Harnessing Proteomics for Precision Medicine in Multiple Sclerosis
Multiple sclerosis (MS) is a complex immune-mediated neurodegenerative disease. It's a chronic, inflammatory, and neurodegenerative condition that attacks the central nervous system, affecting nearly 2.8 million people around the globe. It's more common in young adults, particularly women, and is more prevalent in areas further from the equator. While there's been progress in how we classify, diagnose, and treat MS, many questions remain, and that’s where the exciting field of proteomics comes in.
What is Proteomics Anyway?
Think of our genes as the blueprints of our bodies. Genomics, the study of genes, has been a game-changer. However, genes don't directly do the work; they create proteins, which are the workhorses of our cells. Proteomics is the study of these proteins, including their structure, function, and interactions. Because proteins are closer to the actual biological processes, studying them can provide a wealth of information about diseases like MS.
Why Proteomics Matters for MS
The ultimate goal is personalized medicine, which means tailoring treatments to each patient's specific needs. In MS, there’s a critical need for biomarkers—measurable substances that can indicate disease, predict its course, or monitor the effectiveness of treatments. Proteomics has the potential to find these much-needed biomarkers. Currently, there isn't a specific biomarker for diagnosing MS or for predicting how the disease will progress, which would be invaluable for clinicians.
How Proteomics Works
Proteomics uses several advanced technologies, such as mass spectrometry and gel electrophoresis, to analyze proteins. These methods help researchers to identify and quantify the different proteins that are present in biological samples. There are two main approaches for quantitative proteomic analysis:
* Top-down analysis: This method first separates proteins using gel-based techniques, then uses liquid chromatography-tandem mass spectrometry (LC-TMS) to identify them. An integrative approach uses 2DE to separate proteins by charge, then uses SDS-PAGE to identify proteins by mass, and then uses LC-TMS.
* Bottom-up analysis: This method involves digesting protein extracts into smaller peptides, which are then analyzed by LC-TMS. The amino acid sequences are then inferred using online databases.
Where do the samples come from?
Finding the right sample to analyze is critical in proteomics research. There are several different types of biological samples used, each with its own advantages and disadvantages:
* Blood: Blood is easily accessible and can be sampled repeatedly. However, CNS-related proteins are often present in low quantities, making detection difficult.
* Cerebrospinal fluid (CSF): CSF is in direct contact with the CNS, so it contains more CNS-related proteins than blood. However, obtaining CSF requires a lumbar puncture, which is more invasive.
* Saliva and Tears: These are easy to collect, but they may not contain enough protein for analysis.
* Urine: Urine is easy and non-invasive to collect, but it has low protein content and metabolic variations can complicate analysis.
* CNS Tissue Samples: These samples provide the most detailed information about the affected area of the CNS. However, they are difficult to obtain in living patients, so these samples are primarily used in animal models and post-mortem studies.
What have we learned about MS so far?
Proteomics has already provided valuable insights into the pathogenesis of MS using animal models. For example, studies using experimental autoimmune encephalomyelitis (EAE) and cuprizone models in mice have revealed the dysregulation of a variety of proteins involved in:
* Synaptic transmission.
* Mitochondrial function.
* Blood-brain barrier (BBB) integrity.
* Myelin formation and remyelination.
* Inflammatory pathways
* The renin–angiotensin–aldosterone system (RAAS).
Research on human tissue samples has also identified potential biomarkers for MS:
* Diagnosis and differential diagnosis: Some proteins, such as connective tissue growth factor/cysteine-rich protein/nephroblastoma overexpressed-5 (CCN5), von Willebrand factor (vWF), and glial fibrillary acidic protein (GFAP) are being explored to distinguish MS from other neurological conditions.
* Conversion from CIS to RRMS: Studies have identified several proteins that may predict the conversion of clinically isolated syndrome (CIS) to relapsing-remitting MS (RRMS). One of the most promising candidates is chitinase 3-like 1 (CHI3L1).
* Disease Activity: Secretogranin-1 may be a marker of disease progression.
* Disease Progression: Several proteins have been identified that may be associated with the severity and progression of MS, including CHI3L2.
* Monitoring Therapy: Proteomic analysis is being used to monitor the effects of disease-modifying therapies (DMTs) on the levels of specific proteins in blood and CSF.
What's Next?
Proteomics is a rapidly evolving field with the potential to significantly improve the lives of people living with MS. More research is needed to validate the identified biomarkers and to translate these findings into clinical practice. However, the future is bright. We are starting to understand the complex molecular mechanisms that drive MS and are closer than ever to finding better ways to diagnose, treat, and ultimately prevent this debilitating disease. By continuing to push the boundaries of proteomics, we can unlock the full potential of personalized medicine for MS.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Sandi, D., Kokas, Z., Biernacki, T., Bencsik, K., Klivényi, P., & Vécsei, L. (2022). Proteomics in multiple sclerosis: the perspective of the clinician. International Journal of Molecular Sciences, 23(9), 5162.
