Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability.

Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice PLoS One . 2026 Jun 3;21(6):e0350674. doi: 10.1371/journal.pone.0350674 Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability Marina Pavlova 1 , Irina Shalaginova 1, * , Natalia Dyuzhikova 1 Editor: Jean-Pierre Mothet 2 Author information Article notes Copyright and License information 1 Pavlov Institute of Physiology of the Russian Academy of Sciences, Saint-Petersburg, Russia 2 CNRS - ENS Paris Saclay, FRANCE Competing Interests: The authors have declared that no competing interests exist. ✉ * E-mail: shalaginova_i@mail.ru Roles Marina Pavlova : Conceptualization, Formal analysis, Visualization, Writing – review & editing Irina Shalaginova : Conceptualization, Investigation, Writing – original draft Natalia Dyuzhikova : Conceptualization, Funding acquisition, Writing – review & editing Jean-Pierre Mothet : Editor Received 2026 Jan 26; Accepted 2026 May 16; Collection date 2026. © 2026 Pavlova et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13232848  PMID: 42234683 Abstract Individual variability of reactions to environmental influences which determines the range of the “reaction norm” and the possibilities of adaptation is strongly shaped by inherited properties of the nervous system, including genetically determined differences in excitability. Rat strains selectively bred for contrasting thresholds of neural system excitability provide a model for studying how such inherited differences are reflected at the molecular level. Here, we performed bulk RNA sequencing of the hippocampus in high-excitability (LT) and low-excitability (HT) rats to characterize baseline interstrain transcriptomic divergence. Differential expression analysis revealed strain-specific transcriptional profiles involving not only synapse- and plasticity-related genes, but also non-neuronal components associated with glial/immune functions, intracellular trafficking and protein processing, kinase signaling, extracellular matrix remodeling, and neurovascular regulation. Functional annotation highlighted differences in synaptic organization, neuronal projection development, cellular maintenance pathways, and tissue-level regulatory processes. The analysis motivates testable hypotheses involving synaptic/neurite organization, cellular maintenance pathways (MAPK/PI3K-linked trafficking and redox regulation), and glial/neurovascular components, to be evaluated in follow-up studies using structural and functional tissue-level measurements. This work also provides a reference for cross-model comparisons of polygenic excitability-related traits, as a reference transcriptomic profile from a long-term selective-breeding paradigm. Introduction Individual variability of reactions to environmental influences, including physical, chemical, and psychosocial stimuli, is a fundamental property of organisms with a complex nervous system. One of the main factors determining such variability is the excitability of the nervous system, a genetically encoded trait that is associated with the threshold and dynamics of neural responses [ 1 ]. Thus, a deeper understanding of the molecular and genetic mechanisms underlying inherited differences in excitability is crucial to identify biological risk factors and pathways that determine adaptation reserves and may contribute to vulnerability or resilience to various pathologies, including, but not limited to, stress-related disorders. One of the effective strategies for investigating complex traits is the use of selectively breeding of model organisms, allowing for controlled genetic divergence along specific behavioral or physiological axes. Notable examples include rats bred for high vs. low anxiety-like behavior (HAB vs. LAB), which differ in coping strategies and HPA axis reactivity [ 2 ], and high- vs. low-responder strains (bHR/bLR), which show stable differences in emotional reactivity and PTSD-like behavior [ 3 ]. However, these models are typically based on behavioral selection, which may be confounded by uncontrolled factors and the complexity of interpretation of behavioural tests that were used for selection. In contrast, our model is based on a direct and quantifiable physiological trait – tibial nerve excitability threshold – which reflects fundamental properties of neuronal responsiveness [ 4 ]. Long-term selection for this trait has produced two strains: low-threshold (LT, high-excitability) and high-threshold (HT, low-excitability) rats. These strains exhibit stable differences in CNS excitability and behavioral profiles [ 5 , 6 ], making them a valuable system for dissecting the molecular basis of inherited excitability. Given the hippocampus’s key role in regulating neural excitability, emotional processing, and stress response, and its established involvement in excitability-related pathologies [ 7 ], we selected this structure to investigate transcriptomic differences between the strains. The specific tasks were: 1) to determine which genes show differential expression (DEGs) in the hippocampus between LT and HT strains; 2) to characterize the functional structure of the interstrain transcriptomic divergence by Gene Ontology (GO) annotation of DEGs (biological processes, cellular components, and molecular functions); 3) to explore the organization of DEGs into interaction modules by constructing protein–protein interaction (PPI) networks and identifying clustered modules for key enriched functional categories. This design is not intended to show causal links between gene expression and phenotype, rather, it aims to map interstrain transcriptomic differences in the hippocampus of intact rats formed as a result of selection for high and low thresholds of nervous system excitability. These transcriptional profiles provide a baseline reference for subsequent studies of stress- or aging-related effects in the context of heritable differences in excitability, because they allow to predict the nature of the reaction to stress and other extreme factors depending on the genetic background associated with high and low excitability, as well as to distinguish expression changes that occur along the pre-existing interstrain axes from novel, exposure-induced shifts. In addition, this approach helps to identify a limited set of consistent modules as candidates for subsequent testing of their functional relevance. Methods Animals Male rats (5 months old, n = 5 per group) from two selectively bred strains (F72)— HT (high-threshold, low-excitability) and LT (low-threshold, high-excitability) were used in this study. The strains were developed at the I.P. Pavlov Institute of Physiology, Russian Academy of Sciences, by selective breeding from an outbred Wistar population, with the aim of studying how neural excitability influences behavioral and physiological phenotypes. These strains are included in the official biocollection at the Pavlov Institute of Physiology, RAS (NoGZ 0134-2018-0003), with patents for selective breeding (No 10769 and 10768). The selection criterion was the threshold of neuromuscular excitability, determined by electrical stimulation of the tibial nerve ( n. tibialis ) with 2-ms electrical impulses. The first two generations were obtained by sibling mating, and subsequent breeding was performed within each strain. By the 10th generation, the differences between the strains had stabilized, exceeding the within-strain variability by more than fourfold. The animals used in the present study belonged to the 76th generation of this long-term selective breeding program. Nervous system excitability was routinely assessed both during the breeding program, to select animals for further within-line mating, and during the formation of experimental cohorts. Supplementary material (S_1) shows the individual excitability threshold values of the HT and LT rats used in the present study, confirming the phenotypic separation of the two strains in the analyzed cohort. Animals from both strains were randomly selected for the experiment and kept in a shared vivarium room under controlled conditions (23 ± 2 °C, 12 h light/dark cycle) with free access to standard chow and water. Rats were group-housed (three animals per cage) until 5 months of age, after which they were decapitated in an intact state. Animals were inspected daily by trained animal-care staff, and all procedures were designed to minimize distress. The study involved terminal tissue collection from intact rats at a predefined time point and did not include survival surgery or prolonged invasive manipulations before euthanasia. Decapitation was performed rapidly by experienced personnel to minimize handling time and distress. Anesthesia was not used because agents such as inhalational anaesthetics such as isoflurane can rapidly alter cytokine and other immune-related genes expression [ 8 ] which would compromise the primary transcriptomic endpoint of the study and reduce the scientific validity of the terminal sampling procedure. The hippocampus was dissected according to the coordinates of the stereotactic atlas of the rat brain [ 9 ]. No humane endpoints were required, as animals were euthanized at the planned time point without prior invasive procedures. All procedures complied with the European Community Council Directive 2010/63/EU and were approved by the Institutional Animal Care and Use Committee of the Pavlov Institute of Physiology, RAS (Protocol No. 09/16, 16.09.2021) and follow the ARRIVE 2.0 guidelines for in vivo experiments. All animals were included in the analysis; no pre-established exclusion criteria were applied. Tissue collection, RNA extraction and sequencing Hippocampal tissue was collected under RNase-free conditions. The brain was rapidly removed and placed on an ice-cooled dissection surface. The whole hippocampus was dissected on ice with care to avoid inclusion of ventricular/ependymal tissue and visible large blood vessels. Tissue was transferred into RNAlater (Thermo Fisher Scientific, Ambion). Total RNA was extracted from hippocampal tissue using Trizol reagent followed by the PureLink RNA Micro Kit (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s protocol after tissue homogenization. Sequencing was performed at the Genoanalitika Lab, Moscow, Russia. The quality and quantity of the isolated total RNA were assessed using a BioAnalyzer with the RNA 6000 Nano Kit (Agilent). The poly(A) RNA fraction was then purified using oligo(dT) magnetic beads (Dynabeads® mRNA Purification Kit, Ambion) according to the manufacturer’s instructions. cDNA libraries for high-throughput sequencing were prepared from the poly(A) RNA using the NEBNext® Ultra™ II RNA Library Prep Kit (New England Biolabs). Library concentrations were measured with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific) on a Qubit 2.0 fluorometer, and fragment size distribution was evaluated using the Agilent High Sensitivity DNA Kit (Agilent, CA, USA). Single-end sequencing was performed on an Illumina HiSeq1500 platform (Illumina, San Diego, CA, USA), generating at least 20 million 50-bp reads per sample. RNA extraction, library preparation, and sequencing were performed simultaneously for all hippocampal samples Read mapping and gene-level quantification were carried out with STAR 2.7.9a [ 10 ]. Default settings were applied except for outFilterMismatchNmax = 3, which restricted the number of mismatches per read to three. Reads were aligned to the Rnor_6.0 reference genome using Ensembl v.99 annotation. Raw gene-level counts were generated within STAR [ 10 ] with the quantMode GeneCounts option. Differential expression analysis was performed in R with DESeq2 (v.1.28.1) [ 11 ]. Size-factor normalization was carried out with the estimateSizeFactors() function based on the median-of-ratios approach, and dispersion parameters were estimated using estimateDispersions(). Differentially expressed genes were identified with the DESeq() function. Group comparisons were evaluated with the Wald test, and p values were adjusted for multiple testing by the Benjamini-Hochberg false discovery rate (FDR) procedure. RNA sequencing analyses DEGs were identified using an FDR-adjusted significance threshold of padj < 0.05. This criterion was used as the primary statistical threshold for differential expression and controls the expected proportion of false discoveries among the genes declared significant. No mandatory log2 fold-change cutoff was applied for DEG definition, because the comparison involved intact animals under baseline physiological conditions, where moderate but statistically consistent expression differences may be biologically meaningful. The additional threshold of |log2FoldChange| ≥ 0.379, corresponding approximately to a 1.3-fold change, was used only to reduce the number of genes displayed in the circular heatmap and to improve visualization of genes with more pronounced inter-line differences. Gene Ontology (GO) enrichment analysis was performed using DAVID [ 12 ] (Database for Annotation, Visualization and Integrated Discovery; DAVID Knowledgebase v2025_1, released April 17, 2025) to identify functional categories overrepresented among the DEGs. Up- and down-regulated genes were analyzed together as a single DEG list, because the aim was to characterize the functional composition of genes differing between the lines rather than to infer direction-specific pathway activation. Gene identifiers were analyzed against the full set of expressed genes detected in our RNA-seq dataset, which was used as the background gene set. GO Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories were analyzed. Enrichment significance was assessed using the DAVID functional annotation enrichment procedure, with multiple-testing correction based on the false discovery rate (FDR). Terms were considered significant at p < 0.05 and FDR q < 0.1. Statistically significant GO terms were manually reduced to a representative non-redundant subset by grouping terms with similar biological meaning and/or overlapping DEG composition and retaining one representative term per group, prioritizing higher specificity, lower FDR values, and relevance to the biological focus of the study. Data visualization and plotting were performed using the SRplot online platform ( https://www.bioinformatics.com.cn ; accessed May 1, 2025; updated April 2025) [ 13 ]. The following types of plots were generated: Volcano plot – selection criterion: padj < 0.05 (FDR); Principal Component Analysis (PCA) – selection criterion: padj < 0.05 (FDR); To avoid circularity associated with PCA based on DEGs, PCA was additionally performed using the transcriptome-wide background expression matrix without DEG-based gene selection. For this analysis, all expressed genes were retained after removal of genes with zero expression or zero variance across samples. Circular cluster heatmap – selection criteria: padj < 0.05 (FDR) and |log 2 FC| > 0.379; this threshold corresponds to a change in the level of gene expression by more than 1.3 times (or less than 1/1.3 ≈ 0.77 times). The use of an additional filter made it possible to select DEGs with the most pronounced differences in expression levels in order to reduce their total number to the optimal one for clearer visualization of clusters on a Circular cluster heatmap; clustering based on Euclidean distance, expression data normalized by z -score; GO/ Pathway Enrichment plots – selection criteria for DEGs is padj < 0.05 (FDR); for GO-Enrichment pathways is p value < 0.05; padj < 0.1. The STRING database (version 12.0) was used to construct and analyze the protein–protein interaction (PPI) network. The analysis included both direct (physical) and indirect (functional) interactions derived from curated databases, experimental evidence, and computational predictions. The confidence score threshold was initially set at 0.4 and then increased to 0.7 to retain only high-confidence associations and obtain clearer clustering. Clusters within the PPI network were identified using the Markov Cluster Algorithm (MCL) with the inflation parameter set to 1.5 [ 14 ]. For the construction of PPI networks, we focused on differentially expressed genes (DEGs) associated with specific BP term. Raw and processed RNA-seq data were deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE327807 . Results Differential gene expression in the hippocampus of rat strains with contrast levels of neural excitability The direction and fold changes were determined for the LT strain relative to the HT strain. A total of 654 differentially expressed genes (DEGs) were identified, of which 270 showed higher expression in the LT and 384 in the HT ( padj < 0.05) ( Fig 1 ). Fig 1. Differentially expressed genes in the hippocampus of high-excitability (LT) and low-excitability (HT) strains. Open in a new tab Cut off: padj ≤0,05 (FDR). Inspection of the top DEGs suggested several broad functional groupings: a vascular/immune-associated set; a trafficking/protein-homeostasis-related; and a neuronal signaling/structural. Table 1 presents the top-DEGs highlighted on the Fig 1 . Table 1. ТOP-DEGs: Functional roles and general information*. Gene symbol Gene name Function Preferential localization Higher expression Log2Fold Change Padj Serpinf2 serpin family F member 2 Plasmin fibrinolysis inhibition Extracellular matrix LT 3,33 8,5E-06 Slc35d3 solute carrier family 35, member D3 UDP-glucose transmembrane transporter. Regulating of dopamine signaling. Golgi apparatus Endosome LT 3,28 9,9E-03 Pcdha4 protocadherin alpha 4 Cell adhesion. Specific neuronal connections in the brain. Plasma membrane LT 2,82 7,7E-06 Stab2 stabilin 2 May function in angiogenesis, lymphocyte homing, cell adhesion, or receptor scavenging Cytosol Plasma membrane Macrophages/glia LT 1,82 1,5E-05 Coro7 coronin 7 F-actin regulation Golgi apparatus Cytozol LT 1,53 2,1E-04 Cd55 CD55 molecule (Cromer blood group) Complement cascade inhibition Extracellular matrix Plasma membrane LT 1,21 2,6E-05 Mtmr1 myotubularin related protein 1 Predicted to be involved in signal transduction Сytosol Plasma membrane LT 0,61 1,5E-18 Vgf VGF nerve growth factor inducible Regulation of neuronal activity. neurogenesis and neuroplasticity. Extracellular matrix Golgi apparatus LT 0,48 3,7E-07 Prmt1l1 protein arginine methyltransferase 1 like 1 Еpigenetic regulation and transcriptional control.via histone methylation Cytosol Nucleus HT −2,50 4,0E-02 Tmem258b transmembrane protein 258 B Oligosaccharyltransferase complex binding activity Endoplasmic reticulum Plasma membrane HT −1,94 1,5E-03 Apobec1 apolipoprotein B mRNA editing enzyme catalytic subunit 1 m-RNA editing Epigenetic regulation of gene expression Nucleus Cytozol HT −1,58 2,1E-04 P2rx7 purinergic receptor P2X 7 Purinergic signaling/ Ligand-gated ion channel and is responsible for ATP -possible mechanism of gene expression changes. Plasma membrane. Mitochondrion HT −0,98 2,9E-22 Slc2a5 solute carrier family 2 member 5 Fructose transporter Plasma membrane Extracellular matrix HT −0,83 1,6E-06 Acer2 alkaline ceramidase 2 Ceramide metabolism Golgi apparatus Plasma membrane HT −0,81 1,6E-08 Grin2c glutamate ionotropic receptor NMDA type subunit 2C Encoding of subunit of the N-methyl-D-aspartate (NMDA) receptor Plasma membrane Endoplasmic reticulum HT −0,64 9,2E-05 Ift88 intraflagellar transport 88 Intraflagellar transport. Positively regulates primary cilium biogenesis. Involved in autophagy. Cytosol Cytoskeleton Nucleus HT −0,56 1,1E-08 Open in a new tab *From DAVID and GeneCards. Principal component analysis (PCA) based on the 654 DEGs demonstrated complete separation between LT and HT samples, with the first two principal components explaining 81.7% of the total variance ( Fig 2A ). The additional PCA was performed using the transcriptome-wide background expression matrix without DEG-based gene selection ( Fig 2B ). In this analysis, separation between LT and HT samples was also preserved, although the proportion of variance explained by the first two principal components was lower (32.7%), consistent with the use of an unbiased transcriptome-wide gene set rather than genes selected for differential expression. Fig 2. Principal component analysis (PCA) of hippocampal transcriptomic profiles in HT and LT rat strains. Open in a new tab (A) PCA based on 654 differentially expressed genes (DEGs; padj ≤ 0.05). (B) PCA based on all expressed genes after removal of genes with zero expression or zero variance across samples. LT – high-excitability; HT – low-excitability. Fig 3 presents a circular heatmap of the expression profiles of 167 DEGs. The diagram shows two main gene clusters: (1) genes with higher expression in the LT strain (70 DEGs) and (2) genes with lower expression in the LT strain (97 DEGs). Fig 3. Сirkular claster heatmap for 167 DEGs expression in the hippocampus of HT and LT rat strains. Open in a new tab Cut off: padj ≤0,05 (FDR), |log2fold change| ≥ 0,379). LT – high-excitability; HT – low-excitability. LT1-LT5, HT1-HT5 – samples; red color – up regulation, blue color – down regulation (LT vs HT). Together, the DEGs, PCA, and heatmap results supported the presence of a structured interstrain expression signature in the hippocampus and motivated subsequent functional annotation analysis. Gene ontology (GO) enrichment analysis To explore the biological significance of the 654 differentially expressed genes, GO enrichment analysis was performed ( Fig 4 ). Fig 4. Gene Ontology-based functional annotation of the differentially expressed genes across biological process (A), cellular component (B), and molecular function (C) categories. Open in a new tab The x-axis indicates the GeneRatio, representing the proportion of DEGs annotated to a given GO term. The color gradient of the dots corresponds to the adjusted p-value (FDR), dot size corresponds to the number of genes annotated to each term. Among the identified GO biological process (BP) pathways ( Fig 4A ), the most enriched and biologically relevant include “ regulation of the MAPK cascade” , “ neuron projection morphogenesis” , “ regulation of neuron projection development” , “ supramolecular fiber organization” and “ gliogenesis” . Also, the pathways directly related to the regulation of behavior under normal and pathological conditions, such as “ synapse organization” , “ cognition” , “ learning or memory” , “ regulation of synaptic plasticity” , “ generation of precursor metabolites and energy” , and “ extracellular matrix organization” . The most enriched GO pathways for cellular components (CC) ( Fig 4B ) were “ neuron-to-neuron synapse” , “ side of membrane” , “ extracellular matrix” , “ cell leading edge” , “ distal axon” , “ Golgi apparatus subcompartment” and “ secretory granule” . For molecular functions (MF) ( Fig 4C ), the most significant categories included “ signaling receptor activity” , “ oxidoreductase activity” and “ calcium ion binding” . Overall, the GO analysis indicated several major functional trends in the strain hippocampal transcription profiles. Beyond broad synaptic/plasticity-related categories, the enriched terms also pointed to neuronal process development, glial-associated functions, extracellular matrix organization, kinase-related signaling, intracellular trafficking, and metabolic/redox-related processes. Because GO analysis indicated several coherent functional themes, we next asked whether selected categories were also supported by network-level interactions among their gene products. Protein-protein interactions STRING analysis of the complete DEG set (654 genes, FDR < 0.05) demonstrated significant protein–protein interaction enrichment (639 nodes, 547 edges; 381 expected edges; average node degree: 1.71; PPI enrichment p = 7.77 × 10 −16 ), indicating that the transcriptomic differences between HT and LT strains are not randomly distributed across independent genes but are partially organized into interconnected molecular systems ( S2 Fig ). To further examine the functional organization of selected DEG subsets associated with biologically relevant GO categories, we next constructed focused PPI networks for genes involved in regulation of synaptic plasticity and regulation of the MAPK cascade ( Figs 5 and 6 ). Fig 5. Protein–protein interaction network for genes involved in regulation of synaptic plasticity. Open in a new tab Cluster 1 (red, 18 genes) – Regulation of synaptic plasticity. Cluster 2 (green, 2 genes: Grin2c, Grik1 ) – Ionotropic glutamate receptor activity. Cluster 3 (blue, Penk and Vgf ) – not annotated in STRING. Fig 6. Protein–protein interaction network for genes involved in regulation of MAPK cascade. Open in a new tab Cluster 1 (red, 12 genes) – regulation of phospholipase activity, Cluster 2 (yellow, 7 genes) – positive regulation of myeloid leukocyte differentiation, Cluster 3 (green, 5 genes) – receptors involved in neurotrophic and GPCR signaling, Cluster 4 (cyan, 3 genes) – cytoskeletal components involved in microvillus assembly and myosin II binding, Cluster 5 (purple, 3 genes) – transcriptional regulators of early cell fate decisions. Genes not integrated into any cluster due to insufficient interaction evidence are shown as unconnected white nodes. To explore functional relationships among the genes contributing to the enriched GO term “regulation of synaptic plasticity” (GO:0048167), we constructed a PPI network using the STRING database. The network included 22 nodes connected by experimentally validated or predicted interactions. MCL clustering revealed three functional modules ( Fig 5 ). The network of 29 nodes connected by 51 edges, whereas only 6 edges would be expected for a random set of this size. The network showed highly significant PPI enrichment (p < 1.0 × 10 −16 ), with an average node degree of 3.52 and a local clustering coefficient of 0.497, supporting the presence of a non-random and moderately clustered interaction structure. Cluster 1 (red, 18 genes) represents the core synaptic plasticity module, encompassing key regulators of neurotransmission and cytoskeletal organization such as Snap25 , Camk2a , Ntrk2 , Mapt , and Gfap . Cluster 2 (green, 2 genes: Grin2c , Grik1 ) corresponds to ionotropic glutamate receptor activity, directly linking to postsynaptic excitatory signaling. Cluster 3 (blue, Penk and Vgf ) includes neuropeptide-encoding genes associated with neurotrophic signaling and plasticity modulation. The dense interconnections within Cluster 1 highlight a coordinated regulatory network that integrates synaptic vesicle trafficking, receptor signaling, and structural remodeling processes. PPI analysis of the functional coherence of differentially expressed genes associated with the GO:0043408 ~ regulation of MAPK cascade pathway using the STRING database revealed five distinct clusters ( Fig 6 ). The STRING network contained 57 nodes and 136 edges, whereas only 33 edges were expected by chance. The network showed highly significant PPI enrichment (p < 1.0 × 10 −16 ), with an average node degree of 4.77 and a local clustering coefficient of 0.476, indicating a non-random and moderately clustered interaction structure. Cluster 1 corresponds to regulation of phospholipase activity and includes key upstream modulators such as Agt, Ednra, Pdgfrb, Flt1 , and Agtr1 . Cluster 2 reflects genes involved in the positive regulation of myeloid leukocyte differentiation. Cluster 3 comprises receptors involved in neurotrophin and GPCR signaling ( Ntrk2, Ntsr2, Gpr37l1, Rit2 ). Cluster 4 represents cytoskeletal components involved in microvillus assembly and myosin II binding ( Ezr, Sln ). Cluster 5 includes transcriptional regulators of early cell fate decisions ( Sox2, Bmp4 ). Overall, the PPI analysis helped to resolve selected DEG-associated categories into connected modules. The synaptic plasticity-related network contained modules centered on core synaptic genes, glutamate receptor-related components, and neuropeptide-associated genes, whereas the MAPK-related network was organized into smaller signaling, cytoskeletal, and transcription-related modules. Discussion Top DEGs ( Table 1 ) can be used as concrete candidates for follow-up validation and for building minimal marker panels, which we then relate to the broader GO/PPI structure described below. Several top DEGs may be consistent with a non-neuronal vascular/immune-associated component of interstrain divergence: Serpinf2 (α2-antiplasmin; coagulation-related) [ 15 ], with broader serpin-family links to neurodegenerative conditions [ 16 ] and our prior observation of higher Serpinf2 mRNA in LT amygdala [ 17 ]; Stab2 (endothelial/myeloid transmembrane receptor), consistent with a heightened neuroimmune profile previously reported in LT amygdala [ 17 ]; Cd55 (complement regulation) [ 18 , 19 ]; and glia/innate-immunity candidates such as P2rx7 (predominantly glial purinergic receptor) [ 20 – 22 ] with context-dependent signaling states [ 23 ], Slc2a5 /GLUT5 (microglia-enriched fructose transporter) [ 24 – 26 ], and Apobec1 (RNA-editing enzyme with reported roles in microglial inflammatory control and neurodegeneration) [ 27 , 28 ] and an epilepsy-related association [ 29 ]. Acer2 (sphingolipid metabolism) [ 30 , 31 ] may reflect differences in lipid/vascular or blood–brain barrier–related components rather than neuron-intrinsic expression changes. Because the present study is based on bulk hippocampal RNA-seq, these interpretations remain tentative and do not provide direct evidence of cell-type-specific expression changes. Further validation would require single-cell RNA sequencing or deconvolution analysis using suitable reference data. Several top DEGs form a recurring pattern related to – ER-Golgi/endosomal trafficking and protein homeostasis. Slc35d3 links to Golgi/endosomal membranes and receptor trafficking pathways (D1 receptor trafficking shown in D1 neurons) [ 32 ] and has been connected to trafficking/autophagy-related processes [ 33 ]. Coro7 is a Golgi-localized trafficking/structure factor [ 34 , 35 ]. Tmem258b is associated with the oligosaccharyltransferase complex for N-linked glycosylation and ER stress pathways [ 27 ]. Together, these genes provide a short list of candidates for follow-up validation in trafficking/ER stress/protein quality control pathways across strains with contrast excitability. A third group of top-DEGs captures neuronal circuit organization and activity-related signaling/regulation. Pcdha4 – (PCDHA cluster genes) are linked to neuronal connectivity and synapse specificity [ 36 , 37 ], with protocadherin mutations (e.g., PCDH19) associated with epilepsy and network hyperexcitability [ 38 ]. Vgf is an activity-regulated neuropeptide precursor induced by neurotrophic factors [ 39 ] and associated with hippocampal activity-dependent plasticity and learning [ 40 ], with broader biomarker/therapeutic discussions [ 41 ]. Grin2c (GluN2C NMDA receptor subunit) has distinct receptor properties and cell-type–restricted expression patterns [ 42 ], and NMDA receptors are broadly implicated in neuropsychiatric disease contexts [ 43 ]. Ift88 (primary cilia) is linked to neuronal signaling regulation and circuit formation [ 44 – 46 ]. Prmt1l1 (PRMT1-like methyltransferase, poorly characterized) [ 47 ], together with PRMT-family links to excitability-related methylation (e.g., NALCN in a PRMT7 context) [ 48 ], points to a regulatory layer. Additional candidates in this broader “circuit support” space include Mtmr1 (myotubularin family; related family links to myelin regulation) [ 49 , 50 ]. Several enriched GO categories were related to synaptic organization and neuronal signaling, suggesting that one component of the baseline interstrain divergence in the hippocampus involves synaptic/plasticity-associated processes. This interpretation is supported by enrichment across BP, CC, and MF categories, although some of these terms are relatively broad (such as “cognition” and “learning and memory”) and should be viewed as higher-level functional descriptors rather than pathway-specific evidence on their own. In this context, the hippocampal profile may provide a useful reference for future studies asking whether stress exposure modifies pre-existing synaptic/plasticity-related differences between LT and HT rats or instead introduces new changes outside this axis. Interestingly, we also found significantly higher expression of Grin2c in the HT strain, encoding the GluN2C subunit of the NMDA receptor, which may be relevant to differences in glutamatergic signaling and synaptic modulation. Other model demonstrate that resilient mice exposed to chronic stress show compensatory structural plasticity of postsynaptic compartments in the CA1 hippocampus and a shift of the AMPAR/NMDAR ratio toward NMDAR, whereas animals developing anhedonia display an increased AMPAR/NMDAR ratio [ 51 ]. Since AMPARs mediate fast excitatory currents and NMDARs, which require prior depolarization, allow Ca² ⁺ influx and trigger long-term plasticity processes such as LTP [ 52 ], such shifts in their balance raise the possibility of a molecular mechanism that may contribute to differences in the adaptability of hippocampal circuits associated with resilient or vulnerable behavioral phenotypes. Another major enrichment theme involved neuronal process development and glial-related terms, including neuron projection morphogenesis, axon development, gliogenesis, and astrocyte differentiation, with corresponding CC enrichment in the cell projection membrane and cell cortex. The cell cortex is a thin, actin-rich layer underneath the plasma membrane that regulates membrane mechanics and shape; it is key to cell migration, division, and tissue morphogenesis, and has been implicated in cell differentiation during development [ 53 ]. The cell projection membrane refers to the specialized plasma membrane of neuronal extensions, such as axons and dendrites, where ion channels and receptors are concentrated [ 54 ]. Although our data identify this pathway only at the level of enrichment, its biological significance is well documented. This localization is functionally critical, for instance, the axon initial segment contains a high density of voltage-gated channels that makes it the dominant site for action potential initiation and regulation of synaptic inputs integration, intrinsic excitability, and transmitter release [ 55 ]. In hippocampal pyramidal neurons, ion channels such as Kv4.2 and HCN are differentially distributed along dendrites and shape local excitability and input integration [ 56 ]. Thus, enrichment of these СС terms and also “active ion transporter activity” in MF in our RNA seq dataset may reflect genetic differences between LT and HT rats in the molecular organization of neuronal processes, formation of dendritic-axonal architecture pointing to basis to variation in excitability and information processing strategies at the circuit level. In practical terms, these enrichment signals suggest that the interstrain divergence may involve differences in neuronal process architecture and compartmentalized membrane organization; therefore, the most informative follow-up study is at the morphological and functional levels (neurite/axon initial segment organization and circuit excitability measures). Differences in term “gliogenesis” may indicate that the strains have different patterns of glial activation upon stimulus. For example, one possible interpretation is that hippocampal astrocytes may differ numerically or functionally: recent data have shown that hippocampal astrocytes are actively involved in regulating anxiety, responding with calcium waves to disturbing environmental stimuli and influencing behavior [ 57 ]. More reactive astrocytes can better control the concentrations of neurotransmitters (glutamate, GABA) and maintain a balance of excitation/inhibition. A next enrichment clusters stress/adaptive-response terms (e.g., “response to wounding”, MAPK and PI3K/AKT regulation) together with Golgi/endosomal compartments (trans-Golgi network, secretory granules, multivesicular bodies) and oxidoreductase activity. While the Golgi apparatus and multivesicular bodies are known to participate in receptor trafficking and thereby contribute to synaptic plasticity under physiological conditions, they also play a crucial role in neuronal defense by regulating protein turnover, receptor degradation, and adaptive membrane remodeling during cellular stress [ 58 ]. Notably, in models of systemic inflammation/hepatic encephalopathy, increased multivesicular bodies in astrocytes has been interpreted as a compensatory response supporting gliovascular homeostasis [ 59 ], suggesting that astroglial endosomal stress-handling pathways may differ between LT and HT strains at baseline. Similarly, MAPK and PI3K/AKT signaling pathways are not only central to long-term synaptic plasticity but also constitute major survival and stress-response cascades in neurons [ 60 ]. Genetic alterations in the MAP-ERK pathway are associated with neurodevelopmental disorders, while its aberrant activation in glial cells contributes to neuroinflammatory mechanisms of major neurodegenerative diseases such as Alzheimer’s, Parkinson’s, Huntington’s disease [ 61 ]. Enrichment of the MF term “oxidoreductase activity” suggests the involvement of antioxidant and redox-regulatory processes that may support neuronal homeostasis under conditions of increased excitability or metabolic demand in the brain [ 62 ]. Together with enrichment of Golgi/endosomal and MAPK/PI3K-related categories, this points to a baseline cellular-maintenance component of the interstrain hippocampal divergence. An important next step is to determine whether this signal is linked to specific cellular compartments, such as astrocytes, endothelium, or microglia, and whether it is reflected at the structural or protein level in the hippocampus. Another enrichment theme was related to extracellular matrix organization. In the hippocampus, ECM, including perineuronal nets, not only provides structural support but also modulates plasticity, memory-related processes, receptor mobility, and the balance between excitation and inhibition [ 63 ]. Its role in plasticity regulation and in stress- or depression-related remodeling has been well documented [ 64 ]. Enrichment of metabolism-related categories further suggests strain-specific differences in energetic support and neurotransmitter-associated metabolic processes. For example, mitochondrial calcium uptake can enhance electron transport chain activity and ATP production during high-frequency firing in hippocampal neurons [ 65 ], while amino acid transporters involved in the glutamate-glutamine cycle help maintain transmitter supply under sustained synaptic load [ 66 ]. Thus, changes in gene expression in the hippocampus of LT and HT rats affect processes at several levels, from synaptic transmission and process morphogenesis to regulation of the extracellular environment and metabolic support, which provides the basis for further study of their functional significance. The significant PPI enrichment of the complete DEG set indicates that the hippocampal differences between HT and LT rats are organized beyond the level of individual differentially expressed genes. The higher-than-expected number of interactions suggests that the DEG set is concentrated within functionally connected molecular neighborhoods. This is consistent with the nature of inherited nervous system excitability as a polygenic trait and selection is unlikely to produce a transcriptional signature restricted to a single canonical pathway. Instead, the enriched interaction structure suggests that baseline hippocampal divergence is distributed across connected molecular neighborhoods involving synaptic organization, signaling, cellular-maintenance, and glial/neurovascular components. This supports the view that the interstrain difference represents a coordinated tissue-level transcriptomic state linked to the long-term selective-breeding history of the strains, and provides the context for the focused analyses of synaptic plasticity- and MAPK-related DEG subsets. In the synaptic plasticity-related network, several proteins point to processes such as vesicle cycling, calcium signaling, and synapse structural organization. Among them, SNAP25 (node degree = 9, S3 Table ) is especially notable because, as a core SNARE-complex protein, it is directly involved in synaptic vesicle fusion and neurotransmitter release. Altered SNAP25 expression has been linked to cognitive and emotional disorders [ 67 ], and SNAP-25 also contributes to postsynaptic receptor trafficking and dendritic spine organization, indicating that its variation may influence both pre- and postsynaptic components of plasticity [ 68 ]. Another important node of this cluster is NTRK2 (TrkB, node degree = 9) – a BDNF receptor associated with plasticity, synaptic stability, and adaptive response to stress [ 69 ]. MAPT (tau, node degree = 10) is now considered not only as a protein “associated with microtubules”. There is increasing evidence of its involvement in synaptic regulation, microtubule stabilization, dendritic spike support, and memory consolidation. Tau removal in mice leads to age-related disorders of short-term memory and defects in synaptic plasticity [ 70 ]. The role of synaptic tau, i.e., tau localized in synapses, in the coordination of synaptic responses and plasticity is also discussed [ 71 ]. GFAP (glial fibrillar acid protein, node degree = 12) is a marker and structural component of astrocytes involved in the morphological plasticity of astrocytic processes and the regulation of homeostasis. Astrocytes and GFAP are important not only for supporting neurons, but also for modulating plasticity (for example, through environmental regulation, glutamate clearance, Ca2 ⁺ signals) [ 72 ]. Mutant GFAP mice models show decreased synaptic plasticity and impaired astrocyte function [ 73 ]. The differences in Gfap expression between the strains are consistent with the astrocyte stress response that we found earlier: 7 days after stress, the LT strain showed a decrease in the number of GFAP+ cells in the prefrontal cortex, hippocampus, and amygdala, whereas in HT rats such a decrease was limited only by the hippocampus [ 6 ]. The presence of MAPT and GFAP in this cluster indicates that the plastic changes reflected in the transcriptome are not limited to neurotransmitter mechanisms in synaptic transmission, as well as stabilization of the cytoskeleton, changes in astrocytic support. Such nodes are particularly interesting as candidates for subsequent verification: how can interstrain differences in MAPT expression affect the structural stability of dendrites in response to stress, and variations in GFAP expression affect glial reactivity and its ability to “service” neural networks? Among the genes contributing to the MAPK cascade-related network, angiotensinogen (AGT, node degree = 12, S6 Table ) is notable because angiotensinogen is expressed in both astrocytes and neurons [ 74 ]. Interstrain differences in basal AGT mRNA levels have been reported previously in rat: in Wistar astrocytes expression was higher compared to the spontaneously hypertensive rats (SHR) strain [ 75 ]. AGT is a precursor molecule of Ang II, which in astrocytes causes activation of several intracellular signaling pathways involving mitogen activated protein (MAP) kinases, tyrosine kinases, protein kinase C (PKC), immediate early response genes [ 76 ]. FLT1 (VEGFR1, node degree = 5), a receptor for vascular growth factor, is involved in neurovascular plasticity: its expression in the hippocampus increases after ischemic damage, which may reflect an attempt to regenerate or restructure the vascular network [ 77 ]. Differences in the expression of AGT and FLT1 suggest that the interstrain divergence may extend beyond synaptic genes to include pathways related to vascular signaling and possibly glia–vascular interactions within hippocampal tissue. PDGFRB (platelet growth factor β-form receptor, node degree = 10) is involved in MAPK activation through binding to the growth factor receptor binding protein 2 (GRB2) and triggering the Ras/MAPK cascade. Multiple transcription factors, such as Elk-1, c-Jun and c-Myc, are catalyzed by MAPK activation and control cell metabolism, growth, and proliferation [ 78 ]. In nervous tissue, PDGF/PDGFR signaling regulates neurogenesis, cell survival, and remodeling of neural networks [ 79 ]. Thus, the largest cluster identified by the PPI analysis of genes associated with GO:0043408 “regulation of the MAPK cascade” included many modulators of kinase signaling pathways potentially relevant to vascular, glial, and hormonal responses. Conclusion The identified hippocampal transcriptomic profile provides a molecular baseline for the interstrain differences that have emerged through selection and helps identify candidate pathways that may contribute to differential responses to stress and other extreme factors depending on the differences in genetic background associated with high and low excitability. Transcriptomic analysis of the hippocampus in rat strains with genetically determined differences in nervous excitability revealed divergence in molecular profiles not only at the level of neuronal factors related to neurons excitability, synaptic organization and structural neuroplasticity, but also non-neuronal factors related to glial function, cellular stress responses, MAPK signaling, extracellular matrix remodeling, and vascular regulation. This baseline profile provides a reference framework for interpreting future studies in this model, helping to distinguish constitutive interstrain differences from changes induced by experimental manipulations. Although the present bulk RNA-seq approach does not provide direct cell-type resolution and therefore requires further validation by cell-type-resolved methods, it help identify the major functional domains in which interstrain hippocampal divergence is organized. The analysis motivates testable hypotheses involving synaptic/neurite organization, cellular maintenance pathways (MAPK/PI3K-linked trafficking and redox regulation), and glial/neurovascular components, to be evaluated in follow-up studies using structural and functional tissue-level measurements. This work also provides a reference for cross-model comparisons of polygenic excitability-related traits, as a reference transcriptomic profile from a long-term selective-breeding paradigm. Supporting information S1 Fig. Individual excitability thresholds in selectively bred HT and LT rats. (DOCX) pone.0350674.s001.docx (67.1KB, docx) S2 Fig. STRING PPI network from the full set of DEGs identified between HT and LT hippocampus samples. (PNG) pone.0350674.s002.png (11.1MB, png) S1 Table. Gene Lists. (XLSX) pone.0350674.s003.xlsx (242.7KB, xlsx) S2 Table. Fig 5 string MCL cluster descriptions. (XLS) pone.0350674.s004.xls (18.5KB, xls) S3 Table. Fig 5 string MCL clusters. (XLS) pone.0350674.s005.xls (39KB, xls) S4 Table. Fig 5 string node degrees. (XLSX) pone.0350674.s006.xlsx (7.3KB, xlsx) S5 Table. Fig 6 string MCL cluster descriptions. (XLS) pone.0350674.s007.xls (27KB, xls) S6 Table. Fig 6 string MCL clusters. (XLS) pone.0350674.s008.xls (42.5KB, xls) S7 Table. Fig 6 string node degrees. (XLSX) pone.0350674.s009.xlsx (8.7KB, xlsx) S8 Table. String node degrees of PPI network from the full set of DEGs. (TSV) pone.0350674.s010.tsv (20.8KB, tsv) Acknowledgments We would like to thank Genoanalytica Lab, Moscow, Russia, where the samples were sequenced and the initial data processing was carried out. We would also like to express our sincere gratitude to Professor Natalia Kudryavtseva, Doctor of Biological Sciences (Pavlov Institute of Physiology of the Russian Academy of Sciences), Olga Redina, Doctor of Biological Sciences (Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences) and Artur Shvetcov, Doctor of Philosophy (Senior Bioinformatician in Westmead Institute for Medical Research, Sydney, Australia) for their assistance and support in working with transcriptome analysis data. Data Availability All relevant data are within the manuscript and its Supporting information files. The raw and processed data in NCBI - GSE327807. Funding Statement This research was funded by the State funding of the Pavlov Institute of the Physiology, Russian Academy of Sciences (N 1021062411629-7-3.1.4). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References 1. Dyuzhikova N, Lopatina N. Genetically determined excitability of the nervous system: impact on brain function and behavior. Usp Fiziol Nauk. 2024;55:88–104. doi: 10.31857/S0301179824010085 [ DOI ] [ Google Scholar ] 2. Gryksa K, Schmidtner AK, Masís-Calvo M, Rodríguez-Villagra OA, Havasi A, Wirobski G, et al. Selective breeding of rats for high (HAB) and low (LAB) anxiety-related behaviour: a unique model for comorbid depression and social dysfunctions. Neurosci Biobehav Rev. 2023;152:105292. doi: 10.1016/j.neubiorev.2023.105292 [ DOI ] [ PubMed ] [ Google Scholar ] 3. Prater KE, Aurbach EL, Larcinese HK, Smith TN, Turner CA, Blandino P Jr, et al. Selectively bred rats provide a unique model of vulnerability to PTSD-like behavior and respond differentially to FGF2 augmentation early in life. Neuropsychopharmacology. 2017;42(8):1706–14. doi: 10.1038/npp.2017.37 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Vaido A, Shiryaeva N, Pavlova M, Levina A, Khlebaeva D, Lyubashina O, et al. Selected rat strains Ht, Lt as a model for the study of dysadaptation states dependent on the level of excitability of the nervous system. Lab Anim Sci Rus. 2018;1(3):12–22. doi: 10.29296/2618723x-2018-03-02 [ DOI ] [ Google Scholar ] 5. Sivachenko I, et al. Impulse activity and genome instability in neurons of the amygdala complex in rats of selected strains with contrast excitability of the nervous system in normal and stress conditions. Zh Vyssh Nerv Deyat Im IP Pavlova. 2020;70:655–67. [ Google Scholar ] 6. Vylegzhanina A, Shalaginova I, Korolevich D, Katserov D, Semenova A, Sidorova M, et al. Strain-specific trajectories of behavioural, neuroinflammatory, and microbiota changes under chronic stress in rats with contrast levels of nervous system excitability. BIOCELL. 2025;0(0):1–10. doi: 10.32604/biocell.2025.071198 [ DOI ] [ Google Scholar ] 7. Ghasemi M, Navidhamidi M, Rezaei F, Azizikia A, Mehranfard N. Anxiety and hippocampal neuronal activity: relationship and potential mechanisms. Cogn Affect Behav Neurosci. 2022;22(3):431–49. doi: 10.3758/s13415-021-00973-y [ DOI ] [ PubMed ] [ Google Scholar ] 8. Wu X, Lu Y, Dong Y, Zhang G, Zhang Y, Xu Z, et al. The inhalation anesthetic isoflurane increases levels of proinflammatory TNF-α, IL-6, and IL-1β. Neurobiol Aging. 2012;33(7):1364–78. doi: 10.1016/j.neurobiolaging.2010.11.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Paxinos G, Watson C. The rat brain in stereotaxic coordinates. 6th ed. Academic Press; 2007. [ DOI ] [ PubMed ] [ Google Scholar ] 10. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):P3. doi: 10.1186/gb-2003-4-5-p3 [ DOI ] [ PubMed ] [ Google Scholar ] 13. Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, et al. SRplot: a free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. doi: 10.1371/journal.pone.0294236 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30(7):1575–84. doi: 10.1093/nar/30.7.1575 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Pechlivani N, Kearney KJ, Ajjan RA. Fibrinogen and antifibrinolytic proteins: interactions and future therapeutics. Int J Mol Sci. 2021;22(22):12537. doi: 10.3390/ijms222212537 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Zattoni M, Mearelli M, Vanni S, Colini Baldeschi A, Tran TH, Ferracin C, et al. Serpin signatures in prion and Alzheimer’s diseases. Mol Neurobiol. 2022;59(6):3778–99. doi: 10.1007/s12035-022-02817-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Shalaginova I, Pavlova M, Dyuzhikova N. Аmygdala DEGs are associated with the immune system function: a comparative transcriptomic study of high- and low-excitability rat strains. PLoS One. 2025;20(5):e0323325. doi: 10.1371/journal.pone.0323325 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Lee I, Wang X, Ling I, Verma S, Eck C, Blanche W, et al. The complement regulator CD55 modulates TLR9 signaling and supports survival in marginal zone B cells. J Immunol. 2025;214(9):2211–20. doi: 10.1093/jimmun/vkaf124 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. van Beek J, van Meurs M, ’t Hart BA, Brok HPM, Neal JW, Chatagner A, et al. Decay-accelerating factor (CD55) is expressed by neurons in response to chronic but not acute autoimmune central nervous system inflammation associated with complement activation. J Immunol. 2005;174(4):2353–65. doi: 10.4049/jimmunol.174.4.2353 [ DOI ] [ PubMed ] [ Google Scholar ] 20. Illes P, Khan TM, Rubini P. Neuronal P2X7 receptors revisited: do they really exist? J Neurosci. 2017;37:7049–62. doi: 10.1523/JNEUROSCI.3103-16.2017 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Kaczmarek-Hajek K, Zhang J, Kopp R, Grosche A, Rissiek B, Saul A, et al. Re-evaluation of neuronal P2X7 expression using novel mouse models and a P2X7-specific nanobody. Elife. 2018;7:e36217. doi: 10.7554/eLife.36217 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Huang S, Dong W, Lin X, Xu K, Li K, Xiong S, et al. Disruption of the Na+/K+-ATPase-purinergic P2X7 receptor complex in microglia promotes stress-induced anxiety. Immunity. 2024;57(3):495-512.e11. doi: 10.1016/j.immuni.2024.01.018 [ DOI ] [ PubMed ] [ Google Scholar ] 23. Campagno KE, Lu W, Jassim AH, Albalawi F, Cenaj A, Tso H-Y, et al. Rapid morphologic changes to microglial cells and upregulation of mixed microglial activation state markers induced by P2X7 receptor stimulation and increased intraocular pressure. J Neuroinflammation. 2021;18(1):217. doi: 10.1186/s12974-021-02251-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Sturno AM, Hassell JE Jr, Lanaspa MA, Bruce KD. Do microglia metabolize fructose in Alzheimer’s disease? J Neuroinflammation. 2025;22(1):85. doi: 10.1186/s12974-025-03401-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Kojo A, Yamada K, Yamamoto T. Glucose transporter 5 (GLUT5)-like immunoreactivity is localized in subsets of neurons and glia in the rat brain. J Chem Neuroanat. 2016;74:55–70. doi: 10.1016/j.jchemneu.2016.03.004 [ DOI ] [ PubMed ] [ Google Scholar ] 26. Mizuno TM, Lew PS, Jhanji G. Regulation of the fructose transporter gene Slc2a5 expression by glucose in cultured microglial cells. Int J Mol Sci. 2021;22(23):12668. doi: 10.3390/ijms222312668 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Sjöstedt E, Zhong W, Fagerberg L, Karlsson M, Mitsios N, Adori C, et al. An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. 2020;367(6482):eaay5947. doi: 10.1126/science.aay5947 [ DOI ] [ PubMed ] [ Google Scholar ] 28. Cole DC, Chung Y, Gagnidze K, Hajdarovic KH, Rayon-Estrada V, Harjanto D, et al. Loss of APOBEC1 RNA-editing function in microglia exacerbates age-related CNS pathophysiology. Proc Natl Acad Sci U S A. 2017;114(50):13272–7. doi: 10.1073/pnas.1710493114 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Kankowski S, Förstera B, Winkelmann A, Knauff P, Wanker EE, You XA, et al. A novel RNA editing sensor tool and a specific agonist determine neuronal protein expression of RNA-edited glycine receptors and identify a genomic APOBEC1 dimorphism as a new genetic risk factor of epilepsy. Front Mol Neurosci. 2018;10:439. doi: 10.3389/fnmol.2017.00439 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Parveen F, Bender D, Law S-H, Mishra VK, Chen C-C, Ke L-Y. Role of ceramidases in sphingolipid metabolism and human diseases. Cells. 2019;8(12):1573. doi: 10.3390/cells8121573 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Lin H, Wu S, Weng Z, Wang H, Shi R, Tian M, et al. Tianma formula alleviates dementia via ACER2-mediated sphingolipid signaling pathway involving Aβ. Evid Based Complement Alternat Med. 2021;2021:6029237. doi: 10.1155/2021/6029237 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Zhang Z, Hao C-J, Li C-G, Zang D-J, Zhao J, Li X-N, et al. Mutation of SLC35D3 causes metabolic syndrome by impairing dopamine signaling in striatal D1 neurons. PLoS Genet. 2014;10(2):e1004124. doi: 10.1371/journal.pgen.1004124 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Wei Z-B, Yuan Y-F, Jaouen F, Ma M-S, Hao C-J, Zhang Z, et al. SLC35D3 increases autophagic activity in midbrain dopaminergic neurons by enhancing BECN1-ATG14-PIK3C3 complex formation. Autophagy. 2016;12(7):1168–79. doi: 10.1080/15548627.2016.1179402 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Rybakin V, Stumpf M, Schulze A, Majoul IV, Noegel AA, Hasse A. Coronin 7, the mammalian POD-1 homologue, localizes to the Golgi apparatus. FEBS Lett. 2004;573(1–3):161–7. doi: 10.1016/j.febslet.2004.07.066 [ DOI ] [ PubMed ] [ Google Scholar ] 35. Bhattacharya K, Swaminathan K, Peche VS, Clemen CS, Knyphausen P, Lammers M, et al. Novel Coronin7 interactions with Cdc42 and N-WASP regulate actin organization and Golgi morphology. Sci Rep. 2016;6:25411. doi: 10.1038/srep25411 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Wu Q, Maniatis T. A striking organization of a large family of human neural cadherin-like cell adhesion genes. Cell. 1999;97(6):779–90. doi: 10.1016/s0092-8674(00)80789-8 [ DOI ] [ PubMed ] [ Google Scholar ] 37. Osuka T, Yamamoto H, Yagi T. Diversity of clustered protocadherin-α genes in neuronal identity and its role in short-term specific associative memory formation. Sci Rep. 2025;15(1):19334. doi: 10.1038/s41598-025-02546-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Serratto GM, Pizzi E, Murru L, Mazzoleni S, Pelucchi S, Marcello E, et al. The epilepsy-related protein PCDH19 regulates tonic inhibition, GABAAR kinetics, and the intrinsic excitability of hippocampal neurons. Mol Neurobiol. 2020;57(12):5336–51. doi: 10.1007/s12035-020-02099-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Alder J, Thakker-Varia S, Bangasser DA, Kuroiwa M, Plummer MR, Shors TJ, et al. Brain-derived neurotrophic factor-induced gene expression reveals novel actions of VGF in hippocampal synaptic plasticity. J Neurosci. 2003;23(34):10800–8. doi: 10.1523/JNEUROSCI.23-34-10800.2003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Moss A, Ingram R, Koch S, Theodorou A, Low L, Baccei M, et al. Origins, actions and dynamic expression patterns of the neuropeptide VGF in rat peripheral and central sensory neurones following peripheral nerve injury. Mol Pain. 2008;4:62. doi: 10.1186/1744-8069-4-62 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Quinn JP, Kandigian SE, Trombetta BA, Arnold SE, Carlyle BC. VGF as a biomarker and therapeutic target in neurodegenerative and psychiatric diseases. Brain Commun. 2021;3(4):fcab261. doi: 10.1093/braincomms/fcab261 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Chipman PH, Fung CCA, Pazo Fernandez A, Sawant A, Tedoldi A, Kawai A, et al. Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum. Elife. 2021;10:e70818. doi: 10.7554/eLife.70818 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Traynelis SF, Wollmuth LP, McBain CJ, Menniti FS, Vance KM, Ogden KK, et al. Glutamate receptor ion channels: structure, regulation, and function. Pharmacol Rev. 2010;62(3):405–96. doi: 10.1124/pr.109.002451 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Stubbs T, Koemeter-Cox A, Bingman JI, Zhao F, Kalyanasundaram A, Rowland LA, et al. Disruption of dopamine receptor 1 localization to primary cilia impairs signaling in striatal neurons. J Neurosci. 2022;42(35):6692–705. doi: 10.1523/JNEUROSCI.0497-22.2022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Wang H, Li Y, Li X, Sun Z, Yu F, Pashang A, et al. The primary cilia are associated with the axon initial segment in neurons. Adv Sci (Weinh). 2025;12(9):e2407405. doi: 10.1002/advs.202407405 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Strobel MR, Zhou Y, Qiu L, Hofer AM, Chen X. Temporal ablation of the ciliary protein IFT88 alters normal brainwave patterns. Sci Rep. 2025;15(1):347. doi: 10.1038/s41598-024-83432-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Tang J, Frankel A, Cook RJ, Kim S, Paik WK, Williams KR, et al. PRMT1 is the predominant type I protein arginine methyltransferase in mammalian cells. J Biol Chem. 2000;275(11):7723–30. doi: 10.1074/jbc.275.11.7723 [ DOI ] [ PubMed ] [ Google Scholar ] 48. Lee S-Y, Vuong TA, Wen X, Jeong H-J, So H-K, Kwon I, et al. Methylation determines the extracellular calcium sensitivity of the leak channel NALCN in hippocampal dentate granule cells. Exp Mol Med. 2019;51(10):1–14. doi: 10.1038/s12276-019-0325-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Raess MA, Friant S, Cowling BS, Laporte J. WANTED - Dead or alive: Myotubularins, a large disease-associated protein family. Adv Biol Regul. 2017;63:49–58. doi: 10.1016/j.jbior.2016.09.001 [ DOI ] [ PubMed ] [ Google Scholar ] 50. Bolis A, Coviello S, Visigalli I, Taveggia C, Bachi A, Chishti AH, et al. Dlg1, Sec8, and Mtmr2 regulate membrane homeostasis in Schwann cell myelination. J Neurosci. 2009;29(27):8858–70. doi: 10.1523/JNEUROSCI.1423-09.2009 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Bączyńska E, Zaręba-Kozioł M, Ruszczycki B, Krzystyniak A, Wójtowicz T, Bijata K, et al. Stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses. Neurobiol Stress. 2024;33:100683. doi: 10.1016/j.ynstr.2024.100683 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Chater TE, Goda Y. The shaping of AMPA receptor surface distribution by neuronal activity. Front Synaptic Neurosci. 2022;14:833782. doi: 10.3389/fnsyn.2022.833782 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Chugh P, Paluch EK. The actin cortex at a glance. J Cell Sci. 2018;131(14):jcs186254. doi: 10.1242/jcs.186254 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021;49(D1):D325–34. doi: 10.1093/nar/gkaa1113 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Kole MHP, Stuart GJ. Signal processing in the axon initial segment. Neuron. 2012;73(2):235–47. doi: 10.1016/j.neuron.2012.01.007 [ DOI ] [ PubMed ] [ Google Scholar ] 56. Marcelin B, et al. Differential dorso-ventral distributions of Kv4.2 and HCN proteins confer distinct integrative properties to hippocampal CA1 pyramidal cell distal dendrites. J Biol Chem. 2012;287:17656–61. doi: 10.1074/jbc.C112.367110 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 57. Cho W-H, Noh K, Lee BH, Barcelon E, Jun SB, Park HY, et al. Hippocampal astrocytes modulate anxiety-like behavior. Nat Commun. 2022;13(1):6536. doi: 10.1038/s41467-022-34201-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Bonifacino JS, Rojas R. Retrograde transport from endosomes to the trans-Golgi network. Nat Rev Mol Cell Biol. 2006;7(8):568–79. doi: 10.1038/nrm1985 [ DOI ] [ PubMed ] [ Google Scholar ] 59. Shulyatnikova T, Shavrin V. Mobilisation and redistribution of multivesicular bodies to the endfeet of reactive astrocytes in acute endogenous toxic encephalopathies. Brain Res. 2021;1751:147174. doi: 10.1016/j.brainres.2020.147174 [ DOI ] [ PubMed ] [ Google Scholar ] 60. Kim EK, Choi E-J. Pathological roles of MAPK signaling pathways in human diseases. Biochim Biophys Acta. 2010;1802(4):396–405. doi: 10.1016/j.bbadis.2009.12.009 [ DOI ] [ PubMed ] [ Google Scholar ] 61. Albert-Gascó H, Ros-Bernal F, Castillo-Gómez E, Olucha-Bordonau FE. MAP/ERK signaling in developing cognitive and emotional function and its effect on pathological and neurodegenerative processes. Int J Mol Sci. 2020;21(12):4471. doi: 10.3390/ijms21124471 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Cobley JN, Fiorello ML, Bailey DM. 13 reasons why the brain is susceptible to oxidative stress. Redox Biol. 2018;15:490–503. doi: 10.1016/j.redox.2018.01.008 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Sorg BA, Berretta S, Blacktop JM, Fawcett JW, Kitagawa H, Kwok JCF, et al. Casting a wide net: role of perineuronal nets in neural plasticity. J Neurosci. 2016;36(45):11459–68. doi: 10.1523/JNEUROSCI.2351-16.2016 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Fawcett JW, Oohashi T, Pizzorusso T. The roles of perineuronal nets and the perinodal extracellular matrix in neuronal function. Nat Rev Neurosci. 2019;20:451–65. doi: 10.1038/s41583-019-0196-3 [ DOI ] [ PubMed ] [ Google Scholar ] 65. Groten CJ, MacVicar BA. Mitochondrial Ca2 uptake by the MCU facilitates pyramidal neuron excitability and metabolism during action potential firing. Commun Biol. 2022;5:900. doi: 10.1038/s42003-022-03848-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Nicoli AL, Al Abed AS, Hulme SR, Das A, Gauthier-Coles G, Bröer A, et al. Glutamine transport via amino acid transporter NTT4 (SLC6A17) maintains presynaptic glutamate supply at excitatory synapses in the CNS. Prog Neurobiol. 2025;250:102785. doi: 10.1016/j.pneurobio.2025.102785 [ DOI ] [ PubMed ] [ Google Scholar ] 67. Najera K, Fagan BM, Thompson PM. SNAP-25 in major psychiatric disorders: a review. Neuroscience. 2019;420:79–85. doi: 10.1016/j.neuroscience.2019.02.008 [ DOI ] [ PubMed ] [ Google Scholar ] 68. Antonucci F, Corradini I, Fossati G, Tomasoni R, Menna E, Matteoli M. SNAP-25, a known presynaptic protein with emerging postsynaptic functions. Front Synaptic Neurosci. 2016;8:7. doi: 10.3389/fnsyn.2016.00007 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Numakawa T, Kajihara R. Involvement of brain-derived neurotrophic factor signaling in the pathogenesis of stress-related brain diseases. Front Mol Neurosci. 2023;16:1247422. doi: 10.3389/fnmol.2023.1247422 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Biundo F, Del Prete D, Zhang H, Arancio O, D’Adamio L. A role for tau in learning, memory and synaptic plasticity. Sci Rep. 2018;8(1):3184. doi: 10.1038/s41598-018-21596-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Robbins M, Clayton E, Kaminski Schierle GS. Synaptic tau: a pathological or physiological phenomenon? Acta Neuropathol Commun. 2021;9:149. doi: 10.1186/s40478-021-01246-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Sanz-Gálvez R, Falardeau D, Kolta A, Inglebert Y. The role of astrocytes from synaptic to non-synaptic plasticity. Front Cell Neurosci. 2024;18:1477985. doi: 10.3389/fncel.2024.1477985 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Berman RF, et al. Gfap mutation and astrocyte dysfunction lead to a neurodegenerative profile with impaired synaptic plasticity and cognitive deficits in a rat model of Alexander disease. eNeuro. 2025;12(ENEURO.0504-24.2025):1–15. doi: 10.1523/ENEURO.0504-24.2025 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Yang G, Gray TS, Sigmund CD, Cassell MD. The angiotensinogen gene is expressed in both astrocytes and neurons in murine central nervous system. Brain Res. 1999;817(1–2):123–31. doi: 10.1016/s0006-8993(98)01236-0 [ DOI ] [ PubMed ] [ Google Scholar ] 75. Gowrisankar YV, Clark MA. Regulation of angiotensinogen expression by angiotensin II in spontaneously hypertensive rat primary astrocyte cultures. Brain Res. 2016;1643:51–8. doi: 10.1016/j.brainres.2016.04.059 [ DOI ] [ PubMed ] [ Google Scholar ] 76. Karnik SS, et al. International Union of Basic and Clinical Pharmacology. XCIX. angiotensin receptors: interpreters of pathophysiological angiotensinergic stimuli. Pharmacol Rev. 2015;67:754–819. doi: 10.1124/pr.114.010454 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. Choi J-S, Kim H-Y, Cha J-H, Choi J-Y, Chun M-H, Lee M-Y. Upregulation of vascular endothelial growth factor receptors Flt-1 and Flk-1 in rat hippocampus after transient forebrain ischemia. J Neurotrauma. 2007;24(3):521–31. doi: 10.1089/neu.2006.0139 [ DOI ] [ PubMed ] [ Google Scholar ] 78. Pandey P, Khan F, Upadhyay TK, Seungjoon M, Park MN, Kim B. New insights about the PDGF/PDGFR signaling pathway as a promising target to develop cancer therapeutic strategies. Biomed Pharmacother. 2023;161:114491. doi: 10.1016/j.biopha.2023.114491 [ DOI ] [ PubMed ] [ Google Scholar ] 79. Sil S, Periyasamy P, Thangaraj A, Chivero ET, Buch S. PDGF/PDGFR axis in the neural systems. Mol Aspects Med. 2018;62:63–74. doi: 10.1016/j.mam.2018.01.006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] PLoS One. doi: 10.1371/journal.pone.0350674.r001 Decision Letter 0 Jean-Pierre Mothet Author information Copyright and License information Roles Jean-Pierre Mothet : Academic Editor © 2026 Jean-Pierre Mothet This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 17 Mar 2026 -->PONE-D-26-04533-->-->Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability-->-->PLOS One Dear Dr. Shalaginova, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->--> Please submit your revised manuscript by May 01 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:--> A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Jean-Pierre Mothet, Ph.D Academic Editor PLOS One Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please note that PLOS One has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed: https://doi.org/10.1371/journal.pone.0323325 In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed. 4. Please note that funding information should not appear in any section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript. 5. Please ensure that you refer to Figure 6 in your text as, if accepted, production will need this reference to link the reader to the figure. 6. Please upload a new copy of Figures 5 and 6 as the detail is not clear. Please follow the link for more information: https://journals.plos.org/plosone/s/figures 7. We are unable to open your Supporting Information file ( Supplementary_1 string_MCL_2_GO_1.tsv and Supplementary_2 string_MCL_2_GO_6.tsv) Please kindly revise as necessary and re-upload. 8. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Additional Editor Comment: The two reviewers raised several concerns about methodologies that are presented without enough details notably regarding animal welfare, description and justification of the selected model, and for the GO analysis. Furthermore, they found that PPI analysis does not reflect an independent layer of experimental evidence from the present study. Finally, it is not clear why the differential gene expression analysis has been performed with a log2FoldChange filter >0.379 and not >1 as classically used and how it may impact the conclusion. Although, I personally think that your manuscript has some merits, I invite you to address those important issues during revision. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions --> Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. --> Reviewer #1: Partly Reviewer #2: Partly Reviewer #3: Partly ********** -->2. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #1: Yes Reviewer #2: N/A Reviewer #3: I Don't Know ********** -->3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ********** -->4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ********** -->5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: The presented manuscript aims to study the inherited properties of the nervous system to respond to external environmental cues. To address this question, the authors took advantage of rat strains presenting differences in neural system excitability, selected by a quantifiable physiological trait - tibial nerve excitability threshold. Hence, they have collected bulk transcriptomic profiles of the hippocampus in high-excitability (LT) and low-excitability (HT) rats to characterize baseline inter-strain transcriptomic divergence. Main comments - The transcriptomic study fails to count with readouts of another part of the brain that could be considered as non-relevant for the excitability response, thus working as a negative control for potential differences among animals. - Surprisingly, the differential gene expression analysis has been performed with a log2FoldChange filter >0.379…i.e. that the authors conserve/consider as differentially expressed genes those that have a fold-change of at least 1.3. This threshold appears quite mild. It would be interesting to know how many genes do pass a log2FoldChange filter >1; which is more classically used. Could such more stringent threshold still conserve the differences described by the authors? - - The discussion part contains several elements that should be retrieved in the results section. Minor comments - The presented figures are not ordered, and their quality are quite low. - Figure 5&6 are missing color legends for edges and nodes. Reviewer #2: The presented manuscript aims to study the inherited properties of the nervous system to respond to external environmental cues. To address this question, the authors took advantage of rat strains presenting differences in neural system excitability, selected by a quantifiable physiological trait - tibial nerve excitability threshold. Hence, they have collected bulk transcriptomic profiles of the hippocampus in high-excitability (LT) and low-excitability (HT) rats to characterize baseline inter-strain transcriptomic divergence. Main comments - The transcriptomic study fails to count with readouts of another part of the brain that could be considered as non-relevant for the excitability response, thus working as a negative control for potential differences among animals. - Surprisingly, the differential gene expression analysis has been performed with a log2FoldChange filter >0.379…i.e. that the authors conserve/consider as differentially expressed genes those that have a fold-change of at least 1.3. This threshold appears quite mild. It would be interesting to know how many genes do pass a log2FoldChange filter >1; which is more classically used. Could such more stringent threshold still conserve the differences described by the authors? - - The discussion part contains several elements that should be retrieved in the results section. Minor comments - The presented figures are not ordered, and their quality are quite low. - Figure 5&6 are missing color legends for edges and nodes. Reviewer #3: This manuscript by Pavlova and colleagues presents a bulk RNA sequencing analysis of the hippocampus in rat strains selectively bred for contrasting levels of nervous system excitability, with the aim of characterising baseline interstrain transcriptomic divergence. The multi-layered analytical approach combines differential expression analysis, GO enrichment, and PPI network clustering. Several substantive concerns regarding animal welfare reporting, model characterisation, methodological transparency, and interpretive discipline must be addressed prior to publication. Main Comments 1. PLOS ONE requires that manuscripts reporting animal research explicitly state, within the Methods section, the steps taken to ameliorate animal suffering. As currently written, the Methods do not adequately address this requirement. Specifically, the described euthanasia procedure — decapitation by guillotine without prior anaesthesia and in the absence of explicitly defined humane endpoints — appears difficult to reconcile with the requirements of the current European Directive 2010/63/EU, which mandates explicit humane endpoints and imposes stricter conditions on the justification of painful or distressing procedures than its predecessor, Directive 86/609/EEC. 2. The authors should expand their description and justification of the selective breeding model. A previous publication by Vylegzhanina and colleagues analysed rat strains with contrasting excitability levels across approximately 70 generations, whereas the present manuscript states that interstrain differences stabilised at the 10th generation, at which point between-strain variability exceeded within-strain variability by more than fourfold. This apparent discrepancy is not discussed, and the relationship between the earlier long-term breeding programme and the present cohort is unclear. The authors should explicitly clarify the generational history of the strains used here, discuss how phenotypic stability was monitored across generations, and situate the present cohort within the broader longitudinal context of the model. To this end, it would be highly valuable to include a figure or supplementary table illustrating the trajectory of excitability threshold divergence and within-strain variance across at least the first ten generations of selection, as this would provide readers with the empirical basis for the claim of phenotypic stabilisation and strengthen confidence in the genetic model underlying the transcriptomic comparisons. 3. The Results section would benefit from expansion and improved internal signposting. At present, the transition between analytical stages — from differential expression to GO enrichment to PPI network analysis — is abrupt, and summary statements foregrounding the key findings of each subsection are largely absent. Adding brief transitional paragraphs that synthesise the reasoning and highlight the principal outcomes of each analytical layer would substantially improve readability and logical flow. Furthermore, the RNA sequencing data generated in this study do not appear to have been deposited in a public repository. Deposition in an appropriate archive (e.g., NCBI GEO or ArrayExpress) prior to publication is strongly encouraged and is consistent with open-science standards increasingly required by journals in this field. The accession number should be reported in the Methods section. 4. The Discussion makes repeated cell-type-specific interpretive claims that are not supported by the bulk sequencing methodology employed. Attributing differences in Gfap expression to astrocytic reactivity, Slc2a5 to microglial metabolic state, P2rx7 to glial purinergic signalling, and Stab2 to an endothelial or myeloid neuroimmune profile implies a cellular resolution that bulk hippocampal RNA-seq fundamentally cannot provide. While the authors appropriately acknowledge in the Conclusion that their findings "motivate testable hypotheses to be evaluated in follow-up studies," this caveat is insufficiently foregrounded in the body of the Discussion, where speculative inferences frequently appear without explicit epistemic qualification. The authors are encouraged to systematically reframe cell-type-specific and mechanistic claims as hypotheses, accompanied in each case by a brief statement of what additional evidence — such as single-nucleus RNA sequencing, cell-type-resolved proteomics, or targeted immunohistochemistry — would be required to substantiate them. 5. The GO enrichment analysis is presented without adequate methodological detail or critical evaluation of its inherent limitations. The background gene set used is not reported, precluding reproducibility assessment. More substantively, terms such as "cognition," "learning and memory," and "regulation of synaptic plasticity" are among the most annotation-dense in GO databases and are prone to enrichment in virtually any transcriptomic dataset derived from neural tissue; their appearance cannot be interpreted as strong evidence of pathway-specific divergence without further qualification. The authors should report all enrichment parameters, apply a redundancy-reduction approach (e.g., semantic clustering via REVIGO or equivalent) to present a non-redundant summary of enriched terms, and explicitly distinguish high-specificity terms — which carry stronger inferential weight — from broad, annotation-rich terms that may reflect database structure as much as biology. 6. The protein-protein interaction network analysis using the STRING database with the discussion of individual cluster nodes — including SNAP25, NTRK2, MAPT, GFAP, AGT, FLT1, and PDGFRB — focuses primarily on their established biological roles as reported in the broader literature, rather than on their network-level properties (e.g., degree, betweenness centrality) or their specific quantitative contribution to interstrain transcriptional divergence. As a result, the PPI analysis functions largely as a thematically organised literature review rather than as an analytically independent layer of evidence. Connecting network topology explicitly to the magnitude of differential expression for each highlighted node would substantially strengthen the analytical value of this section. ********** -->6. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No Reviewer #2: No Reviewer #3: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. PLoS One. 2026 Jun 3;21(6):e0350674. doi: 10.1371/journal.pone.0350674.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 26 Apr 2026 Editor We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed: https://doi.org/10.1371/journal.pone.0323325 In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed. We re-examined the revised manuscript for any duplicated wording outside the Methods section, including overlap with our own previously published work, and corrected the passages that could be considered too close in phrasing. Most of the overlap was limited to short, highly conventional technical or descriptive phrases, mainly in figure legends and brief transition sentences in the Results section. Wherever possible, we rephrased these passages to avoid unnecessary textual similarity. 4. Please note that funding information should not appear in any section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript. Done 5. Please ensure that you refer to Figure 6 in your text as, if accepted, production will need this reference to link the reader to the figure. Done 6. Please upload a new copy of Figures 5 and 6 as the detail is not clear. Please follow the link for more information: https://journals.plos.org/plosone/s/figures Done 7. We are unable to open your Supporting Information file ( Supplementary_1 string_MCL_2_GO_1.tsv and Supplementary_2 string_MCL_2_GO_6.tsv) Please kindly revise as necessary and re-upload. We have revised the Supporting Information files and converted the supplementary tables, including Supplementary_1 string_MCL_2_GO_1 and Supplementary_2 string_MCL_2_GO_6, into Excel to ensure they can be opened without technical difficulty. The two reviewers raised several concerns about methodologies that are presented without enough details notably regarding animal welfare, description and justification of the selected model, and for the GO analysis. Furthermore, they found that PPI analysis does not reflect an independent layer of experimental evidence from the present study. Finally, it is not clear why the differential gene expression analysis has been performed with a log2FoldChange filter >0.379 and not >1 as classically used and how it may impact the conclusion. Although, I personally think that your manuscript has some merits, I invite you to address those important issues during revision. We believe that these points have been addressed in the revised manuscript. In response to the reviewers’ comments, we clarified the animal welfare reporting, improved the description and justification of the selective breeding model, revised the GO analysis section for greater methodological transparency and interpretive caution, strengthened the PPI analysis by connecting network properties with differential expression metrics, and clarified the rationale for the selected log2FoldChange threshold. Reviewer #1: Main comments - The transcriptomic study fails to count with readouts of another part of the brain that could be considered as non-relevant for the excitability response, thus working as a negative control for potential differences among animals. We agree that analysis of an additional brain region could help to assess regional specificity more fully. However, in this model, another brain area cannot be considered a true negative control, because inherited differences in nervous system excitability may affect multiple brain structures. At the same time, our previous transcriptomic study of the amygdala (Shalaginova, 2025 in References) showed a different pattern, with a much stronger immune-related signature, although the amygdala is also involved in stress-related regulation. This supports the view that the observed changes are not simply uniform brain-wide differences, but may vary across regions. - Surprisingly, the differential gene expression analysis has been performed with a log2FoldChange filter >0.379…i.e. that the authors conserve/consider as differentially expressed genes those that have a fold-change of at least 1.3. This threshold appears quite mild. It would be interesting to know how many genes do pass a log2FoldChange filter >1; which is more classically used. Could such more stringent threshold still conserve the differences described by the authors? - We thank the reviewer for this important comment regarding the stringency of the criteria used to select differentially expressed genes when assessing inter-strain differences. We have now clarified that |log2FC| > 0.379 was not used as the primary criterion for DEG detection. Differential expression was defined primarily by FDR-adjusted significance (padj < 0.05), whereas the |log2FC| > 0.379 filter was applied only for visualization in the circular heatmap to reduce the number of displayed genes and highlight those with more pronounced between-strain differences. The choice of a moderate fold-change threshold was based on the biological context of the study. The aim of our study was to compare the transcriptomic profiles of intact rats from selectively bred strains contrasting in nervous system excitability under normal physiological conditions. In this context, central regulatory mechanisms are expected to operate within the limits of homeostatic stability, supported by the evolutionary conservation of molecular and transcriptional processes in the brain. Therefore, large two-fold changes in gene expression are not necessarily expected, whereas moderate but statistically robust expression shifts may still be biologically meaningful. More stringent thresholds such as |log2FC| > 1 are useful for identifying large-amplitude transcriptional responses and may be particularly appropriate in models of severe brain injury, stress exposure or pathology. We believe that using that using of this approach to the criterion for selecting DEGs allows us to retain genes with less pronounced but potentially biologically meaningful expression differences, as well as the pathways with which they are associated. This approach may provide insight into more subtle mechanisms regulating brain function under selective breeding for nervous system excitability and thereby enrich the interpretation of the results. Methodological studies in RNA-seq analysis have cautioned that stringent fold-change filtering may remove genes with genuine differential expression, particularly when expression changes are moderate but consistent across samples (Anqi Zhu, et al, Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences, Bioinformatics, Volume 35, Issue 12, June 2019, Pages 2084–2092, https://doi.org/10.1093/bioinformatics/bty895 ). To further support this rationale, we cite several neurotranscriptomic studies in which differential expression analyses were performed using low fold-change thresholds, including thresholds around 0.3 or without an explicit fold-change cutoff. This suggests that mild log2FC filtering is an accepted analytical option in neuroscience when the aim is to detect coordinated, moderate transcriptional differences rather than only large-magnitude expression changes. • Goh JY, et al. Transcriptomic analysis of rat prefrontal cortex following chronic stress induced by social isolation - Relevance to psychiatric and neurodevelopmental illness, and implications for treatment. Neurobiol Stress. 2024 Oct 17;33:100679. doi: 10.1016/j.ynstr.2024.100679 • Chen MB, et al. Persistent transcriptional programmes are associated with remote memory. Nature. 2020 Nov;587(7834):437-442. doi: 10.1038/s41586-020-2905-5. Epub 2020 Nov 11. Erratum in: Nature. 2025 Aug;644(8077):E37. doi: 10.1038/s41586-025-09463-4 • Ayhan F, et al. Resolving cellular and molecular diversity along the hippocampal anterior-to-posterior axis in humans. Neuron. 2021 Jul 7;109(13):2091-2105.e6. doi: 10.1016/j.neuron.2021.05.003 • Mathys H, et al. Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019 Jun;570(7761):332-337. doi: 10.1038/s41586-019-1195-2 • Makovka YV, et al. Effect of Short-Term Restraint Stress on the Expression of Genes Associated with the Response to Oxidative Stress in the Hypothalamus of Hypertensive ISIAH and Normotensive WAG Rats. Antioxidants (Basel). 2024 Oct 26;13(11):1302. doi: 10.3390/antiox13111302 As suggested by the reviewer, we additionally applied a more stringent |log2FC| > 1 threshold. This resulted in 38 genes, now listed in Supplementary Table 1. GO enrichment analysis of this restricted gene set produced only a small number of enriched terms, many of which were weakly related to nervous system function. Thus, applying a stringent |log2FC| > 1 threshold would address a different question: which genes show large-amplitude expression changes. Whereas our study aimed to characterize the functional organization of moderate but coordinated inter-strain differences in gene expression. The discussion part contains several elements that should be retrieved in the results section. We revised the manuscript to improve the separation between the Results and Discussion sections. Specifically, we moved or condensed several result-like summary elements that had previously appeared in the Discussion, especially where the text described functional groupings of DEGs, enriched GO categories, or the structure of PPI clusters. Minor comments - The presented figures are not ordered, and their quality are quite low. Fixed - Figure 5&6 are missing color legends for edges and nodes. Fixed Reviewer #2: Main comments 1. PLOS ONE requires that manuscripts reporting animal research explicitly state, within the Methods section, the steps taken to ameliorate animal suffering. As currently written, the Methods do not adequately address this requirement. Specifically, the described euthanasia procedure — decapitation by guillotine without prior anaesthesia and in the absence of explicitly defined humane endpoints — appears difficult to reconcile with the requirements of the current European Directive 2010/63/EU, which mandates explicit humane endpoints and imposes stricter conditions on the justification of painful or distressing procedures than its predecessor, Directive 86/609/EEC. We re-examined this point in light of the current European Directive 2010/63/EU. We note that the Directive requires that methods of euthanasia should minimize pain, suffering, and distress, and that the appropriate species-specific method should be used. Also, decapitation in rodents is treated as a method to be used when other approaches are not suitable in the specific experimental context. In our study, the terminal procedure was selected on the basis of harm-benefit balance and endpoint validity. The primary outcome was transcriptomic analysis of immune- and cytokine-related signals in intact hippocampal tissue. Inhalational anesthesia such as isoflurane can rapidly alter pro-inflammatory gene expression and therefore would directly compromise the main molecular readout of the study. Under these conditions, the use of prior anesthesia would reduce the scientific validity of the terminal sampling procedure and could lead to animal use without obtaining interpretable data. We have clarified this rationale in the revised Methods and have expanded the description of measures taken to minimize suffering, including routine welfare monitoring, minimization of handling time, and rapid performance of the terminal procedure by experienced personnel. 2. The authors should expand their description and justification of the selective breeding model. A previous publication by Vylegzhanina and colleagues analysed rat strains with contrasting excitability levels across approximately 70 generations, whereas the present manuscript states that interstrain differences stabilised at the 10th generation, at which point between-strain variability exceeded within-strain variability by more than fourfold. This apparent discrepancy is not discussed, and the relationship between the earlier long-term breeding programme and the present cohort is unclear. The authors should explicitly clarify the generational history of the strains used here, discuss how phenotypic stability was monitored across generations, and situate the present cohort within the broader longitudinal context of the model. To this end, it would be highly valuable to include a figure or supplementary table illustrating the trajectory of excitability threshold divergence and within-strain variance across at least the first ten generations of selection, as this would provide readers with the empirical basis for the claim of phenotypic stabilisation and strengthen confidence in the genetic model underlying the transcriptomic comparisons. Thank you for this important comment. We agree that the wording in the original version was not sufficiently clear and could create confusion regarding the generational history of the model. The statement that interstrain differences had stabilized by the 10th generation refers to the early phase of the selective breeding program, that is, the point at which divergence between the strains became clearly established and exceeded within-strain variability by more than fourfold. It does not refer to the generation used in the present experiment. The animals studied in the current study belonged to the 76th generation of the long-term breeding program. We have clarified this explicitly in the revised manuscript. We have also added text explaining that nervous system excitability was monitored both during the selective breeding process, when animals were chosen for further breeding, and during the formation of experimental groups. In addition, we have included supplementary material showing individual excitability thresholds, to better place the present cohort within the broader context of the model (Supplementary Figure 1). 3. The Results section would benefit from expansion and improved internal signposting. At present, the transition between analytical stages — from differential expression to GO enrichment to PPI network analysis — is abrupt, and summary statements foregrounding the key findings of each subsection are largely absent. Adding brief transitional paragraphs that synthesise the reasoning and highlight the principal outcomes of each analytical layer would substantially improve readability and logical flow. In the revised manuscript, we added brief transitional and summary sentences to improve the logical flow between differential expression analysis, functional enrichment, and PPI analysis. Furthermore, the RNA sequencing data generated in this study do not appear to have been deposited in a public repository. Deposition in an appropriate archive (e.g., NCBI GEO or ArrayExpress) prior to publication is strongly encouraged and is consistent with open-science standards increasingly required by journals in this field. The accession number should be reported in the Methods section. We deposited the raw and processed data in NCBI and added the corresponding link in the Methods section. ( GSE327807 ). 4. The Discussion makes repeated cell-type-specific interpretive claims that are not supported by the bulk sequencing methodology employed. Attributing differences in Gfap expression to astrocytic reactivity, Slc2a5 to microglial metabolic state, P2rx7 to glial purinergic signalling, and Stab2 to an endothelial or myeloid neuroimmune profile implies a cellular resolution that bulk hippocampal RNA-seq fundamentally cannot provide. While the authors appropriately acknowledge in the Conclusion that their findings "motivate testable hypotheses to be evaluated in follow-up studies," this caveat is insufficiently foregrounded in the body of the Discussion, where speculative inferences frequently appear without explicit epistemic qualification. The authors are encouraged to systematically reframe cell-type-specific and mechanistic claims as hypotheses, accompanied in each case by a brief statement of what additional evidence — such as single-nucleus RNA sequencing, cell-type-resolved proteomics Attachment Submitted filename: Response.docx pone.0350674.s012.docx (27.2KB, docx) PLoS One. doi: 10.1371/journal.pone.0350674.r003 Decision Letter 1 Jean-Pierre Mothet Author information Copyright and License information Roles Jean-Pierre Mothet : Academic Editor © 2026 Jean-Pierre Mothet This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 13 May 2026 -->PONE-D-26-04533R1-->-->Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability-->-->PLOS One Dear Dr. Shalaginova, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jun 27 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:--> A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. --> If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only the individual author can complete the verification step; PLOS staff cannot verify ORCID iDs on behalf of authors. We look forward to receiving your revised manuscript. Kind regards, Jean-Pierre Mothet, Ph.D Academic Editor PLOS One Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments: The reviewer and I appreciate your efforts in addressing adequately the original concerns. We particularly acknowledge the integration of new figures, tables and detailed revised sections. Accordingly, the revised version of your manuscript has been largely improved. However, there are still few minor comments raised by the reviewer that require your attention. Therefore, I kindly ask you to address these remaining comments before final acceptance. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions --> Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.--> Reviewer #3: (No Response) ********** -->2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. --> Reviewer #3: Yes ********** -->3. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #3: Yes ********** -->4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #3: Yes ********** -->5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #3: Yes ********** -->6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #3: The authors have largely addressed my previous comments satisfactorily. The deposition of RNA sequencing data in the NCBI GEO public repository under accession number GSE327807 is particularly appreciated. The revised manuscript, which now includes additional figures, tables, and revised sections, is in my view near ready for acceptance. I have a few follow-up remarks and comments that I would ask the authors to address before final acceptance. 1. PCA analysis The current presentation of the PCA based solely on the 654 differentially expressed genes (DEGs) is methodologically problematic, as it introduces a circular analysis issue — also known as "double dipping": since genes were selected precisely because they differ between groups, the resulting separation and the variance explained by the first principal components are artificially inflated. Importantly, a PCA performed on all expressed and variable genes used as background for the DESeq2 analysis already achieves a clear and complete separation of the HT and LT groups into non-overlapping clusters, which constitutes a far more rigorous and compelling demonstration of transcriptomic divergence between the two conditions. The authors should therefore present the PCA based on all background genes as the primary analysis, and may retain the DEG-based PCA as a supplementary figure for comparison, clearly contrasting the variance explained in both cases and explicitly discussing the methodological distinction. 2. Protein-protein interaction (PPI) enrichment analysis The significant PPI enrichments reported for networks derived from GO biological process gene sets are fully expected by construction, as genes sharing a biological process annotation are inherently more likely to interact, and therefore these results add limited interpretive value to the manuscript. A more informative and meaningful observation would be to highlight that the full set of 654 DEGs (FDR < 0.05) itself shows significant PPI enrichment, which provides evidence that the transcriptional response to the experimental contrast is not a collection of independent gene-level changes, but rather reflects the perturbation of coordinated biological networks. STRING network: number of nodes: 639 number of edges: 547 expected number of edges: 381 PPI enrichment p-value: 7.77e-16 https://version-12-0.string-db.org/cgi/network?networkId=bsNRxR3QJrRd This PPI enrichment should be presented as a key result, as it both strengthens the biological relevance of the findings and directly justifies the more focused PPI analyses conducted on the DEGs associated with the GO biological processes "regulation of the MAPK cascade" and "regulation of synaptic plasticity." ********** -->7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #3: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. --> PLoS One. 2026 Jun 3;21(6):e0350674. doi: 10.1371/journal.pone.0350674.r004 Author response to Decision Letter 2 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 15 May 2026 Reviewer #3: 1. PCA analysis The current presentation of the PCA based solely on the 654 differentially expressed genes (DEGs) is methodologically problematic, as it introduces a circular analysis issue — also known as "double dipping": since genes were selected precisely because they differ between groups, the resulting separation and the variance explained by the first principal components are artificially inflated. Importantly, a PCA performed on all expressed and variable genes used as background for the DESeq2 analysis already achieves a clear and complete separation of the HT and LT groups into non-overlapping clusters, which constitutes a far more rigorous and compelling demonstration of transcriptomic divergence between the two conditions. The authors should therefore present the PCA based on all background genes as the primary analysis, and may retain the DEG-based PCA as a supplementary figure for comparison, clearly contrasting the variance explained in both cases and explicitly discussing the methodological distinction. We thank the reviewer for this important methodological comment. We performed an additional PCA using the transcriptome-wide background expression matrix without DEG-based gene selection. All expressed genes were included after removal of genes with zero expression or zero variance across samples. The revised manuscript now presents both the DEG-based PCA (Fig. 2A) and the background-gene PCA (Fig. 2B). Importantly, clear separation between LT and HT samples was also preserved in the unbiased background PCA, although the variance explained by the first principal components was lower, as expected for a transcriptome-wide analysis not restricted to DEGs. The Methods and Results sections were revised accordingly. 2. Protein-protein interaction (PPI) enrichment analysis The significant PPI enrichments reported for networks derived from GO biological process gene sets are fully expected by construction, as genes sharing a biological process annotation are inherently more likely to interact, and therefore these results add limited interpretive value to the manuscript. A more informative and meaningful observation would be to highlight that the full set of 654 DEGs (FDR < 0.05) itself shows significant PPI enrichment, which provides evidence that the transcriptional response to the experimental contrast is not a collection of independent gene-level changes, but rather reflects the perturbation of coordinated biological networks. STRING network: number of nodes: 639 number of edges: 547 expected number of edges: 381 PPI enrichment p-value: 7.77e-16 https://version-12-0.string-db.org/cgi/network?networkId=bsNRxR3QJrRd This PPI enrichment should be presented as a key result, as it both strengthens the biological relevance of the findings and directly justifies the more focused PPI analyses conducted on the DEGs associated with the GO biological processes "regulation of the MAPK cascade" and "regulation of synaptic plasticity." We thank the reviewer for this helpful suggestion and analysis. The Results section was revised to include this global PPI enrichment before the focused PPI analyses. The Discussion was also modified to interpret this result in the context of inherited nervous system excitability as a polygenic trait. The global STRING network was added as Supplementary Fig. 2 and node degrees as Supplementary Table 8. Attachment Submitted filename: Response_auresp_2.docx pone.0350674.s013.docx (15.2KB, docx) PLoS One. doi: 10.1371/journal.pone.0350674.r005 Decision Letter 2 Jean-Pierre Mothet Author information Copyright and License information Roles Jean-Pierre Mothet : Academic Editor © 2026 Jean-Pierre Mothet This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 18 May 2026 <p>Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability PONE-D-26-04533R2 Dear Dr. Shalaginova, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support . If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Jean-Pierre Mothet, Ph.D Academic Editor PLOS One Additional Editor Comments (optional): The authors have satisfactorily addressed all concerns PLoS One. doi: 10.1371/journal.pone.0350674.r006 Acceptance letter Jean-Pierre Mothet Author information Copyright and License information Roles Jean-Pierre Mothet : Academic Editor © 2026 Jean-Pierre Mothet This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PONE-D-26-04533R2 PLOS One Dear Dr. Shalaginova, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing . If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr Jean-Pierre Mothet Academic Editor PLOS One Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 Fig. Individual excitability thresholds in selectively bred HT and LT rats. (DOCX) pone.0350674.s001.docx (67.1KB, docx) S2 Fig. STRING PPI network from the full set of DEGs identified between HT and LT hippocampus samples. (PNG) pone.0350674.s002.png (11.1MB, png) S1 Table. Gene Lists. (XLSX) pone.0350674.s003.xlsx (242.7KB, xlsx) S2 Table. Fig 5 string MCL cluster descriptions. (XLS) pone.0350674.s004.xls (18.5KB, xls) S3 Table. Fig 5 string MCL clusters. (XLS) pone.0350674.s005.xls (39KB, xls) S4 Table. Fig 5 string node degrees. (XLSX) pone.0350674.s006.xlsx (7.3KB, xlsx) S5 Table. Fig 6 string MCL cluster descriptions. (XLS) pone.0350674.s007.xls (27KB, xls) S6 Table. Fig 6 string MCL clusters. (XLS) pone.0350674.s008.xls (42.5KB, xls) S7 Table. Fig 6 string node degrees. (XLSX) pone.0350674.s009.xlsx (8.7KB, xlsx) S8 Table. String node degrees of PPI network from the full set of DEGs. (TSV) pone.0350674.s010.tsv (20.8KB, tsv) Attachment Submitted filename: Response.docx pone.0350674.s012.docx (27.2KB, docx) Attachment Submitted filename: Response_auresp_2.docx pone.0350674.s013.docx (15.2KB, docx) Data Availability Statement All relevant data are within the manuscript and its Supporting information files. The raw and processed data in NCBI - GSE327807. Articles from PLOS One are provided here courtesy of PLOS ACTIONS View on publisher site PDF (1.6 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Transcriptomic profile of the hippocampus of rat strains with contrasting nervous system excitability#255570
• Reduced inhibition, bursting, and accelerated oscillations drive early hippocampal hyperactivity in Alzheimer's disease in vivo.#220914
• Recovery after stress on HPA activity, memory and associated molecular changes in LPS-challenged rats.#631716
• Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells#684669
• DNA methylation signatures of bilateral hippocampal volume, asymmetry and atrophy: a cross-omics analysis in the general population.#218812