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Record W2070860432 · doi:10.1186/1472-6750-13-7

Development of a versatile TaqMan™ real-time quantitative PCR (RT-qPCR) compliant anchor sequence to quantify bacterial gene transcripts from RNA samples containing carryover genomic DNA

2013· article· en· W2070860432 on OpenAlexafffund
Vijay Gadkar, Martin Filion

Bibliographic record

VenueBMC Biotechnology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversité de Moncton
FundersInstitute of GeneticsNatural Sciences and Engineering Research Council of Canada
KeywordsBiologygenomic DNATaqManRNAGeneDNAComplementary DNAGenBankMolecular biologyComputational biologyGeneticsReal-time polymerase chain reaction

Abstract

fetched live from OpenAlex

BACKGROUND: In bacterial systems, the sequence congruence of genomic DNA (gDNA) and cDNA obtained following reverse transcription of RNA, makes gDNA an automatic target for qPCR primers. This could lead to aberrant gene expression quantification. This is why a rigorous treatment of bacterial RNA with DNase I is usually required to remove any traces of carryover gDNA. As bacterial RNA is known to be extremely labile, any procedure that affects RNA yield, such as DNase I treatment, can be logically assumed to also influence detection and quantification of gene transcripts, leading to either an underestimation or no detection at all. To address such problems, we have developed a novel and versatile TaqMan RT-qPCR compliant anchor sequence (MYT4) for quantifying bacterial gene transcripts without the need for DNase I treatment. RESULTS: A non-genomic anchor sequence, henceforth referred to as MYT4 was designed using a synthetic DNA sequence called myIC, previously shown to share no significant homology to any known accession in the GenBank database. The sequence characteristic of MYT4 was kept within the design parameters required for the TaqMan RT-qPCR platform. The specificity and robustness of the novel MYT4 sequence was validated on RNA extracted from the bacterium Pseudomonas sp. LBUM300, grown under liquid culture and spiked soil conditions. Two transcripts, namely hcnC and phlD, were quantified from these two experimental systems. Using the MYT4 anchor, no RT-qPCR signal was detected from non-DNase I treated RNA, while strong signals were obtained using conventional reverse primers and RT-qPCR, indicating the presence of carryover gDNA in the RNA, extracted from either liquid culture or soil. Serial treatment of the RNA samples with DNase I (required to achieve absolute gDNA elimination) resulted in 50-70% loss of RNA which, when submitted to conventional RT-qPCR, significantly altered the transcript numbers detected when compared to the MYT4-based approach. CONCLUSIONS: Implementation of the versatile approach described in this study, which can be "retrofitted" to any existing TaqMan RT-qPCR system, should contribute to reducing the time and lowering the costs required to perform adequate bacterial RNA purification for downstream quantification of gene transcripts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.055
GPT teacher head0.273
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations9
Published2013
Admission routes2
Has abstractyes

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