Using Epidemiology, Immunology, and Genomics to Study the Biology of Chlamydia trachomatis
Bibliographic record
Abstract
ABSTRACT: The traditional framework in which to study the biology of human infectious diseases involves characterizing interactions and features of the host, pathogen, and environment. Using the tools of epidemiology, immunology, and genomics allows one to study the biology of infectious disease within this framework. The study of Chlamydia trachomatis biology vividly illustrates the usefulness for the approach. I note key findings from my own studies on C. trachomatis epidemiology, immunology, and genomics to show how important light has been shed on its biology and how this has impacted the Chlamydia field generally. In particular, the epidemiology of C. trachomatis diseases in women shows its impact on reproduction and how public health programs to detect and treat infection has reduced that impact but at the cost of arresting the development of protective immunity and increasing the risk of infection and reinfection. Immunological studies demonstrate the importance of CD4 Th1 cells in protection and that antibiotic treatment interferes with the development of protective immunity when given early in the course of infection. Evaluating the T-cell antigen landscape for C. trachomatis and Chlamydia muridarum demonstrates the role of surface proteins such as the major outer-membrane protein and the polymorphic membrane proteins as major protective CD4 T-cell antigens. Genomic studies reveal that the genome of organism has 3 loci of immunological interest. The antigen loci of the major outer-membrane protein and polymorphic membrane proteins are hotspots for both mutation and recombination, and the plasticity zone contains immune evasion genes that are highly variable from species to species. Interestingly, these 3 loci seem to have entered the Chlamydia phylum at the time of the evolution of the Chlamydiaceae when they became pathogens of vertebrates and encountered the adaptive immune system. In aggregate, these 3 approaches have shed light on human C. trachomatis infections and suggest paths for vaccine development. These approaches are likely to remain useful for the further study of C. trachomatis and for other human pathogens.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".