Evidence map›Paper›PMID 21324174›Full record

ArticleBMC molecular biology2011

Assessment and validation of a suite of reverse transcription-quantitative PCR reference genes for analyses of density-dependent behavioural plasticity in the Australian plague locust.

Marie-Pierre Chapuis, Donya Tohidi-Esfahani, Tim Dodgson, Laurence Blondin, Fleur Ponton, Darron Cullen, Stephen J Simpson, Gregory A Sword

Open access · hybridAbstract read
In one paragraph

Article in BMC molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.

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  14. Improving the standardization of mRNA measurement by RT-qPCR.Biomolecular detection and quantification · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 4 countries.

Marie-Pierre ChapuisSchool of Biological Sciences, University of Sydney, Sydney, NSW 2006, Australia. marie-pierre.chapuis@cirad.fr
Donya Tohidi-Esfahani
Tim Dodgson
Laurence Blondin
Fleur Ponton
Darron Cullen
Stephen J Simpson
Gregory A Sword
The University of Sydney · AUCentre de Coopération Internationale en Recherche Agronomique pour le Développement · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Australian plague locust, Chortoicetes terminifera, is among the most promising species to unravel the suites of genes underling the density-dependent shift from shy and cryptic solitarious behaviour to the highly active and aggregating gregarious behaviour that is characteristic of locusts. This is because it lacks many of the major phenotypic changes in colour and morphology that accompany phase change in other locust species. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the most sensitive method available for determining changes in gene expression. However, to accurately monitor the expression of target genes, it is essential to select an appropriate normalization strategy to control for non-specific variation between samples. Here we identify eight potential reference genes and examine their expression stability at different rearing density treatments in neural tissue of the Australian plague locust.

resultsTaking advantage of the new orthologous DNA sequences available in locusts, we developed primers for genes encoding 18SrRNA, ribosomal protein L32 (RpL32), armadillo (Arm), actin 5C (Actin), succinate dehydrogenase (SDHa), glyceraldehyde-3P-dehydrogenase (GAPDH), elongation factor 1 alpha (EF1a) and annexin IX (AnnIX). The relative transcription levels of these eight genes were then analyzed in three treatment groups differing in rearing density (isolated, short- and long-term crowded), each made up of five pools of four neural tissue samples from 5th instar nymphs. SDHa and GAPDH, which are both involved in metabolic pathways, were identified as the least stable in expression levels, challenging their usefulness in normalization. Based on calculations performed with the geNorm and NormFinder programs, the best combination of two genes for normalization of gene expression data following crowding in the Australian plague locust was EF1a and Arm. We applied their use to studying a target gene that encodes a Ca2+ binding glycoprotein, SPARC, which was previously found to be up-regulated in brains of gregarious desert locusts, Schistocerca gregaria. Interestingly, expression of this gene did not vary with rearing density in the same way in brains of the two locust species. Unlike S. gregaria, there was no effect of any crowding treatment in the Australian plague locust.

conclusionArm and EF1a is the most stably expressed combination of two reference genes of the eight examined for reliable normalization of RT-qPCR assays studying density-dependent behavioural change in the Australian plague locust. Such normalization allowed us to show that C. terminifera crowding did not change the neuronal expression of the SPARC gene, a gregarious phase-specific gene identified in brains of the desert locust, S. gregaria. Such comparative results on density-dependent gene regulation provide insights into the evolution of gregarious behaviour and mass migration of locusts. The eight identified genes we evaluated are also candidates as normalization genes for use in experiments involving other Oedipodinae species, but the rank order of gene stability must necessarily be determined on a case-by-case basis.

Indexed as

AnimalsCrowdingGenome, InsectGrasshoppersInsect ProteinsOsteonectinReverse Transcriptase Polymerase Chain ReactionInsect ProteinsOsteonectin

Identifiers

PMID21324174
PMCPMC3048552
OpenAlexW2076832582

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.