Interspecific interactions and aging: Prediction of gerogenic bacteria and critical human protein targets of microbial infections
Bibliographic record
Abstract
Bacteria permeate every niche of the human body with major consequences on our health and senescence that have not been fully described. Here, we predict which bacteria and which bacterial proteins could interfere with proteins associated with human aging using bipartite networks showing interspecific protein interactions coupled with investigations of published experimental evidence and transcriptomic data. We introduce the term of "gerogenic" bacteria, literally bacteria that could induce some aging in their host and discuss the mechanisms by which such bacteria could serve as age-distorters of humans. Salmonella, Escherichia and Shigella appear as major candidate age-distorters, characterized by a higher experimentally demonstrated potential than other bacteria to interact with human proteins associated with human aging and human cellular senescence. Our analysis also highlights an evolutionary convergence among bacterial and viral candidate age-distorting proteins, since 14 human proteins associated with aging can be commonly targeted by bacteria and viruses in case of microbial infection. Since infections are common and Salmonella, Escherichia and Shigella are frequently found as pathogens in our microbiomes, characterizing bacterial influence on our aging and our cellular senescence through molecular hijacking could enhance the understanding of the causes of aging and suggest new anti-aging therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".