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Analysis of in vivo expressed genes in Mannheimia haemolytica A1

2006· article· en· W1718106869 on OpenAlexafffund
Reggie Y.C. Lo, S. Sathiamoorthy, Patricia E. Shewen

Bibliographic record

VenueFEMS Microbiology Letters · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyGeneVirulenceGene expressionPolymerase chain reactionRegulator geneIn vivoMicrobiologyReverse transcriptaseReal-time polymerase chain reactionGenetics

Abstract

fetched live from OpenAlex

The expression of Mannheimia haemolytica A1 genes during in vivo growth was examined by reverse transcriptase-polymerase chain reaction (RT-PCR) using total RNA extracted directly from M. haemolytica A1 recovered from pneumonic lungs of cattle. Primers specific for three groups of genes were used. Group 1 includes virulence-related genes: lktC, tbpB, ahs, nmaA, gs60 and gcp. Group 2 includes genes that code for putative two-component regulatory systems: narP, narQ, ttrR, ttrS, phoB and phoR. Group 3 includes genes involved in regular cellular functions such as plp4, thiL and rrf. The RT-PCR data were examined in conjunction with the percent pneumonic lesion in each lung scored during necropsy. The analysis showed that lungs with a higher percent pneumonic score exhibit expression of more M. haemolytica A1 genes. For group 1 genes, lktC was expressed in the majority of samples, whereas the other genes were only expressed in some samples. This was not unexpected as the leukotoxin is a major virulence factor of the bacterium. The genes encoding the response regulators for the putative two-component regulatory systems were found to be expressed in more samples than the genes encoding the sensor proteins. The regulator proteins may be required in higher levels to regulate expression of target genes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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.

Study designBench or experimental
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

Citations15
Published2006
Admission routes2
Has abstractyes

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