ABSTRACT

In an attempt to understand the role of dysregulated circadian rhythm in glioma, our recent findings highlighted the existence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK. To further elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this lactate dehydrogenase A (LDHA)-IL-1β-CLOCK/BMAL1 circuit and predicts potential therapeutic targets. The model was calibrated on quantitative western blotting data in two glioma cell lines in response to either lactate inhibition or IL-1β stimulation. The calibrated model described the experimental data well and most of the parameters were identifiable, thus the model was predictive. Sensitivity analysis identified IL-1β and LDHA as potential intervention points. Mathematical models described here can be useful to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in designing effective therapeutic strategies. Our findings underscore the importance of including the circadian clock when developing pharmacological approaches that target aberrant tumour metabolism and inflammation. Insight box The complex interplay of metabolism-inflammation-circadian rhythm in tumours is not well understood. Our recent findings provided evidence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK/BMAL1 in glioma. To elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this LDHA-IL-1β-CLOCK/BMAL1 circuit and integrates experimental data to predict potential therapeutic targets. The study employed a multi-start optimization strategy and profile likelihood estimations for parameter estimation and assessing identifiability. The simulations are in reasonable agreement with the experimental data. Sensitivity analysis found LDHA and IL-1β as potential therapeutic points. Mathematical models described here can provide insights to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in identifying effective therapeutic targets.

PMID:37449740 | DOI:10.1093/intbio/zyad008

Immunol Rev. 2023 Jul 14. doi: 10.1111/imr.13241. Online ahead of print.

ABSTRACT

CRISPR technology has transformed multiple fields, including cancer and immunology. CRISPR-based gene editing and screening empowers direct genomic manipulation of immune cells, opening doors to unbiased functional genetic screens. These screens aid in the discovery of novel factors that regulate and reprogram immune responses, offering novel drug targets. The engineering of immune cells using CRISPR has sparked a transformation in the cellular immunotherapy field, resulting in a multitude of ongoing clinical trials. In this review, we discuss the development and applications of CRISPR and related gene editing technologies in immune cells, focusing on functional genomics screening, gene editing-based cell therapies, as well as future directions in this rapidly advancing field.

PMID:37449673 | DOI:10.1111/imr.13241

Front Immunol. 2023 Jun 28;14:1186580. doi: 10.3389/fimmu.2023.1186580. eCollection 2023.

ABSTRACT

T-bet-expressing Th17 (T-bet+RORγt+) cells are associated with the induction of pathology during experimental autoimmune encephalomyelitis (EAE) and the encephalitic nature of these Th17 cells can be explained by their ability to produce GM-CSF. However, the upstream regulatory mechanisms that control Csf2 (gene encoding GM-CSF) expression are still unclear. In this study, we found that Th17 cells dynamically expressed GATA3, the master transcription factor for Th2 cell differentiation, during their differentiation both in vitro and in vivo. Early deletion of Gata3 in three complimentary conditional knockout models by Cre-ERT2, hCd2 Cre and Tbx21 Cre, respectively, limited the pathogenicity of Th17 cells during EAE, which was correlated with a defect in generating pathogenic T-bet-expressing Th17 cells. These results indicate that early GATA3-dependent gene regulation is critically required to generate a de novo encephalitogenic Th17 response. Furthermore, a late deletion of Gata3 via Cre-ERT2 in the adoptive transfer EAE model resulted in a cell intrinsic failure to induce EAE symptoms which was correlated with a substantial reduction in GM-CSF production without affecting the generation and/or maintenance of T-bet-expressing Th17 cells. RNA-Seq analysis of Gata3-sufficient and Gata3-deficient CNS-infiltrating CD4+ effector T cells from mixed congenic co-transfer recipient mice revealed an important, cell-intrinsic, function of GATA3 in regulating the expression of Egr2, Bhlhe40, and Csf2. Thus, our data highlights a novel role for GATA3 in promoting and maintaining the pathogenicity of T-bet-expressing Th17 cells in EAE, via putative regulation of Egr2, Bhlhe40, and GM-CSF expression.

PMID:37449212 | PMC:PMC10337884 | DOI:10.3389/fimmu.2023.1186580

PNAS Nexus. 2023 Jul 4;2(7):pgad220. doi: 10.1093/pnasnexus/pgad220. eCollection 2023 Jul.

ABSTRACT

Mammalian genomes encode large number of long noncoding RNAs (lncRNAs) that play key roles in various biological processes, including proliferation, differentiation, and stem cell pluripotency. Recent studies have addressed that some lncRNAs are dysregulated in human cancers and may play crucial roles in tumor development and progression. Here, we show that the lncRNA ZNNT1 is required for the proliferation and tumorigenicity of colon cancer cells with wild-type p53. ZNNT1 knockdown leads to decreased ubiquitination and stabilization of p53 protein. Moreover, we demonstrate that ZNNT1 needs to interact with SART3 to destabilize p53 and to promote the proliferation and tumorigenicity of colon cancer cells. We further show that SART3 is associated with the ubiquitin-specific peptidase USP15 and that ZNNT1 may induce p53 destabilization by inhibiting this interaction. These results suggest that ZNNT1 interferes with the SART3-USP15 complex-mediated stabilization of p53 protein and thereby plays important roles in the proliferation and tumorigenicity of colon cancer cells. Our findings suggest that ZNNT1 may be a promising molecular target for the therapy of colon cancer.

PMID:37448957 | PMC:PMC10337854 | DOI:10.1093/pnasnexus/pgad220

Data Brief. 2023 Jun 24;49:109343. doi: 10.1016/j.dib.2023.109343. eCollection 2023 Aug.

ABSTRACT

[This corrects the article DOI: 10.1016/j.dib.2023.109069.].

PMID:37448733 | PMC:PMC10336392 | DOI:10.1016/j.dib.2023.109343

Mol Ther Methods Clin Dev. 2023 Jun 19;30:147-160. doi: 10.1016/j.omtm.2023.06.007. eCollection 2023 Sep 14.

ABSTRACT

Adoptive cell therapy of donor-derived, antigen-specific T cells expressing native T cell receptors (TCRs) is a powerful strategy to fight viral infections in immunocompromised patients. Determining the fate of T cells following patient infusion hinges on the ability to track them in vivo. While this is possible by genetic labeling of parent cells, the applicability of this approach has been limited by the non-specificity of the edited T cells. Here, we devised a method for CRISPR-targeted genome integration of a barcoded gene into Epstein-Barr virus-antigen-stimulated T cells and demonstrated its use for exclusively identifying expanded virus-specific cell lineages. Our method facilitated the enrichment of antigen-specific T cells, which then mediated improved cytotoxicity against Epstein-Barr virus-transformed target cells. Single-cell and deep sequencing for lineage tracing revealed the expansion profile of specific T cell clones and their corresponding gene expression signature. This approach has the potential to enhance the traceability and the monitoring capabilities during immunotherapeutic T cell regimens.

PMID:37448595 | PMC:PMC10336339 | DOI:10.1016/j.omtm.2023.06.007

Int J Mol Sci. 2023 Jul 5;24(13):11113. doi: 10.3390/ijms241311113.

ABSTRACT

Dopamine (DA) inhibits excitatory synaptic transmission in the anterior cingulate cortex (ACC), a brain region involved in the sensory and affective processing of pain. However, the DA modulation of inhibitory synaptic transmission in the ACC and its alteration of the excitatory/inhibitory (E/I) balance remains relatively understudied. Using patch-clamp recordings, we demonstrate that neither DA applied directly to the tissue slice nor complete Freund's adjuvant (CFA) injected into the hind paw significantly impacted excitatory currents (eEPSCs) in the ACC, when recorded without pharmacological isolation. However, individual neurons exhibited varied responses to DA, with some showing inhibition, potentiation, or no response. The degree of eEPSC inhibition by DA was higher in naïve slices compared to that in the CFA condition. The baseline inhibitory currents (eIPSCs) were greater in the CFA-treated slices, and DA specifically inhibited eIPSCs in the CFA-treated, but not naïve group. DA and CFA treatment did not alter the balance between excitatory and inhibitory currents. Spontaneous synaptic activity revealed that DA reduced the frequency of the excitatory currents in CFA-treated mice and decreased the amplitude of the inhibitory currents, specifically in CFA-treated mice. However, the overall synaptic drive remained similar between the naïve and CFA-treated mice. Additionally, GABAergic currents were pharmacologically isolated and found to be robustly inhibited by DA through postsynaptic D2 receptors and G-protein activity. Overall, the study suggests that CFA-induced inflammation and DA do not significantly affect the balance between excitatory and inhibitory currents in ACC neurons, but activity-dependent changes may be observed in the DA modulation of presynaptic glutamate release in the presence of inflammation.

PMID:37446289 | DOI:10.3390/ijms241311113