Comprehensive analysis of the miRNA-mRNA pathological regulatory network of intestinal CD4+ T cells in Parkinson's disease

doi:10.21203/rs.3.rs-2717723/v1

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Comprehensive analysis of the miRNA-mRNA pathological regulatory network of intestinal CD4+ T cells in Parkinson's disease

https://doi.org/10.21203/rs.3.rs-2717723/v1

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Infiltration of CD4+ T cells was found in brain tissue samples from PD patients, suggesting their involvement in developing central nervous system (CNS) disease. The idea of the gut-brain axis further corroborates intestinal T cells' activation as the central immune response initiation. However, the specific factors and molecular pathways regulating intestinal T-cell activation are unclear. We used the GSE156287 and GSE145814 datasets from the GEO database to analyze and obtain the miRNAs, which are aberrantly expressed in intestinal CD4+ T cells in PD patients and predict their regulatory target mRNAs. Further, combined with the GSE174473 dataset of CD4+ T cells sequencing in PD patients, we finally clarified the aberrant genes expressed in CD4+ T cells from the intestine of PD patients and constructed a miRNA-mRNA regulatory network.

The highlight of our findings showed pathways, networks, biological functions and key molecules potentially involved in the miRNA-mediated functional effects in CD4+ T cell from intestin of PD patients. The hsa-miR-3180-3p mediated CBX8, etc were determined as most effective in enhancing T cell survial. PEG10 and, etc regulated by hsa-miR-20a-3p targets were possibly involved in T cell differentiation.The JPT2 regulated by hsa-miR-1281 were involved in influencing T cell infiltration;

The discovery of this interaction between miRNA and mRNA in CD4+ T cell has important implications for understanding the intestinal initial of PD pathological molecular and anti-inflammation of T cell activation.

Parkinson's disease

CD4+T cell

miRNA-mRNA regulatory network

immunity

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's. The abnormal accumulation and misfolding of α-synuclein (α-Syn), a neuro-specific protein that regulates synaptic function, is the main feature of PD[1]. This misfolded α-synuclein activates microglia. Currently, there is conclusive evidence that lymphocytes can enter the brain through the blood-brain barrier, and infiltration of CD8+ T cells and CD4+ T cells have been detected in autopsy specimens from PD patients and brain tissue samples from MPTP murine models. Moreover, it has been reported that only the addition of CD4+ T cells to α-synuclein-treated microglia induced a robust immune response, suggesting that the synergistic effect of α-synuclein on microglia and CD4+ T cells is crucial in triggering the immune response[2]. The process that microglia clear dopaminergic neurons, which secret α-synuclein to protect the central nervous system, and the absence of T cells may lead to a consequence that microglia can not clear diseased dopaminergic neurons in time, then leads to more neuronal degeneration.

In contrast, the different differentiation of microglia and T cells can have different effects on the subsequent development of the disease. For example, TH1 and TH17 cells help create M1-type microglia, which damage dopaminergic neurons, while TH2 and Treg cells help create M2-type microglia, which preserve neurons[3]. In addition, it has been recognized that gut flora can significantly modulate brain-gut axis interactions through immune responses. Dysregulation of the brain-gut-flora axis in PD may be related to the gastrointestinal manifestations that commonly precede motor symptoms and to the pathogenesis of PD, supporting the hypothesis that the pathological process spreads from the gut to the brain[4]. Dysregulation of the gut flora may affect the activation and differentiation of CD4+ T cells, which induces systemic inflammation.

Many studies have reported that some kind of flora can target miRNAs to activate signalling pathways and modulate immune responses[5]. Many studies have identified regulatory abnormalities of miRNAs in disease, highlighting their potential as diagnostic and prognostic biomarkers in immune-related diseases. Moreover, individual miRNAs can target different mRNA in complex regulatory networks and have great potential to control immune function and inflammatory pathways[6]. This topic suggests that dysregulation of intestinal flora activates miRNAs in CD4+ T cells, which regulates mRNAs and thus affects CD4+ T cells. Then it is essential to analyze the miRNA-mRNA network of intestinal T cells in PD to understand the direction of CD4+ T cells toward different differentiation and activation statuses to study PD development.

Data processing

GEO is a gene expression database, including gene microarray and high-throughput sequencing data. According to the study: whether there are specific miRNAs in the intestine of PD patients involved in activating specific genes in CD4+ T cells, we selected three datasets (GSE156287, GSE145814, GSE174473) from the GEO database for analysis. GSE156287 is a collection of miRNA samples from resting CD4+ T cells obtained after anti-CD 3/anti-CD28 or IFN I stimulation for 3h, 6h and 24h or no stimulation (0h). This dataset was used to analyze the expression of miRNA in CD4+ T cells which were stimulated with different antibodies and times. GSE145814 was used to obtain miRNA expression in Routine colon biopsy of tissue from 13 PD patients and 17 healthy controls. This dataset was used to obtain differential miRNA expression in the intestine of PD patients. The combined GSE156287 and GSE145814 datasets can be used to screen miRNAs co-expressed by both intestinal and CD4+ T cells from PD patients. GSE174473 is RNA sequencing of peripheral CD4 memory T cell subsets from 34 PD patients and 19 healthy controls. This dataset can be used to analyze the expression of differential genes in CD4+ T cells from PD patients.

Screening of differential miRNAs and differential genes

Based on the GSE156287 data, we used LianChuan BioCloud platform to plot box-line plots, principal component analysis (PCA) and Uniform Manifold Approximation and Projection(UMAP), allow us visually compare the different miRNA expression in CD4+ T cells from each sample as a whole. Through the Hiplot platform, the differential miRNAs obtained after anti-CD 3/anti-CD28 or IFN I stimulation for 3h, 6h and 24h, respectively, were screened using the P<0.05 and |log2FC|>=1 condition to produce volcano plots and heat maps. Through the Lianchuan BioCloud platform, based on the two sets of data, miRNAs expressed in the intestine of PD patients and differential miRNAs in CD4+ T cells that had been screened, we drawn Venn diagrams to identify differential miRNAs co-expressed in both the intestine of PD patients and CD4+ T cells.

The target genes of differentially expressed miRNAs in the intestinal CD4+ T cells of PD patients were predicted by four databases: miRDB, mirecords, mirtarbase, and starbase. Based on GSE174473 data, the differential genes in CD4+ T cells of PD patients were screened by the Hiplot platform using the condition of P<0.05 and |log2FC|>=1, and volcano and heat maps were drawn. We draw Venn diagrams through the Lianchuan BioCloud platform based on the differential expression of miRNA target genes in intestinal CD4+ T cells in PD patients has been predicted and differential genes in CD4+ T cells of PD patients that have been screened to identify differential genes co-expressed by intestinal and CD4+ T cells in PD patients.

GO enrichment analysis and KEGG pathway analysis of differential genes.

DAVID is a publicly available database integrating biological data and analysis tools for online gene and pathway function annotation. GO and KEGG enrichment analysis of identified differential genes co-expressed by intestinal and CD4+ T cells in PD patients is performed using DAVID. GO is a bioinformatics analysis tool for gene annotation and analysis of their biological processes and functions, and GO functional enrichment analysis includes three modules: biological process (BP), cellular composition (CC) and molecular function (MF). Kegg database allows the analysis of relevant signalling pathways for a large amount of molecular data obtained by high-throughput experimental techniques. In this analysis, P<0.05 was considered as differential genes significantly enriched in function or KEGG pathway. Gene functions and pathways significantly enriched by differential genes were visualized by Microbiot and Hiplot platforms.

MiRNA-mRNA regulatory network and selection of hub genes

Cytoscape is a software platform dedicated to bioinformatics analysis to visualize molecular networks of action and integrate modular networks. Cytoscape software was used to map the regulatory network of differential miRNAs with differential genes in intestinal CD4+ T cells of PD patients by importing miRNA and mRNA action network data. The six MCC, Degree, DMNC, MNC, EPC, and Closeness algorithms were integrated using the cytohhubba plugin to obtain hub genes.

To determine the differential miRNAs co-expressed by intestinal and post-activated CD4+ T cells in PD patients

Based on the GSE156287 dataset, we grouped the samples according to CD4+ T cells stimulated by different times in 4 groups, namely Control group (stimulation 0h), Exp-3 (stimulation 3h), Exp-6 (stimulation 6h) and Exp-24 (stimulation 24h). The expression of miRNAs among the samples was visually reflected by the box line plot, PCA and UMAP with variability. Up-regulated differential miRNAs and down-regulated differential miRNAs were obtained from the volcano plot at different times of stimulation. A total of 5 down-regulated differential miRNAs were obtained in the Exp-3 group, 18 down-regulated differential miRNAs were obtained in Exp-6, and 2 up-regulated differential miRNAs were obtained in Exp-24. Thus, a total of 23 down-regulated differential miRNAs and 2 up-regulated miRNAs were screened in CD4+ T cells miRNAs, and the expression of differential miRNAs in the samples was visualized by heat map. Combined with the differential miRNAs in the intestinal of PD patients already obtained from the GSE145814 dataset, 21 differential miRNAs were co-expressed in the intestinal and CD4+ T cells of PD patients by the Venn diagram.

Identification of differential genes co-expressed by intestinal and activated CD4+ T cells in PD patients

Based on the GSE174473 dataset, as shown in the volcano plot, we screened the differential genes in CD4+ T cells of PD patients by the condition of P<0.05 and |log2FC|>=1. A total of 275 up-regulated differential genes and 298 down-regulated differential genes were obtained. The expression of differential genes in the samples was visualized by heat map. Four databases, miRDB, mirecords, mirtarbase, and tarbase, were used to predict the target genes of 21 co-expressed differential miRNAs in intestinal and CD4+ T cells of PD patients, and a total of 3731 target genes were obtained. Combined with the screened differential genes in CD4+ T cells of PD patients, 53 co-expressed differential genes in the intestinal and CD4+ T cells of PD patients were obtained from the Venn diagram.

Determine GO function and KEGG pathway enrichment analysis of differential genes

We use DAVID to perform GO functional enrichment and KEGG pathway enrichment analysis of 53 co-expressed differential genes. The results revealed that the differential genes were mainly involved in axon guidance pathway, in biological processes such as vesicle-mediated transport reaction and activation of GTPase activity, in cell composition such as cell cortex, cytoplasm and plasma membrane, and in molecular functions such as small GTPase binding.

Differential genes are involved in the regulation of T-cell differentiation and infiltration

In this study, we use Cytoscape software to construct the interaction network of differential miRNAs and differential genes co-expressed in intestinal and CD4+ T cells of PD patients. The results showed a close interaction between miRNAs and mRNAs. Cytohhubba plugin was used to synthesize six algorithms: MCC, Degree, DMNC, MNC, EPC, and Closeness to obtain the key interaction relationships between miRNA and mRNA.

Primitive unactivated CD4+ T cells can differentiate into different subtypes according to different stimuli and signals, which can be classified and summarized into 3 categories: TH1 cells: secrete IL-2, IFN-γ and TNF-α; TH2 cells: secrete IL-4, IL-5 and IL-13; TH17 cells: secrete IL-17 and IL-22, etc. T cells differentiate to produce cells with different functions; TH17 cells secrete inflammatory factors, while regulatory T cells(Treg) secrete IL-4 to exert anti-inflammatory effects. Therefore, the different differentiation of T cells is crucial for maintaining the homeostasis of the intracellular and extracellular environment. After the above analysis and screening, the differential genes common to PD patients' intestinal and activated CD4+ T cells were summarized. By establishing miRNA and mRNA regulatory networks and selection of hub genes, we found that miRNA-regulated differential genes can be broadly classified into the following categories: T cell survival, T cell differentiation, T cell infiltration, and T cell activation.

In this project, we identified differential genes co-expressed by intestinal and CD4+ T cells in PD patients. We classified miRNAs and their regulated mRNAs and concluded that hsa-miR-3180-3p-regulated CBX8, TP73, BICDL1, GPSM1 are involved in affecting T cell survival; hsa-miR-20a-3p-regulated PEG10, TPM2, NECTIN4, OTUD1, NEK9, PGM3, POU2AF1, MAP1B, ASNS, RAPH1, PLXND1 are involved in influencing T cell differentiation and infiltration; hsa-miR-1281-regulated JPT2, MPZ, SLC43A2, SIGLEC9, NCKAP1, ZNF556 are involved in affecting T cell infiltration; hsa-miR-574-5p-regulated RPH3A, EFNB1, TBC1D16, SEMA6C, CRISPLD2, GPR37L1, FOSL1, ADAT2 are involved in affecting T cell viability and differentiation.

T cell activation and infiltration are crucial for the development of neurodegenerative diseases such as Parkinson's disease. It has been shown that overexpression of α-synuclein in vivo induces elevated expression of major histocompatibility complex II (MHCII) and increased proliferation of CD4+ T cells. This suggests that α-synuclein may act as an antigen to elicit an immune response. However, increasing monomeric α-synuclein expression alone does not induce the immune response, and experiments have shown that the immune response acts only on aggregated α-synuclein. When α-synuclein interacted with microglia and migrated to the lysosome, α-synuclein alone did not activate microglia or elicit an inflammatory response, and CD4+ T cells had to be added to α-synuclein-treated microglia to elicit a strong immune response, suggesting that α-synuclein triggers the immune response The involvement of CD4+ T cells is crucial in the process of the α-synuclein-triggered immune response[2]. In our results, has-miR-3180-3p-regulated CBX8 is involved in cellular communication and immune response regulation and affects CD4+ T cell depletion[7]; has-miR-3180-3p-regulated GPSM1, which encodes the activator of G-protein signalling 3 (AGS3), regulates G-protein signalling in the immune system and thus regulates the activation process of T cells[8]; has-miR-20a-3p-regulated aberrant expression of PEG10 increased T cell size and promoted T cell proliferation and infiltration[9]; has-miR-20a-3p-regulated TPM2 was significantly and negatively correlated with the degree of CD8+ T cell infiltration[10]; the mutation of PGM3 which regulated by hsa-miR-20a-3p have recently been shown to lead to disorders of glycosylation, lymphopenia and impairment in T cell proliferation[11]; has-miR-1281-regulated NCKAP1 is significantly associated with the level of T cell infiltration[12]. The above mRNAs are involved in T-cell activation and infiltration, and changes in the expression of these mRNAs produced under miRNA regulation may lead to impaired T-cell activation and reduced infiltration, which may eventually lead to an immune response by T cells absent of α-synuclein-activated microglia, and the inability of microglia to clear α-synuclein-expressing neurons in a timely manner. This may lead to the spread of α-synuclein, with undesirable consequences; alternatively, altered mRNA expression may also lead to excessive activation and infiltration of T cells, resulting in an excessive immune response by microglia, clearing healthy neurons and advancing the pathogenesis of Parkinson's disease.

Different differentiation outcomes of T cells play a crucial role in the development of neurodegenerative diseases such as Parkinson's disease. For example, when CD4+ T cells differentiate into TH1 and TH17 cells will cause damage to dopaminergic neuronal cells, whereas differentiation into TH2 and Treg cells protects neurons. hsa-miR-20a-3p-regulated OTUD1 is a deubiquitinase that drives (Notch1770-ICD) NICD signaling, the signal could promote Th17 and Th1 cell differentiation and function[13]. Signalling (Sema) 4A is a transmembrane glycoprotein; Sema4A has a key role in regulating Th1 and Th2 differentiation. PlexinD1, regulated by hsa-miR-20a-3p, is a receptor for Sema4A, and PlexinD1 induces differentiation of T cell into TH1 cell while reducing differentiation into Th2 and Th17, and Sema4A- PlexinD1 signalling acts as a negative regulator of Th1 differentiation but is also a key mediator of Th2 and Th17 differentiation, suggesting that dysregulation of this axis may be implicated in the pathogenesis of CD4+ T cell-mediated disease[14]. Has-miR-574-5p-regulated EFNB1 is highly expressed in Th1, and Th17 cells, and T cells expressing EFNB1 are highly expressed in multiple sclerosis (MS) lesions found in immune cell infiltration. It has been shown that EFNB1 expression is associated with Th-cell differentiation and migration to sites of inflammation[15]. Aberrant expression of these mRNAs leads to differentiating CD4+ T cells into TH1 and TH17. It has been reported that M1-type microglia may act on TH1 and TH17 cells and activated TH1 and TH17 cells in turn, keep microglia M1-type through IFN-γ production, M1-type microglia release NO and O2, and these oxidative stress (OS) compounds damage dopaminergic neurons, M1-type microglia also release inflammatory factors (e.g. INF-α and IL-1β) that exacerbate inflammatory responses[3]. These mRNAs, which regulate the differentiation of T cells to TH1 and TH17, are abundantly expressed, exacerbating the neuroinflammatory response of M1 microglia and advancing the pathogenesis of neurodegenerative diseases such as Parkinson's disease.

In addition, has-miR-20a-3p-regulated Pou2af1 mutations lead to impaired T cells, such as impairment in cytokine production and T follicular helper (Tfh) differentiation[16]. Has-miR-20a-3p-regulated MAP1B upregulation leads to a lower proportion of plasma cells, CD8+ T cells and T cell follicular helper cells, suggesting that MAP1B may be involved in regulating T cell differentiation[17]. It has been reported that asparagine synthetase (Asns), whose expression peaks in effector T cells, may determine the outcome of T cell differentiation, leading to a decrease in the differentiation of TH2 and Treg cells[18]. In contrast, in the MPTP-induced PD immunoprotective model, firstly, TH2 and Treg cells release IL-4, IL-10 and TGF-β to maintain microglia in a neuroprotective M2 phenotype, and secondly, Treg cells inhibit microglial activation and release of ROS induced by misfolded α-synuclein. TH2 and Treg cells may also protect neurons through cell contact mechanisms or the release of neurotrophic factors such as BDNF. The damaged neurons themselves also have mechanisms that may cause M2 microglia to emerge or release chemokines to recruit more TH2 and Treg cells[3]. The expression of these mRNAs, which regulate the differentiation of T cells to TH2 and Treg cells, may be reduced when the above mRNAs are regulated by miRNAs, making microglia unable to maintain the M2 phenotype and with reduced neuroprotective capacity. the different differentiation outcomes of T cells have significant implications for neurodegenerative diseases, and therefore these mRNAs regulating T cell differentiation have great clinical potential as key targets for the treatment of diseases. great clinical potential.

We believe that biological processes such as activation and differentiation of T cells in the gut are closely related to the intestinal flora, that the intestinal flora can maintain a delicate balance between pro-inflammatory and anti-inflammatory mechanisms, and that specific members of the community can enhance the production of anti-inflammatory Treg or pro-inflammatory T helper cells 17 (TH17). In addition to the intestinal flora itself, its metabolites also influence the activation, differentiation and other important biological processes of T cells in the intestine. The metabolic by-products of the flora can be sensed by immune system cells and affect the balance between pro-inflammatory and anti-inflammatory cells. It has been reported that short-chain fatty acid (SCFA) butyrate produced by symbiotic microorganisms during starch fermentation in the mouse intestine promotes the extracellular production of Treg cells. In addition to butyrate, peripheral nascent Treg cell production was also enhanced by propionate, another microbial source of SCFA. These results suggest that flora metabolites mediate communication between the symbiotic microbiota and the immune system, affecting the balance between pro-inflammatory and anti-inflammatory mechanisms. However, evidence for specific factors triggering intestinal T-cell activation and differentiation remains to be explored[19].

Finally, our analysis focuses on constructing a network between miRNA and mRNA of intestinal CD4+T cells in PD and further refines the molecular targets of T cell activation, differentiation and other effects to point the way to mechanistic studies for T cell-mediated progression of Parkinson's disease.

Auther contributions

Conceptualization, Chuanxi Tang; Methodology, Xinyu Fan; Writing-Original Draft, Kaiquan Shao, Zipeng Wang, Ruiao Sun; Statistical analyses, Kaiquan Shao, Xinyu Fan; Writing-Reviewing & Editing, Chuanxi Tang; Funding Acquisition, Chuanxi Tang; Supervision, Chuanxi Tang.

Funding

National Natural Science Foundation of China (grant no: 82101263, to CX Tang); Jiangsu Province Science Foundation for Youths (BK20210903, to CX Tang); Research Foundation for Talented Scholars of Xuzhou Medical University (RC20552114, to CX Tang)

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No competing interests reported.

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Comprehensive analysis of the miRNA-mRNA pathological regulatory network of intestinal CD4+ T cells in Parkinson's disease

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