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Record W4412702970 · doi:10.1021/acsptsci.5c00403

Use of Proteomics to Explore Biomarkers of Amyotrophic Lateral Sclerosis (ALS): Proof of Principle from Humanized SOD1 Mouse to Human ALS

2025· review· en· W4412702970 on OpenAlexafffund
Nitesh Sanghai, Geoffrey K. Tranmer

Bibliographic record

VenueACS Pharmacology & Translational Science · 2025
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchResearch Manitoba
KeywordsAmyotrophic lateral sclerosisSOD1MedicineProteomicsProof of conceptPreclinical researchNeuroscienceBiologyDiseasePathologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.239
GPT teacher head0.448
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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