Use of Proteomics to Explore Biomarkers of Amyotrophic Lateral Sclerosis (ALS): Proof of Principle from Humanized SOD1 Mouse to Human ALS
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a rare motor neurodegenerative disease affecting multiple cellular proteins during the progression of the disease. ALS was first discovered by Charcot in 1869, and since then, scientists have been unable to identify a singular cause of the disease. Further, there are no effective treatments available to cure ALS. The benchmark discovery of humanized preclinical SOD1 mouse models, which recapitulates the clinical and pathological phenotypes of human ALS, gives hope to medicinal chemists and neuroscientists around the globe that a suitable drug-like molecule can be discovered and translated into human beings as a means to slow down the progression of the disease. However, little success has been achieved until now in terms of finding an effective treatment for heterogenic and incurable ALS. One area marked for improvement is the use of semiquantitative, antibody-based targeted Western blotting (WB) experiments, which lack the power to analyze multiple cellular events within the entire dysregulated proteomic system. With the inconsistency of WB experiments, unexpected cellular pathways go undiscovered, and hence, loss of translation with no target engagement is seen from preclinical to human clinical ALS. Recent advancements in discovery-based quantitative proteomics have many advantages over WB. These innovative techniques could help solve the inherent problem in WB and their inability to discover multiple altered proteins with the added capability of longitudinal analysis in preclinical SOD1 models, further validating the findings in human ALS. Herein, we applied a holistic approach to summarize various reports on the use of proteomics in ALS from the published literature, and importantly, we found that using a discovery-based proteomics approach in SOD1 preclinical ALS models has revealed a more diverse and global picture of pathological proteins that affect multiple pathways during different stages of disease progression. Furthermore, we found that the proteomic profiling of the humanized SOD1 mouse model provided a proof of principle for translating the diverse pathological biomarker proteins identified in clinical human ALS cases. Moreover, we believe that advancements in the proteomics approach toward ALS biomarkers could bridge the gap between preclinical and clinical studies, enabling scientists worldwide to discover novel biomarkers and treatments that modify the progression of ALS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".