Analysis of the lake trout heart, blood, liver, and brain proteome using evolutionary proteomics
Bibliographic record
Abstract
Introduction Salvelinus namaycush (lake trout) is a top‐predator fish in the Great Lakes region. The Great Lakes Fish Monitoring and Surveillance Program (GLFMSP) use lake trout as a bioindicator of chemical stress by monitoring the concentrations of persistent, bioaccumulative and toxic (PBT) chemicals in their bodies. Studies have shown that elevated concentrations of PBTs can cause changes in transcribed genes, translated mRNAs, proteins produced, and post‐translational modifications in aquatic species. Though lake trout is used to monitor chemical concentrations for over 50 years, there is very little information available on the proteome of this species. A well‐developed protein database for the lake trout would help elucidate the effects of PBT chemicals on this species providing a direct health metric for the Great Lakes ecosystem. Methods In this study, heart, blood, brain, and liver samples from lake trout were analyzed by SDS‐PAGE, followed by in‐gel trypsin digestion and analysis by nanoLC‐MS/MS. The raw data was searched against different NCBI and UniProtKB databases in Mascot Daemon and the output was analyzed by Scaffold 4.3 software. Databases used include Actinopterygii, Salmonidae, Salvelinus , as well as the highly studied species Oncorhynchus mykiss and Danio rerio . Preliminary Data Previously, our group has reported a large number of proteins for lake trout liver, heart and blood (Dupree et al., Proteomics, 2019, PMID: 31578773 and Dupree et al., Proteomics, 2021, PMID: 34676671). In lake trout liver we identified 4194 proteins using the NCBI databases and 3811 potential protein hits in the UniProtKB databases. In the blood and heart we identified 838 and 580 proteins, respectively, using NCBI databases and 1180 and 561 potential protein hits for the heart and blood, respectively in BR2. A lake trout brain proteomics analysis completed using an in‐house SDS‐PAGE, followed by in gel digestion and nanoLC‐MS/MS analysis. The Mascot database search is underway. Once protein hits are cataloged, BLAST and Scaffold‐based comparisons of the liver, heart, blood and brain will be performed. Additional search of the data against the other fish relatives will also be completed. Through this process, evolutionary relationships for the lake trout species will also be explored. The current study will add to our understanding of the lake trout proteome that will be used to understand the biochemical/physiological effects of legacy PBT chemicals in the Great Lakes ecosystem. Novel Aspect Use of four types of lake trout tissue together to develop a protein database for the species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".