Insights into the Regulation of Algal Proteins and Bioactive Peptides Using Proteomic and Transcriptomic Approaches
Bibliographic record
Abstract
Oceans abound in resources of various kinds for R&D and for commercial applications. Monitoring and bioprospecting allow the identification of an increasing number of key natural resources. Macroalgae are essential elements of marine ecosystems as well as a natural resource influenced by dynamic environmental factors. They are not only nutritionally attractive but have also demonstrated potential health benefits such as antioxidant, antihypertensive, and anti-inflammatory activities. Several bioactive peptides have been observed following enzymatic hydrolysis of macroalgal proteins. In addition, significant differences in protein bioactivities and peptide extracts of wild and cultivated macroalgae have been highlighted, but the metabolic pathways giving rise to these bioactive molecules remain largely elusive. Surprisingly, the biochemistry that underlies the environmental stress tolerance of macroalgae has not been well investigated and remains poorly understood. Proteomic and functional genomic approaches based on identifying precursor proteins and bioactive peptides of macroalgae through integrated multi-omics analysis can give insights into their regulation as influenced by abiotic factors. These strategies allow evaluating the proteomics profile of regulation of macroalgae in response to different growth conditions as well as establishing a comparative transcriptome profiling targeting structural protein-coding genes. Elucidation of biochemical pathways in macroalgae could provide an innovative means of enhancing the protein quality of edible macroalgae. This could be ultimately viewed as a powerful way to drive the development of a tailored production and extraction of high value molecules. This review provides an overview of algal proteins and bioactive peptide characterization using proteomics and transcriptomic analyses.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".