Targeted Quantitative Lipidomics of Cold Stress and the Development of Methods to Increase the Sensitivity of Proteomics Analyses Using Mass Spectrometry
Bibliographic record
Abstract
This thesis focuses on mass spectrometry (MS) based techniques and uses them to discover new lipidomic profiles of species that adapt to cold temperatures in the northern climates.Additionally, techniques were developed to enhance MS-based proteomics analyses for better protein identification.This research looks at both qualitative and quantitative analysis techniques: the focus in the lipidomic work is mainly on relative quantification, while the proteomics work enhances qualitative analyses.A lipid bilayer is of interest and will be examined in the tissues of several animal models.The composition of lipid bilayers is investigated in three different cold stress adaptation mechanisms including hibernation, freeze tolerance and freeze avoidance.Also examined are lipid bilayer differences in cold adaption in vital versus non-vital organs.The research was also conducted on how certain seasonal rhythms are preserved even in the absence of environmental cues.The study models that were used were the thirteen-lined ground squirrel for hibernation, the wood frog for freeze tolerance and the goldfish for freeze avoidance traits.Our results elucidate some exciting patterns of lipid bilayers in adaptation to cold stress.Increases in the concentration of unsaturated phospholipids in cold temperatures, particularly in the squirrel and frog liver tissues were observed.Also, in some cases increases in phosphatidylethanolamine lipids were observed in the lipid bilayers during winter months in comparison to summer months.These biomolecular dynamics are linked with increases in the fluidity of the lipid bilayer which is necessary for a continued physiological function at lower temperatures.iii Proteomics work was focused further developments of the Trimethylation Enhancement using Diazomethane (TrEnDi) technique.Diazomethane was used to methylate tryptically digested peptides or commercial peptides.TrEnDi derivatization allows for the formation of fixed permanent charges on the peptides making them more sensitive in MS analyses.These results also highlight a novel method to identify the phosphorylation of peptides, which holds a significant interest in a great deal of clinical and health-based research as dysfunctional phosphorylation pathways are linked to numerous diseases.Although TrEnDi derivatization requires further optimization on peptide samples at this point in time, the developments described herein demonstrate that it is a unique method that can enhance the sensitivity of MS-based peptide analysis in numerous ways. 4.8
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".