A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Dysregulated lipid metabolism networks modulate T-cell function in people with relapsing-remitting multiple sclerosis

Altered cholesterol, oxysterol, sphingolipid, and fatty acid concentrations are reported in blood, cerebrospinal fluid, and brain tissue of people with relapsing-remitting multiple sclerosis (RRMS) and are linked to disease progression and treatment responses. CD4 + T cells are pathogenic in RRMS, and defective T-cell function could be mediated in part by liver X receptors (LXRs)-nuclear receptors that regulate lipid homeostasis and immunity. RNA-sequencing and pathway analysis identified that genes within the 'lipid metabolism' and 'signalling of nuclear receptors' pathways were dysregulated in CD4 + T cells isolated from RRMS patients compared with healthy donors. While LXRB and genes associated with cholesterol metabolism were upregulated, other T-cell LXR-target genes, including genes involved in cellular lipid uptake (inducible degrader of the LDL receptor, IDOL), and the rate-limiting enzyme for glycosphingolipid biosynthesis (UDP-glucosylceramide synthase, UGCG) were downregulated in T cells from patients with RRMS compared to healthy donors. Correspondingly, plasma membrane glycosphingolipids were reduced, and cholesterol levels increased in RRMS CD4 + T cells, an effect partially recapitulated in healthy T cells by in vitro culture with T-cell receptor stimulation in the presence of serum from RRMS patients. Notably, stimulation with LXR-agonist GW3965 normalized membrane cholesterol levels, and reduced proliferation and IL17 cytokine production in RRMS CD4 + T-cells. Thus, LXR-mediated lipid metabolism pathways were dysregulated in T cells from patients with RRMS and could contribute to RRMS pathogenesis. Therapies that modify lipid metabolism could help restore immune cell function.

Multiple Sclerosis: From the Application of Oligoclonal Bands to Novel Potential Biomarkers

Multiple sclerosis is a chronic immune-mediated disorder of the central nervous system with a high heterogeneity among patients. In the clinical setting, one of the main challenges is a proper and early diagnosis for the prediction of disease activity. Current diagnosis is based on the integration of clinical, imaging, and laboratory results, with the latter based on the presence of intrathecal IgG oligoclonal bands in the cerebrospinal fluid whose detection via isoelectric focusing followed by immunoblotting represents the gold standard. Intrathecal synthesis can also be evidenced by the measurement of kappa free light chains in the cerebrospinal fluid, which has reached similar diagnostic accuracy compared to that of oligoclonal bands in the identification of patients with multiple sclerosis; moreover, recent studies have also highlighted its value for early disease activity prediction. This strategy has significant advantages as compared to using oligoclonal band detection, even though some issues remain open. Here, we discuss the current methods applied for cerebrospinal fluid analysis to achieve the most accurate diagnosis and for follow-up and prognosis evaluation. In addition, we describe new promising biomarkers, currently under investigation, that could contribute both to a better diagnosis of multiple sclerosis and to its monitoring of the therapeutic treatment response.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Predicting glucocorticoid resistance in multiple sclerosis relapse via a whole blood transcriptomic analysis

AIMS

Treatment of multiple sclerosis (MS) relapses consists of short-term administration of high-dose glucocorticoids (GCs). However, over 40% of patients show an insufficient response to GC treatment. We aimed to develop a predictive model for such GC resistance.

METHODS

We performed a receiver operating characteristic (ROC) curve analysis following the transcriptomic assay of whole blood samples from stable, relapsing GC-sensitive and relapsing GC-resistant patients with MS in two different European centers.

RESULTS

We identified 12 genes being regulated during a relapse and differentially expressed between GC-sensitive and GC-resistant patients with MS. Using these genes, we defined a statistical model to predict GC resistance with an area under the curve (AUC) of the ROC analysis of 0.913. Furthermore, we observed that relapsing GC-resistant patients with MS have decreased GR, DUSP1, and TSC22D3 mRNA levels compared with relapsing GC-sensitive patients with MS. Finally, we showed that the transcriptome of relapsing GC-resistant patients with MS resembles those of stable patients with MS.

CONCLUSION

Predicting GC resistance would allow patients to benefit from prompt initiation of an alternative relapse treatment leading to increased treatment efficacy. Thus, we think our model could contribute to reducing disability development in people with MS.

Mitochondrial Dynamics in Neurodegenerative Diseases: Unraveling the Role of Fusion and Fission Processes

Neurodegenerative diseases (NDs) are a diverse group of disorders characterized by the progressive degeneration and death of neurons, leading to a range of neurological symptoms. Despite the heterogeneity of these conditions, a common denominator is the implication of mitochondrial dysfunction in their pathogenesis. Mitochondria play a crucial role in creating biomolecules, providing energy through adenosine triphosphate (ATP) generated by oxidative phosphorylation (OXPHOS), and producing reactive oxygen species (ROS). When they're not functioning correctly, becoming fragmented and losing their membrane potential, they contribute to these diseases. In this review, we explore how mitochondria fuse and undergo fission, especially in the context of NDs. We discuss the genetic and protein mutations linked to these diseases and how they impact mitochondrial dynamics. We also look at the key regulatory proteins in fusion (MFN1, MFN2, and OPA1) and fission (DRP1 and FIS1), including their post-translational modifications. Furthermore, we highlight potential drugs that can influence mitochondrial dynamics. By unpacking these complex processes, we aim to direct research towards treatments that can improve life quality for people with these challenging conditions.

Advanced methods and novel biomarkers in autoimmune diseases ‑ a review of the recent years progress in systemic lupus erythematosus

There are several autoimmune and rheumatic diseases affecting different organs of the human body. Multiple sclerosis (MS) mainly affects brain, rheumatoid arthritis (RA) mainly affects joints, Type 1 diabetes (T1D) mainly affects pancreas, Sjogren's syndrome (SS) mainly affects salivary glands, while systemic lupus erythematosus (SLE) affects almost every organ of the body. Autoimmune diseases are characterized by production of autoantibodies, activation of immune cells, increased expression of pro-inflammatory cytokines, and activation of type I interferons. Despite improvements in treatments and diagnostic tools, the time it takes for the patients to be diagnosed is too long, and the main treatment for these diseases is still non-specific anti-inflammatory drugs. Thus, there is an urgent need for better biomarkers, as well as tailored, personalized treatment. This review focus on SLE and the organs affected in this disease. We have used the results from various rheumatic and autoimmune diseases and the organs involved with an aim to identify advanced methods and possible biomarkers to be utilized in the diagnosis of SLE, disease monitoring, and response to treatment.