Background: Multiple sclerosis is characterized by both neuroinflammation and accelerated brain atrophy. These two processes can be quantified by MRI, are at least partially independent, and have different underlying pathological mechanisms. MicroRNA (miRNA) have previously shown strong ties to various neurological disease processes, and have potential as biomarkers in MS.
Objectives: To classify and immunologically characterize persons with MS based on serum miRNA profiles in conjunction with MRI phenotypes, as defined by relative burden of cerebral T2-hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF).
Methods: Cerebral T2LV and BPF were retrospectively quantified from 1.5T MRI, and used to define the following MRI phenotypes. Type I: low T2LV, low atrophy; type II: high T2LV, low atrophy; type III: low T2LV, high atrophy; type IV: high T2LV, high atrophy, in a large cross-sectional cohort (n = 1,088) and a subset with 5-year longitudinal follow-up (n = 153). Serum miRNAs were assessed on a third MS cohort with 2-year MRI phenotype stability (n = 98). A proportional odds logistic regression model was used to determine significant associations been MRI features and miRNA expression.
Results: One-third of the patients showed dissociation between lesion burden and atrophy severity as defined by MRI phenotypes II or III. At 5-year follow-up, all phenotypes showed increased atrophy (p < 0.001), disproportionally in type II (BPF −2.28%). Only type IV experienced significantly worse neurological disability scores. Types I and II had a 5-year MRI phenotype conversion rate of 33% and 46%, whereas III and IV had >90% stability. Type II switched primarily to IV (91%); type I switched primarily to II (47%) or III (37%). Baseline higher age (p = 0.006) and lower BPF (p < 0.001) predicted 5-year phenotype conversion. MicroRNA analysis revealed sixteen miRNA differentially expressed (p < 0.05, uncorrected) between the four phenotypes. Each phenotype demonstrated a distinct miRNA signature. Biological interpretation of these miRNA suggest a role for blood-brain barrier pathology. miR-22-3p, miR-361-5p, and miR-345-5p were the most valid differentiators.
Conclusions: MRI-defined MS phenotypes show high conversion rates characterized by relentless brain atrophy with or without ongoing inflammation, and results support the partial independence of these two features. Differentially expressed serum microRNA for the MRI phenotypes implicates the blood-brain barrier as an important mechanism determining pathological course. MicroRNA are promising as biomarkers in MS but require significant further verification and methodological standardization.
Neurodegeneration in Multiple Sclerosis (MS) occurs from early disease stages. Cerebrospinal fluid (CSF) Tau protein and beta-amyloid
protein (Abeta) are currently markers used in other neurodegenerative diseases. Several molecules, including Tau and Abeta, have been
investigated as suitable biomarkers of axonal damage in MS, but none is routinely used in clinical practice, also due to conflicting results.
To evaluate if CSF Tau and Abeta protein, evaluated at the diagnosis, could predict early MS disability obtained at last clinical follow-up and to evaluate a possible correlation between CSF Tau and Abeta protein with radiological prognostic markers also collected at the diagnosis (baseline).
CSF Abeta and Tau levels were determined with commercial enzyme-linked immunosorbent assay in newly diagnosed MS patients. We collected demographic, clinical, and radiological data at baseline and at last clinical follow-up. We evaluated early disability using the MS severity score (MSSS) and the MSSS age-related score (ARMSS) at last follow-up as disability outcome, and global T2 white matter lesion load (LL) with a cut-off of 9 lesions and the presence or absence of spinal cord lesions as radiological baseline prognostic markers
We enrolled 109 patients, 82 with a relapsing-remitting MS and with a mean follow-up of 4 years (SD±5y). Mean CSF values of Tau and Abeta were respectively 128,5±69 pg/ml and 557,7±258,6 pg/ml. Patients with higher CSF Tau levels at diagnosis developed higher disability evaluated with MSSS (R:0,3361, p=0,0003) and ARMSS (R:0,3088, p=0,001). At the moment, no correlations were found for Abeta and early disability markers. We also found a trend of higher Tau level and lower Abeta levels with higher T2 white matter LL and spinal cord involvement, still statistically not significant.
Our results showed a predictive role of neurodegenerative CSF markers, in particular Tau protein, in identifying early disability and worse prognosis in MS patients, indipendently from age. To our knowledge no other studies report a correlation of CSF Tau with both MSSS and ARMSS. Longer follow-up, larger population and extended analysis of radiological data are needed, to confirm a predictive and prognostic role of our biomarkers both at baseline and follow up.
The difference between Radiologically isolated syndrome (RIS) and Clinically isolated syndrome (CIS) is lack of clinical symptomatology. RIS patients have increased risk progression to CIS, however the underlying molecular mechanisms of RIS has not been yet elucidated.
To investigate peripheral blood mononuclear cells (PBMC) transcriptional profile of patients with RIS using high throughput RNA-Seq platform.
Samples of peripheral blood mononuclear cells (PBMC) were obtained from 14 RIS subjects (9 females, 31.5±3.6 years). The comparisons were performed with 26 disease modifying drugs free CIS patients (21 females, 33.2±1.9 years, EDSS 1.0±0.2, disease duration 1.6±0.5 years) and 16 age and gender matched healthy subjects (HS). All samples were applied for PBMC transcriptome analysis using Illumina RNA-Seq technology. Differentially expressed genes (DEGs, false discovery rate≥0.1, Fold change≥1.5) were obtained using DESeq2 software and functional analysis was performed by Ingenuity Pathway Analysis software.
RIS and CIS patients were characterized by 455 and 125 DEGs, respectively as compared to HS. Among CIS associated DEGs, 65 (52%) were common with RIS group. RIS transcriptional profile was enriched by genes known to be associated with inflammatory response (p=2.3E-18), including antiviral response (p=2.3E-18), antimicrobial response (p=1.9E-15), immune cell trafficking (p=5.7E-09), activation of leukocytes (p=7.1E-11), phagocytes (p=2.9E-8), antigen presenting cells (p=7.6E-08) and attraction of mononuclear leukocytes (p=1.3E-07). Moreover, the specific RIS related transcriptional profile was associated with activation of bacterial and viruses pattern recognition receptors mechanism (p=7.8E-07), interferon signaling pathway (p=1.1E-07) leading to activation of antimicrobial and antiviral mechanisms.
RIS subjects have common cross-disease blood transcriptional profile with CIS. RIS specific PBMC transcriptome, suggests the occurrence of an initial infection that triggers immune mechanisms operating in the preclinical stage of multiple sclerosis.
Neuromyelitis optica spectrum disorders (NMOSD) are autoimmune mediated astrocytopathies. Glial fibrillary acidic protein (GFAP) and S100B, two astrocyte specific, and neurofilament light chain (NfL), a neuron specific biomarker are reported to be elevated in CSF and serum or plasma in acute phases of NMOSD. Serum NfL is a predictor of relapse activity in multiple sclerosis (MS). However, whether serum levels of NfL (sNfL), GFAP (sGFAP) or S100B (sS100B) levels can be prognostic biomarkers for future acute disease activity in NMOSD has not been elucidated.
To test the prognostic potential of sGFAP, sS100B and sNfL levels during remission phase as biomarker for future relapses in NMOSD with aquaporin-4-IgG.
Median values of sGFAP, sS100B and sNfL were calculated from 47 serum samples from 18 patients in remission (>180 days after last relapse), followed for up to 10 years, and marked cut-off levels for “high” and “low” sGFAP (141.6 pg/mL), sS100B (8.6 pg/mL), and sNfL (33.9 pg/mL), respectively. Kaplan-Meier analysis, univariable and a multivariable (adjusted for age, sex, time from recent relapse and treatment) Cox-hazard model were used to compare the time to and hazard risk of the next relapse between the high and low groups for all three markers.
Twenty-five first post-relapse/remission phase samples from these 18 patients (11 had one relapse, 7 had two relapses) were selected for analyses. Patients in the high sGFAP group experienced future relapses earlier than those with low sGFAP levels (median 3710 versus 922 days, p = 0.0047) and had higher risk of future relapses (unadjusted hazard ratio (HR): 5.6 [95% CI 1.5–21.0], p = 0.010; adjusted hazard ratio: 9.5 [95% CI 1.9–47.0], p = 0.0061). In contrast, high sS100B and sNfL levels were unable to identify patients at increased risk for relapses.
We illustrate the prognostic capacity of an astrocyte specific marker, sGFAP, for future relapses in stable NMOSD. This further supports the value of this blood biomarker for potential future clinical application in guiding and monitoring treatment response of patients with NMOSD. The failure of sNfL as a neuronal marker in this capacity for NMOSD may reflect the pathogenetic differences between this disease and MS. The failure of sS100B may result from its pharmacokinetic profile as a short-lived marker in acute NMOSD, but not in stable disease.