Peripheral CD4 (+) T Cell Immunity and Brain Microglial Activation Associated with Cognitive Heterogeneity in Aged Rats

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Although aging is a natural process, there is significant heterogeneity in cognition levels among individuals; however, the underlying mechanisms remain uncertain. In our study, we classified aged male Sprague-Dawley rats into aged cognition-unimpaired (AU) group and aged cognition-impaired (AI) group, by using an attentional set-shifting task. The transcriptome sequencing results of medial prefrontal cortex (mPFC) demonstrated significant differences in microglial activation and inflammatory response pathways between the two groups. Specifically, compared to AU rats, AI rats exhibited a greater presence of CD86-positive microglia and major histocompatibility complex class II (MHC-II)-positive microglia, along with elevated inflammatory molecules, in mPFC. Conversely, AI rats exhibited a reduction in the amount of microglia expressing CD200R and the anti-inflammatory molecules Arg-1 and TGF-β. Additionally, peripheral blood analysis of AI rats demonstrated elevated levels of Th17 and Th1 cells, along with proinflammatory molecules; however, decreased levels of Treg cells, along with anti-inflammatory molecules, were observed in AI rats. Our research suggested that peripheral Th17/Treg cells and central microglial activation were associated with cognitive heterogeneity in aged rats. This may provide a new target for healthy aging. Aging-associated cognitive decline Cognitive Heterogeneity Microglial activation Th17/Treg cells Inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction With the aggravation of population aging, the problems associated with aging are becoming increasingly prominent. Cognitive decline is a significant characteristic of aging. It has been estimated that approximately 130 million elderly all over the world will experience aging-associated cognitive decline by 2050, which has become a serious public health problem [ 1 ]. Although aging is a universal phenomenon, there is significant heterogeneity in cognition levels among aged individuals [ 2 ]. Some individuals show aging-associated cognitive decline, while others are able to sustain optimal health and cognitive abilities as they age [ 3 ]. Several studies have reported the possible reason for such differences. For example, old participants with poor performance in memory measures only recruit the right prefrontal cortex (PFC), but old participants who perform well engage PFC regions bilaterally [ 4 ]. And this general increase in prefrontal activation is an adaptive and compensatory alteration to counteract aging-associated neural decline [ 5 ]. Animal studies have also shown that cortical functional connectivity (FC) reduced significantly in aged cognition-impaired (AI) rats, while cortical FC is relatively preserved in aged cognition-unimpaired (AU) rats [ 6 ]. However, these imaging data still explain little about the mechanisms of cognitive differences in the elderly. Immunity and inflammation play important roles in aging and cognitive decline. CD4 + T cells mediated immunity in the periphery, including different types of cells like Th17, Th1, and Treg cells, is considered a key factor in aging [ 7 ]. Th17 cells have been demonstrated to exacerbate inflammation in different neurological disorders. In contrast, Tregs have anti-inflammatory effects and can inhibit Th17 cells, although they exhibit shared transcriptomic characteristics [ 8 , 9 ]. Maintaining the equilibrium between Th17 and Treg cells, along with their corresponding transcription factors and cytokines, is essential. When Th17/Treg cells are imbalanced, circulating inflammatory cytokine levels can increase, thus leading to an inflammatory response. Upregulation of inflammatory cytokine levels in circulation enhances blood-brain barrier permeability and enable inflammatory cells and factors to enter into brain tissue [ 10 ]. In brain, microglia are the main immune cells, can be activated by peripheral infiltrating T cells or inflammatory factors, thus releasing inflammatory factors and leading to neuroinflammation. Neuroinflammation can further damage neuronal and synaptic structures, thus affecting cognitive function [ 11 ]. Therefore, we speculate that microglial activation mediated by peripheral CD4 + T cells may be an important reason for aging-associated cognitive heterogeneity. Previous research has demonstrated that aged rats display individual cognitive differences in attentional set-shifting tasks, thus providing a good tool for studying aging-associated cognitive decline [ 12 ]. Thus, this study mainly utilizes behavioral attentional set-shifting task and analyzes the impact of CD4 + T cells in peripheral blood and microglial activation in brain on cognitive heterogeneity in aged rats, thereby providing new interventional targets for achieving healthy aging. Materials and Methods Animals 3-month-old young (n = 8) and 22-month-old aged male Sprague-Dawley rats (n = 20) were used in our study. They were purchased from Chengdu Dashuo Laboratory Animal Company in China. The rats were raised in Experimental Animal Center of Shanxi Medical University. And the room temperature was set at approximately 22°C ~ 25°C. The relative humidity was set at 45% ~ 55%. The day-night cycle was maintained at 12/12 hours. All treatments given to animals were approved by the Ethics Committee of the First Hospital of Shanxi Medical University. Attentional set-shifting task based on operant conditioning The attentional set-shifting task was performed as previously described [ 12 ]. In operant conditioning chambers (Med Associates, USA) as shown in Fig. 1 A, rats learned to press the levers firstly, and then each rat’s side bias was determined (more prefer to the left lever or right lever). The task is formed in two stages. In the 1st stage, rats were trained to press the specific lever with visual cue light. During this discrimination task, the cue light above a lever indicated the appropriate reaction (Fig. 1 B). Once the rats achieved the required level, they were then tested the following day in the second stage. In this stage, rats had to disregard the visual signal and consistently select either the left or right lever (the correct lever being the unbiased side that was defined during training) (Fig. 1 B). When achieving eight consecutive correct trials, the rats were determined to have achieved the set-shift criterion. The number of required trials to achieve the criterion was analyzed. Golgi staining It was performed according to the guidelines from the manufacturer (Hito Golgi-Cox OptimStainTM PreKit). Brain tissues were processed according to the procedure and then were cut into 100 µm-thick slices. The slices were stained and photographed at 40×, 200× and 1000× original magnifications by using TissueFAXS Plus S (TissueGnostics GMBH, Austria). The images were analyzed by using Image J software. And the spine number per 10 µm dendrite was calculated, that is the dendritic spine density. For quantification, 3 rats were used in each group, and 6 complete and clearly visible neurons were selected per rat. Transcriptome sequencing After rats were euthanized, brain tissues were collected, and medial prefrontal cortex (mPFC) was isolated. RNA was isolated and then assessed of quality, integrity and concentration with a NanoDrop spectrophotometer. Libraries for sequencing were created and then sequenced using Illumina NovaSeq 6000 at Shanghai Personal Biotechnology Company. Principal component analysis (PCA) was carried out based on gene expression data. Based on the criteria of |log2FoldChange| > 1 and adjusted P value < 0.05, differentially expressed genes (DEGs) were identified and then visualized in a volcano plot. The software TopGO version 2.50.0 was utilized for conducting GO enrichment analysis on DEGs with P value < 0.05. The GO terms containing significantly enriched DEGs were then identified to ascertain the primary biological functions linked to the DEGs. The Gene Set Enrichment Analysis (GSEA) was conducted on all genes, which were visualized in the pathway map for the enrichment analysis. Flow cytometry As previously described, flow cytometry was utilized to detect peripheral T immune cells [ 13 , 14 ]. Following the behavioral experiments, rats were anesthetized and the peripheral blood was taken. Mononuclear cells were isolated with lymphocyte separation solution (P8360, Solarbio), some of which were used for Treg detection, and the others were treated with a cell stimulation cocktail (00-4975-93, eBioscience) for 6 hours and then used for the detection of Th1 and Th17 cells. First, antibodies against cell surface molecules, including anti-CD3-PerCP-Cy5.5 (201418, Biolegend), anti-CD4-FITC (11-0040-85, eBioscience) and anti-CD25-APC (17-0390-82, eBioscience), were used. Following a 30-minute incubation in darkness, the cells were fixed, permeabilized and then stained with antibodies against intracellular or nuclear factors, including anti-Foxp3-PE (12-4774-42, eBioscience), anti-IFN-γ-APC (50-7310-80, eBioscience) and anti-IL-17-PE (12-7177-81, eBioscience). As previously described, flow cytometry was utilized to detect microglial activation in brain [ 15 ]. The mPFC tissues were digested with collagenase IV to prepare a single-cell suspension. After myelin was removed via gradient density centrifugation with 30% and 70% Percoll separation solution, the microglial cell layer was isolated. Purified anti-rat CD32 (550271, BD Biosciences) was added to block the Fc receptor. Afterwards, surface staining was performed with the following reagents: Zombie NIR ™ Fixable Viability Kit (423105, Biolegend), anti-CD11b-PerCP-Cy5.5 (201819, Biolegend), anti-CD45-Pacific Blue (202225, Biolegend), anti-CD86-PE (12-0860-83, eBioscience), anti-MHC-II-APC (17-0920-82, eBioscience), and anti-CD200R-FITC (204905, Biolegend). The data were acquired via BD FACSCanto II flow cytometer (BD, USA) and then processed with FlowJo 10.8.1 software. Enzyme-linked immunosorbent assay Blood was centrifuged for collecting serum. The concentrations of IL-10 (JL13427, Jianglai Bio), IL-17 (JL20879, Jianglai Bio), IFN-γ (JL13241, Jianglai Bio) and TGF-β (JL13643, Jianglai Bio) in serum were analyzed according to the step-by-step instructions. Real-time PCR RNA of mPFC was extracted (9108Q, TakaRa), and concentration as well as purity of RNA were detected. Subsequently, the RNA was reverse transcribed to cDNA (RR047A, TaKaRa). And cDNA was then amplified by using real-time PCR master mix (RR820A, TaKaRa) and gene-specific primers. The LightCycler 480 II instrument (Roche Diagnostics GmbH, Germany) was used to conduct real-time PCR. The relative expression of a gene was calculated by 2 -ΔΔCt . Four rats from each group were tested. The utilized primers: CD86 F: 5’-AGACATGTGTAACCTGCACCAT-3’, R: 5’-ACTTTTTCCGGTCCTGCCAA-3’; IL-1β F: 5’-CAGCTTTCGACAGTGAGGAGA-3’, R: 5’-TGTCGAGATGCTGCTGTGAG-3’; TNF-α F: 5’-CTCAAGCCCTGGTATGAGCC-3’, R: 5’-GGCTGGGTAGAGAACGGATG-3’; IL-6 F: 5’-TCCTACCCCAACTTCCAATGC-3’, R: 5’- TAGCACACTAGGTTTGCCGAG-3’; Arg-1 F: 5’-CCAGTATTCACCCCGGCTAC-3’, R: 5’-GTCCTGAAAGTAGCCCTGTCT-3’; TGF-β F: 5’-GACCGCAACAACGCAATCTA-3’, R: 5’-CGTGTTGCTCCACAGTTGAC-3’; GAPDH F: 5’-GGCACAGTCAAGGCTGAGAATG-3’, R: 5’-ATGGTGGTGAAGACGCCAGTA-3’. Statistical analysis The data analysis was carried out by using GraphPad Prism 9. All the data were normally distributed and had homogeneous variance; thus, the data were presented as means ± standard deviations ( \(\:\stackrel{-}{X}\pm\:S\) ). Independent two-sample t tests were used to compare the differences between two groups. One-way analysis of variance (ANOVA) was applied for comparisons among multiple groups. P values < 0.05 were considered significant. Results Individual differences in the cognitive function of aged rats Attentional set-shifting task was used to detect the cognitive flexibility. Fewer trials are needed to reach the criterion, which represents better cognitive function. Compared with that of young rats, cognitive performance of aged rats was impaired, thus they needed more trials to achieve the desired performance level. Furthermore, the set-shifting performance of aged rats was more heterogeneous compared to young rats (Fig. 2 A). With a standard deviation (SD) from the average performance in young rats as a control, the aged rats were divided into AU group and AI group. When the trials to criterion exceeded a SD from average number in young rats, it was considered an AI rat (n = 9). All of the other aged rats were AU rats (n = 11). The cognitive performance of AI rats was significantly worse than young rats and AU rats ( P < 0.01, Fig. 2 B). The mPFC is the main brain area performing attentional set-shifting tasks in rodents. The dendritic spine density in mPFC was observed via Golgi staining (Fig. 2 C-F). Compared to that in AU group, the dendritic spine density in AI group was dramatically lower ( P < 0.01, Fig. 2 E-F). Transcriptome sequencing of the mPFC in AU and AI rats RNA was isolated from the mPFC of AU and AI rats for transcriptome sequencing. Principal component analysis (PCA) demonstrated an obvious difference in gene expression between AU and AI rats (Fig. 3 A). A volcano plot demonstrated that 139 DEGs were identified, including 128 up-regulated genes and 11 down-regulated genes, in AI rats compared with AU rats (Fig. 3 B). Further GO analysis demonstrated that the DEGs were closely enriched in innate immune response, positive regulation of cytokine production and inflammatory response (Fig. 3 C). Gene set enrichment analysis (GSEA) demonstrated that glial activation, microglial activation, the neuroinflammatory response and the TNF-mediated signaling pathway were upregulated in AI rats compared with AU rats (Fig. 3 D). Microglial activation differed between AU and AI rats To more specifically assess microglial activation, flow cytometry was used to detect the marker for activated microglia in the mPFC. CD86 and MHC-II have been reported as proinflammatory markers, while CD200R is recognized as anti-inflammatory marker [ 16 , 17 ]. Among microglia (CD11b + CD45 int ), there were more microglia expressing CD86, MHC-II and CD200R in both AI and AU rats than in young rats (Fig. 4 ). Compared with those in AU rats, the number of CD86-positive microglia was greater ( P < 0.05) and the number of MHC-II-positive microglia was greater ( P < 0.01), whereas CD200R-positive microglia was lower in AI rats ( P < 0.05), suggesting an increase in inflammatory activity while a reduction in anti-inflammatory activity in AI rats. Differential expression of central inflammatory factors in AU and AI rats After microglia are activated, the expression of CD86 and MHC-II promotes neuroinflammation by increasing the secretion of inflammatory factors, thus resulting in aggravated neuroinflammation [ 18 – 20 ]. However, the expression of CD200R inhibits the neuroinflammation and reduces the production of proinflammatory factors [ 21 ]. The expression of inflammation-related factors in mPFC were detected with RT-PCR (Fig. 5 ). Compared to those in AU rats, AI rats exhibited higher levels of proinflammatory markers CD86 ( P < 0.01), IL-1β, TNF-α and IL-6 ( P < 0.05), while showing lower levels of anti-inflammatory markers Arg-1 ( P < 0.01) and TGF-β ( P < 0.05). The percentages of Treg, Th1 and Th17 cells in the peripheral blood of AU and AI rats were different Among CD4 + T cells (CD3 + CD4 + ), CD25 + Foxp3 + represents Treg cells, IFN-γ + represents Th1 cells and IL-17 + represents Th17 cells. The percentages of Treg, Th1 and Th17 in CD4 + T cells were greater in both AI and AU rats than in young rats. Compared to those in AU group, the percentage of Treg was lower ( P < 0.01), while the percentages of Th1 and Th17 were greater in AI group ( P < 0.01, Fig. 6 ). Serum inflammatory factor levels differed between AU and AI rats In the peripheral blood, Treg cells secrete anti-inflammatory IL-10 and TGF-β, Th1 cells and Th17 cells secrete proinflammatory factors IFN-γ and IL-17 respectively. These factors in serum were detected by ELISA. In aged rats, IFN-γ and IL-17 increased (Fig. 7 A, C, P < 0.01), IL-10 and TGF-β also increased (Fig. 7 E, G, P < 0.05). Compared with those in AU rats, IFN-γ and IL-17 increased (Fig. 7 B, D, P < 0.01), whereas IL-10 decreased (Fig. 7 F, P < 0.01), and TGF-β decreased (Fig. 7 H, P < 0.05), in AI rats. Discussion The mPFC is important for cognitive function in rodents [ 22 ]. Injury to the mPFC can affect cognitive flexibility. Cognitive flexibility refers to the ability to cope with sudden alterations in the environment by effectively updating internal representations and changing behavioral responses and is a part of executive function [ 23 ]. Previous studies on rats have shown that cognitive flexibility is highly susceptible to aging [ 24 ]. The attentional set-shifting task, which is an important indicator for evaluating cognitive flexibility, is sensitive to mPFC damage [ 25 ]. In this study, aged rats showed individual differences in attentional set-shifting task. Compared with performance of young rats, the AI and AU rats could be distinguished, which is consistent with the previous result [ 12 ]. The acquisition of initial rules remains unaffected, while the ability to adjust a shift is selectively impaired, thus reflecting the differences in mPFC functional activity between the two groups of rats [ 25 ]. To explore the mechanism of individual differences, transcriptome sequencing of the mPFC was performed. There were differences in the pathways of microglial activation and the inflammatory response between AU and AI rats. Microglia, which is the main immune cells in brain, make up around 10% of the brain's cells [ 26 ]. Under physiological conditions, microglia have small cell bodies and maintain homeostasis of the nervous system by continuously monitoring the surrounding environment. When microglia are activated, their morphology rapidly changes, which is manifested as cell body enlargement; additionally, gene expression also changes [ 27 ]. During aging, microglia are activated, and are expressed CD86 and MHC-II, which interact with T cells entering the brain to release inflammatory factors and exacerbate neuroinflammation [ 18 – 20 ], are significantly upregulated. CD200R on the surface of microglia is a receptor for CD200 [ 28 ]. Interaction with neuronal CD200 can not only inhibit microglial activation but can also suppress Ras-ERK and Ras-PI3K pathways to decrease inflammatory molecules [ 21 ]. In this study, compared with those in young rats, the number of microglia expressing CD86 or MHC-II increased in aged rats, which is consistent with previous research [ 29 ]. The expression of CD200R in the microglia with age also increased in an attempt to inhibit microglial activation and proinflammatory factors production. Interestingly, compared with those of AU rats, the mPFC of AI rats had more microglia expressing CD86 or MHC-II and fewer microglia expressing CD200R, which contributed to the increase in IL-1β, TNF-α and IL-6. The balance between different phenotypes of microglia is crucial in the aging process. During the early period of inflammation, the initial response of activated microglia is proinflammatory. Subsequently, anti-inflammatory microglia also increase to inhibit the inflammatory response, which is necessary for maintaining balance in the body [ 30 ]. However, during long-term chronic inflammation, microglia are continuously activated, and inflammatory factors continue to be produced, thus ultimately exacerbating neuroinflammation and cognitive impairment. Immune dysregulation and inflammation in the circulation play important roles in aging. Compared with those in young individuals, CD4 + T cells in elderly individuals produce more Th17 cell-related proinflammatory factors, including IL-6 and IL-17A, which drive the body to exhibit an inflammatory state [ 31 , 32 ]. However, inflammatory aging is not solely reflected in an increase in proinflammatory markers [ 33 ]. Treg cells are involved in the body's protection and can produce IL-10 to inhibit inflammation and prevent excessive immune responses [ 34 ]. Recently, single-cell RNA sequencing analysis from human peripheral blood showed the proportion of Tregs increased with age [ 35 ]. Similarly, in old mice, three CD4 + T-cell subsets (exhausted, anti-inflammatory regulatory T cells, and proinflammatory cytotoxic) were shown to gradually accumulate with age [ 36 ]. Consistent with these results, in our study, aged rats exhibited increases in the levels of proinflammatory Th17 and Th1 cells, as well as inflammatory molecules IL-17 and IFN-γ. Similarly, anti-inflammatory Treg cells, as well as anti-inflammatory factors IL-10 and TGF-β also increase with age, which reflects the body's adaptive response to proinflammatory stimuli that attempt to suppress inflammation [ 37 ]. More importantly, compared with AU rats, AI rats have more Th17 and Th1 cells, which produce more IL-17 and IFN-γ, whereas fewer Tregs as well as TGF-β and IL-10 are unable to suppress aging-related inflammation. This may be the reason for severe inflammation in the body of AI rats. Immune and inflammatory levels are key factors contributing to individual differences in the elderly, which can predict the development of aging-associated diseases. From an evolutionary perspective, inflammation has been regarded as an adaptation/remodeling because it may activate anti-inflammatory responses to counteract aging-associated pro-inflammatory context. For example, centenarians have numerous anti-inflammatory factors in the circulation, such as IL-10, TGF-β1 and IL-1 receptor antagonists [ 38 – 40 ]. The anti-inflammatory state is activated to down-regulate the elevated inflammatory factors, such as C-reactive protein, IL-6 and IL-18 [ 41 – 43 ]. This implies that immune system will have an adaptive/maladaptive change in the process of aging. The health of an individual depends on the adaptive/maladaptive consequence. It is determined by the ability to adapt and reshape harmful stimuli [ 44 ]. In conclusion, chronic low-grade inflammation plays a significant role on aging-associated cognitive decline. Aging disrupts the equilibrium of peripheral Th17/Treg cells, thus resulting in elevated inflammation levels. Inflammation leads to heightened microglial activation in the mPFC, thus ultimately contributing to cognitive decline. Our study underscores the association between peripheral and central immune state and aging-associated cognitive heterogeneity. Additionally, our research also indicates that healthy aging is not a sustained state of youthfulness but rather an adaptive reshaping of the body's aging state. The promotion of the Th17/Treg balance, rather than restoring it to a youthful state, may become a therapeutic target for aging-associated cognitive decline. Nevertheless, this study still has some limitations. How CD4 + T cells activate microglia, and whether regulating the ratio of Th17/Treg can influence the activation of microglia, still need further research and exploration. Declarations Author contributions Lian Yu, Miao-Miao Liu and Yan-Li Li conceived the study and designed the experiments. Rui Wang, Xiao-Rong Yang and Jun-Hong Guo guided the experimental design. Lian Yu, Miao-Miao Liu and Mei-Qi Guan performed the experiments. Rui Wang, Xiu-Min Zhang, Hong Gu and Qiang Fu carried out data analysis. Lian Yu, Miao-Miao Liu, Xiao-Rong Yang and Shu-Fen Wu carried out image processing. Lian Yu, Miao-Miao Liu and Mei-Qi Guan wrote the manuscript. Jing-Jing Wei, Jun-Hong Guo and Yan-Li Li reviewed and edited the manuscript. All authors have agreed the manuscript to be published. Conflicts of interest statement All authors declare no conflicts of interest. Funding This research was funded by National Natural Science Foundation of China, grant number 82101641; the Natural Science Foundation of Shanxi Province, China, grant number 20210302124173; Open Fund from Key Laboratory of Cellular Physiology (Shanxi Medical University), Ministry of Education, China, grant number KLMEC/SXMU-202010; National major R&D projects of China-Scientific technological innovation 2030, grant number 2021ZD0201801. Acknowledgments We would like to give thanks to all people participating in the study for their support and cooperation. 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Aging Cell. 2004;3:443–8. Cavallone L, Bonafè M, Olivieri F, Cardelli M, Marchegiani F, Giovagnetti S, et al. The role of IL-1 gene cluster in longevity: a study in Italian population. Mech Ageing Dev. 2003;124:533–8. Gao D, Ni X, Fang S, Wang Z, Jiao J, Liu D, et al. Exploration for the reference interval of C-reactive protein in the Chinese longevity people over 90 years of age. Diabetes Metab Syndr. 2023;17:102817. Bonafè M, Olivieri F, Cavallone L, Giovagnetti S, Mayegiani F, Cardelli M, et al. A gender–dependent genetic predisposition to produce high levels of IL-6 is detrimental for longevity. Eur J Immunol. 2001;31:2357–61. Gangemi S, Basile G, Merendino RA, Minciullo PL, Novick D, Rubinstein M, et al. Increased circulating Interleukin-18 levels in centenarians with no signs of vascular disease: another paradox of longevity? Exp Gerontol. 2003;38:669–72. Fulop T, Larbi A, Hirokawa K, Cohen AA, Witkowski JM. Immunosenescence is both functional/adaptive and dysfunctional/maladaptive. Semin Immunopathol. 2020;42:521–36. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2024 Read the published version in Immunity & Ageing → Version 1 posted Editorial decision: Revision requested 19 Oct, 2024 Reviews received at journal 18 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 15 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4743495","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336247895,"identity":"af8ca6f0-b0f6-4e54-8eec-73fb263e5ae5","order_by":0,"name":"Lian Yu","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Yu","suffix":""},{"id":336247897,"identity":"2ca97247-9a6d-4dc5-99b1-1b07aa4c5299","order_by":1,"name":"Miao-Miao Liu","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Miao-Miao","middleName":"","lastName":"Liu","suffix":""},{"id":336247900,"identity":"af0252c6-6bec-46e4-835f-4b2a08c343b5","order_by":2,"name":"Mei-Qi Guan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mei-Qi","middleName":"","lastName":"Guan","suffix":""},{"id":336247901,"identity":"4669ece7-86a5-46c7-8094-d0b9184df856","order_by":3,"name":"Rui Wang","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":336247902,"identity":"04d1a622-a29f-4f54-a98b-6a5e549ab379","order_by":4,"name":"Xiao-Rong Yang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Rong","middleName":"","lastName":"Yang","suffix":""},{"id":336247903,"identity":"c69f67bd-7111-497b-b9b7-37c48648aa39","order_by":5,"name":"Xiu-Min Zhang","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiu-Min","middleName":"","lastName":"Zhang","suffix":""},{"id":336247904,"identity":"d956ce5f-0676-41f4-9927-b9a1f0eb3cde","order_by":6,"name":"Jing-Jing Wei","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing-Jing","middleName":"","lastName":"Wei","suffix":""},{"id":336247906,"identity":"934de9af-4082-4fb6-ad74-670fc48c47f2","order_by":7,"name":"Shu-Fen Wu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shu-Fen","middleName":"","lastName":"Wu","suffix":""},{"id":336247907,"identity":"542c6b51-4043-43fa-aba4-b22c613710de","order_by":8,"name":"Hong Gu","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Gu","suffix":""},{"id":336247908,"identity":"2ffd91b7-cc99-4b1e-bfe4-ec9becfe753c","order_by":9,"name":"Qiang Fu","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Fu","suffix":""},{"id":336247909,"identity":"5d785063-0d60-4ca6-a45d-d8c418667640","order_by":10,"name":"Jun-Hong Guo","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun-Hong","middleName":"","lastName":"Guo","suffix":""},{"id":336247910,"identity":"e3f5e6d6-536b-4a7a-81c8-a0222473f872","order_by":11,"name":"Yan-Li Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIie3QPQrCMBTA8QeBtMPTbJLSoVd4pSAFPUy9QSdxUsFVcG3xEoIXqOQYXQpeIOBScLFpcU5GwfyHB4H8yAeAz/eDzZmZpQTOWNPo3oHwkZAEEfDNoz67kHESQHTBTIXchQSYvkrKgRRqBQiJWDS2i2EWV8PFSM1uqswhra+FlVCMX1IhFNTaSfaeCHYKuRtZjqdEJwRXwrcrQwTjNHyytL9FCHVvcbcHLtRT636diNhCAEIy83CcVtK23RR0Lrt8Pp/vn/sAD1s0llI1DpgAAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Shanxi Medical University, Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yan-Li","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-15 14:14:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4743495/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4743495/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12979-024-00486-5","type":"published","date":"2024-11-14T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62654273,"identity":"f6b87244-69dc-4ece-bea0-319efd0fbbf0","added_by":"auto","created_at":"2024-08-17 01:22:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":294269,"visible":true,"origin":"","legend":"\u003cp\u003eAttentional set-shifting task based on operant conditioning. (\u003cstrong\u003eA\u003c/strong\u003e) Operant conditioning chambers. (\u003cstrong\u003eB\u003c/strong\u003e) attentional set-shifting task program. It consists of two stages: the first stage (visual cue discrimination) and the second stage (set-shift).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/3be1b19690f20845386184ca.png"},{"id":62655085,"identity":"3c681e53-699d-426b-b8cf-d05904ef36cb","added_by":"auto","created_at":"2024-08-17 01:30:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600745,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive function in young and aged rats. (A) Cognitive performance in attentional set-shifting task. (B) Statistical analysis of the trials to criterion. Representative images of dendrites in the mPFC at 40× (C), 200× (D) and 1000× (E). (F) Statistical analysis of the dendritic spine density in each group. AU: aged cognition-unimpaired; AI: aged cogni-tion-impaired. ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/83e1207f6ace60dee18427ed.png"},{"id":62654279,"identity":"18ea3e7e-232a-4587-9110-c806fa7eaae1","added_by":"auto","created_at":"2024-08-17 01:22:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":655251,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome difference analysis of the mPFC between AU and AI rats. (A) PCA plot. (B) Volcano plot of DEGs in each group. (C) GO enrichment analysis. (D) GSEA.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/d6ad3d2d920d7133616fc971.png"},{"id":62654275,"identity":"f991c311-c470-4569-aa49-42352d917dd3","added_by":"auto","created_at":"2024-08-17 01:22:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":424732,"visible":true,"origin":"","legend":"\u003cp\u003eMicroglial activation was detected by using flow cytometry. (A) The gating strategy for microglia. The gate represents CD11b+CD45int-expressing microglia. (B) Flow cytometry plots showing CD86, MHC-II and CD200R expression on the surface of microglia. (C) The numbers of CD86+, MHC-II+ and CD200R+ cells among the CD11b+CD45int microglia. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. FMO: Fluorescence-Minus-One Control.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/346ee17db3349ae7eb472ca0.png"},{"id":62655086,"identity":"9d2b9bb6-6fa4-429f-88f3-8dfbd756a65e","added_by":"auto","created_at":"2024-08-17 01:30:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103482,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of central inflammatory factors in young, AU and AI rats. (A) CD86. (B) IL-1β. (C) TNF-α. (D) IL-6. (E) Arg-1. (F) TGF-β. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/d321c25f44a4faa15eb036b1.png"},{"id":62654277,"identity":"921f606a-2854-4dad-a4ce-71be91e8926a","added_by":"auto","created_at":"2024-08-17 01:22:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":507877,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentages of Treg, Th1 and Th17 in peripheral blood of young, AU, AI rats. (A) Gate strategy. The gate represents CD4+ T cells. (B) Flow cytometry plots and the proportion of Treg. (C) Flow cytometry plots and the percentage of Th1. (D) Flow cytometry plots and the percentage of Th17. ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/ecbee036d6b00684bc0e6c3e.png"},{"id":62654278,"identity":"ce169e86-e212-48c3-b8ae-5bbfff18c5a9","added_by":"auto","created_at":"2024-08-17 01:22:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":138892,"visible":true,"origin":"","legend":"\u003cp\u003eThe concentrations of inflammatory factors in serum. (A-B) IFN-γ. (C-D) IL-17. (E-F) IL-10. (G-H) TGF-β. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/6655287be5ae2409a9e38fd8.png"},{"id":69274748,"identity":"d5008f8a-9666-4f52-a2a7-521b70202597","added_by":"auto","created_at":"2024-11-18 16:21:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3394937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4743495/v1/da7848a0-e093-44c1-aa0e-f9524d98a425.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Peripheral CD4 (+) T Cell Immunity and Brain Microglial Activation Associated with Cognitive Heterogeneity in Aged Rats","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith the aggravation of population aging, the problems associated with aging are becoming increasingly prominent. Cognitive decline is a significant characteristic of aging. It has been estimated that approximately 130\u0026nbsp;million elderly all over the world will experience aging-associated cognitive decline by 2050, which has become a serious public health problem [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough aging is a universal phenomenon, there is significant heterogeneity in cognition levels among aged individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Some individuals show aging-associated cognitive decline, while others are able to sustain optimal health and cognitive abilities as they age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Several studies have reported the possible reason for such differences. For example, old participants with poor performance in memory measures only recruit the right prefrontal cortex (PFC), but old participants who perform well engage PFC regions bilaterally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. And this general increase in prefrontal activation is an adaptive and compensatory alteration to counteract aging-associated neural decline [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Animal studies have also shown that cortical functional connectivity (FC) reduced significantly in aged cognition-impaired (AI) rats, while cortical FC is relatively preserved in aged cognition-unimpaired (AU) rats [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these imaging data still explain little about the mechanisms of cognitive differences in the elderly.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eImmunity and inflammation play important roles in aging and cognitive decline. CD4\u003csup\u003e+\u003c/sup\u003eT cells mediated immunity in the periphery, including different types of cells like Th17, Th1, and Treg cells, is considered a key factor in aging [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Th17 cells have been demonstrated to exacerbate inflammation in different neurological disorders. In contrast, Tregs have anti-inflammatory effects and can inhibit Th17 cells, although they exhibit shared transcriptomic characteristics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Maintaining the equilibrium between Th17 and Treg cells, along with their corresponding transcription factors and cytokines, is essential. When Th17/Treg cells are imbalanced, circulating inflammatory cytokine levels can increase, thus leading to an inflammatory response. Upregulation of inflammatory cytokine levels in circulation enhances blood-brain barrier permeability and enable inflammatory cells and factors to enter into brain tissue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In brain, microglia are the main immune cells, can be activated by peripheral infiltrating T cells or inflammatory factors, thus releasing inflammatory factors and leading to neuroinflammation. Neuroinflammation can further damage neuronal and synaptic structures, thus affecting cognitive function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, we speculate that microglial activation mediated by peripheral CD4\u003csup\u003e+\u003c/sup\u003eT cells may be an important reason for aging-associated cognitive heterogeneity.\u003c/p\u003e\u003cp\u003ePrevious research has demonstrated that aged rats display individual cognitive differences in attentional set-shifting tasks, thus providing a good tool for studying aging-associated cognitive decline [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, this study mainly utilizes behavioral attentional set-shifting task and analyzes the impact of CD4\u003csup\u003e+\u003c/sup\u003eT cells in peripheral blood and microglial activation in brain on cognitive heterogeneity in aged rats, thereby providing new interventional targets for achieving healthy aging.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003e3-month-old young (n\u0026thinsp;=\u0026thinsp;8) and 22-month-old aged male Sprague-Dawley rats (n\u0026thinsp;=\u0026thinsp;20) were used in our study. They were purchased from Chengdu Dashuo Laboratory Animal Company in China. The rats were raised in Experimental Animal Center of Shanxi Medical University. And the room temperature was set at approximately 22\u0026deg;C\u0026thinsp;~\u0026thinsp;25\u0026deg;C. The relative humidity was set at 45% ~ 55%. The day-night cycle was maintained at 12/12 hours. All treatments given to animals were approved by the Ethics Committee of the First Hospital of Shanxi Medical University.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAttentional set-shifting task based on operant conditioning\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe attentional set-shifting task was performed as previously described [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In operant conditioning chambers (Med Associates, USA) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, rats learned to press the levers firstly, and then each rat\u0026rsquo;s side bias was determined (more prefer to the left lever or right lever). The task is formed in two stages. In the 1st stage, rats were trained to press the specific lever with visual cue light. During this discrimination task, the cue light above a lever indicated the appropriate reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Once the rats achieved the required level, they were then tested the following day in the second stage. In this stage, rats had to disregard the visual signal and consistently select either the left or right lever (the correct lever being the unbiased side that was defined during training) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). When achieving eight consecutive correct trials, the rats were determined to have achieved the set-shift criterion. The number of required trials to achieve the criterion was analyzed.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGolgi staining\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIt was performed according to the guidelines from the manufacturer (Hito Golgi-Cox OptimStainTM PreKit). Brain tissues were processed according to the procedure and then were cut into 100 \u0026micro;m-thick slices. The slices were stained and photographed at 40\u0026times;, 200\u0026times; and 1000\u0026times; original magnifications by using TissueFAXS Plus S (TissueGnostics GMBH, Austria). The images were analyzed by using Image J software. And the spine number per 10 \u0026micro;m dendrite was calculated, that is the dendritic spine density. For quantification, 3 rats were used in each group, and 6 complete and clearly visible neurons were selected per rat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome sequencing\u003c/h2\u003e\u003cp\u003eAfter rats were euthanized, brain tissues were collected, and medial prefrontal cortex (mPFC) was isolated. RNA was isolated and then assessed of quality, integrity and concentration with a NanoDrop spectrophotometer. Libraries for sequencing were created and then sequenced using Illumina NovaSeq 6000 at Shanghai Personal Biotechnology Company. Principal component analysis (PCA) was carried out based on gene expression data. Based on the criteria of |log2FoldChange| \u0026gt; 1 and adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, differentially expressed genes (DEGs) were identified and then visualized in a volcano plot. The software TopGO version 2.50.0 was utilized for conducting GO enrichment analysis on DEGs with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The GO terms containing significantly enriched DEGs were then identified to ascertain the primary biological functions linked to the DEGs. The Gene Set Enrichment Analysis (GSEA) was conducted on all genes, which were visualized in the pathway map for the enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs previously described, flow cytometry was utilized to detect peripheral T immune cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Following the behavioral experiments, rats were anesthetized and the peripheral blood was taken. Mononuclear cells were isolated with lymphocyte separation solution (P8360, Solarbio), some of which were used for Treg detection, and the others were treated with a cell stimulation cocktail (00-4975-93, eBioscience) for 6 hours and then used for the detection of Th1 and Th17 cells. First, antibodies against cell surface molecules, including anti-CD3-PerCP-Cy5.5 (201418, Biolegend), anti-CD4-FITC (11-0040-85, eBioscience) and anti-CD25-APC (17-0390-82, eBioscience), were used. Following a 30-minute incubation in darkness, the cells were fixed, permeabilized and then stained with antibodies against intracellular or nuclear factors, including anti-Foxp3-PE (12-4774-42, eBioscience), anti-IFN-γ-APC (50-7310-80, eBioscience) and anti-IL-17-PE (12-7177-81, eBioscience).\u003c/p\u003e \u003cp\u003eAs previously described, flow cytometry was utilized to detect microglial activation in brain [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The mPFC tissues were digested with collagenase IV to prepare a single-cell suspension. After myelin was removed via gradient density centrifugation with 30% and 70% Percoll separation solution, the microglial cell layer was isolated. Purified anti-rat CD32 (550271, BD Biosciences) was added to block the Fc receptor. Afterwards, surface staining was performed with the following reagents: Zombie NIR\u003csup\u003e\u0026trade;\u003c/sup\u003e Fixable Viability Kit (423105, Biolegend), anti-CD11b-PerCP-Cy5.5 (201819, Biolegend), anti-CD45-Pacific Blue (202225, Biolegend), anti-CD86-PE (12-0860-83, eBioscience), anti-MHC-II-APC (17-0920-82, eBioscience), and anti-CD200R-FITC (204905, Biolegend).\u003c/p\u003e \u003cp\u003eThe data were acquired via BD FACSCanto II flow cytometer (BD, USA) and then processed with FlowJo 10.8.1 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnzyme-linked immunosorbent assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBlood was centrifuged for collecting serum. The concentrations of IL-10 (JL13427, Jianglai Bio), IL-17 (JL20879, Jianglai Bio), IFN-γ (JL13241, Jianglai Bio) and TGF-β (JL13643, Jianglai Bio) in serum were analyzed according to the step-by-step instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eReal-time PCR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRNA of mPFC was extracted (9108Q, TakaRa), and concentration as well as purity of RNA were detected. Subsequently, the RNA was reverse transcribed to cDNA (RR047A, TaKaRa). And cDNA was then amplified by using real-time PCR master mix (RR820A, TaKaRa) and gene-specific primers. The LightCycler 480 II instrument (Roche Diagnostics GmbH, Germany) was used to conduct real-time PCR. The relative expression of a gene was calculated by 2\u003csup\u003e-ΔΔCt\u003c/sup\u003e. Four rats from each group were tested. The utilized primers: CD86 F: 5\u0026rsquo;-AGACATGTGTAACCTGCACCAT-3\u0026rsquo;, R: 5\u0026rsquo;-ACTTTTTCCGGTCCTGCCAA-3\u0026rsquo;; IL-1β F: 5\u0026rsquo;-CAGCTTTCGACAGTGAGGAGA-3\u0026rsquo;, R: 5\u0026rsquo;-TGTCGAGATGCTGCTGTGAG-3\u0026rsquo;; TNF-α F: 5\u0026rsquo;-CTCAAGCCCTGGTATGAGCC-3\u0026rsquo;, R: 5\u0026rsquo;-GGCTGGGTAGAGAACGGATG-3\u0026rsquo;; IL-6 F: 5\u0026rsquo;-TCCTACCCCAACTTCCAATGC-3\u0026rsquo;, R: 5\u0026rsquo;- TAGCACACTAGGTTTGCCGAG-3\u0026rsquo;; Arg-1 F: 5\u0026rsquo;-CCAGTATTCACCCCGGCTAC-3\u0026rsquo;, R: 5\u0026rsquo;-GTCCTGAAAGTAGCCCTGTCT-3\u0026rsquo;; TGF-β F: 5\u0026rsquo;-GACCGCAACAACGCAATCTA-3\u0026rsquo;, R: 5\u0026rsquo;-CGTGTTGCTCCACAGTTGAC-3\u0026rsquo;; GAPDH F: 5\u0026rsquo;-GGCACAGTCAAGGCTGAGAATG-3\u0026rsquo;, R: 5\u0026rsquo;-ATGGTGGTGAAGACGCCAGTA-3\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data analysis was carried out by using GraphPad Prism 9. All the data were normally distributed and had homogeneous variance; thus, the data were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\pm\\:S\\)\u003c/span\u003e\u003c/span\u003e). Independent two-sample \u003cem\u003et\u003c/em\u003e tests were used to compare the differences between two groups. One-way analysis of variance (ANOVA) was applied for comparisons among multiple groups. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndividual differences in the cognitive function of aged rats\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAttentional set-shifting task was used to detect the cognitive flexibility. Fewer trials are needed to reach the criterion, which represents better cognitive function. Compared with that of young rats, cognitive performance of aged rats was impaired, thus they needed more trials to achieve the desired performance level. Furthermore, the set-shifting performance of aged rats was more heterogeneous compared to young rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). With a standard deviation (SD) from the average performance in young rats as a control, the aged rats were divided into AU group and AI group. When the trials to criterion exceeded a SD from average number in young rats, it was considered an AI rat (n\u0026thinsp;=\u0026thinsp;9). All of the other aged rats were AU rats (n\u0026thinsp;=\u0026thinsp;11). The cognitive performance of AI rats was significantly worse than young rats and AU rats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe mPFC is the main brain area performing attentional set-shifting tasks in rodents. The dendritic spine density in mPFC was observed via Golgi staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-F). Compared to that in AU group, the dendritic spine density in AI group was dramatically lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome sequencing of the mPFC in AU and AI rats\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRNA was isolated from the mPFC of AU and AI rats for transcriptome sequencing. Principal component analysis (PCA) demonstrated an obvious difference in gene expression between AU and AI rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A volcano plot demonstrated that 139 DEGs were identified, including 128 up-regulated genes and 11 down-regulated genes, in AI rats compared with AU rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Further GO analysis demonstrated that the DEGs were closely enriched in innate immune response, positive regulation of cytokine production and inflammatory response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Gene set enrichment analysis (GSEA) demonstrated that glial activation, microglial activation, the neuroinflammatory response and the TNF-mediated signaling pathway were upregulated in AI rats compared with AU rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMicroglial activation differed between AU and AI rats\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo more specifically assess microglial activation, flow cytometry was used to detect the marker for activated microglia in the mPFC. CD86 and MHC-II have been reported as proinflammatory markers, while CD200R is recognized as anti-inflammatory marker [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among microglia (CD11b\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003eint\u003c/sup\u003e), there were more microglia expressing CD86, MHC-II and CD200R in both AI and AU rats than in young rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared with those in AU rats, the number of CD86-positive microglia was greater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the number of MHC-II-positive microglia was greater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas CD200R-positive microglia was lower in AI rats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting an increase in inflammatory activity while a reduction in anti-inflammatory activity in AI rats.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression of central inflammatory factors in AU and AI rats\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter microglia are activated, the expression of CD86 and MHC-II promotes neuroinflammation by increasing the secretion of inflammatory factors, thus resulting in aggravated neuroinflammation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the expression of CD200R inhibits the neuroinflammation and reduces the production of proinflammatory factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe expression of inflammation-related factors in mPFC were detected with RT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared to those in AU rats, AI rats exhibited higher levels of proinflammatory markers CD86 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), IL-1β, TNF-α and IL-6 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while showing lower levels of anti-inflammatory markers Arg-1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and TGF-β (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eThe percentages of Treg, Th1 and Th17 cells in the peripheral blood of AU and AI rats were different\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong CD4\u003csup\u003e+\u003c/sup\u003e T cells (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e), CD25\u003csup\u003e+\u003c/sup\u003eFoxp3\u003csup\u003e+\u003c/sup\u003e represents Treg cells, IFN-γ\u003csup\u003e+\u003c/sup\u003e represents Th1 cells and IL-17\u003csup\u003e+\u003c/sup\u003e represents Th17 cells. The percentages of Treg, Th1 and Th17 in CD4\u003csup\u003e+\u003c/sup\u003e T cells were greater in both AI and AU rats than in young rats. Compared to those in AU group, the percentage of Treg was lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the percentages of Th1 and Th17 were greater in AI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSerum inflammatory factor levels differed between AU and AI rats\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the peripheral blood, Treg cells secrete anti-inflammatory IL-10 and TGF-β, Th1 cells and Th17 cells secrete proinflammatory factors IFN-γ and IL-17 respectively. These factors in serum were detected by ELISA. In aged rats, IFN-γ and IL-17 increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, C, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), IL-10 and TGF-β also increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, G, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with those in AU rats, IFN-γ and IL-17 increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, D, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas IL-10 decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and TGF-β decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), in AI rats.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mPFC is important for cognitive function in rodents [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Injury to the mPFC can affect cognitive flexibility. Cognitive flexibility refers to the ability to cope with sudden alterations in the environment by effectively updating internal representations and changing behavioral responses and is a part of executive function [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Previous studies on rats have shown that cognitive flexibility is highly susceptible to aging [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The attentional set-shifting task, which is an important indicator for evaluating cognitive flexibility, is sensitive to mPFC damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, aged rats showed individual differences in attentional set-shifting task. Compared with performance of young rats, the AI and AU rats could be distinguished, which is consistent with the previous result [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The acquisition of initial rules remains unaffected, while the ability to adjust a shift is selectively impaired, thus reflecting the differences in mPFC functional activity between the two groups of rats [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To explore the mechanism of individual differences, transcriptome sequencing of the mPFC was performed. There were differences in the pathways of microglial activation and the inflammatory response between AU and AI rats.\u003c/p\u003e \u003cp\u003eMicroglia, which is the main immune cells in brain, make up around 10% of the brain's cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Under physiological conditions, microglia have small cell bodies and maintain homeostasis of the nervous system by continuously monitoring the surrounding environment. When microglia are activated, their morphology rapidly changes, which is manifested as cell body enlargement; additionally, gene expression also changes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. During aging, microglia are activated, and are expressed CD86 and MHC-II, which interact with T cells entering the brain to release inflammatory factors and exacerbate neuroinflammation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], are significantly upregulated. CD200R on the surface of microglia is a receptor for CD200 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Interaction with neuronal CD200 can not only inhibit microglial activation but can also suppress Ras-ERK and Ras-PI3K pathways to decrease inflammatory molecules [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, compared with those in young rats, the number of microglia expressing CD86 or MHC-II increased in aged rats, which is consistent with previous research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The expression of CD200R in the microglia with age also increased in an attempt to inhibit microglial activation and proinflammatory factors production. Interestingly, compared with those of AU rats, the mPFC of AI rats had more microglia expressing CD86 or MHC-II and fewer microglia expressing CD200R, which contributed to the increase in IL-1β, TNF-α and IL-6. The balance between different phenotypes of microglia is crucial in the aging process. During the early period of inflammation, the initial response of activated microglia is proinflammatory. Subsequently, anti-inflammatory microglia also increase to inhibit the inflammatory response, which is necessary for maintaining balance in the body [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, during long-term chronic inflammation, microglia are continuously activated, and inflammatory factors continue to be produced, thus ultimately exacerbating neuroinflammation and cognitive impairment.\u003c/p\u003e \u003cp\u003eImmune dysregulation and inflammation in the circulation play important roles in aging. Compared with those in young individuals, CD4\u003csup\u003e+\u003c/sup\u003e T cells in elderly individuals produce more Th17 cell-related proinflammatory factors, including IL-6 and IL-17A, which drive the body to exhibit an inflammatory state [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, inflammatory aging is not solely reflected in an increase in proinflammatory markers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Treg cells are involved in the body's protection and can produce IL-10 to inhibit inflammation and prevent excessive immune responses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recently, single-cell RNA sequencing analysis from human peripheral blood showed the proportion of Tregs increased with age [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, in old mice, three CD4\u003csup\u003e+\u003c/sup\u003e T-cell subsets (exhausted, anti-inflammatory regulatory T cells, and proinflammatory cytotoxic) were shown to gradually accumulate with age [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consistent with these results, in our study, aged rats exhibited increases in the levels of proinflammatory Th17 and Th1 cells, as well as inflammatory molecules IL-17 and IFN-γ. Similarly, anti-inflammatory Treg cells, as well as anti-inflammatory factors IL-10 and TGF-β also increase with age, which reflects the body's adaptive response to proinflammatory stimuli that attempt to suppress inflammation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. More importantly, compared with AU rats, AI rats have more Th17 and Th1 cells, which produce more IL-17 and IFN-γ, whereas fewer Tregs as well as TGF-β and IL-10 are unable to suppress aging-related inflammation. This may be the reason for severe inflammation in the body of AI rats.\u003c/p\u003e \u003cp\u003eImmune and inflammatory levels are key factors contributing to individual differences in the elderly, which can predict the development of aging-associated diseases. From an evolutionary perspective, inflammation has been regarded as an adaptation/remodeling because it may activate anti-inflammatory responses to counteract aging-associated pro-inflammatory context. For example, centenarians have numerous anti-inflammatory factors in the circulation, such as IL-10, TGF-β1 and IL-1 receptor antagonists [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The anti-inflammatory state is activated to down-regulate the elevated inflammatory factors, such as C-reactive protein, IL-6 and IL-18 [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This implies that immune system will have an adaptive/maladaptive change in the process of aging. The health of an individual depends on the adaptive/maladaptive consequence. It is determined by the ability to adapt and reshape harmful stimuli [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, chronic low-grade inflammation plays a significant role on aging-associated cognitive decline. Aging disrupts the equilibrium of peripheral Th17/Treg cells, thus resulting in elevated inflammation levels. Inflammation leads to heightened microglial activation in the mPFC, thus ultimately contributing to cognitive decline. Our study underscores the association between peripheral and central immune state and aging-associated cognitive heterogeneity. Additionally, our research also indicates that healthy aging is not a sustained state of youthfulness but rather an adaptive reshaping of the body's aging state. The promotion of the Th17/Treg balance, rather than restoring it to a youthful state, may become a therapeutic target for aging-associated cognitive decline. Nevertheless, this study still has some limitations. How CD4\u003csup\u003e+\u003c/sup\u003eT cells activate microglia, and whether regulating the ratio of Th17/Treg can influence the activation of microglia, still need further research and exploration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLian Yu, Miao-Miao Liu and Yan-Li Li conceived the study and designed the experiments. Rui Wang, Xiao-Rong Yang and Jun-Hong Guo guided the experimental design. Lian Yu, Miao-Miao Liu and Mei-Qi Guan performed the experiments. Rui Wang, Xiu-Min Zhang, Hong Gu and Qiang Fu carried out data analysis. Lian Yu, Miao-Miao Liu, Xiao-Rong Yang and Shu-Fen Wu carried out image processing. Lian Yu, Miao-Miao Liu and Mei-Qi Guan wrote the manuscript. Jing-Jing Wei, Jun-Hong Guo and Yan-Li Li reviewed and edited the manuscript. All authors have agreed the manuscript to be published. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by National Natural Science Foundation of China, grant number 82101641; the Natural Science Foundation of Shanxi Province, China, grant number 20210302124173; Open Fund from Key Laboratory of Cellular Physiology (Shanxi Medical University), Ministry of Education, China, grant number KLMEC/SXMU-202010; National major R\u0026amp;D projects of China-Scientific technological innovation 2030, grant number 2021ZD0201801.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to give thanks to all people participating in the study for their support and cooperation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Ethics Committee of First Hospital of Shanxi Medical University (DWLL-2023-006).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHay M, Barnes C, Huentelman M, Brinton R, Ryan L. 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Semin Immunopathol. 2020;42:521\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"immunity-and-ageing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iage","sideBox":"Learn more about [Immunity \u0026 Ageing](http://immunityageing.biomedcentral.com/)","snPcode":"12979","submissionUrl":"https://submission.nature.com/new-submission/12979/3","title":"Immunity \u0026 Ageing","twitterHandle":"@ImmunAllergyBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aging-associated cognitive decline, Cognitive Heterogeneity, Microglial activation, Th17/Treg cells, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-4743495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4743495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive decline is a critical hallmark of brain aging. Although aging is a natural process, there is significant heterogeneity in cognition levels among individuals; however, the underlying mechanisms remain uncertain. In our study, we classified aged male Sprague-Dawley rats into aged cognition-unimpaired (AU) group and aged cognition-impaired (AI) group, by using an attentional set-shifting task. The transcriptome sequencing results of medial prefrontal cortex (mPFC) demonstrated significant differences in microglial activation and inflammatory response pathways between the two groups. Specifically, compared to AU rats, AI rats exhibited a greater presence of CD86-positive microglia and major histocompatibility complex class II (MHC-II)-positive microglia, along with elevated inflammatory molecules, in mPFC. Conversely, AI rats exhibited a reduction in the amount of microglia expressing CD200R and the anti-inflammatory molecules Arg-1 and TGF-β. Additionally, peripheral blood analysis of AI rats demonstrated elevated levels of Th17 and Th1 cells, along with proinflammatory molecules; however, decreased levels of Treg cells, along with anti-inflammatory molecules, were observed in AI rats. Our research suggested that peripheral Th17/Treg cells and central microglial activation were associated with cognitive heterogeneity in aged rats. This may provide a new target for healthy aging.\u003c/p\u003e","manuscriptTitle":"Peripheral CD4 (+) T Cell Immunity and Brain Microglial Activation Associated with Cognitive Heterogeneity in Aged Rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:22:33","doi":"10.21203/rs.3.rs-4743495/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-19T09:26:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-18T17:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54507006429049034983953605858575039692","date":"2024-09-30T23:32:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248777615699610328725293726757114962253","date":"2024-09-30T20:41:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-09T17:18:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183356566240729627119697470688075068809","date":"2024-07-22T18:09:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146502771687559425414902926401790076998","date":"2024-07-17T15:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-17T14:10:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T06:15:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-17T06:13:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Immunity \u0026 Ageing","date":"2024-07-15T14:12:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"immunity-and-ageing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iage","sideBox":"Learn more about [Immunity \u0026 Ageing](http://immunityageing.biomedcentral.com/)","snPcode":"12979","submissionUrl":"https://submission.nature.com/new-submission/12979/3","title":"Immunity \u0026 Ageing","twitterHandle":"@ImmunAllergyBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04086b80-a7cb-4638-af0e-2b2265db085a","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T15:59:16+00:00","versionOfRecord":{"articleIdentity":"rs-4743495","link":"https://doi.org/10.1186/s12979-024-00486-5","journal":{"identity":"immunity-and-ageing","isVorOnly":false,"title":"Immunity \u0026 Ageing"},"publishedOn":"2024-11-14 15:56:58","publishedOnDateReadable":"November 14th, 2024"},"versionCreatedAt":"2024-08-17 01:22:33","video":"","vorDoi":"10.1186/s12979-024-00486-5","vorDoiUrl":"https://doi.org/10.1186/s12979-024-00486-5","workflowStages":[]},"version":"v1","identity":"rs-4743495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4743495","identity":"rs-4743495","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}