Correlation of Cry1Ac mRNA and protein abundance in transgenic Gossypium hirsutum plant

Transcription and translation in eukaryotes are distinct processes of the molecular cascade leading to protein production from genetic material. However, establishing correlation between mRNA expression and protein abundance, the end results of the two processes of central dogma, remains a challenge. For transgenic plants, such correlation between mRNA and protein expression serves as a guide to design the transgene, in particular the choices of promoter and codon usage to ensure stable expression of the target protein in relevant tissues under various stress conditions. To elucidate level of mRNA-protein correlation in a commercial transgenic cotton plant Gossypium hirsutum, Bollgard II^® (MON15985), we present the results of Cry1Ac protein expression correlating with corresponding mRNA levels. Protein was quantitated using a home-grown validated ELISA assay with a monoclonal-polyclonal antibody pair, whereas mRNA level was detected by a real-time quantitative PCR assay using standardized reference genes. Our results indicate that protein and mRNA levels are highly correlated in the leaves, but not in squares and stem. The correlations seem to be consistent between young and mature leaves and increase over time of harvesting of samples from months 1-3. These findings demonstrate that transcript level measurement could serve as a proxy to protein abundance for this commercially important cotton species, particularly for leaf tissues which are the most vulnerable organs to cotton bollworms and other pathogens.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02828-2.

Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

BACKGROUND

Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the "house-keeping genes", involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of > 50,000 genes in a publicly available RNA-seq dataset of cotton species Gossypium hirsutum.

RESULT

Five genes (TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6) identified by data-science driven analysis, along with two commonly used reference genes found in literature (PP2A1 and UBQ14) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm, BestKeeper, NormFinder and RefFinder.

CONCLUSION

Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.