Identification of TEX101-associated Proteins Through Proteomic Measurement of Human Spermatozoa Homozygous for the Missense Variant rs35033974*
Bibliographic record
Abstract
TEX101 is a germ-cell-specific protein and a validated biomarker of male infertility. Mouse TEX101 was found essential for male fertility and was suggested to function as a cell surface chaperone involved in maturation of proteins required for sperm migration and sperm–oocyte interaction. However, the precise functional role of human TEX101 is not known and cannot be studied in vitro due to the lack of human germ cell lines. Here, we genotyped 386 men for a common missense variant rs35033974 of TEX101 and identified 52 heterozygous and 4 homozygous men. We then discovered by targeted proteomics that the variant allele rs35033974 was associated with the near-complete degradation (>97%) of the corresponding G99V TEX101 form and suggested that spermatozoa of homozygous men could serve as a knockdown model to study TEX101 function in humans. Differential proteomic profiling with label-free quantification measured 8,046 proteins in spermatozoa of eight men and identified eight cell-surface and nine secreted testis-specific proteins significantly down-regulated in four patients homozygous for rs35033974. Substantially reduced levels of testis-specific cell-surface proteins potentially involved in sperm migration and sperm–oocyte interaction (including LY6K and ADAM29) were confirmed by targeted proteomics and Western blotting assays. Because recent population-scale genomic data revealed homozygous fathers with biological children, rs35033974 is not a monogenic factor of male infertility in humans. However, median TEX101 levels in seminal plasma were found fivefold lower ( p = 0.0005) in heterozygous than in wild-type men of European ancestry. We conclude that spermatozoa of rs35033974 homozygous men have substantially reduced levels of TEX101 and could be used as a model to elucidate the precise TEX101 function, which will advance biology of human reproduction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".