Myelin Repair Foundation

Author Of 1 Presentation

Biomarkers and Bioinformatics Poster Presentation

P0082 - From proteome to interactome: a mechanistic approach to MS biomarker discovery (ID 1215)

Speakers
Presentation Number
P0082
Presentation Topic
Biomarkers and Bioinformatics

Abstract

Background

Multiple sclerosis (MS) is a multifaceted disease with an intricate pathophysiology that lies at the intersection of autoimmunity, inflammation, redox imbalance, demyelination, and neurodegeneration. The varying interplay of distinct and converging mechanistic profiles in MS is believed to contribute to the heterogeneity observed in disease course and outcomes, clinical presentation, and therapeutic response. With this high degree of undeciphered molecular complexity, identifying biomolecular markers that are reproducible as well as specific to even the major subclasses of MS has been problematic. These difficulties have hindered the clinical translation of biomarkers and their use to aid in disease assessment and treatment strategies for individual MS patients.

Objectives

We posit that a biocentric framework can be leveraged to augment the prognostic capacity of MS biomarkers. By coupling machine learning findings with a computational illustration of their dynamical interactions within disease-perturbed networks, we hope to extract biomarkers that convey the full spectrum of MS pathophysiology—from disease activity to worsening and progression. Furthermore, by honing in on biomarkers that reflect the true underlying biology, we may be able to expand on the standard list of MS clinical and surrogate endpoints, further guiding patient stratification and informing targeted therapeutic selection and drug repurposing.

Methods

Using a panel of 21 protein serum biomarkers that were pre-selected per radiographic and clinical endpoints, we ran spatial expression correlation to extract a proteomic signature specific to MS organs and cell types. We modeled the functional connectivity of these proteins and then performed unsupervised clustering and network centrality analysis to identify motifs of interconnected proteins. Network motifs were later annotated using significantly enriched gene ontology terms and pathways in order to contextualize their mechanism-of-action with respect to MS.

Results

Topological mapping of protein functional interactions uncovered 10 major pathological profiles in MS: (1) myelin integrity; (2) lipid metabolism; (3) immune modulation; (4) inflammation; (5) cerebrovascular function; (6) cell-cell communication; (7) cellular energetics; (8) synaptic dynamics; (9) neuroaxonal integrity; and (10) gut microbiota. Their shifting degree of involvement was captured to characterize the different paths to disease activity and progression. A rich repertoire of immune, glial, and neuronal cells was implicated as being critical in orchestrating the synergistically evolving crosstalk between these various disease mechanisms.

Conclusions

Through a complementary integration of data analytics and systems biology, we were able to shift the focus from that of single proteins to disease processes, identifying clinical biomarkers that appear to fully recapitulate hallmarks of MS.

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