Ether lipids and a peroxisomal riddle in sperm
Bibliographic record
Abstract
Sperm are terminally differentiated cells that lack most of the membranous organelles, resulting in a high abundance of ether glycerolipids found across different species. Ether lipids include plasmalogens, platelet activating factor, GPI-anchors and seminolipid. These lipids play important roles in sperm function and performance, and thus are of special interest as potential fertility markers and therapeutic targets. In the present article, we first review the existing knowledge on the relevance of the different types of ether lipids for sperm production, maturation and function. To further understand ether-lipid metabolism in sperm, we then query available proteomic data from highly purified sperm, and produce a map of metabolic steps retained in these cells. Our analysis pinpoints the presence of a truncated ether lipid biosynthetic pathway that would be competent for the production of precursors through the initial peroxisomal core steps, but devoid of subsequent microsomal enzymes responsible for the final synthesis of all complex ether-lipids. Despite the widely accepted notion that sperm lack peroxisomes, the thorough analysis of published data conducted herein identifies nearly 70% of all known peroxisomal resident proteins as part of the sperm proteome. In view of this, we highlight open questions related to lipid metabolism and possible peroxisomal functions in sperm. We propose a repurposed role for the truncated peroxisomal ether-lipid pathway in detoxification of products from oxidative stress, which is known to critically influence sperm function. The likely presence of a peroxisomal-derived remnant compartment that could act as a sink for toxic fatty alcohols and fatty aldehydes generated by mitochondrial activity is discussed. With this perspective, our review provides a comprehensive metabolic map associated with ether-lipids and peroxisomal-related functions in sperm and offers new insights into potentially relevant antioxidant mechanisms that warrant further research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".