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Record W3208989175 · doi:10.1186/s13007-021-00808-4

Genome-wide screening of novel RT-qPCR reference genes for study of GLRaV-3 infection in wine grapes and refinement of an RNA isolation protocol for grape berries

2021· article· en· W3208989175 on OpenAlexafffund
Yashu Song, Robert Hanner, Baozhong Meng

Bibliographic record

VenuePlant Methods · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Food Inspection AgencyUniversity of Guelph
KeywordsWineBiologyIsolation (microbiology)GeneRNA extractionComputational biologyVitis viniferaWine grapeGenomeGeneticsRNARNA-SeqHorticultureBioinformaticsFood scienceGene expressionTranscriptome

Abstract

fetched live from OpenAlex

BACKGROUND: Grapevine, as an essential fruit crop with high economic values, has been the focus of molecular studies in diverse areas. Two challenges exist in the grapevine research field: (i) the lack of a rapid, user-friendly and effective RNA isolation protocol for mature dark-skinned berries and, (ii) the lack of validated reference genes that are stable for quantification of gene expression across desired experimental conditions. Successful isolation of RNA with sufficient yield and quality is essential for downstream analyses involving nucleic acids. However, ripe berries of dark-skinned grape cultivars are notoriously challenging in RNA isolation due to high contents of polyphenolics, polysaccharides, RNase and water. RESULTS: We have optimized an RNA isolation protocol through modulating two factors at the lysis step that could impact results of RNA isolation - 2-ME concentration and berry mass. By finding the optimal combination among the two factors, our refined protocol was highly effective in isolating total RNA with high yield and quality from whole mature berries of an array of dark-skinned wine grape cultivars. Our protocol takes a much shorter time to complete, is highly effective, and eliminates the requirement for hazardous organic solvents. We have also shown that the resulting RNA preps were suitable for multiple downstream analyses, including the detection of viruses and amplification of grapevine genes using reverse transcription-polymerase chain reaction (RT-PCR), gene expression analysis via quantitative reverse transcription PCR (RT-qPCR), and RNA Sequencing (RNA-Seq). By using RNA-Seq data derived from Cabernet Franc, we have identified seven novel reference gene candidates (CYSP, NDUFS8, YLS8, EIF5A2, Gluc, GDT1, and EF-Hand) with stable expression across two tissue types, three developmental stages and status of infection with grapevine leafroll-associated virus 3 (GLRaV-3). We evaluated the stability of these candidate genes together with two conventional reference genes (actin and NAD5) using geNorm, NormFinder and BestKeeper. We found that the novel reference gene candidates outperformed both actin and NAD5. The three most stable reference genes were CYSP, NDUFS8 and YSL8, whereas actin and NAD5 were among the least stable. We further tested if there would be a difference in RT-qPCR quantification results when the most stable (CYSP) and the least stable (actin and NAD5) genes were used for normalization. We concluded that both actin and NAD5 led to erroneous RT-qPCR results in determining the statistical significance and fold-change values of gene expressional change. CONCLUSIONS: We have formulated a rapid, safe and highly effective protocol for isolating RNA from recalcitrant berry tissue of wine grapes. The resulting RNA is of high quality and suitable for RT-qPCR and RNA-Seq. We have identified and validated a set of novel reference genes based on RNA-Seq dataset. We have shown that these new reference genes are superior over actin and NAD5, two of the conventional reference genes commonly used in early studies.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.084
GPT teacher head0.420
Teacher spread0.336 · 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

Citations23
Published2021
Admission routes2
Has abstractyes

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