Mammalian NLR proteins; discriminating foe from friend
Bibliographic record
Abstract
Eukaryotic organisms of the plant and animal kingdoms have developed evolutionarily conserved systems of defence against microbial pathogens. These systems depend on the specific recognition of microbial products or structures by molecules of the host innate immune system. The first mammalian molecules shown to be involved in innate immune recognition of, and defence against, microbial pathogens were the Toll-like receptors (TLRs). These proteins are predominantly but not exclusively located in the transmembrane region of host cells. Interestingly, mammalian hosts were subsequently found to also harbour cytosolic proteins with analogous structures and functions to plant defence molecules. The members of this protein family exhibit a tripartite domain structure and are characterized by a central nucleotide-binding oligomerization domain (NOD). Moreover, in common with TLRs, most NOD proteins possess a C-terminal leucine-rich repeat (LRR) domain, which is required for the sensing of microbial products and structures. Recently, the name 'nucleotide-binding domain and LRR' (NLR) was coined to describe this family of proteins. It is now clear that NLR proteins play key roles in the cytoplasmic recognition of whole bacteria or their products. Moreover, it has been demonstrated in animal studies that NLRs are important for host defence against bacterial infection. This review will particularly focus on two subfamilies of NLR proteins, the NODs and 'NALPs', which specifically recognize bacterial products, including cell wall peptidoglycan and flagellin. We will discuss the downstream signalling events and host cell responses to NLR recognition of such products, as well as the strategies that bacterial pathogens employ to trigger NLR signalling in host cells. Cytosolic recognition of microbial factors by NLR proteins appears to be one mechanism whereby the innate immune system is able to discriminate between pathogenic bacteria ('foe') and commensal ('friendly') members of the host microflora.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".