# MicroRNA signatures of CD4+ T cell subsets in healthy and multiple sclerosis subjects determined by small RNA-sequencing

Slobodan Culina, Pierre-Henri Commere, Elodie Turc, Axel Jouy, Sandra Pellegrini, Thomas Roux, Milena Hasan, Marc Monot, Frédérique Michel

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Slobodan Culina, Pierre-Henri Commere, Elodie Turc, Axel Jouy, Sandra Pellegrini, et al.. MicroRNA signatures of CD4+ T cell subsets in healthy and multiple sclerosis subjects determined by small RNA-sequencing. Journal of Neuroimmunology, 2025, 401, pp.578531. 10.1016/j.jneuroim.2025.578531. pasteur-05028720

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Full Length Article

# MicroRNA signatures of CD4⁺ T cell subsets in healthy and multiple sclerosis subjects determined by small RNA-sequencing

Slobodan Culina<sup>a,1</sup>, Pierre-Henri Commère<sup>b,1</sup>, Elodie Turc<sup>c,1</sup>, Axel Jouy<sup>d,g</sup>, Sandra Pellegrini<sup>e</sup>, Thomas Roux<sup>f</sup>, Milena Hasan<sup>a</sup>, Marc Monot<sup>c</sup>, Frédérique Michel<sup>e,g,*</sup>

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ARTICLE INFO

Keywords:

CD4⁺ T cell

T cell differentiation

T cell function

microRNA (miRNA)

Multiple sclerosis

Biomarker

ABSTRACT

Diverse CD4⁺ T cell subsets with specialized functions operate at different phases of the immune response. Among these are phenotypically and functionally characterized naïve, central memory (CM), effector memory (EM), and regulatory (Treg) cells. Using small RNA-sequencing, we have profiled miRNAs in these cell subsets from healthy subjects and untreated patients with relapsing-remitting multiple sclerosis (RRMS). MiRNA genomic clustering and abundance were also investigated. From the 60 most differentially expressed miRNAs, broad and highly selective core signatures were determined for naïve and memory cells at homeostasis, while miR-146a-5p was strongly upregulated in Treg cells. In line with other studies, a 5-miRNA core was identified for naïve cells (miR-125b-5p, miR-99a-5p, miR-365a-3p, miR-365b-3p, miR-193b-3p). In memory cells, a number of identical miRNAs were more expressed in EM than CM cells, supporting the progressive T cell differentiation model. This was particularly the case for an 8-miRNA core (members from miR-23a~27a~24-2, miR-23b~27b~24-1, miR-221~222 clusters, miR-22-3p, miR-181c-5p) and for the large ChrXq27.3 miR-506~514 cluster. Interestingly, most of these miRNAs were reported to negatively regulate cell proliferation and survival. Finally, we found that the miRNA core signatures of naïve and memory CD4⁺ T cells were conserved in RRMS patients. Only few miRNAs were quantitatively modified and, among these, miR-1248 was validated to be downregulated in EM cells. Overall, this study expands and provides novel insights into miRNA profiling of CD4⁺ T cell subsets that may be useful for further investigations.

1. Introduction

To oppose danger signals and diseases while avoiding chronic
inflammation, tissue damage and potential autoimmunity, the immune
system needs to develop a tailored response, memorize information, and
return to homeostasis. CD4⁺ T cells play a key role in these processes by
developing fitted and flexible differentiation programs that are driven
by signaling pathways and gene networks. Upon recognition of a
cognate antigen by the T cell receptor, activated naïve CD4⁺ T cells
clonally expand and, under specific cytokine environments, differentiate
into various types of T helper (Th) cells and induced suppressive

regulatory T cells (iTreg). These cell subsets have been defined by the
expression of transcription factors, cytokines and cell surface receptors
that are essential for their localization and effector function. A
contraction phase follows where most of the previously activated cells
die, while surviving cells persist as a heterogeneous pool of memory cells
at different stages of differentiation. Sallusto et al. were first to char-
acterize two major subsets of memory CD4⁺ T cells in human blood
according to their homing capacity, effector activity and survival po-
tential (i.e., central (CM) and effector (EM) memory cells, (Sallusto
et al., 1999). Other memory cell subtypes were later identified such as
the rare stem cell-like memory cells (SCM), transitional memory cells

<footnote><sup>a</sup> Single Cell Biomarkers UTechS, Paris Cité University, Institut Pasteur, Paris, France</footnote>

<footnote><sup>b</sup> Flow Cytometry Platform, Paris Cité University, Institut Pasteur, Paris, France</footnote>

<footnote><sup>c</sup> Biomics Technological Platform, Paris Cité University, Institut Pasteur, Paris, France</footnote>

<footnote><sup>d</sup> Paris Saclay University, Saclay, France</footnote>

<footnote><sup>e</sup> Unit of Cytokine Signaling, Inserm U1224, Paris Cité University, Institut Pasteur, Paris, France</footnote>

<footnote><sup>f</sup> CRC-SEP, Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France</footnote>

<footnote><sup>g</sup> T cell activation and function DIO3 team, Department of Immunology, Paris Cité University, Institut Pasteur, Paris, France</footnote>

<footnote>* Corresponding author at: Unit of Cytokine Signaling, Inserm U1224, Paris Cité University, Institut Pasteur, Paris, France.
E-mail address: frederique.michel@pasteur.fr (F. Michel).</footnote>

<footnote n="1">¹ These authors contributed equally to this work</footnote>
(TM), and tissue-resident memory T cells (TRM) predominant in body tissues (Mahnke et al., 2013; Poon et al., 2023). Different models of memory T cell differentiation have been proposed (Ahmed et al., 2009; Geginat et al., 2014; Kaech et al., 2002; Lam et al., 2024; Lanzavecchia and Sallusto, 2000). A widely considered model is that of progressive differentiation, where memory T cells differentiate in a continuum, depending on the extent and the quality of naïve T cell activation (i.e., N, SCM, CM, EM, TRM) (Farber et al., 2014; Lanzavecchia and Sallusto, 2000).

A hallmark of memory CD4⁺ T cells is their fast and enhanced antigen-responsiveness, which is crucial for protective immunity but can contribute to unbalanced CD4⁺ T cell responses that occur in autoimmune and inflammatory disorders. Treg cells as well can be dysfunctional in the context of immunological disorders. To improve our understanding of the mechanisms underlying the complexity of CD4⁺ T cell responses, novel markers of CD4⁺ T cell subsets are needed in pathophysiological conditions. MicroRNAs (miRNAs) represent attractive candidates since they play a critical role in almost all biological processes in health and disease states. These small non-coding RNAs (~22 nt) regulate gene expression at the post-transcriptional level, usually by incomplete binding to the 3' untranslated region of mRNAs, resulting in mRNA degradation and/or translational repression. They have been proposed to regulate over 60 % of human protein coding genes (Friedman et al., 2009). Notably, they modulate signaling and gene networks that control CD4⁺ T cell differentiation programs (Baumjohann and Ansel, 2013; Jeker and Bluestone, 2013; Pagani et al., 2013; Simpson and Ansel, 2015). Yet, their specific activity is rather difficult to establish since one miRNA can directly or indirectly repress numerous genes involved in distinct pathways of CD4⁺ T cell differentiation. In addition, several miRNAs can collectively target one mRNA during this process (Baumjohann and Ansel, 2013; Xu et al., 2023). To better understand the functional impact of miRNAs, one approach is to profile their expression. This has proven to be useful to map different stages of T cell differentiation and Th cell identity according to enrichment or downregulation of specific miRNAs. This was initially shown by Monticelli et al. in a study of 181 miRNAs on murine effector Th cells (Monticelli et al., 2005). Subsequently, small RNA-sequencing (sRNA-seq) was used to compare miRNA landscapes of various human lymphocyte subsets (de Rie et al., 2017; Juzenas et al., 2017; Landgraf et al., 2007; Rodriguez-Galan et al., 2021). Other studies used microarrays and qPCR-based technologies to profile miRNAs in well-defined human CD4⁺ T cell subsets and upon T cell activation (Bronevetsky et al., 2013; De Simone et al., 2021; Rodriguez-Galan et al., 2021; Rossi et al., 2011). Collectively, these investigations provided crucial insights on miRNA expression changes in T cells, yet a portion of the currently known miRNAs or selected miRNAs were investigated. To date, 2883 mature miRNAs are annotated in humans (miRbase v22.1). Here, we have used high-throughput sRNA-seq to compare the miRNA transcriptomic profiles of naïve, CM, EM and Treg CD4⁺ T cell subsets between healthy subjects and patients with the neuroinflammatory and autoimmune disease, multiple sclerosis (MS). Among key players involved in this disease are CM and EM CD4⁺ T cells that comprise autoreactive cells and proinflammatory Th cells, and Treg cells with a defective suppressive function (Dendrou et al., 2015; Dominguez-Villar et al., 2011; Galli et al., 2019; Lorenzini et al., 2024; Morille et al., 2022; Paroni et al., 2017). Relapsing-remitting MS (RRMS), the most common form of the disease, can be more or less active and can worsen in time in a complex manner (Calabrese et al., 2024). Thus, there is a need for prognostic biomarkers and a better understanding of immunopathological mechanisms of MS. Here, we report on broad miRNA signatures and highly-subset selective miRNA cores of naïve, memory and Treg CD4⁺ T cells. In untreated patients with RRMS, the miRNA signatures were largely conserved, and we identified a set of miRNAs that were differentially expressed, of which some may be potential disease biomarkers.

## 2. Methods

### 2.1. Healthy subjects and MS patients

Healthy women volunteers (n = 6, median age 46 years, range 34–54 years) were from the CoSImmGEn cohort (Clinical trials NCT 03925272, CPP Ile-de-France 1–12,483) of ICAReB-Clin (Clinical investigation platform, Institut Pasteur). RRMS patients were diagnosed according to 2010 McDonald criteria and recruited at the MS expert center CRC-SEP, hospital Pitié-Salpetrière, Paris, France. RRMS patients were women at 60 % (n = 7, median age 38 years, range 30–50 years) with disease duration less than 2 years and median 1.5 EDSS. For sRNA-seq studies, RRMS patients (3 women, 1 man) and healthy subjects (3 women) were age-matched (median age 38 years). All patients were in remission and did not receive disease-modifying drugs or other treatments including steroids for at least 3 months before blood collection. Infectious diseases, autoimmune and inflammatory diseases, cancer, pregnancy, and breastfeeding were other exclusion criteria. To avoid potential hormonal influence in women, lack of pregnancy was verified using a urinary test before enrollment in the study. Blood samples of all subjects were used a few hours after their collection on heparin-tubes. This study was approved by the CPP 1–22-004/22.00257.000083. Some preliminary assays were performed with peripheral blood mononuclear cells (PBMC) samples from the French OFSEP MS network.

### 2.2. FACS sorting of CD4⁺ T cell subsets

PBMCs were obtained by Ficoll gradient centrifugation of blood samples and CD4⁺ T cells were isolated by negative magnetic selection using the CD4⁺ T cell isolation kit and an autoMACS Pro Separator (Miltenyi Biotec). Isolated CD4⁺ T cells were stained as reported (Devi-Marulkar et al., 2021) with the following antibodies: CD4-VioGreen (REA623), CD25-PE (REA945), CD127-APC (REA614), CD45RA-FITC, (REA562), CD27-PerCP-Vio700 (REA499) (Miltenyi Biotec). CD4⁺ T cells subsets were sorted using a MoFlo Astrios EQ, Summit (Beckman Coulter) at the IP Flow cytometry platform. Treg cells were sorted as CD4⁺CD25<sup>high</sup>CD127<sup>low</sup>. Naïve (CD45RA<sup>+</sup>CD27<sup>+</sup>), central memory (CM, CD45RA<sup>-</sup>CD27<sup>+</sup>), and effector memory cells including EM and EMRA (CD27<sup>-</sup>CD45RA<sup>-</sup> and CD27<sup>-</sup>CD45RA<sup>+</sup>) were sorted from conventional CD4⁺ T cells (CD4<sup>+</sup>CD25<sup>-</sup>CD127<sup>+</sup>). Following resting for at least 2 h in a complete medium (RPMI 1640 GlutaMAX 10 % iFCS, 1 mM sodium pyruvate, 1 % non-essential amino acids and antibiotics), cell pellets were lysed in QIAzol reagent and frozen at -80 °C. Sorted CD4⁺ T cell subsets were from the same subject.

### 2.3. Small RNA libraries, sequencing, and miRNA analysis

Total RNA was extracted from thawed QIAzol cell lysates using miRNeasy Mini or Micro kits according to the manufacturer instructions (Qiagen). RNA concentration was determined with the Qubit fluorimeter (ThermoFisher Scientific) and purity was obtained with the NanoDrop2000 spectrophotometer (Thermo Fisher Scientific). Small RNA libraries were prepared from 100 ng of total RNA using the Netflex Small RNA-Seq Kit v4 with UDIs (PerkinElmer). RNA sequencing was performed on the Illumina NextSeq 2000 platform using 67 bases for a target of 10 M and 20 M reads per sample.

The RNA-seq analyses were performed with sequana_rnaseq 0.15.2 from the pipeline Sequana 0.14.1 (https://joss.theoj.org/papers/10.21105/joss.00352). Reads were trimmed from adapters using Cutadapt 2.10 then mapped to the human genome (hg38) using STAR 2.7.8 with the following parameters "-outFilterMismatchNoverLmax 0.05 -outFilterMatchNmin 16 -outFilterScoreMinOverLread 0 -outFilterMatchNminOverLread 0 -alignIntronMax 1 -clip3pAdapterSeq TGGAATTCTCGGGTGCCAAGG TGGAATTCTCGGGTGCCAAGG -clip3pAdapterMMp 0.1 0.1". FeatureCounts 2.0.1 with the multimapper option was used to produce the
count matrix, assigning reads to features using corresponding primary annotation with strand-specificity information. Quality control statistics were summarized using MultiQC 1.12, statistical analysis on the count matrix was performed to identify differentially regulated genes. Differential expression testing was conducted using DESeq2 (Love et al., 2014) library indicating the significance (Benjamini-Hochberg adjusted p-values < 0.05) and the effect size (fold-change) for each comparison.

## 2.4. miRNA real time qPCR

TaqMan small RNA assays were used, including stem-loop primers for reverse transcription and a sequence-specific assay for detection of mature miRNA and the U6b snRNA (U6) used for normalization (Thermo Fisher Scientific). qPCR reactions were performed in a QuantStudio 3 system on 96-well plates and analyzed using the standard comparative CT program.

## 2.5. Statistical analyses

Principal component analysis and heatmap analyses of mature miRNA expression were performed using one-way or two-way ANOVA with correction tests for multiple comparison with Qtlucore v3.9. Other statistical tests were performed using GraphPad Prism v10.2.2 and their results are presented in the Figure legends.

## 2.6. Ethics statement

The study was recorded on [Clinicaltrials.gov](http://Clinicaltrials.gov) (ID: NCT05290688) and approved by the CPP Sud Ouest et Outre Mer I° Ethical Research Committee (No.CPP 1-22-004 / 22.00257.000083; N°ID-RCB 2021-A02983-38). Personal data processing for this study complies with the requirements of the “reference methodology MR-001” established by the French Data Protection Authority (CNIL) regarding data processing in health research. Study of healthy volunteers from the CoSImmGEn cohort (clinical trials NCT 03925272) was approved by the CPP Ile-de-France 1-12,483. All volunteers gave a written informed consent to participate in the study. Studies in this work abide by the Declaration of Helsinki principles.

# 3. Results

## 3.1. Broad and core miRNA signatures of naïve and memory CD4⁺ T cells in healthy subjects

MiRNA profiling of naïve (N), central memory (CM), effector memory (EM) and regulatory (Treg) CD4⁺ T cells, purified from fresh blood samples of healthy subjects, was investigated by high-throughput sRNA-seq. The four T cell subsets were sorted by flow cytometry from negatively selected CD4⁺ T cells based on differential expression of CD45RA, CD27, CD25 and CD127 receptors as reported (Devi-Marulkar et al., 2021). Due to very low frequency, EMRA cells were sorted together with

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Unsupervised cluster analysis of miRNA expression in CD4⁺ T cell subsets of healthy subjects. (A) two-dimensional PCA of normalized sequencing counts (log₂) in FACS-sorted CD4⁺ T cell subsets from healthy subjects (HC, *n* = 3). Anova test, adjusted *p* value < 0.05, Qtlucore v3.9. (B) synchronized heatmap showing hierarchical clustering of the top 60 DE miRNAs. Left colored squares indicate miRNAs associated with naïve (blue), CM/EM memory (green), Treg (pink) cells, and poorly defined clusters (purple). (C) pie charts represent miRNA percentages relative to the 60 DE miRNAs (adj. *p* < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<table><caption>**Table 1**
miRNA signatures associated with human naïve CD4⁺ T cells.</caption><thead><tr><th>miRNAs</th><th>ID</th><th>q-value</th><th>Naïve counts</th><th>Naïve %</th><th>CM %</th><th>EM %</th><th>Treg %</th><th>locus</th><th>genomic cluster</th><th>Ref.</th></tr></thead><tbody><tr><td>miR-142-5p</td><td>MI0000458</td><td>0.05</td><td>67988</td><td>100</td><td>65</td><td>56</td><td>82</td><td>chr17: 58331232-58331318 [-]</td><td>mir-142, 4736</td><td>SmigIELSKa et al.</td></tr><tr><td>miR-186-5p</td><td>MI0000483</td><td>0.047</td><td>23106</td><td>100</td><td>81</td><td>77</td><td>75</td><td>chr1: 71067631-71067716 [-]</td><td>-</td><td>Rossi et al.</td></tr><tr><td>miR-31-5p</td><td>MI0000089</td><td>0.027</td><td>19797</td><td>100</td><td>55</td><td>31</td><td>20</td><td>chr9: 21512115-21512185 [-]</td><td>-</td><td>SmigIELSKa et al.</td></tr><tr><td><b>miR-125b-5p</b></td><td>MI0000470</td><td>0.017</td><td>18104</td><td><b>100</b></td><td><b>21</b></td><td><b>10</b></td><td><b>17</b></td><td>chr21: 16590237-16590325 [+]</td><td>-</td><td>Rossi, SmigIELSKa</td></tr><tr><td><b>miR-99a-5p</b></td><td>MI0000101</td><td>0.026</td><td>15297</td><td><b>100</b></td><td><b>19</b></td><td><b>8</b></td><td><b>15</b></td><td>chr21: 16539089-16539169 [+]</td><td>let-7c, 99a</td><td>Rossi et al.</td></tr><tr><td>let-7c-5p</td><td>MI0000064</td><td>0.05</td><td>826</td><td>100</td><td>42</td><td>26</td><td>32</td><td>chr21: 16539828-16539911 [+]</td><td>"</td><td>Rossi et al.</td></tr><tr><td>miR-335-5p</td><td>MI0000816</td><td>0.01</td><td>4381</td><td>100</td><td>40</td><td>22</td><td>44</td><td>chr7: 130496111-130496204 [+]</td><td>-</td><td>Rossi et al.</td></tr><tr><td>miR-335-3p</td><td>MI0000816</td><td>0.05</td><td>295</td><td>100</td><td>72</td><td>45</td><td>50</td><td>chr7: 130496111-130496204 [+]</td><td>-</td><td>-</td></tr><tr><td>miR-151a-3p</td><td>MI0000809</td><td>0.01</td><td>3070</td><td>100</td><td>47</td><td>18</td><td>24</td><td>chr8: 140732564-140732653 [-]</td><td>-</td><td>-</td></tr><tr><td>miR-151a-5p</td><td>MI0000809</td><td>0.01</td><td>2480</td><td>100</td><td>51</td><td>19</td><td>23</td><td>chr8: 140732564-140732653 [+]</td><td>-</td><td>SmigIELSKa et al.</td></tr><tr><td>miR-362-5p</td><td>MI0000762</td><td>0.01</td><td>1820</td><td>100</td><td>53</td><td>45</td><td>65</td><td>chrX: 50008964-50009028 [+]</td><td>miR-188, 362, 500a, 500b, 501, 502, 532, 660</td><td>-</td></tr><tr><td>miR-532-5p</td><td>MI0003205</td><td>0.026</td><td>1200</td><td>100</td><td>54</td><td>48</td><td>77</td><td>chrX: 50003148-50003238 [+]</td><td>"</td><td>Rossi et al.</td></tr><tr><td>miR-17-3p</td><td>MI0000071</td><td>0.026</td><td>1630</td><td>100</td><td>73</td><td>48</td><td>83</td><td>chr13: 91350605-91350688 [+]</td><td>miR-17, 18a, 19a, 19b-1, 20a, 92a-1</td><td>-</td></tr><tr><td><b>miR-193b-3p</b></td><td>MI0003137</td><td>0.028</td><td>658</td><td><b>100</b></td><td><b>17</b></td><td><b>16</b></td><td><b>23</b></td><td>chr16: 14303967-14304049 [+]</td><td>miR-193b, 365a</td><td>Rossi et al.</td></tr><tr><td><b>miR-365a-3p</b></td><td>MI0000767</td><td>0.01</td><td>892</td><td><b>100</b></td><td><b>15</b></td><td><b>11</b></td><td><b>15</b></td><td>chr16: 14309285-14309371 [+]</td><td>"</td><td>Rossi et al.</td></tr><tr><td><b>miR-365b-3p</b></td><td>MI0000769</td><td>0.01</td><td>884</td><td><b>100</b></td><td><b>15</b></td><td><b>10</b></td><td><b>15</b></td><td>chr17: 31575411-31575521 [+]</td><td>miR-365b, 4725</td><td>-</td></tr><tr><td>miR-181a-2-3p</td><td>MI0000269</td><td>0.015</td><td>523</td><td>100</td><td>23</td><td>15</td><td>45</td><td>chr9: 124692442-124692551 [+]</td><td>miR-181a-2, 181b-2</td><td>Rossi et al.</td></tr><tr><td>miR-153-3p</td><td>MI0000464</td><td>0.037</td><td>483</td><td>100</td><td>48</td><td>29</td><td>62</td><td>chr2: 21929411-219294200 [-]</td><td>-</td><td>-</td></tr><tr><td>miR-641</td><td>MI0003656</td><td>0.024</td><td>121</td><td>100</td><td>63</td><td>50</td><td>65</td><td>chr19: 40282543-40282641 [-]</td><td>-</td><td>-</td></tr></tbody></table>

EM cells (Methods section). Sequencing reads (61 nt, ~6 million/sample) were mapped to mature miRNAs (miRBase v22.1). Normalized counts between CD4⁺ T cell subsets were analyzed in an unsupervised manner using Qlucore v3.9, resulting in the investigation of 575 miRNAs. First, two-dimensional principal component analysis (PCA) of the data showed a very good separation of naïve, memory (CM/EM) and Treg cells with a large proportion of data variance explained (PCA1 65 %, PCA2 18 %; multigroup Anova test, adj. *p* value <0.05). CM and EM cells remained close together, suggesting similarity of their miRNA profiles (Fig. 1A). The synchronized heatmap analysis revealed 60 differentially expressed (DE) mature miRNAs that were distributed into three major clusters corresponding to broad signatures of naïve, memory, and Treg cells (Fig. 1B and Supplementary Table 1 for data ordered as in the heatmap). The naïve cell cluster was particularly well-defined, while the CM cluster displayed an EM-like profile with expression levels intermediate between naïve and EM cells. Two additional cell clusters could not be related to a specific CD4⁺ T cell subset (unselective clusters). Of note, the proportion of miRNAs was nearly identical in the naïve and memory cell clusters (32 % and 30 %, respectively), while it was lower in the less well defined Treg cell cluster (15 %) (Fig. 1C).

DE miRNAs were examined for their genomic location, signature potential and abundance, the latter being well reflected by sequencing counts. Nineteen miRNAs were enriched in naïve CD4⁺ T cells (Fig. 1B). These miRNAs are encoded by 17 genes, 10 of which are positioned within 7 miRNA clusters (Table 1). To determine which miRNAs were the most selectively expressed in naïve cells, sequencing counts in CM, EM and Treg cells were compared relatively to naïve cells (set to 100 %, Table 1). Interestingly, the two *MIR* genes of the miR-193b~365a cluster presented the same pattern of differential expression among cell subsets. However, for other *MIR* gene clusters, only specific members were detected. This was the case, for example, for miR-17-3p in the large miR-17~92a cluster comprising 6 genes, and for miR-362-5p and miR-532-5p in the miR-188~660 cluster comprising 8 genes. Based on the lowest miRNA expression in CM, EM and Treg cells, we determined a 5-miRNA core signature of naïve CD4⁺ T cells (miR-125b-5p, miR-99a-5p, paralogous miR-365a-3p and miR-365b-3p, and miR-193b-3p, Table 1 and Fig. 2A). This signature was the most selective for naïve cells as shown by the strong reduction (> 75 %) of the 5 miRNAs in CM, EM and Treg cells. Regarding abundance, miR-125b-5p and miR-99a-5p were markedly expressed (>10⁵ counts), while miR-365a-3p, miR-365b-3p and miR-193b-3p were moderately expressed (<10³ counts) but still highly discriminative of naïve CD4⁺ T cells.

Eighteen miRNAs were upregulated in the CM/EM memory cell

cluster. These are encoded by 15 genes, of which 11 are positioned within 6 miRNA clusters (Table 2). All members of the miR-23a~27a~24~2, miR-125a~let-7e~99b and miR-221~222 clusters exhibited a similar differential pattern among CD4⁺ T cell subsets but at variable levels. As for naïve cells, only specific members of other clusters were detected. Based on the strongest miRNA enrichment in memory cells, we determined an 8-miRNA core (miRNAs from three clusters: miR-23a~27a~24~2, miR-23b~27b~24~1 and miR-221~222; and miR-22-3p, miR-181c-5p). As shown in Fig. 2B, miR-24-3p and miR-23a-3p were by far the most expressed (~10⁵~10⁶ counts) while miR-181c-5p and miR-24-2-5p were lowly expressed (<10³ counts). Of note, the expression level of these 8 miRNAs was more elevated in EM than CM cells, which suggests that this signature may be particularly relevant in EM cells.

Finally, we found that miR-146a-5p was strongly upregulated in Treg cells, as reported in other studies (Lu et al., 2010; Rossi et al., 2011; SmigIELSKa-Czepiel et al., 2014). Of note, its level was significantly higher in CM and EM compared to naïve cells but much less so than in Treg cells (Fig. 2C). Other miRNAs associated with the Treg cell cluster were not selective enough or very weakly expressed (Supplementary Table 1). Altogether, the analysis of the top 60 DE miRNAs between naïve, memory and Treg CD4⁺ T cell subsets revealed broad miRNA signatures and highly subset-selective cores, including novel and previously reported subset-specific miRNAs (Tables 1 and 2).

### 3.2. miRNA core signatures are conserved in CD4⁺ T cell subsets of RRMS patients

Next, analysis of sRNA-seq data from untreated RRMS patients was carried out to assess potential modifications of broad and core miRNA signatures of each CD4⁺ T cell subset in patients. As was done for healthy subjects, cell subsets were sorted by flow cytometry from fresh blood samples and profiled for miRNA expression by sRNA-sequencing (~12 million reads/sample). Following filtering and normalization of sequencing counts, 662 mature miRNAs were analyzed in an unsupervised manner using Qlucore v3.9. The two-dimensional PCA showed that naïve, memory (CM/EM) and Treg cells were well separated (PCA1 60 %, PCA2 22 %, multigroup Anova test, adj. *p* < 0.01) but, as for healthy subjects, CM and EM cells were not (Fig. 3A). The synchronized heatmap revealed that the top 60 DE miRNAs were distributed, as for healthy subjects, into three major clusters corresponding to naïve, memory and Treg cells, and two unselective clusters (Fig. 3B and Supplementary Table 2 for full data). Interestingly, the 5-miRNA core
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** MiRNA signatures of naïve and memory CD4⁺ T cells in healthy subjects. (A) abundance and genomic location of miRNAs in naïve CD4⁺ T cells. Clustered MIR genes (<10 kb) are similarly colored (left squares). The miRNA core signature is highlighted in bold and illustrated by scatter bar graphs (right). (B) miRNA signatures of CM/EM memory cells are presented as in A. (C) miR-146a-5p enrichment in Treg cells. Horizontal lines represent medians. Data were analyzed by RM one-way Anova with Tukey's multiple comparisons test, \* adj. *p* < 0.05, \*\* adj. *p* < 0.01, \*\*\* adj. *p* < 0.001, \*\*\*\* adj. *p* < 0.0001.</figcaption></figure>
<table><caption>**Table 2**
miRNA signatures associated with human memory CD4⁺ T cells.</caption><thead><tr><th>miRNAs</th><th>ID</th><th>q-value</th><th>EM counts</th><th>Naive %</th><th>CM %</th><th>EM %</th><th>Treg %</th><th>locus</th><th>genomic clusters</th><th>Ref.</th></tr></thead><tbody><tr><td><b>miR-24-3p</b></td><td>MI0000080</td><td>0.01</td><td>175312</td><td>100</td><td><b>233</b></td><td><b>528</b></td><td>150</td><td>chr9: 95086021-95086088 [+]</td><td>miR-23b, 27b, 3074, 24-1</td><td>-</td></tr><tr><td><b>miR-23a-3p</b></td><td>MI0000079</td><td>0.002</td><td>88339</td><td>100</td><td><b>250</b></td><td><b>439</b></td><td>150</td><td>chr19: 13836587-13836659 [-]</td><td>miR-23a, 27a, 24-2</td><td>Smigielska et al.</td></tr><tr><td><b>miR-27a-3p</b></td><td>MI0000085</td><td>0.05</td><td>37866</td><td>100</td><td><b>209</b></td><td><b>307</b></td><td>133</td><td>chr19: 13836440-13836517 [-]</td><td>"</td><td>Rossi, Smigielska</td></tr><tr><td><b>miR-24-2-5p</b></td><td>MI0000081</td><td>0.016</td><td>633</td><td>100</td><td><b>347</b></td><td><b>592</b></td><td>235</td><td>chr19: 13836287-13836359 [-]</td><td>"</td><td>-</td></tr><tr><td>miR-125a-5p</td><td>MI0000469</td><td>0.035</td><td>54299</td><td>100</td><td>137</td><td>165</td><td>73</td><td>chr19: 51693254-51693339 [+]</td><td>miR-125a, let-7e, 99b</td><td>Rossi et al.</td></tr><tr><td>miR-125a-3p</td><td>MI0000469</td><td>0.024</td><td>122</td><td>100</td><td>235</td><td>358</td><td>106</td><td>chr19: 51693254-51693339 [+]</td><td>"</td><td>-</td></tr><tr><td>miR-99b-5p</td><td>MI0000746</td><td>0.027</td><td>2563</td><td>100</td><td>145</td><td>166</td><td>69</td><td>chr19: 51692612-51692681 [+]</td><td>"</td><td>Rossi et al.</td></tr><tr><td>let-7e-5p</td><td>MI0000066</td><td>0.024</td><td>1085</td><td>100</td><td>138</td><td>195</td><td>69</td><td>chr19: 51692786-51692864 [+]</td><td>"</td><td>-</td></tr><tr><td><b>miR-22-3p</b></td><td>MI0000078</td><td>0.05</td><td>31536</td><td>100</td><td><b>335</b></td><td><b>765</b></td><td>191</td><td>chr17: 1713903-1713987 [-]</td><td>-</td><td>-</td></tr><tr><td>miR-22-5p</td><td>MI0000078</td><td>0.028</td><td>866</td><td>100</td><td>370</td><td>769</td><td>201</td><td>chr17: 1713903-1713987 [-]</td><td>-</td><td>-</td></tr><tr><td><b>miR-221-3p</b></td><td>MI0000298</td><td>0.016</td><td>9992</td><td>100</td><td><b>213</b></td><td><b>281</b></td><td>145</td><td>chrX: 45746157-45746266 [-]</td><td>miR-221, 222</td><td>-</td></tr><tr><td><b>miR-222-3p</b></td><td>MI0000299</td><td>0.028</td><td>10377</td><td>100</td><td><b>226</b></td><td><b>298</b></td><td>161</td><td>chrX: 45747015-45747124 [-]</td><td>"</td><td>Rossi, Smigielska</td></tr><tr><td>miR-221-5p</td><td>MI0000298</td><td>0.026</td><td>785</td><td>100</td><td>195</td><td>279</td><td>149</td><td>chrX: 45746157-45746266 [-]</td><td>"</td><td>-</td></tr><tr><td>miR-1307-3p</td><td>MI0006444</td><td>0.032</td><td>912</td><td>100</td><td>176</td><td>314</td><td>176</td><td>chr10:103394253-103394401[-]</td><td>-</td><td>-</td></tr><tr><td><b>miR-181c-5p</b></td><td>MI0000271</td><td>0.016</td><td>706</td><td>100</td><td><b>326</b></td><td><b>787</b></td><td>180</td><td>chr19: 13874699-13874808 [+]</td><td>miR-181c, 181d</td><td>-</td></tr><tr><td>miR-29b-2-5p</td><td>MI0000107</td><td>0.01</td><td>332</td><td>100</td><td>156</td><td>143</td><td>102</td><td>chr1: 207802443-207802523 [-]</td><td>miR-29b-2, 29c</td><td>-</td></tr><tr><td>miR-330-5p</td><td>MI0000803</td><td>0.028</td><td>156</td><td>100</td><td>134</td><td>232</td><td>143</td><td>chr19: 45638994-45639087 [-]</td><td>-</td><td>-</td></tr><tr><td>let-7i-3p</td><td>MI0000434</td><td>0.032</td><td>136</td><td>100</td><td>295</td><td>351</td><td>216</td><td>chr12: 62603686-62603769 [+]</td><td>-</td><td>-</td></tr></tbody></table>

signature that was identified for naïve cells of healthy subjects was well preserved in naïve cells of patients. This was also the case for the 8-miRNA core and broad miRNA signatures of memory cells, and for the Treg marker, miR-146a-5p. Importantly, no significant quantitative differences in the expression of miRNA cores were found between patients and healthy subjects (cf. below section 3.4). Thus, these results show that miRNA cores were conserved in CD4⁺ T cell subsets of MS patients and indicate their robustness as candidate T cell differentiation markers.

However, in patients the profile of miRNA expression showed some alterations. In particular, the proportion of miRNAs was higher in the CM/EM memory cluster as compared to naïve cells (Fig. 3C, 48% vs 13% respectively), and to healthy subjects (Fig. 1C, 30% vs 32%). In fact, the higher number of miRNAs in patient's memory CD4⁺ T cells (29 vs 18 in HC) was mainly related to 9 miRNAs of low abundance (< 100 counts, highlighted by an asterisk in Fig. 3B and Supplementary Table 2).

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** miRNA profiling in CD4⁺ T cell subsets of RRMS patients. (A) two-dimensional PCA of normalized sequencing counts in FACS-sorted CD4⁺ T cell subsets (n = 4). Anova test, Adj. *p* < 0.01, Qlucore v3.9. (B) synchronized heatmap and hierarchical clustering of the top 60 DE miRNAs. *Lowly abundant miRNAs, **miR-146a-5p. (C) miRNA percentages relative to the 60 DE miRNAs (adj. *p* < 0.01).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Activation of the X-located miR-506~514 cluster in effector memory CD4⁺ T cells. sRNA-sequencing of EM and naïve cells from RRMS and healthy subjects was replicated at a high sequencing depth (~21 M reads/RRMS, n = 4; ~30 M reads/HC, n = 3). Numbers of miRNAs significantly upregulated in each cell subset are indicated within circles (Benjamini-Hochberg, adj. *p* < 0.05, log2(|FC|) ≥ 1). Similar colored squares indicate miRNAs and encoding MIRs.</figcaption></figure>

### 3.3. The miR-506~514 cluster on chromosome Xq27.3 is activated in effector memory CD4⁺ T cells of both RRMS and healthy subjects

To obtain a better detection of lowly expressed miRNAs, sRNA-sequencing was replicated using the same library preparation at a higher sequencing depth for naïve and EM CD4⁺ T cells of patients and healthy subjects (Run 2, ~21 and ~30 million reads/sample, respectively). In patients, 104 miRNAs were differentially expressed between EM and naïve cells (Benjamini-Hochberg adj. *p* < 0.05, log2(|FC|) > 1) with a higher percentage of upregulated miRNAs in EM cells (62 % up) compared to naïve cells (38 % up, Fig. 4). In healthy subjects, 99 miRNAs were differentially expressed between EM and naïve cells, and consistently with our first sRNA-seq data (Run 1), similar proportions of miRNAs were upregulated in the two cell subsets (47 % vs 53 %), and Supplementary Table 3.

As found in Run 1 for MS patients (Fig. 3), there was an increase of lowly expressed miRNAs in patient's EM cells compared to patient's naïve cells, in particular ten members of the miR-506~514 cluster (adj. *p* 2.4 10⁻²⁴ to 4.9 10⁻²). This cluster is located on the chromosome Xq27.3 region and contains 15 MIR genes that are well conserved among primate species (Yoshida et al., 2021). Interestingly, seven members of the miR-506~514 cluster were also significantly expressed in EM cells of healthy subjects (adj. *p* 6.8 10⁻⁴ to 1 10⁻²). Thus, the analysis of deeper sRNA-sequencing data revealed the activation of the miR-506~514 cluster in EM cells of both RRMS and healthy subjects. Hence, this cluster may represent a novel marker of EM CD4⁺ T cells.

### 3.4. A set of miRNAs is modulated in CD4⁺ T cell subsets of RRMS patients

Next, we searched for quantitative differences of miRNA expression between CD4⁺ T cell subsets of RRMS patients and healthy subjects. Normalization of sequencing counts and differential expression testing were conducted using sDESeq2. Statistically significant differences are reported in Table 3 for sRNA-seq Runs 1 and 2, (Benjamini-Hochberg test, adj. *p* < 0.05, log2(|FC|) ≥ 1). Two miRNAs of low abundance (<100 counts) were consistently modulated in naïve CD4⁺ T cells (Runs 1 and 2), i.e., miR-4485-5p (down in MS/up in HC) and miR-6868-3p (up in MS/down in HC). Of the six miRNAs differentially expressed in

patient's memory CM and EM cells (Run 1), three were found in patient's EM cells in sRNA-seq Run 2, i.e., miR-1248 and miR-10395-3p (both down in MS), and miR-6868-3p (up in MS). Of note, no significant change of miRNA expression was found in Treg between patients and healthy subjects. A closer examination at the expression profile of DE miRNAs among the patient's four CD4⁺ T cell subsets (Fig. 5A) revealed that miR-10395-3p and miR-4485-5p were strongly underexpressed, while miR-6868-3p was upregulated in all CD4⁺ T cell subsets. Finally, we validated the downregulation of miR-1248 expression in patient's EM cells using a stem-loop RT-qPCR assay (Fig. 5B). Overall, these data in RRMS patients point to the differential expression of a small set of miRNAs, most of them downregulated, except miR-6868-3p.

## 4. Discussion

Here we have expanded current knowledge on miRNA profiles in functionally distinct naïve, memory and regulatory CD4⁺ T cells from peripheral blood of healthy and untreated RRMS subjects. We identified broad miRNA signatures by unsupervised cluster analysis of sRNA-seq data and miRNA core signatures based on the highest miRNA enrichment in each cell subset. MiRNA abundance and genomic location were also considered. Indeed, while miRNAs selectively expressed at a high level in a particular cell subset are expected to be involved in the subset development or maintenance, lowly expressed miRNAs may reflect a rare cell subset or cell state (e.g., a molecular process). Many MIR genes are non-randomly distributed on chromosomes, and up to 40 % are located in genomic clusters in humans (Altuvia et al., 2005; Calin et al., 2004; Ghorai and Ghosh, 2014). Clustered miRNAs often belong to the same family and regulate common molecular pathways (Kabekkodu et al., 2018), but are not necessarily co-transcribed.

By analyzing the top 60 most DE miRNAs among CD4⁺ T cell subsets, we identified a broad naïve cell signature composed of 19 miRNAs, of which 7 were not previously detected (Table 1). We derived a selective core signature composed of 5 miRNAs that were the most strongly downregulated in CM, EM and Treg as compared to naïve cells (miR-125b-5p, miR-99a-5p, miR-365a-3p, miR-365b-3p, miR-193b-3p). This finding is in line with previous studies that reported enrichment of these miRNAs in human naïve CD4⁺ T cells, in particular, miR-125b and miR-193b (Pagani et al., 2013; Rossi et al., 2011). MiR-125b was proposed to control the naïve cell state by targeting genes involved in T cell responses (IFNG, IL2RB, IL10RA, PRDM1). In addition, the sustained down-modulation of miR-125b and miR-193b during several days following naïve CD4⁺ T cell activation was associated with the acquisition of an effector-memory phenotype (Rossi et al., 2011). Regarding the other three miRNAs core, miR-99a-5p was suggested to exert an immunosuppressive activity through its canonical target mTOR, resulting in the inhibition of Th1 differentiation and promotion of Treg cells (Gu et al., 2021). Little is known on the role of miR-365a-3p and miR-365b-3p in T cell immunity. We found that miR-365a-3p and miR-193b were co-transcribed from the miR-193b~365a cluster with a similar cell enrichment pattern. Given that these two miRNAs are encoded from the same genomic cluster but belong to distinct miR families, their co-expression suggests involvement in a common pathway regulating the naïve cell state, likely by targeting distinct mRNA transcripts.

A broad signature composed of 18 miRNAs including an 8-miRNA core was found to be associated with memory cells. Notably, the same miRNAs were expressed in CM and EM cells but at a higher level in EM cells, suggesting that miRNAs may accumulate, be more actively transcribed and/or stabilized at this late stage of CD4⁺ T cell differentiation. These data are consistent with the progressive model of memory T cell differentiation (Farber et al., 2014; Lanzavecchia and Sallusto, 2000). Interestingly, the three miRNAs from the miR-23a~27a~24-2 cluster were co-transcribed with a strong enrichment pattern in EM cells, suggesting the critical activity of this cluster. Notably, miR-24-3p, which is encoded by the two highly homologous miR-23a~27a~24-2 and miR-
<table><caption>**Table 3**
Differentially expressed miRNAs between CD4⁺ T cells subsets of healthy and RRMS subjects.</caption><thead><tr><th>sRNA-seq Run 1</th><th>baseMean</th><th>log2FC</th><th>FC</th><th>pvalue</th><th>padj</th></tr></thead><tbody><tr><td><strong>Naive HC vs MS</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hsa-miR-4485-5p</td><td>83</td><td>2,5</td><td>5,7</td><td>3,4E-05</td><td>0,02</td></tr><tr><td>hsa-miR-6868-3p</td><td>11</td><td>-6,4</td><td>0,01</td><td>1,9E-06</td><td>0,002</td></tr><tr><td><strong>CM HC vs MS</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hsa-miR-106b-5p</td><td>7261</td><td>1,1</td><td>2,1</td><td>1,2E-04</td><td>0,04</td></tr><tr><td>hsa-miR-1248</td><td>2621</td><td>1,6</td><td>3,0</td><td>3,8E-05</td><td>0,02</td></tr><tr><td>hsa-miR-10395-3p</td><td>450</td><td>1,5</td><td>2,9</td><td>5,3E-07</td><td>6,13E-04</td></tr><tr><td>hsa-miR-10395-5p</td><td>219</td><td>1,0</td><td>1,9</td><td>1,7E-04</td><td>0,04</td></tr><tr><td>hsa-miR-34a-5p</td><td>310</td><td>1,6</td><td>3,0</td><td>5,3E-05</td><td>0,02</td></tr><tr><td><strong>EM HC vs MS</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hsa-miR-1248</td><td>4907</td><td>3,4</td><td>10,6</td><td>1,0E-14</td><td>1,16E-11</td></tr><tr><td><strong>sRNA-seq Run 2</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Naive HC vs MS</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hsa-miR-4485-5p</td><td>203</td><td>2,5</td><td>5,5</td><td>6,9E-06</td><td>4,39E-03</td></tr><tr><td>hsa-miR-6868-3p</td><td>23</td><td>-8,4</td><td>0,003</td><td>3,4E-10</td><td>4,34E-07</td></tr><tr><td><strong>EM HC vs MS</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hsa-miR-1248</td><td>5177</td><td>3,2</td><td>9,5</td><td>1,9E-13</td><td>2,31E-10</td></tr><tr><td>hsa-miR-10395-3p</td><td>932</td><td>1,6</td><td>3,0</td><td>8,3E-05</td><td>0,03</td></tr><tr><td>hsa-miR-6868-3p</td><td>12</td><td>-7,4</td><td>0,01</td><td>1,4E-06</td><td>0,001</td></tr></tbody></table>

Healthy Controls (HC) vs RRMS patients (MS).

DESeq2 analysis, Benjamini-Hochberg test, adj. $p < 0.05$, $log2(|FC|) \ge 1$.

23b~27b~24~1 clusters (located on Chr 19 and Chr 9, respectively), was the most highly expressed in EM cells. MiR-24 was reported to be upregulated during terminal differentiation of multiple cell lineages, to inhibit cell proliferation by targeting cell-cycle regulatory genes and to

promote cell death by targeting H2AX, a key double strand break DNA repair protein (Lal et al., 2009a; Lal et al., 2009b). Strikingly, reduced proliferative potential, increased propensity to apoptosis, and immediate effector response are important features of EM cells (Sallusto et al., 2004). In line with the latter activity, miR-24 overexpression in mouse T cells was found to promote the production of Th1 (IFN-γ) and Th17 (IL-17) effector cytokines and, to limit Th2 responses in cooperation with miR-27 (Cho et al., 2017; Cho et al., 2016; Pua et al., 2016). However, the whole miR-23~27~24 family was shown to negatively impact differentiation and function of multiple T cell lineages (Cho et al., 2016). Thus, the exact role of the miR-23~27~24 clusters and their members still requires a better understanding, in particular in human CD4⁺ T cells.

Four other miRNAs of the 8-miRNA memory core were enriched at intermediate and low expression levels in EM cells (miR-22-3p, miR-221-3p, miR-222-3p and miR-181c-5p, respectively). MiR-22 was suggested as an emerging biomarker in leukemia. It was found to be dysregulated and to inhibit cell-cycle progression in various leukemia subtypes, notably T cell acute lymphoblastic leukemia (Saccomani et al., 2020; Xia et al., 2023). We found that the miR-221~222 cluster was fully activated as shown by co-expression of miR-221-3p and miR-222-3p with the same enrichment pattern in CM and EM cells compared to naïve and Treg cells. This result corroborates and complements previous reports showing that miR-222 is upregulated in human memory CD4⁺ T cells and in activated CD4⁺ T cells (Rodriguez-Galan et al., 2021; Rossi et al., 2011). Little is known on the functional impact of the miR-221~222 cluster in T cells. In non-immune cells, miR-222 was shown to inhibit cell proliferation and to promote cell death, while shortening of its 3'end was associated with a decreased apoptotic activity (Yu et al.,

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Differential miRNA expression in CD4⁺ T cell subsets of RRMS patients. (A, B) normalized sequencing counts between healthy and RRMS subjects were compared using two-way Anova with Sidak's multiple comparisons test, * adj. $p < 0.05$, ** adj. $p < 0.01$. Horizontal lines represent medians. (B, right) relative expression of miR-1248 measured by stem loop RT-qPCR and normalized to U6b snRNA. Data are presented as fold change (FC) relative to naïve cells. Unpaired t-test with Holm-Sidak's multiple comparison test, ** adj. $p < 0.01$. HC, $n = 3$; RRMS, $n = 4$.</figcaption></figure>
2017). However, the cluster may also be pro-oncogenic in certain cancers (le Sage et al., 2007). In mice, it was shown to negatively regulate Th17 cells (Mikami et al., 2021) and to positively regulate effector B cell responses (Wigton et al., 2021). The precise activity of the miR-221~222 cluster in human CD4⁺T cell responses remains to be addressed. MiR-181c-5p is encoded by the miR-181c~181d cluster and belongs to a family of four related members. This miRNA has been shown to reduce proliferation of human activated CD4⁺T cells, possibly by targeting IL-2 (Xue et al., 2011). In mice, miR-181c-5p was reported to promote Th17 cells by targeting the negative regulator Smad7 of TGF-β signaling and/or IL-2 (Zhang et al., 2018).

Finally, we identified seven miRNAs from the miR-506~514 cluster that were enriched in EM cells, which suggests their involvement in effector T cell responses. These miRNAs have not been previously documented, probably because their expression is very low. Interestingly, this cluster is located on the well-conserved ChrXq27.3 region close to the *FMR1* gene whose mutations underlie the fragile-X developmental and mental retardation syndrome. It is also interesting that certain sex-biased autoimmune diseases have been proposed to be under the influence of the X chromosome, possibly through the high number of X-encoded miRNAs (Miquel et al., 2023; Pinheiro et al., 2011). The impact of the miR-506~514 cluster is well reported in various cancers, where a downregulated expression, most often of miR-506-3p, was associated with poor prognosis. Consistently, the overexpression of miR-506~514 was linked with a tumor-suppressive activity, repressing genes involved in cell cycle progression and metastasis (Yoshida et al., 2021). Overall, many of the miRNAs that we found to be strongly enriched in EM cells appear to be involved in negative regulation of cell proliferation. While this further suggests that these miRNAs may sign memory CD4⁺T cells differentiating toward a terminal state, it should be noted that many published studies were performed in cells other than human primary T cells.

When comparing naïve, memory and regulatory CD4⁺T cell subsets in healthy and RRMS subjects, we found conservation of the miRNA core signatures, including activation of the miR-506~514 cluster. Despite a tendency to a higher miRNA diversity in EM cells of patients, we did not identify drastic quantitative changes of their expression in comparison to healthy subjects. These results were quite unexpected since several studies have reported on differential expression in RRMS patients. However, most studies were performed with whole blood leukocytes, PBMCs, total T cells and CD4⁺T cells, and used various technologies. Although such experimental settings provided important insights, the interpretation of the data is complex due to immune cell-type heterogeneity. In particular, regarding specific CD4⁺T cell subsets, few miRNAs were reported to be dysregulated in RRMS patients (Chen et al., 2018; Guerau-de-Arellano et al., 2011; Martin and Illes, 2014; Smith et al., 2012).

MS is a chronic disease whose mechanisms of pathogenesis are heterogeneous and involve almost all immune cell types infiltrating the central nervous system. Pathogenic CM and EM CD4⁺T cells were determined as discrete subsets circulating in peripheral blood with a high proinflammatory potential (e.g., Th1/Th17, Th-GM, Tfh) and brain-homing capacity (Galli et al., 2019; Morille et al., 2022; Paroni et al., 2017). In contrast, other cell subsets were found to be enriched in the cerebrospinal fluid and reduced in patient blood (Hoeks et al., 2024). Recently, three blood endophenotypes were identified, each showing distinct changes of memory CD4⁺T cell phenotypes (Gross et al., 2024). Overall, the high heterogeneity of the disease hampers the determination of robust cellular and molecular signatures in non-stratified patients. Nevertheless, we were able to identify a set of miRNAs, of which most were downregulated in CD4⁺T cell subsets of patients. Among these, miR-1248 may be a candidate disease biomarker, since it was well expressed in EM cells of healthy subjects but not patients. Interestingly, miR-1248 was reported to positively regulate IL-5 expression in T cells and to be increased in serum of patients with asthma (Panganiban et al., 2012). This raises the possibility that its lower expression in EM cells of

RRMS patients may be associated with reduced anti-inflammatory Th2 responses and/or might result from exosomal secretion. Our finding that the set of differentially expressed miRNAs in CD4⁺T cell subsets of patients and healthy subjects was relatively modest may be explained by the limitations of our study, notably the small number of participants. Another limitation is that miRNA changes were mainly determined by sRNA-sequencing but not fully validated by qPCR assays. Moreover, it will be important to assess the clinical relevance of dysregulated miRNAs, their potential association with disease activity and progression, and their functional impact in inflammatory and autoreactive CD4⁺T cells. Overall, our findings provide expanded insights into CD4⁺T cell subset-selective miRNA expression that could be useful for monitoring the CD4⁺T cell response in pathophysiological conditions and, potentially, for modulating it through miRNA-targeted immunotherapies.

### CRediT authorship contribution statement

**Slobodan Culina:** Investigation. **Pierre-Henri Commère:** Investigation. **Elodie Turc:** Investigation. **Axel Jouy:** Investigation. **Sandra Pellegrini:** Writing – review & editing. **Thomas Roux:** Resources. **Milena Hasan:** Writing – review & editing. **Marc Monot:** Supervision, Software, Methodology, Formal analysis. **Frédérique Michel:** Writing – original draft, Visualization, Supervision, Project administration, Funding acquisition, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare that they have no conflicts of interest with the contents of this article.

### Data availability

Small RNA-sequencing datasets have been deposited with the GEO accession number, GSE270689

### Acknowledgments

We are grateful to healthy and patient volunteers. At the Institut Pasteur, we thank Nathalie Clément from the Clinical coordination core for her involvement in ethical, regulatory and operational management of the study; the ICAREB-Clin of the Medical Direction and ICAREB-biobank; Germano Cecere, Jean-Yves Coppée, Odile Sismeiro and Hugot Varet for advice and preliminary assays. The Biomics Platform, C2RT, Institut Pasteur, is supported by France Génomique (ANR-10-INBS-09) and IBISA. We are grateful to the multiple sclerosis center CRC-SEP, Pitié-Salpêtrière hospital, Paris, for providing blood samples and managing participants' visits. This work was funded by the Institut Pasteur, Paris, and the EDMUS foundation in partnership with the France Sclérose en Plaques foundation.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jneuroim.2025.578531.

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