Роль Ocrelizumab в модуляции гена и микроRNA-выражения в множестве склероза.

Introduction

Multiple sclerosis (MS) is a neurodegenerative disorder of the CNS. The disease is characterized primarily by immune-mediated inflammation and demyelination. As described in the Lublin–Reingold classification (revised in 2011), MS shows different clinical courses. Relapsing-remitting MS (RRMS) is characterized by relapsed inflammatory attacks causing neurological symptoms, followed by periods of remission [ 1 ]. In primary progressive MS (PPMS) the progressive accumulation of disability is evident from the onset, while in the secondary progressive MS (SPMS) the progressive accumulation of disability displays occurs after the initial relapsing course [ 2 ]. Initially, the brain atrophy is not associated with neurologic worsening, due to the brain's ability to compensate through neurological reserve. After exhausting the reserve owing to MS and aging, neuronal loss is clearly visible as a progression of MS disease [ 1 ].

As reported in the Atlas of MS, published by the International Multiple Sclerosis Federation (MSIF) and WHO in 2008, and updated in 2013, the incidence of MS has increased over time and this growth is primarily due to women, who on a global scale, get sick more than twice compared to men. MS is widespread throughout the world, but the distribution of the disease is not uniform; it is more prevalent in areas far from the Equator, particularly in Northern Europe, Canada, the United States, New Zealand, and Australia. Italy is considered a high-risk area for MS, with a prevalence of 122–232 cases per 100 thousand inhabitants and with the highest incidence rate in Sardinia [ 3 ].

The onset of MS is a precise combination of genetic and environmental factors [ 4 ] Genome-wide association studies have identified in HLA-DR and HLA-DQ variants the major risk alleles predisposing to MS, with a particular attention for DR15 haplotype because is associated with estrogen receptor encoding gene and immune-related genes as IL1, IL1RA, CTLA-4, TNF and TGFB1 and consequently a both adaptive and innate immune response. Non genetic factors as latitude and low vitamin D levels contribute to disease development, but also comorbidities and/or infection (Epstein-Barr virus) and lifestyles (smoking, childhood obesity) seem to influence the incidence of MS by activating pro inflammatory pathways. For example, it has been suggested that inflammatory events associated with infections may activate autoreactive CNS cells [ 5 ]. A dysregulated immune response is central to MS pathogenesis; actually, it is unclear whether MS initiates from an immune attack to CNS or begins as an intrinsic disease of CNS with subsequent peripheral immune activation [ 6 ]. Probably, after a peripheral activation in lymph nodes, due to an immune cross-reactivity with extraneous antigens (EBV), immune cells migrate across the BBB (blood-brain barrier) and cause CNS damage. Here, activated T cells exert specific functions; CD4 + Th17 serves a double function: activate CD8 + T cells releasing perforin and granzymes that destroy oligodendrocytes and release IL-17, IL-21 and IL-22 acting on glia; IL-17 and IFN-γ secreting CD4 + Th1 cells, also activate local glia and antigen-presenting cells (APCs), up-regulating major histocompatibility complex (MHC) class I/II molecules on APCs, allowing them to restimulate myelin-reactive effector T cells [ 7 ]. These events determine oligodendrocyte death, demyelination, and neuronal loss.

MicroRNAs and long non-coding RNAs are non-coding RNAs (ncRNAs) playing a critical role in physiology, but when dysregulated, acquire a pathological function as in the pathogenesis of MS [ 8 ]. miRNAs are ncRNAs approximately 22 nucleotides in length, playing essentially regulatory roles in cellular activity (development, differentiation, proliferation, apoptosis, and genomic stability) through a complementary interaction with the target at transcriptional and post-transcriptional level [ 9 ]. LncRNAs are long RNA transcripts (∼200 nucleotides) that do not encode proteins. They can act as scaffold inducing epigenetic modification or chromatin modification through recruitment of remodelers; decoy lncRNAs induce transcriptional inhibition and/or mRNA degradation; guide-lncRNAs regulate splicing, editing and turnover of small and medium ncRNAs and signal lncRNAs inducing gene activation and/or modulation of miRNA regulation [ 10 ]. Several studies have evaluated ncRNAs expression level in demyelinating lesions and body fluids (CSF and whole blood) from MS patients and identified a large set of dysregulated miRNAs and lncRNAs [ 11 , 12 ]. In the immune cells of MS patients, upregulated miR-155 and miR-326 promote Th17 cells differentiation and IL-17 production, while miR-448 can increase Th17 differentiation indirectly through the inhibition of the anti-inflammatory protein PTPN2. Again, overexpressed let-7e increases Th1 and Th17 cells. Also, downregulation of many miRNAs influence Th1 or Th17 cell differentiation. For example, under normal conditions, miR-1405p inhibits the differentiation of Th1 cells by downregulating STAT1, but its expression is significantly downregulated in MS. Dysregulated miRNAs were found also in other T cells types; miR-128, miR-27b and miR-340 inhibit the differentiation of Th2 cells by directly decreasing the expression of IL-4 and BMI1 (B lymphoma Mo-MLV insertion region 1 homolog); miR-106b and miR-25 regulate Treg cells in MS through TGF-β signaling pathway [ 8 ]. Some dysregulated lncRNAs are, for example, RMRP (RNA Component Of Mitochondrial RNA Processing Endoribonuclease) and FLICR (FOXP3 Regulating Long Intergenic Non-Coding RNA), that were found overexpressed in MS patients compared to healthy controls; upregulated FLICR reflects altered Tregs function in MS patients [ 13 ]; instead Rahmani S. et al., reported a crucial role of RMRP in the RORC-DDX5 assembly, a complex that activates transcriptional processes leading to development of Th17 cells [ 14 ]. In the last 25 years, disease-modifying therapies (DMTs) are used for the management of MS; each DMTs has a different mechanism of action, mode of administration, dosing and efficacy. Recently, it was seen that MS is not only a T-cell-mediated disorder, but B cells play a crucial role in the pathogenesis of MS via antigen-driven autoantibody responses and through the cross regulation of T-helper cells. B cells express the surface molecule CD20 which is the target of Ocrelizumab [ 15 ]. Ocrelizumab was approved in 2017 in the USA; it is a humanized anti-CD20 monoclonal antibody binding different epitope demonstrating effectiveness in both RRMS and PPMS. Compared with IFN-β1a, Ocrelizumab was associated with significantly reduced annualized relapse rate (∼45 %) and reduced confirmed disability progression (∼34 %) at 6 months; again, it reduced the number of Gadolinium-enhancing lesions and new T2 lesions [ 16 ]. Although the clinical efficacy of Ocrelizumab and its general impact on immune-related gene expression have been previously studied, including recent work by Arisi et al. [ 17 ], the molecular mechanisms underlying its action remain incompletely understood. In particular, there is limited insight into how Ocrelizumab may regulate specific inflammatory and neurodegenerative pathways through microRNA-mediated mechanisms. Our study offers a focused analysis of key markers such as TNF-alpha, NEFL, IL-6, and SLC16A10, and identifies a strong inverse relationship with let-7a-5p. By integrating expression data with correlation analysis, interaction networks, and pathway enrichment, we aim to uncover potential secondary effects of Ocrelizumab at the molecular level that are not addressed in previous works. This provides a novel contribution to the current understanding of Ocrelizumab's role in MS beyond its established clinical application.

Materials and methods

Recruitment of patients

For this study, 10 MS patients, aged 42 ± 10.1, treated with DMTs Ocrelizumab were selected among a larger cohort of MS patients treated at the Neurology Unit of Policlinic Federico II, Naples. Ocrelizumab-treated patients (7 male and 3 females) were recruited because they reflected clinical, radiographic, and laboratory criteria (McDonald diagnostic criteria) used in the diagnosis of multiple sclerosis and had a significant score in the neurological examination (SDMT, CVLT II, BVTM R and EDSS basal). All patients signed an informed consent, and the study was conducted according to the guidelines of The Ethics Committee of the University of Study of Campania “Luigi Vanvitelli” (Prot. 12478/20).

Blood collection

For microRNAs and mRNAs quantification, we collected two venous blood sample from each of the 10 MS patients, at the moment of diagnosis and after 12 months of Ocrelizumab treatment. Samples were handled according to the guidelines of the Helsinki Declaration. PBMCs (peripheral blood mononuclear cells) were isolated from EDTA blood via one wash with 1× RBC lysis buffer followed by one wash with 1× PBS. Approximately 1 × 10 7  cells were resuspended in Qiazol® (Qiagen, Hilden, Germany) and stored at −80 °C.

RNA extraction

Total RNA was isolated from PBMCs of the same 10-patient cohort using Qiazol® protocol, plus an optional DNase digestion step, in accordance with manufacturer's protocol. RNA quality and quantity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific Inc., USA). 1 μg of total RNA was reverse-transcribed for genes expression analysis, using Takara-Bio PrimeScript RT Reagent Kit with gDNA for genes, while 1 μg of total RNA was used with Takara-Bio Mir-X™ miRNA First-Strand Synthesis Kit to obtain complementary microRNAs.

MicroRNAs profiling and validation of microRNAs and mRNAs expression

For miRNAs, we used a miRCURY LNA miRNA Focus PCR Panels, investigating 84 microRNAs, to perform a targeted microRNA profiling from the 10 patients, before and after treatment. RT-qPCR array reactions were performed on the ABI real-time instrument (ABI StepOnePlus, USA) with a mixture containing 500 μL of (2×) Fast SYBR-Green PCR Mix (Qiagen), 450 μL of RNase free water and 5 μL of cDNA undiluted, corresponding to 1 μg of T0 RNA (first time point, before the first Ocrelizumab administration) and to 1 μg of T12 RNA (12 months after the beginning of Ocrelizumab administration). SNORD44, SNORD38B, SNORD49A and U6 snRNA were used as internal control to calculate ΔCt. Quantitative results were then examined for statistical significance. Data corresponding to differentially expressed (p < 0.05) microRNAs, before and after Ocrelizumab treatment, were selected.

In order to validate the microRNA profiling results, we performed a RT-qPCR quantification of differentially expressed microRNAs and microRNA-related genes, in duplicate for each sample using microRNA and mRNAs assays (ABI StepOnePlus, Applied Biosystems Inc., Foster City, CA, USA), and then we estimated the specificity of the RT-qPCR product through the melting curve. For reactions, we used a mixture (20 μL) containing 2 μL cDNA template, 10 μL (2×) Fast SYBR-Green PCR Mix (Qiagen), and 1 μL each of sense and antisense primers. Forward primer sequences of target MicroRNAs are shown in Table 1 . MicroRNAs expression level were obtained, using U6 as the internal control, using the 2 −ΔΔCt method.

Table 1 Primer sequences used for RT-qPCR of miRNAs. Table 1 miRNA Primer sequence let-7a-5p 5′-TGA GGT AGT AGG TTG TAT AGT T-3′ miR-338-3p 5′-TCC AGC ATC AGT GAT TTT GTT G-3′ miR-21a-5p 5′-ACA CTC CAG CTG GGT AGC TTA TCA GAC TGA-3′ miR-146a-5p 5′-TGA GAA CTG AAT TCC ATG GGT T-3′ Universal Real Time Reverse Primer 5′-GTGTCGTGGAGTCGG CAATTC-3′

Primer sequences used for RT-qPCR of miRNAs.

Quantitative real-time PCR (RT-qPCR) analysis of diagnostic genes

We performed RT-qPCR analysis to verify the expression of diagnostic genes in peripheral blood of MS patients treated with Ocrelizumab. The blood samples were obtained from 10 MS patients at the moment of diagnosis and after 12 months of Ocrelizumab treatment. Briefly, total RNA from peripheral blood was extracted using Takara RNAiso Plus (9108) Trizol reagent. After assessment of RNA quality and concentration, reverse transcription and RT-qPCR were performed using the Takara Prime-Script RT Master Mix (RR036A) and SYBR Green Premix (RR420A), respectively. Primer sequences of target genes are listed in Table 2 . RT-qPCR was performed in technical triplicate for per target gene. Relative transcript abundance was determined by using the 2 −ΔΔCt method and normalized to the averaged mRNA expression levels of β-actin.

Table 2 Primer sequences used for RT-qPCR. Table 2 Gene name Primer sequence NEFL FW: 5′-GAC AAG CAG AAC GCC GAC AT-3′ RW: 5′-CTT TGT GGT CCT CAA TTC ATT TTC TA-3′ NESTIN FW: 5′-GCG GTG GCT CCA AGA CTT C-3′ RW: 5′-ACT GGG AGC AAA GAT CCA AGA C-3′ SLC16A10 FW: 5′-GGT GTG AAG AAG GTT TAT CTA CAG G-3′ RW: 5′-AGG GCC CCA AAG ATG CTA-3′ NEAT1 FW: 5′-AGT GTG AGT CCT AGC ATT G-3′ RW: 5′-GAA CTT CCT CCT CCT AAG C-3′ TNF-alpha FW: 5′-GAA AGC ATG ATC CGG GAC G-3′ RW: 5′-GAT GGC AGA GAG GAG GTT G-3′ IL-6 FW: 5′-AAG CCA GAG CTG TGC AGA TGA GTA-3′ RW: 5′-AGC TGC GCA GAA TGA GAT GAG TT-3′

Primer sequences used for RT-qPCR.

Statistical analysis

A paired t -test was conducted on the normalized delta CT values from the MCI and control groups to determine the fold change in average gene expression levels between the groups, as well as the associated p-values. Raw Ct values were first normalized using the 2^−ΔΔCt method, and the distribution of ΔCt values was evaluated using the Shapiro-Wilk test to confirm the assumptions for parametric testing. To assess expression changes between the pre- and post-treatment samples, a paired two-tailed Student's t -test was applied, and results were considered statistically significant at a threshold of p < 0.05. In addition, Pearson correlation coefficients were calculated to explore relationships between let-7a-5p and each significantly modulated gene, as well as between the genes themselves. The statistical analysis was carried out using Python version 3.7.0, along with its numpy [ 18 ], SciPy [ 19 ], and Pandas [ 20 ] modules. For data visualization and graph creation, the Matplotlib [ 21 ] and Seaborn [ 22 ] libraries were employed.

Bioinformatics analyses

Bioinformatics analyses were designed to interpret the functional significance of the differentially expressed miRNAs and genes. To identify potential interacting partners, we utilized RNAInter v4.0 [ 23 , 24 ] to retrieve experimentally validated and high-confidence predicted miRNA-mRNA interactions, focusing specifically on human targets supported by robust evidence such as CLIP-seq and reporter assays. For protein-protein interactions, we consulted the Human Reference Protein Interactome Mapping Project (HuRI) [ 25 ] incorporating only interactions validated by binary assays. All these tools were set to search for interactions reported solely in experimental studies employing robust detection methods.

We then conducted a pathway analysis on the compiled list of interactions through the DAVID [ 26 , 27 ] online toolkit for miRNAs. This analysis included sources such as Gene Ontology's molecular function and biological process, KEGG, Reactome, WikiPathways for biological pathways. Analyses were performed using the Homo sapiens background, filtering for significance using an FDR-adjusted p-value cutoff of 0.01 and a maximum set size of 800. Gene enrichment pathway analysis was performed using g:Profiler [ 28 ], selecting the same sources used for DAVID and adding TRANSFAC and miRTarBase for DNA regulatory motifs. Network visualization was achieved with Cytoscape version 3.10.2 [ 29 ]. The results from g:Profiler and DAVID were integrated using the Enrichment Map [ 30 ] add-on version 3.3.6, which enabled clustering of related pathways. CytoHubba [ 31 ] helped to identify key hub genes and regulatory bottlenecks using the MCC algorithm, enhancing the biological interpretation of our dataset.

Results

Multiple sclerosis microRNAs profiling

To perform our study, we sought to enroll a heterogeneous cohort of patients with different subtypes of Multiple Sclerosis, ensuring a broad representation across various demographics and disease stages ( Table 3 ). Our cohort consisted of 12 individuals, balanced in gender with 5 females and 7 males, reflecting common age-related disease onset patterns with an average age of 41.0 years and an onset ranging from 20 to 57 years, with the average at 39.4. Clinically, the group varied not only in MS subtypes—comprising 2 with Primary Progressive (PP), 2 with Secondary Progressive (SP), and 8 with Relapsing-Remitting (RR)—but also in disease severity and progression. This variability is reflected also in their initial average Expanded Disability Status Scale (EDSS) score of 2.3, which increased to 3.4 after one year, and an average disease duration of 2 years.

Table 3 Details of the enrolled cohort. Table 3 n mean SD Range (min – max) Sex (females/males) 5/7 Age 41.0 12.5 20–59 Age at onset 39.4 11.6 20–57 EDSS 2.3 2.3 0–5.5 EDSS after 1 year 3.4 2.6 1–5.5 Years of disease 2 2 0–8 Disease form 2 PP; 2 SP; 8 RR

Details of the enrolled cohort.

Differentially expressed microRNAs and mRNAs between pre-treatment and 12 months ocrelizumab treatment patients

As first step, we analyzed the expression levels of four miRNAs (let-7a-5p, miR-14a-5p, miR-21a-5p and miR-338-3p) and seven genes (IL-1, IL-6, NEAT1, NEFL, NESTIN, SLC16A10 and TNF-alpha), to observe an eventual change in their expression levels in MC patients before and after being treated for twelve months with Ocrelizumab. Results, as shown in Fig. 1 and Table 4 , illustrate a significant changes in the expression level of only one miRNA, let-7a-5p, whose levels in patient's whole blood samples appeared to be increased after the treatment. Among the genes assessed, five of them showed a significant change in expression upon Ocrelizumab treatment: IL-6, SLC16A10, NEFL and TNF-alpha, whose expression was decreased.

Column histograms displaying the expression levels in fold change of the miRNAs and genes on the y-axis and the sample label on the x-axis. Significance is marked as follows: not significant (ns) for p-values over 0.05; ∗ for p-values from 0.05 to 0.01; ∗∗ for p-values from 0.01 to 0.001; ∗∗∗ for p-values from 0.001 to 0.0001; and ∗∗∗∗ for p-values less than 0.0001.

Table 4 Table of gene expression p-values. Table 4 Gene name fold change (2^−ΔΔCt) p-Value SLC16A10 0.009 0.001 NESTIN 1.355 0.653 NEFL 0.461 0.008 TNF-alpha 0.182 0.005 NEAT1 2.487 0.206 IL-6 0.008 0.028 let-7a-5p 2.415 0.010 miR-338-3p 3.321 0.147 miR-21a-5p 3.576 0.132 miR-146a-5p 1.189 0.458

Table of gene expression p-values.

To assess the presence of eventual direct of inverse correlation among let-7a-5p and the genes who showed a significant change in their expression, we performed a correlation analysis using Pearson Correlation. Interestingly, results ( Table 5 ) indicate a robust negative correlation between let-7a-5p and the four genes analyzed, with NEFL showing the strongest inverse correlation, with a correlation coefficient of −0.962, followed by TNF-alpha with a coefficient of −0.874, SLC16A10 having a correlation coefficient of −0.842 and IL-6 with a coefficient of −0.838, thus suggesting a role of let-7a-5p as possible regulator of SCL16A10, NEFL, IL-6 and TNF-alpha. We then conducted a Pearson correlation analysis comparing all the significant genes and let-7a-5p. The correlation matrix, visible in Fig. 2 (b) , evidenced a robust positive correlation between TNF-alpha and IL-6, indicated by a high correlation coefficient of 0.96, suggesting that these two genes are co-expressed. Additionally, significant positive correlations were also observed between SLC16A10 and NEFL, SLC16A10 and IL-6, as well as SLC16A10 and TNF-alpha.

Table 5 Pearson correlation analysis among let-7a-5p and under expressed genes. Table 5 Gene name Correlation coefficient p-Value SLC16A10 −0.842340 0.035326 NEFL −0.962350 0.002100 TNF-alpha −0.873581 0.022962 IL-6 −0.838116 0.037189

Pearson correlation analysis among let-7a-5p and under expressed genes.

Heatmap of gene correlations. Blue color indicates an inverse correlation, while the red indicates a direct correlation, Intensity of the color is proportional to the robustness of the relationship. (a) Visualization of the Correlation coefficient between SLC16A10, NEFL, TNF-alpha and IL-6 with let-7. (b) Matrix of the correlation among all the significant genes and let-7.

Functional annotations and microRNA–mRNA interaction networks

Subsequently, we sought to investigate the presence of a direct or indirect interaction between let-7a-5p, TNF-alpha, NEFL and SLC16A10 through mining miRNA-mRNA interaction databases. Screening of validated interaction datasets and predictive datasets revealed the presence of a direct interaction among the miRNA and SLC16, TNF-alpha and IL-6, with details visible in Table 6 . On the other hand, NEFL failed to show validated interaction and putative target sites on its 3′UTR, making a direct interaction unlikely. So, for this gene, we focused on searching the presence of an undirect interaction. To achieve this, we initially identified their known interactors, followed by an analysis to determine any overlapping interactions. Notably, the analysis revealed the presence of three shared interactors between let-7a-5p and NEFL: APP, PDE4DIP and NEFM ( Fig. 3 ), indicating a potential indirect interaction between the two entities.

Table 6 Details of the direct interactions of the genes with let-7a-5p. Table 6 Interactor Confidence score RNAInter ID IL-6 0.5988 RR00749335 SLC16A10 0.4932 RR01306756 TNF-alpha 0.4457 RR01464575

Details of the direct interactions of the genes with let-7a-5p.

Interaction Network of let-7a-5pand NEFL (highlighted in yellow), in the center of the network are visible their shared interactors: APP, PDE4DIP and NEFM. To enhance readability, only the interactors of let-7a-5pwith a score of 3 or higher are displayed, aside from the three shared interactors.

Then we conducted a comprehensive pathway analysis to explore the biological processes in which the miRNA, the four genes, and their interactors participate. The findings, depicted in Fig. 4 , evidence that both let-7a-5p (highlighted in red) and the four genes (highlighted in green) play crucial roles in mechanisms related to signal transduction and immune response. Furthermore, let-7a-5p is involved in pathways integral to the progression of various cancers, including regulation of signal the cell cycle, apoptosis, cellular senescence, the immune response to viral infections, transcriptional regulation, chromatin remodeling and the DNA damage repair process. Conversely, the four genes are overall implicated in pathways critical for inflammation, encompassing regulation interleukin and cytokines, response to corticosteroids, and the regulation of the neuroinflammatory response. Interestingly the analysis evidenced clusters of pathways involved in regulation of neurogenesis, leukocyte activation in inflammatory response, and glial cell and astrocyte activation and proliferation.

Outcome of the pathway analysis displayed as a network. The width of each node is directly related to the count of its connected genes, miRNA or interactors. Pathways that share interactors are linked by lines (or edges), where the line thickness reflects the number of shared interactors. Pathways associated with NEFL, SLC16A10, TNF-alpha and IL-1 are highlighted in green, whereas those connected to let-7a-5pfeature a red color.

Discussion

Multiple sclerosis is an autoimmune disease characterized by chronic inflammation, demyelination, and scarring that affects the central nervous system (CNS), and it can lead to severe physical and neurological disabilities [ 32 ]. Current therapies aim to slow disease progression and relapsing rate. Ocrelizumab is humanized monoclonal antibody developed to selectively target CD20-positive B cells. CD20 is a surface antigen found on pre-B and mature B lymphocytes but not on stem cells, pro-B cells, or plasma cells. The mechanism by which ocrelizumab exerts its therapeutic effects involves the depletion of these CD20-positive B cells through antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) [ 33 ]. This is significant because B cells are implicated in the pathogenesis of multiple sclerosis, both through antibody production and as antigen-presenting cells that can activate T cells and promote inflammation [ 6 ]. Ocrelizumab has been studied extensively in clinical trials. For RRMS, key studies like the OPERA I and OPERA II trials demonstrated that ocrelizumab significantly reduced relapse rates, MRI lesion activity, and progression of disability compared to interferon beta-1a, a commonly used MS treatment [ 34 ]. For PPMS, the ORATORIO trial showed that ocrelizumab slowed disease progression compared to a placebo, which was a notable achievement given the challenging nature of treating PPMS [ 35 ]. In our study after one year of treatment, we have 75 % of NEDA-3 patients, in line with real world evidence [ 36 ]. Given these promising results, we wanted to investigate the effects of Ocrelizumab on inflammatory and neuroinflammatory markers, as well as some of the most known miRNAs, in order to expand the knowledge of its impact beyond the targeting of CS20-positive B-cells. So, we analyzed by RT-qPCR MS patients’ blood samples before and after a year of Ocrelizumab treatment.

Among the genes assessed, we found the expression of SLC16A10, NEFL, IL-6 and TNF-alpha to be significantly decreased upon treatment with Ocrelizumab. Among the tested miRNAs, let-7a-5p was the only one found to have a significant change in expression, and its levels were higher after a year of treatment.

SLC16A10 is part of the SLC16 gene family and encodes an aromatic amino acid transporter protein. Members of this family play a role in various metabolic processes, such as the energy metabolism in the brain, muscle, heart, and cancer cells, as well as gluconeogenesis, activation of T-lymphocytes, intestinal metabolism, sperm production, dysfunction in pancreatic β-cells, metabolism of thyroid hormones, and the transportation of drugs. The potential of these proteins as targets for drug development was highlighted by the identification of strong and specific inhibitors of Monocarboxilate Transporter 1 (MCT1), a member of this family, which serve as immunosuppressive drugs by inhibiting the growth of T-lymphocytes [ 37 ]. For example, a promising group of thieno[2,3-d]pyrimidinedione-derived inhibitors targeting MCT (monocarboxylate transporters) was developed from compounds originally designed to prevent organ transplant rejection. These inhibitors are particularly effective at blocking the MCT1-mediated removal of L-lactate from T lymphocytes [ 38 ]. Considering this, the downregulation of SLC16A10 after Ocrelizumab treatment may suggest that this drug may indirectly also affect T lymphocyte function, potentially influencing the immune responses more broadly than expected.

Neurofilament light chain (NEFL) is a protein found in the cytoplasm of neurons, especially abundant in large, myelinated axons. The levels of NEFL in cerebrospinal fluid (CSF) and blood rise in relation to the extent of axonal injury in neurological conditions, such as diseases that are inflammatory, neurodegenerative, traumatic, or related to cerebrovascular issues [ 39 ]. Given its well established role and its increasing use as biomarker for MS, it has been suggested to incorporate blood NEFL measurements into the current criteria for assessing 'no evidence of disease activity' in MS patients [ 40 ]. In fact, in a study involving patients with clinically isolated syndrome (CIS) and relapsing-remitting multiple sclerosis (RRMS), a significant trend of decreasing serum Neurofilament light chain (NfL) levels was observed over time. Since patients had active disease initially and started other disease-modifying therapies (DMT) after the baseline measurement, these decreases might reflect either a natural regression to the mean or the therapeutic effect of the DMT [ 41 ]. In light of this, the lower expression of NEFL after a year of treatment with Ocrelizumab could indicate the drug's effectiveness in reducing axonal damage.

Tumor necrosis factor-alpha (TNF-alpha) plays a critical role in the pathology of autoimmune diseases, including peripheral nervous system disorders like Guillain-Barré syndrome (GBS) and chronic inflammatory demyelinating polyneuropathy (CIDP). In a study utilizing an Aire-deficient mouse model to examine inflammatory neuropathy, it was discovered that TNF-alpha signaling is significantly elevated in the immune cells of the sciatic nerves during autoimmunity, driven by interferon-gamma (IFN-γ) from autoreactive T cells, which stimulates TNF-alpha production in macrophages. Interestingly, reducing TNF-alpha activity through genetic deletion or antibody blockade effectively protected against autoimmune neuropathy [ 42 ]. This suggests that targeting TNF-alpha could be a viable therapeutic approach not only in PNS autoimmune diseases but also in CNS diseases like MS, where similar immune mechanisms cause tissue damage. Thus, the decrease of TNF-alpha after Ocrelizumab treatment observed in our results, may represent another potential way in which the drug modulates the inflammatory response.

Last of the significantly downregulated genes, IL-6 is known to engage in controlling the acute-phase reaction to damage and infection [ 43 ], and play a multifaceted role in the pathophysiology of multiple sclerosis (MS). IL-6 is primarily recognized for its involvement in the inflammatory processes within the central nervous system (CNS) that characterize MS. Notably, IL-6 influences the differentiation and regulation of T cell subsets, including promoting their switch into pathogenic Th17 cells, while inhibiting regulatory T cells (Tregs) [ 44 ]. This dual role enhances autoimmune responses and impedes regulatory mechanisms that are crucial for maintaining immune tolerance in MS. Additionally, IL-6 is involved in B cell differentiation and activation, contributing to the neuroinflammatory processes, making it a promising target for treatment strategies [ 45 ]. The reduction of IL-6 following treatment with Ocrelizumab, as observed in our findings, could represent a signal of the ability of the drug to address both the immune dysregulation at the heart of the disease and the downstream effectors of inflammation.

Given the inverse trend in expression of let-7a-5p respect to SLC16A10, NEFL, TNF-alpha and IL-6, along with its capability to interact with them both directly [ [46] , [47] , [48] , [49] ] or indirectly (with NEFL) [ 25 , 50 , 51 ], it may indicate that Ocrelizumab may trigger a secondary mechanism that involves the upregulation of let-7a-5p, which in turn negatively modulates the expression of these four critical genes, eventually contributing to slowing disease progression. Moreover, the strong positive correlation shown by SCL16, NEFL, TNF-alpha and IL-6 among themselves, further reinforce this hypothesis, suggesting that their common trend of expression could be the result of the action elicited by a common modulator. However, further experimental evidence is needed to validate this hypothesis.

This study provides new insights into the potential molecular effects of Ocrelizumab in MS, particularly in relation to inflammation- and neurodegeneration-associated pathways. While previous work has demonstrated global immune modulation, our findings suggest a specific regulatory axis involving let-7a-5p and several key inflammatory genes. These molecular changes may contribute to the broader clinical efficacy observed with Ocrelizumab.

Pathway analysis performed on let-7a-5p and its interactors, as well as on the four genes, highlights their common involvement in pathways related to viral infection, underscoring the significant role of viral mechanisms in the disease's etiology [ 52 ] in fact, there are increasing evidences showing that the onset and progression of multiple sclerosis are significantly influenced by viral infections and the subsequent immune responses, arising from the presence in serum and CSF of increased levels of viral-specific immunoglobulins for viruses, including Epstein-Barr Virus (EBV), Herpes viruses, Measles Virus (MeV) and Mumps Virus (MuV) [ 53 ]. For example, it has been hypothesized that B-cell targeting MS therapies may elicit their effect by eliminating EBV-infected B cells in the periphery, avoiding their entrance in the CNS [ 51 ]. Another evidence supporting the viral hypothesis, involves the effectiveness of interferon-based therapies, considering its antiviral activity [ 54 ]. Interestingly, let-7 family has been shown play a role in viral response, let-7b significantly suppress HCV replication and works synergistically with the antiviral cytokine interferon-α-2a (IFN-α-2a) to inhibit HCV infection by binding to conserved sites on the HCV genome, particularly in the coding region of NS5B and the 5′-untranslated region (UTR); similarly, let-7c overexpression represses HCV replication by inducing heme oxygenase-1 (HO-1) through inhibition of Bach1, which enhances the interferon response and suppresses viral protease activity) [ 55 ]. let-7b and let-7f also upregulate IFN-β and other interferon-stimulated genes (ISGs) like Mx1 and IFITM3, amplifying the type I interferon response against RNA viruses such as Influenza A Virus (IAV) [ 56 ]. With regard of this, we may speculate that Ocrelizumab-mediated overexpression of let-7a-5p could possibly also promote interferon response.

Two note-worthy clusters of pathways are those involved in glial, microglial, and astrocyte cell proliferation and activation ( Fig. 4 (b) ). Indeed, their role in the onset and progression of MS is significant. Microglia, the resident immune cells of the central nervous system (CNS), are among the first to respond to pathological changes. In MS, microglial activation precedes T-cell infiltration and demyelination, and this activation is detected using biomarkers like cerebrospinal fluid (CSF) sTREM2 [ 57 ]. Moreover, activated microglia produce pro-inflammatory molecules such as nitric oxide (NO) and TNF-alpha, which contribute to the destruction of myelin and neurons [ 58 ]. Astrocytes, another crucial glial cell type, also play a vital role in MS. These cells respond to inflammatory signals and can promote inflammation themselves, exacerbating CNS pathology; in fact, astrocyte activation, marked by increased expression of glial fibrillary acidic protein (GFAP), contributes to disease progression by maintaining the blood-brain barrier and modulating synaptic transmission [ 59 ]. Recent studies have shown that astrocytes can interact with other CNS-resident cells and peripheral immune cells, influencing disease outcomes through these interactions [ 59 ]. Elevated serum NEFL levels are associated with greater microglial activation, particularly at the rims of chronic lesions and in the perilesional normal-appearing white matter (NAWM), indicating ongoing inflammation and neuroaxonal damage [ 60 ]. Overall, taking our results into account, we might infer that Ocrelizumab treatment could mediate the downregulation of TNF-alpha and NEFL, thereby contrasting their negative effects on myelin and neurons. At the same time, the decrease in TNF-alpha and NEFL levels could serve as a sign that the drug is effectively reducing inflammation.

Conclusions

To summarize, we have performed RT-qPCR analysis on blood samples from a cohort of MS patients, before and after a year of treatment with Ocrelizumab, and observed a significant decrease in the expression of four genes (TNF-alpha, SLC16A10, NEFL and IL-6) and a significant increase of let-7a-5p expression. Statistical analysis showed a strong inverse correlation among the miRNA and the four genes, and a positive correlation when comparing the genes between themselves, thus suggesting that let-7a-5p may be a common modulator. This hypothesis is reinforced from miRNA-mRNA Database mining, that revealed indeed experimentally validated interactions of TNF-alpha, SLC16A10 and IL-6, while the interaction with NEFL could be indirect via one or more of their three common interactors.

These findings offer a mechanistic perspective on Ocrelizumab's potential molecular effects in MS; however, some limitations should be acknowledged: our analysis was based on a targeted panel rather than whole-transcriptome data, and even if the regulatory relationships inferred through in silico tools are coming from experimentally validated interactions, future studies using in vitro or in vivo models will be essential to confirm the functional roles of let-7a-5p and its putative gene targets. Nonetheless, in view of our findings, it could be worthy to continue investigating if the drug has the ability to directly upregulate let-7a-5p, and in turn downregulate the four genes eliciting secondary mechanism of action, or if the observed expression modulation is an indicator of the effective reduction of overall inflammation. Still, both interpretations potentially highlight the drug's beneficial role in managing the disease.

CRediT authorship contribution statement

Bruna De Felice: Writing – original draft, Supervision, Formal analysis, Conceptualization. Elisabetta Signoriello: Conceptualization. Concetta Montanino: Formal analysis. Giuseppe Romano: Data curation. Deborah Archetto: Methodology. Elisabetta Maida: Conceptualization. Martina Marciano: Conceptualization. Simona Bonavita: Conceptualization. Giacomo Lus: Conceptualization. Federica Farinella: Formal analysis. Cinzia Coppola: Conceptualization.

Data availability

The corresponding author can provide the datasets created and examined during the current investigation upon reasonable request.

Ethics approval

Written consent was obtained from all the participant in the study. The entire work was conducted according to the guidelines of The Ethics Committee of the University of Study of Campania “Luigi Vanvitelli” (Prot. 12478/20), as well as the Helsinki Declaration's guidelines for handling samples.

Funding

All authors declare that they did not accept any grants, funding, or other forms of assistance in order to prepare this paper.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Peer review under the responsibility of Editorial Board of Non-coding RNA Research.

References

Associated Data

Data Availability Statement

The corresponding author can provide the datasets created and examined during the current investigation upon reasonable request.