Active Brain & Nervous System Genetics & Molecular Biology

Exploiting state-of-the-art mass spectrometry to identify blood-based biomarkers for TDP-43 driven Motor Neuron Disease.

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A simple blood test could cut the year-long diagnostic delay for motor neuron disease (MND) by detecting telltale protein fragments shed by dying nerve cells. The problem is that MND diagnosis currently relies on clinical observation and excluding other conditions, with no blood-based test to confirm it or predict how the disease will progress. This matters because early, accurate diagnosis could allow patients to access treatments and plan care sooner, while prognostic information would help them and their clinicians make informed decisions. The researchers will search for “cryptic peptides”—protein fragments that appear only when the key MND-driving protein TDP-43 malfunctions—in blood samples from people with MND. They will first identify these fragments in lab-grown immune cells engineered to mimic TDP-43 pathology, then develop a highly sensitive mass spectrometry assay to detect them in patient blood. If successful, this project would produce a scalable, multiplexed blood test that not only diagnoses MND but also distinguishes subtypes and predicts disease course. The long-term goal is to translate these findings into clinical-grade antibody tests for routine use, giving neurologists a practical tool to monitor disease pathology in real time.

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People living with MND (pwMND) rightly are concerned by the time and delay in reaching a diagnosis (1year or more) and the absence of prognostic information at diagnosis. This proposal seeks to generate a simple blood-based tests that will not only be potentially diagnostic but also inform on prognosis and / or sub-type of MND. Recent studies have identified fragments of proteins called "cryptic peptides" that are uniquely present in MND post-mortem brain tissue. We will use state-of-the-art techniques to determine first whether these same protein fragments / cryptic peptides can be found in human blood where we have simulated the key driving pathology found in MND. This will then allow us to develop a targeted and measurable test for quantifying these fragments in the blood of pwMND. In summary, this proposal aims to develop new simple and scalable diagnostic and predictive tools for pwMND. Cytoplasmic mis-localisation and aggregation of TDP-43 is the key pathological signature of ca. 97% of MND including both genetic and sporadic cases. Recent studies have shown TDP-43 pathology leads to its nuclear loss-of-function and subsequent cryptic splicing of a plethora of genes. Some of those mis-spliced transcripts are translated into de novo proteins that are uniquely observed in the pathological MND cases which can be exploited for developing biomarkers that link to TDP-43 LOF. The aim of this project is to identify such TDP-43 pathology driven cryptic peptides that are uniquely present in blood samples. First, we will identify TDP-43 LOF driven cryptic peptides in human peripheral macrophages using proteomics and transcriptomic approaches on TDP-43 depleted macrophages. Second, we will develop a unique and highly sensitive multiplexed parallel reaction monitoring mass spectrometry (PRM-MS) assays to quantitatively detect panel of cryptic peptides. Finally, we will apply the PRM-MS assay to detect and quantify specific levels of cryptic peptides in peripheral macrophages and serum/plasma of deeply phenotyped and genotyped pwMND samples obtained through MND-SMART and CARE-MND. The long-term goal for this research project, beyond the goal of the current proposal, is to develop clinical grade monoclonal antibodies more easily monitor the levels of mis-spliced biomarker proteins resulting from TDP-43 LOF levels in clinical setting. Ultimately, this project is the first step in establishing a multiplexed blood-based biomarker assay for MND disease pathology.

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Researchers

Bhuvaneish Selvaraj (Co-Investigator)Dario Alessi (Co-Investigator)RAJA SEKHAR NIRUJOGI (Principal Investigator)Suvankar Pal (Co-Investigator)

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Original classification

Research and Innovation

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