The Interplay of Human and Mycobacterium Tuberculosis Genomic Variability
Bibliographic record
Abstract
Tuberculosis (TB), caused by the human pathogens Mycobacterium tuberculosis (Mtb) and Mycobacterium africanum, has plagued humanity for millennia and remains the deadliest infectious disease in the modern world. Mtb and M. africanum can be subdivided phylogenetically into seven lineages exhibiting a low but significant degree of genomic diversity and preferential geographic distributions. Human genetic variability impacts all stages of TB pathogenesis ranging from susceptibility to infection with Mtb, progression of infection to disease, and the development of distinct clinical sub-types. The genetic study of severe childhood TB identified strong inborn single gene errors revealing crucial pathways of vulnerability to TB. However, the identification of major TB-susceptibility genes on the population level has remained elusive. In particular, the replication of findings from candidate and genome-wide association studies across distinct human populations has proven difficult, thus hampering the characterization of reliable host molecular markers of susceptibility. Among the possible confounding factors of genetic association studies is Mtb genomic variability which generally was not taken into account by human genetic studies. In support of this possibility, Mtb lineage was found to be a contributing factor to clinical presentation of TB and epidemiological spread of Mtb in exposed populations. The confluence of pathogen and human host genetic variability to TB pathogenesis led to the consideration of a possible co-adaptation of Mtb strains and their human hosts which should reveal itself in significant interaction effects between Mtb strain and TB susceptibility/resistance alleles. Here, we present some of the most consistent findings of genetic susceptibility factors in human TB and review studies that point to genome-to-genome interaction between humans and Mtb lineages. The limited results available so far suggest that analyses considering joint human-Mtb genomic variability may provide improved power for the discovery of pathogenic drivers of the ongoing TB epidemic.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".