Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

BACKGROUND

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

METHODS

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

RESULTS

We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.

CONCLUSION

Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Multiscale networks in multiple sclerosis

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Epigallocatechin Gallate: A Multifaceted Molecule for Neurological Disorders and Neurotropic Viral Infections

Epigallocatechin-3-gallate (EGCG), a polyphenolic moiety found in green tea extracts, exhibits pleiotropic bioactivities to combat many diseases including neurological ailments. These neurological diseases include Alzheimer's disease, multiple sclerosis, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. For instance, in the case of Alzheimer's disease, the formation of a β-sheet in the region of the 10th-21st amino acids was significantly reduced in EGCG-induced oligomeric samples of Aβ40. Its interference induces the formation of Aβ structures with an increase in intercenter-of-mass distances, reduction in interchain/intrachain contacts, reduction in β-sheet propensity, and increase in α-helix. Besides, numerous neurotropic viruses are known to instigate or aggravate neurological ailments. It exerts an effect on the oxidative damage caused in neurodegenerative disorders by acting on GSK3-β, PI3K/Akt, and downstream signaling pathways via caspase-3 and cytochrome-c. EGCG also diminishes these viral-mediated effects, such as EGCG delayed HSV-1 infection by blocking the entry for virions, inhibitory effects on NS3/4A protease or NS5B polymerase of HCV and potent inhibitor of ZIKV NS2B-NS3pro/NS3 serine protease (NS3-SP). It showed a reduction in the neurotoxic properties of HIV-gp120 and Tat in the presence of IFN-γ. EGCG also involves numerous viral-mediated inflammatory cascades, such as JAK/STAT. Nonetheless, it also inhibits the Epstein-Barr virus replication protein (Zta and Rta). Moreover, it also impedes certain viruses (influenza A and B strains) by hijacking the endosomal and lysosomal compartments. Therefore, the current article aims to describe the importance of EGCG in numerous neurological diseases and its inhibitory effect against neurotropic viruses.

Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?

Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.

Neuron-immunity communication: mechanism of neuroprotective effects in EGCG

Epigallocatechin gallate (EGCG), a naturally occurring active ingredient unique to tea, has been shown to have neuroprotective potential. There is growing evidence of its potential advantages in the prevention and treatment of neuroinflammation, neurodegenerative diseases, and neurological damage. Neuroimmune communication is an important physiological mechanism in neurological diseases, including immune cell activation and response, cytokine delivery. EGCG shows great neuroprotective potential by modulating signals related to autoimmune response and improving communication between the nervous system and the immune system, effectively reducing the inflammatory state and neurological function. During neuroimmune communication, EGCG promotes the secretion of neurotrophic factors into the repair of damaged neurons, improves intestinal microenvironmental homeostasis, and ameliorates pathological phenotypes through molecular and cellular mechanisms related to the brain-gut axis. Here, we discuss the molecular and cellular mechanisms of inflammatory signaling exchange involving neuroimmune communication. We further emphasize that the neuroprotective role of EGCG is dependent on the modulatory role between immunity and neurology in neurologically related diseases.

Proteomics in Multiple Sclerosis: The Perspective of the Clinician

Multiple sclerosis (MS) is the inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS) that affects approximately 2.8 million people worldwide. In the last decade, a new era was heralded in by a new phenotypic classification, a new diagnostic protocol and the first ever therapeutic guideline, making personalized medicine the aim of MS management. However, despite this great evolution, there are still many aspects of the disease that are unknown and need to be further researched. A hallmark of these research are molecular biomarkers that could help in the diagnosis, differential diagnosis, therapy and prognosis of the disease. Proteomics, a rapidly evolving discipline of molecular biology may fulfill this dire need for the discovery of molecular biomarkers. In this review, we aimed to give a comprehensive summary on the utility of proteomics in the field of MS research. We reviewed the published results of the method in case of the pathogenesis of the disease and for biomarkers of diagnosis, differential diagnosis, conversion of disease courses, disease activity, progression and immunological therapy. We found proteomics to be a highly effective emerging tool that has been providing important findings in the research of MS.

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with structural annotations of protein-protein interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

Circular RNA: A promising new star for the diagnosis and treatment of colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive tract. According to the research of circular RNAs in the CRC field, compared with linear RNAs, circular RNAs are a special type of noncoding RNA that are covalently closed circular structures, which have no 5' cap structure and 3' polyA tail and are not affected by RNA exonuclease and actinomycin D.

Biological functions

Notably, circular RNAs have a high degree of stability and potential effect on gene regulation. Meanwhile, circular RNAs are involved in the sponge action of microRNAs and mediate protein translation and direct binding, alternative splicing, and histone modification.

Relationships with CRC

Studies have shown that circular RNAs are related to the proliferation, invasion, recurrence, metastasis, ferroptosis, apoptosis, and chemotherapy resistance of CRC.

Conclusions

This article provides a brief review based on the source, structural characteristics, mechanisms, biological functions of circular RNAs, and the relationships between CRC.