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Record W2063525719 · doi:10.1186/s13104-015-1045-8

Validation of reference genes for quantitative real-time PCR (qPCR) analysis of Actinobacillus suis

2015· article· en· W2063525719 on OpenAlexafffund
Adina R. Bujold, Janet I. MacInnes

Bibliographic record

VenueBMC Research Notes · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReference genesGeneBiologyAnoxic watersGene expressionComputational biologyGeneticsEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Quantitative real-time PCR is a valuable tool for evaluating bacterial gene expression. However, in order to make best use of this method, endogenous reference genes for expression data normalisation must first be identified by carefully validating the stability of expression under experimental conditions. Therefore, the objective of this study was to validate eight reference genes of the opportunistic swine pathogen, Actinobacillus suis, grown in aerobic cultures with (Epinephrine) or without (Aerobic) epinephrine in the growth medium and in anoxic static cultures (Anoxic), and sampled during exponential and stationary phases. RESULTS: Using the RefFinder tool, expression data were analysed to determine whether comprehensive stability rankings of selected reference genes varied with experimental design. When comparing Aerobic and Epinephrine cultures by growth phase, pyk and rpoB were both among the most stably expressed genes, but when analysing both growth phases together, only pyk remained in the top three rankings. When comparing Aerobic and Anoxic samples, proS ranked among the most stable genes in exponential and stationary phase data sets as well as in combined rankings. When analysing the Aerobic, Epinephrine, and Anoxic samples together, only gyrA ranked consistently among the top three most stably expressed genes during exponential and stationary growth as well as in combined rankings; the rho gene ranked as least stably expressed gene in this data set. CONCLUSIONS: Reference gene stability should be carefully assessed with the design of the experiment in mind. In this study, even the commonly used reference gene 16S rRNA demonstrated large variability in stability depending on the conditions studied and how the data were analysed. As previously suggested, the best approach may be to use a geometric mean of multiple genes to normalise qPCR results. As researchers continue to validate reference genes for various organisms in multiple growth conditions and sampling time points, it may be possible to make informed predictions as to which genes may be most suitable to validate for a given experimental design, but in the meantime, the reference genes used to normalise qPCR data should be selected with caution.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.272
GPT teacher head0.470
Teacher spread0.198 · 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 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

Citations10
Published2015
Admission routes2
Has abstractyes

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