Comprehensive Analysis of the miRNA-mRNA Pathological Regulatory Network of Intestinal CD4 + T Cells in Parkinson’s Disease

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 the intestine of PD patients. The hsa-miR-3180-3p mediated CBX8, etc. were determined as most effective in enhancing T cell survival. PEG10, 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.


Introduction
Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease. Most people with PD exhibit many non-motor manifestations. Among the non-motor manifestations, gastrointestinal dysfunction is particularly important as a potential early biomarker of PD, as they are prevalent in diagnosed patients and occur much earlier than motor symptoms (Campos-Acuna et al. 2019). On the other hand, abnormal accumulation and misfolding of alpha-synuclein (α-Syn) is also a major feature of PD (Liu et al. 2011). This misfolded α-synuclein activates microglia. There is now conclusive evidence that lymphocytes can cross the blood-brain barrier into the brain, with infiltration of CD4 + T cells detected in post-mortem specimens from PD patients and in brain tissue samples from MPTP mouse models (Harms et al. 2013). The α-synuclein produced in 1 3 as the gastrointestinal disorders that occur early in PD, suggesting that the CD4 T cells that activate our microglia may be of intestinal origin. It has been reported in the literature that only the addition of CD4 + T cells to α-synuclein-treated microglia induces a robust immune response, suggesting that the synergistic effect of α-synuclein on microglia and CD4 + T cells is key to triggering an immune response (Harms et al. 2013).
There were reports about increased type-1 IFN signaling in both post mortem human Parkinson's disease samples and in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model, which implicate for a deleterious role for the type-1 IFNs as key modulators of the early neuroinflammatory response in Parkinson's disease (Main et al. 2016). Previous studies have demonstrated that cGAS/STING IFN-I signaling mediates neuroinflammation in PD pathology (Chen et al. 2022a). There were reports that the ability of MPTP to induce dopaminergic degeneration in the enteric nervous system is valuable to investigate and understand the involvement of the whole gastrointestinal tract as a typical non-motor symptom observed in PD patients (Goetze and Woitalla 2008). The gut can initiate immune programs via the cGAS-STING-IFN-I axis (Erttmann, et al. 2022). This means that the intestines of PD patients are stimulated by IFN and activate T cells. After IFN activates T cells, in addition to infiltrating the intestine, T cells will spread along the brain-gut axis to the brain, and the circulating effector group of cytotoxic CD4 + T cells exhibits the characteristics of enhanced metabolic activity (Maehara et al. 2020), so it may have a more profound effect on the brain.
Many studies have reported that some kind of flora can target miRNAs to activate signaling pathways and modulate immune responses (Yu et al. 2017). 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 (Dosil et al. 2022). 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 3 h, 6 h, and 24 h or no stimulation (0 h). 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 Bio-Cloud 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 miR-NAs obtained after anti-CD 3/anti-CD28 or IFN I stimulation for 3 h, 6 h, and 24 h, 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 signaling 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.

The Predicted Docking Sites of miRNA to mRNA
TargetScan is a software for predicting miRNA binding sites, which is very effective for predicting miRNA binding sites in mammals. We used TargetScan to predict the docking sites of miRNA to mRNA. D Volcano plot of differential miRNA in CD4 T cells stimulated with antibodies compared to control groups at 3, 6, and 24 h (P < 0.05, |log2FC|≥ 1). E Heat map of differential miRNA (P < 0.05, |log2FC|≥ 1)

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 0 h), Exp-3 (stimulation 3 h), Exp-6 (stimulation 6 h), and Exp-24 (stimulation 24 h). The expression of miRNAs among the samples was visually reflected by the box-line plot, PCA, and UMAP with variability. Upregulated differential miRNAs and downregulated differential miRNAs were obtained from the volcano plot at different times of stimulation. A total of 5 downregulated differential miRNAs were obtained in the Exp-3 group, 18 downregulated differential miRNAs were obtained in Exp-6, and 2 upregulated differential miRNAs were obtained in Exp-24. Thus, a total of 23 downregulated differential miRNAs and 2 upregulated 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 (Fig. 1).

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  Identification of CD4 T cell differential genes in PD patients. A Volcano plot of differential genes in CD4 T cells (P < 0.05, |log2FC|≥ 1). B Heat map of differential genes and samples in CD4T cells (P < 0.05, |log2FC|≥ 1). C Wayne diagram of all miRNAs in gut versus differential miRNAs in CD4T cells. D Wayne diagrams of twenty-one differential miRNA-predicted target genes versus differential genes in Parkinson's CD4 T cells PD patients by the condition of P < 0.05 and |log2FC|≥ 1. A total of 275 upregulated differential genes and 298 downregulated differential genes were obtained. The expression of differential genes in the samples was visualized by heat map. Four databases, miRDB, mirecords, mirtarbase, and starbase, were used to predict the target genes of 21 coexpressed 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 (Fig. 2).

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 (Figs. 3 and 4).

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 (Fig. 5). Primitive unactivated CD4 + T cells can differentiate into different subtypes according to different stimuli and signals, 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 (Fig. 6).

Discussion
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, and GPSM1 are involved in affecting T cell survival; hsa-miR-20a-3p-regulated PEG10, TPM2, NECTIN4, OTUD1, NEK9, PGM3, POU2AF1, MAP1B, ASNS, RAPH1, and PLXND1 are involved in influencing T cell differentiation and infiltration; hsa-miR-1281-regulated JPT2, MPZ, SLC43A2, SIGLEC9, NCKAP1, and ZNF556 are involved in affecting T cell infiltration; hsa-miR-574-5p-regulated RPH3A, EFNB1, TBC1D16, SEMA6C, CRISPLD2, GPR37L1, FOSL1, and ADAT2 are involved in affecting T cell viability and differentiation. As shown in the volcano map and heat map, the expression of miRNA changes as follows: the expression of has-miR-3180-3p is downregulated, the expression of has-miR-1281 is upregulated, the expression of has-miR-20a-3p is upregulated, and the expression of has-miR-574-5p is downregulated. The involvement of CD4 T cells is critical during the activation of α-synuclein-mediated microglia (Harms et al. 2013). In our results, has-miR-3180-3p-regulated CBX8 is involved in cellular communication and immune response regulation and affects CD4 + T cell depletion (Ascoli et al. 2022); has-miR-3180-3p-regulated GPSM1, which encodes the activator of G-protein signaling 3 (AGS3), regulates G-protein signaling in the immune system and thus regulates the activation process of T cells (Branham-O'Connor et al. 2014); has-miR-20a-3p-regulated aberrant expression of PEG10 increased T cell size and promoted T cell proliferation and infiltration (Liu, et al. 2022); has-miR-20a-3pregulated TPM2 was significantly and negatively correlated with the degree of CD8 + T cell infiltration (Zhao et al. 2021); the mutation of PGM3 which regulated by hsa-miR-20a-3p has recently been shown to lead to disorders of glycosylation, lymphopenia, and impairment in T cell proliferation (Yang et al. 2014); has-miR-1281-regulated NCKAP1 is significantly associated with the level of T cell infiltration (Chen et al. 2022b). The downregulation of has-miR-3180-3p expression may lead to impaired T cell   proliferation, and upregulation of has-miR-20a-3p expression may lead to reduced T cell infiltration and proliferation, 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. 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 (Cheng et al. 2022). Signaling (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 signaling 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 (Carvalheiro et al. 2020). 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 (Luo et al. 2016).
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 (Lombard-Vadnais et al. 2022). Has-miR-20a-3pregulated 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 (Gu et al. 2022). 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 (Fernandez-Garcia et al. 2022). Upregulation of has-miR-20a-3p expression may promote T cell differentiation to Th1 and Th17 and decrease T cell differentiation to Th2, 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. 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 (Arpaia et al. 2013).
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.

Declarations
Ethics Approval This is a biographical analysis study and does not require ethical approval.

Consent to Participate
Informed consent was obtained from all individual participants included in the study.

Consent for Publication
The authors affirm that all individual participants provided informed consent for publication.

Competing Interests
The authors declare no competing interests.