Integrative gene–tissue microarray-based approach for identification of human disease biomarkers: application to amyotrophic lateral sclerosis
Bibliographic record
Abstract
Advances in genomics and proteomics permit rapid identification of disease-relevant genes and proteins. Challenges include biological differences between animal models and human diseases, high discordance between DNA and protein expression data and a lack of experimental models to study human complex diseases. To overcome some of these limitations, we developed an integrative approach using animal models, postmortem human material and a combination of high-throughput microarray methods to identify novel molecular markers of amyotrophic lateral sclerosis (ALS). We used laser capture microdissection coupled with microarrays to identify early transcriptome changes occurring in spinal cord motor neurons or surrounding glial cells. Two models of familial motor neuron disease, SOD1(G93A) and TAU(P301L), transgenic mice were used at the presymptomatic stage. Identified gene expression changes were predominantly model-specific. However, several genes were regulated in both models. The relevance of identified genes as clinical biomarkers was tested in the peripheral blood transcriptome of presymptomatic SOD1(G93A) animals using custom-designed ALS microarray. To confirm the relevance of identified genes in human sporadic ALS (SALS), selected corresponding protein products were examined by high-throughput immunoassays using tissue microarrays constructed from human postmortem spinal cord tissues. Genes that were identified by these experiments and located within a linkage region associated with familial ALS/frontotemporal dementia were sequenced in several families. This large-scale gene and protein expression study pointing to distinct molecular mechanisms of TAU- and SOD1-induced motor neuron degeneration identified several new SALS-relevant proteins (CNGA3, CRB1, OTUB2, MMP14, SLK, DDX58, RSPO2) and putative blood biomarkers, including Nefh, Prph and Mgll.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".