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Proteomics of Multiple Sclerosis: Inherent Issues in Defining the Pathoetiology and Identifying (Early) Biomarkers
Monokesh K Sen1, Mohammed S M Almuslehi1,2, Peter J Shortland3
1School of Medicine, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia.
Abstract:
Multiple Sclerosis (MS) is a demyelinating disease of the human central nervous system having an unconfirmed pathoetiology. Although animal models are used to mimic the pathology and clinical symptoms, no single model successfully replicates the full complexity of MS from its initial clinical identification through disease progression. Most importantly, a lack of preclinical biomarkers is hampering the earliest possible diagnosis and treatment. Notably, the development of rationally targeted therapeutics enabling pre-emptive treatment to halt the disease is also delayed without such biomarkers. Using literature mining and bioinformatic analyses, this review assessed the available proteomic studies of MS patients and animal models to discern (1) whether the models effectively mimic MS; and (2) whether reasonable biomarker candidates have been identified. The implication and necessity of assessing proteoforms and the critical importance of this to identifying rational biomarkers are discussed. Moreover, the challenges of using different proteomic analytical approaches and biological samples are also addressed.
Insights
Multiple Sclerosis (MS) research lacks effective animal models and preclinical biomarkers for early diagnosis and targeted therapies. This review analyzes proteomic data to identify better models and potential biomarkers for MS.
Area of Science:
- Neuroscience
- Immunology
- Proteomics
Background:
- Multiple Sclerosis (MS) is a central nervous system demyelinating disease with unknown causes.
- Current animal models do not fully replicate MS complexity, hindering research.
- A lack of preclinical biomarkers impedes early diagnosis and the development of targeted therapies.
Purpose of the Study:
- To evaluate the efficacy of animal models in mimicking MS.
- To identify potential protein biomarkers for MS diagnosis and treatment.
- To discuss the role of proteoforms in biomarker discovery.
Main Methods:
- Literature mining of MS proteomic studies.
- Bioinformatic analysis of proteomic data from patients and animal models.
- Assessment of different proteomic analytical approaches and sample types.
Main Results:
- Existing animal models have limitations in fully recapitulating MS.
- Several potential biomarker candidates were identified through proteomic analysis.
- The importance of analyzing proteoforms for biomarker discovery was highlighted.
Conclusions:
- Improved animal models and robust preclinical biomarkers are crucial for advancing MS research.
- Proteoform analysis offers a promising avenue for identifying reliable MS biomarkers.
- Addressing challenges in proteomic methodologies is essential for future discoveries.
