Underwhelming or Misunderstood? Genetic Variability of Pattern Recognition Receptors in Immune Responses and Resistance to Mycobacterium tuberculosis
Bibliographic record
Abstract
Human genetic control is thought to affect a considerable part of the outcome of infection withMycobacterium tuberculosis(Mtb). Most of us deal with the pathogen by containment (associated with clinical “latency”) or sterilization, but tragically millions each year do not. After decades of studies on host genetic susceptibility toMtbinfection, genetic variation has been discovered to play a role in tuberculous immunoreactivity and tuberculosis (TB) disease. Genes encoding pattern recognition receptors (PRRs) enable a consistent, molecularly direct interaction between humans andMtbwhich suggests the potential for co-evolution. In this review, we explore the roles ascribed to PRRs duringMtbinfection and ask whether such a longstanding and intimate interface between our immune system and this pathogen plays a critical role in determining the outcome ofMtbinfection. The scientific evidence to date suggests that PRR variation is clearly implicated in altered immunity toMtbbut has a more subtle role in limiting the pathogen and pathogenesis. In contrast to ‘effectors’ like IFN-γ, IL-12, Nitric Oxide and TNF that are critical forMtbcontrol, ‘sensors’ like PRRs are less critical for the outcome ofMtbinfection. This is potentially due to redundancy of the numerous PRRs in the innate arsenal, such thatMtbrarely goes unnoticed. Genetic association studies investigating PRRs duringMtbinfection should therefore be designed to investigate endophenotypes of infection – such as immunological or clinical variation – rather than just TB disease, if we hope to understand the molecular interface between innate immunity andMtb.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".