Single-cell RNA sequencing of cerebrospinal fluid reveals the expansion of innate lymphoid cells with upregulated transposable elements in multiple sclerosis

doi:10.21203/rs.3.rs-2613225/v1

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Single-cell RNA sequencing of cerebrospinal fluid reveals the expansion of innate lymphoid cells with upregulated transposable elements in multiple sclerosis

https://doi.org/10.21203/rs.3.rs-2613225/v1

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Background:

Multiple sclerosis (MS) is a chronic and often immune-mediated demyelinating disease with no absolute treatment. Transposable elements (TEs) are getting more attention as a possible culprit in neurodegenerative disease. However, to the best of our knowledge, there is no study to examine the possible association of TE expression and its potential role in MS pathogenesis at the single-cell level.

Result:

In this study, we reanalyzed single-cell RNA sequencing data of human cerebrospinal fluid (CSF) samples. Our result revealed that TEs are overexpressed in a cluster, annotated as innate lymphoid cells (ILCs). Moreover, the enrichment analysis of the associated transcription factors (TFs) with highly upregulated TEs in ILCs revealed the relevance of the TFs with immune pathways and cis-regulatory regions in DNA.

Conclusions:

We propose that upregulated TEs in ILCs are in accordance with the plasticity of ILCs as TEs could insert themselves in coding or regulatory regions of immune-related genes, and represent themselves as immune-related TF binding sites. We also hypothesize that presenting the TE-derived antigens by ILCs with overexpressed TEs could re-activate T cell-mediated immunity in the CNS of MS patients. So this study could indicate a possible mechanism that is mediated by TEs in ILC plasticity and their possible role in MS pathogenicity. Also, we suggest that repurposing the nucleoside reverse transcriptase inhibitors (NRTIs) or developing new high-efficacy NRTIs would be a feasible approach in MS treatment.

Innate lymphoid cells

Multiple sclerosis

Single-cell RNA sequencing

Transposable elements

Multiple sclerosis (MS) is a chronic and often immune-mediated demyelinating disease of the central nervous system(CNS)(1, 2). It causes nontraumatic neurological disability in young adults with a higher incidence in women than men(1). Although the causes of MS have not been thoroughly identified, genetic predisposition and environmental factors including vitamin D deficiency, obesity, smoking, and viral infection could influence MS susceptibility and the course of the disease(3, 4). Many viruses with controversial associations with MS have been identified. However, Herpesviridae including Epstein-Barr virus (EBV), human herpesvirus 6 (HHV-6), and human endogenous retroviruses (HERVs) received the most plausible support. The association of retrovirus-like particles with MS was first described more than 30 years ago as a new human T-lymphotropic virus (HTLV). Further analysis revealed the resemblance of this MS-associated retroviral element (MRSV) with human HERVs _one group of transposable elements(TEs)_ and is currently called HERV-W(5–7).

TEs are mobile DNAs with a viral origin that can replicate throughout the genome(8). These elements compose nearly 50 percent of the mammalian genome and act as building blocks for cis-regulatory elements(9). TEs are categorized into two major classes including retrotransposons, which require RNA intermediate to replicate and re-insert into the genome, and DNA transposons which directly move through the genome by a cut-and-paste mechanism with the aid of a protein called transposase(10). Notably, retrotransposons are also categorized into long terminal repeat (LTR) retrotransposons such as HERVs, and non-LTR retrotransposons such as long interspersed retrotransposable elements(LINEs) and short interspersed retrotransposable elements (SINEs) (11).

Recently, several studies revealed the role of TEs in fueling genetic innovation in the mammalian immune system as gene regulatory elements (12). For instance, it has been revealed that a subset of the ERVs is involved in regulating vital immune functions such as activation of AIM2 inflammasome(12). In another study, Ye et al.(13) analyzed the genome-wide chromatin profiling data from T cells and demonstrated the higher occurrences of TEs within enhancers that are specific to immune cells. Since most TEs are epigenetically silenced in many cell types and normal conditions, one may speculate that pathological TE dysregulation and the subsequent inappropriate immune gene activation could result in various disorders including autoimmune diseases(14).

Recent advancements in next-generation sequencing have enabled us to uncover the transcriptional state of individual cell gene expression in the subject of interest. Unlike bulk RNA sequencing, single-cell RNA sequencing can give us novel insight into transcriptional heterogeneity, disease immunology, and diagnosis of various diseases including neurological disorders(15).

Cerebrospinal fluid (CSF) surrounds the CNS and contains immune cells such as central memory CD4 + T cells. Because of the proximity to CNS, accessibility, and role in immunosurveillance, CSF could give us invaluable insight into MS immunology and the identification of immune cells involved in MS pathology. However, to the best of our knowledge, there is no single-cell RNA seq study to identify the possible role of TEs dysregulation in cell function and its potential role in MS immune dysregulation(16–18)

Herein, we reanalyzed single-cell RNA-Seq data from the CSF samples obtained from six MS patients and six patients with Idiopathic intracranial hypertension(IIH) as control to assess the probable correlation of TE dysregulation with MS pathogenicity.

To realize the discrete immune cell populations and the probable role of TEs in the MS pathogenicity, scRNA-seq data, including a total of 23423 and 19725 cells, obtained from the CSF of MS patients and patients with IIH as control, were reanalyzed (17). (Table 1)

Table 1

GEO accession number and clinical status of samples reanalyzed.
GEO Accession	clinic status	tissue
GSM4104122	Multiple Sclerosis	CSF
GSM4104123	Multiple Sclerosis	CSF
GSM4104124	Multiple Sclerosis	CSF
GSM4104125	Multiple Sclerosis	CSF
GSM4104126	Multiple Sclerosis	CSF
GSM4104127	Multiple Sclerosis	CSF
GSM4104128	Idiopathic intracranial hypertension	CSF
GSM4104129	Idiopathic intracranial hypertension	CSF
GSM4104130	Idiopathic intracranial hypertension	CSF
GSM4104131	Idiopathic intracranial hypertension	CSF
GSM4104132	Idiopathic intracranial hypertension	CSF
GSM4104133	Idiopathic intracranial hypertension	CSF

Using the k-nearest neighbor (KNN) graph constructed on the principal component analysis

(PCA)-reduced data, single cells were clustered and then labeled based on their canonical cell markers (Supplementary Table 1).

We used the KNN graph which is constructed on the PCA-reduced data to cluster single cells. Then each cluster was labeled based on the marker gene expression (Supplementary Table 1). The average and percent expression of each canonical cell marker in MS and IIH patients are shown in Supplementary Fig. 1 and Supplementary Fig. 2. Moreover, we used uniform manifold approximation and projection (UMAP) plots to visualize the clusters in MS and IIH populations (Fig. 1).

Based on the canonical markers, we identified major clusters including T cells, innate lymphoid cells (ILCs), natural killer (NK) cells, B cells, plasma cells, CD14 + monocytes, dendritic cells, and plasmacytoid dendritic cells(19, 20).

Among all these clusters, ILCs revealed a distinct pattern for TE upregulation(Supplementary Table 2 and Supplementary Table 3). This cluster, differentially expressed a high number of TEs (logFC > 0.5 and adj. p-value < 0.05) (Fig. 2-a and Fig. 2-b). Also, this cluster had a higher frequency in MS (Ctrl n = 485 vs MS n = 1548 ILC cells)(Supplementary Table 4 and Supplementary Table 5).

Various defense mechanisms suppress TE activity including DNA methylation and histone modification(21). Since TEs are upregulated in the ILC cluster, it is reasonable to assume that these mechanisms are reduced in the ILCs. In the next step, we decide to identify the probable role of TEs in influencing the transcriptional activity of nearby genes by providing binding sites for transcription factors. So, we analyzed the probable association of upregulated TEs ( log FC > 0.9 and adust.p.value 0.05) with transcription factors(TFs) using the retrotransposon-transcription factor associations database(RTFAdb) (22). We identified 54 TFs that probably could bind to the TE-enriched locations in the genome and affect the ILCs transcriptional regulatory network (Supplementary Table 6). The enrichment analysis of the associated TFs with TEs is shown in Fig. 3. Cell-type enrichment analysis confirms the association of the identified TFs with immune cells (20). Also, the enrichment analysis indicated that the selected TFs were most significantly associated with immune pathways including the TNF signaling pathway and the IL-17 signaling pathway(23, 24). Moreover, gene ontology (GO) molecular function enrichment analysis revealed the association of the selected TFs with cis-regulatory region sequence-specific DNA binding which affirms the probable role of TEs in providing the cis-regulatory sequences in the genome(25, 26).

In this study, we aimed to uncover the probable role of TEs in MS pathogenicity. We identified a cluster of ILCs with a higher population in MS than the control. ILCs are indeed the non-T cell, non-B cell, and NK cell-like lymphoid cells that are similar to t cells and belong to the innate immune system(27). ILCs are categorized into different subsets including NK cells, lymphoid tissue inducer cells (LTi), and helper ILC1-3(27). Recently, ILCs are considered neglected players in MS pathogenicity(28). We identified that TEs are highly upregulated in ILCs. It seems that TE upregulation in ILCs is in accordance with the ability of these cells to adopt their phenotype and functional capacities in response to microenvironment changes, a phenomenon called plasticity(28, 29). It has been proposed that ILC plasticity needs defined chromatin regions, such as cis-acting enhancers and silencers of gene expression, to be accessible to TFs(30). Since TEs are repetitive and mobile in nature, one may speculate that TEs are involved in the genomic plasticity of ILCs by inserting themselves in coding or regulatory regions of immune-related genes, subsequent functional impact on the gene expression, and the resultant plasticity(31).

It has been revealed that the proportion of LTis was significantly increased in the CSF of patients with early MS suggesting either recruitment of blood-derived ILCs or a local expansion of ILCs in the CSF (32). Also, it has been shown that a subset of blood-derived inflammatory ILC3s, could develop the MS-like disease in mouse models by infiltrating the CNS and presenting the antigen to myelin-specific T cells(33). It has been revealed by many studies that TEs are overexpressed in inflammatory and immune-mediated diseases(11, 34, 35). For instance, TEs upregulation is observed in many cancer which is due to DNA methylation loss in cancer cells. This could lead to the presentation of TE-derived peptides on MHC-1 molecules that are recognized by T cells and the resultant immune cell infiltration and inflammation(36).

ILCs could directly augment T-cell adaptive immunity by upregulating the expression of the MHC class II, especially in inflammation. One may speculate that this process could occur in immune-mediated diseases(37). So, we propose that presenting the TE-derived antigens by ILCs with overexpressed TEs could re-activate T cell-mediated immunity and the subsequent MS progression. Consistent with this assumption, antiretroviral agents which can suppress TE expression, demonstrated potential therapeutic chance in MS treatment (38–44)

The detrimental role of TE activity in neurodegenerative diseases is also revealed by several studies(45–48). The most common explanation for TEs’ role in neurodegenerative diseases is an inflammatory response to double-stranded RNA(dsRNAs) and/or cDNAs, or a direct cytotoxic response to specific TE-derived proteins which are mainly directed by innate immune responses(49). In this regard, it has been revealed that peripheral blood mononuclear cells (PBMCs) obtained from MS patients induce elevated Interferon-gamma (IFNγ), Interleukin 6 (IL-6), and tumor necrosis factor-alpha (TNF- α) expression after the stimulation of these cells with multiple sclerosis-associated retrovirus element (MSRV) Env protein. It seems that HERV-W Env proteins activate innate immune responses by activation of toll-like receptor 4(TLR4) and CD14, which causes substantial proinflammatory cytokines(11). The common hypothesized model of retroelement-mediated neuroinflammation proposes that the resultant proinflammatory cytokines which are produced by the detection of retroviral-derived particles by the innate immune response receptors could exert neuropathophysiological effects(11). However, in this study, we indicate a new possible mechanism that is mediated by functional interactions between ILCs and adaptive immunity in MS pathogenicity.

In this study, we reanalyzed the single-cell RNA sequencing data from the CSF samples obtained from six MS patients and six control to assess the probable correlation of TEs with MS pathogenicity. We find out that ILCs that are expanded in MS revealed a distinct pattern for TE upregulation. The upregulation of TEs in these cells is in accordance with the adaptive phenotype and functional capacities of these cells in response to stimuli(29, 50). One may speculate that the genomic plasticity of ILCs could be obtained by inserting of TEs in coding or regulatory regions of immune-related genes, functional impact on the gene regulation, and the resultant plasticity(31). The enrichment analysis of the associated TFs with highly upregulated TEs in ILCs revealed the relevance of the TFs with immune pathways and cis-regulatory regions in DNA which affirms this hypothesis.

In addition, we proposed a new hypothesis regarding the possible role of TEs in MS pathogeneses. The main model for TEs’ role in neurodegenerative diseases is mainly based on the sensing of TE-derived particles by pattern recognition receptors in innate immune response and the subsequent inflammation and neurodegeneration(11). However, we hypothesize

that presenting the TE-derived antigens by ILCs with overexpressed TEs could re-activate T cell-mediated immunity and the subsequent MS progression. Based on this assumption, the administration of antiretroviral agents which can suppress TE expression could be used in MS treatment(43, 45). Further studies are needed to investigate the probable role of TEs in neurodegenerative diseases' pathogenicity and their relevance to immune system plasticity. Also, it seems that the repurposing of the nucleoside reverse transcriptase inhibitors(NRTIs) or developing new high-efficacy NRTIs which specifically target the retrotransposon reverse transcriptases would be a feasible approach in MS treatment(51).

The sc-RNA sequencing data of CSF samples from six MS patients and six patients with IIP as control were retrieved from the (gene expression omnibus) GEO (at https://www.ncbi.nlm.nih.gov/geo/) database under accession number GSE138266.

Quantifying Te Expression

The aligned sc-RNA sequence reads of samples were directly retrieved from the ENA database.

As conventional aligners ignore multi-mapping reads, which is critical for counting TEs, we used a recently developed aligner, called scTE(52), which is compatible with STARsolo (53) output files and could quantify the reads aligned to TEs, accurately.

Genome indices were built with the scTE-build function via the UCSC genome browser Repeatmasker track (54)and GENCODE in default mode (exclusive). Then BAM files were realigned via the scTE function and the count matrices were applied for further investigations.

Preprocessing, Analysis, And Exploration Of Scrna-seq Data:

The Seurat (20, 55, 56) is an R package (satijalab.org/Seurat) developed for analyzing the count matrixes and visualizing data. First of all, low-quality cells and cell doublets were omitted based on the following criteria: 1–2% of cells with greater read-counts RNA or 2–2% of cells of each sample with low numbers of genes, or 3- cells with more than 40% mitochondrial counts.

The retrieved data were then normalized by the ‘LogNormalize’ method, and 2000 most variable genes were determined via Seurat’s ‘FindVariableFeatures’ function. samples were then integrated using the 'IntegrateData' function to correct the batch effect.

Principal Component Analysis (Pca) And Clustering

In this step, we removed TEs temporarily from the count matrix to ensure that they do not affect our clustering. In the next step, we scaled the data, and then PCA was performed using the RunPCA function in Seurat with default parameters.

The KNN graph was done based on the PCA-reduced data, and unsupervised clustering was performed using 'FindNeighbors' and 'FindClusters,' respectively. In this step, we add the TEs to clustered count matrix for further investigations.

Differential Expression (De)

DE analysis was performed on 'RNA' assays of the count matrix, via the 'MAST' (57) method using 'FindAllMarkers,' and cell type was identified using canonical cell markers.

Analyzing The Te-tf Association:

To analyze the probable association of upregulated TEs in the cluster of interest with TFs we query each of the TE with log FC > 0.9 and adust.p.value < 0.05 in RTFAdb (22).

The list of all TFs with a possible association with upregulated TEs was queried in the gene set enrichment analysis web server, Enrichr (58, 59), to obtain more biological information regarding the gene list.

CNS

central nervous system

CSF

cerebrospinal fluid

Differential expression

dsRNAs

double-stranded RNA

EBV

Epstein-Barr virus

GEO

gene expression omnibus

HERVs

human endogenous retroviruses

HHV-6

human herpesvirus 6

HTLV

human T-lymphotropic virus

IIH

Idiopathic intracranial hypertension

IFN-γ

Interferon gamma

IL-6

Interleukin 6

ILCs

innate lymphoid cells

KNN

k-nearest neighbor

LINEs

long interspersed retrotransposable elements

LTR

long terminal repeat

multiple sclerosis

MSRV

multiple sclerosis-associated retrovirus element

NRTIs

nucleoside reverse transcriptase inhibitors

natural killer

PBMCs

peripheral blood mononuclear cells

PCA

principal component analysis

RTFAdb

Retrotransposon - Transcription Factor Associations database

SINEs

short interspersed retrotransposable elements

Transposable elements

TFs

transcription factors

TNF-α

Tumor Necrosis Factor Alpha

TLR4

toll-like receptor 4

UMAP

uniform manifold approximation and projection

Availability of data and materials

The dataset reanalyzed for this study was retrieved from the gene expression omnibus GEO database under accession numbers GSE138266. All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Funding

Authors of this manuscript wish to express their thanks to Shiraz University of Medcial Sciences, Shiraz, Iran (Project No: 24045).

Ethical Approval and Consent to participate

This work was approved to be performed at Shiraz University of Medical Sciences (Ethical code: IR.SUMS.REC.1400.583.

Consent for Publication

As this work was an In silico study and accordingly, no real subject or patient or participants has been admitted to the investigation, the current protocol is free of any subjects to fulfill the requirments or related forms of consent.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors' contributions

PM and MZ conceived the idea of the research. PM and MF were major contributors to writing the manuscript. PM and MF analyzed data. MZ extensively reviewed the manuscript and analyzed the results. All authors read and approved the final manuscript.

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No competing interests reported.

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Single-cell RNA sequencing of cerebrospinal fluid reveals the expansion of innate lymphoid cells with upregulated transposable elements in multiple sclerosis

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Abstract

Background:

Result:

Conclusions:

Figures

Background

Results

Discussion

Conclusion

Method

Quantifying Te Expression

Preprocessing, Analysis, And Exploration Of Scrna-seq Data:

Principal Component Analysis (Pca) And Clustering

Differential Expression (De)

Analyzing The Te-tf Association:

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1