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Record W2003602685 · doi:10.1186/1471-2148-7-154

Phylogenomic analysis of natural selection pressure in Streptococcus genomes

2007· article· en· W2003602685 on OpenAlexafffund
Maria Anisimova, Joseph P. Bielawski, Katherine A. Dunn, Ziheng Yang

Bibliographic record

VenueBMC Evolutionary Biology · 2007
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsDalhousie University
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesGenome CanadaNatural Sciences and Engineering Research Council of CanadaGlaxoSmithKline
KeywordsBiologyVirulenceGenomeGeneGeneticsNatural selectionSelection (genetic algorithm)Pathogenicity islandEvolutionary biology

Abstract

fetched live from OpenAlex

BACKGROUND: In comparative analyses of bacterial pathogens, it has been common practice to discriminate between two types of genes: (i) those shared by pathogens and their non-pathogenic relatives (core genes), and (ii) those found exclusively in pathogens (pathogen-specific accessory genes). Rather than attempting to a priori delineate genes into sets more or less relevant to pathogenicity, we took a broad approach to the analysis of Streptococcus species by investigating the strength of natural selection in all clusters of homologous genes. The genus Streptococcus is comprised of a wide variety of both pathogenic and commensal lineages, and we relate our findings to the pre-existing knowledge of Streptococcus virulence factors. RESULTS: Our analysis of 1730 gene clusters revealed 136 cases of positive Darwinian selection, which we suggest is most likely to result from an antagonistic interaction between the host and pathogen at the molecular level. A two-step validation procedure suggests that positive selection was robustly identified in our genomic survey. We found no evidence to support the notion that pathogen specific accessory genes are more likely to be subject to positive selection than core genes. Indeed, we even uncovered a few cases of essential gene evolution by positive selection. Among the gene clusters subject to positive selection, a large fraction (29%) can be connected to virulence. The most striking finding was that a considerable fraction of the positively selected genes are also known to have tissue specific patterns of expression during invasive disease. As current expression data is far from comprehensive, we suggest that this fraction was underestimated. CONCLUSION: Our findings suggest that pathogen specific genes, although a popular focus of research, do not provide a complete picture of the evolutionary dynamics of virulence. The results of this study, and others, support the notion that the products of both core and accessory genes participate in complex networks that comprise the molecular basis of virulence. Future work should seek to understand the evolutionary dynamics of both core and accessory genes as a function of the networks in which they participate.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.314
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2007
Admission routes2
Has abstractyes

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