Identification and validation of superior housekeeping gene(s) for qRT-PCR data normalization in Agave sisalana (a CAM-plant) under abiotic stresses

Identification of housekeeping gene for future studies exploring effects of cryopreservation on gene expression in shrimp

Few studies have investigated the subcellular effects of low temperature on gene expression in shrimp and most other crustaceans. Before gene expression analysis is conducted, suitable housekeeping genes (HKGs) must be confirmed to account for differences in reverse transcription process efficiency among samples. Thus, this study aimed to verify five frequently used HKGs, namely 18S ribosomal RNA (18S rRNA), ATPase, histone 3, β-actin, and glyceraldehyde 3-phosphate dehydrogenase (gapdh) for use in experiments for assessing the molecular-scale effects of cryopreservation on coral banded shrimp (Stenopus hispidus) embryos. To conduct chilling studies, we subjected S. hispidus embryos to incubation at either 26 °C (control) or 5 °C for 0, 4, 8, 16, or 32 h. The software tools GeNorm, NormFinder, and Bestkeeper were employed to identify the most suitable HKG. GeNorm identified histone 3 and 18S rRNA as the most stable genes. By contrast, NormFinder determined that 18S rRNA is a stable gene for eye-formation and pre-hatch stage samples. Finally, Bestkeeper determined that gapdh and β-actin are the most suitable genes. This study is the first to identify suitable HKGs for studying shrimp embryos at low temperatures. Its findings can aid future research on evaluating the effects of cryopreservation on gene expression in crustaceans.

Identifying the stability of housekeeping genes to be used for the quantitative real-time PCR normalization in retinal tissue of streptozotocin-induced diabetic rats

AIM

To investigate the stability of the seven housekeeping genes: beta-actin (ActB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18s ribosomal unit 5 (18s), cyclophilin A (CycA), hypoxanthine-guanine phosphoribosyl transferase (HPRT), ribosomal protein large P0 (36B4) and terminal uridylyl transferase 1 (U6) in the diabetic retinal tissue of rat model.

METHODS

The expression of these seven genes in rat retinal tissues was determined using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) in two groups; normal control rats and streptozotocin-induced diabetic rats. The stability analysis of gene expression was investigated using geNorm, NormFinder, BestKeeper, and comparative delta-Ct (ΔCt) algorithms.

RESULTS

The 36B4 gene was stably expressed in the retinal tissues of normal control animals; however, it was less stable in diabetic retinas. The 18s gene was expressed consistently in both normal control and diabetic rats' retinal tissue. That this gene was the best reference for data normalisation in RT-qPCR studies that used the retinal tissue of streptozotocin-induced diabetic rats. Furthermore, there was no ideal gene stably expressed for use in all experimental settings.

CONCLUSION

Identifying relevant genes is a need for achieving RT-qPCR validity and reliability and must be appropriately achieved based on a specific experimental setting.

Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the “Normics” algorithm: Proteins are ranked based on their expression level–corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood.

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Normics is a tool for the normalization of proteomic data based on existing algorithms.

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Specifically addresses data with high shares of differential expression.

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Combines variance and data-inherent correlation structure.

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Provides a ranking of differential expression likelihood.

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Enables normalization based on the most stable proteins.

Transcriptome Mining Provides Insights into Cell Wall Metabolism and Fiber Lignification in Agave tequilana Weber

Resilience of growing in arid and semiarid regions and a high capacity of accumulating sugar-rich biomass with low lignin percentages have placed Agave species as an emerging bioenergy crop. Although transcriptome sequencing of fiber-producing agave species has been explored, molecular bases that control wall cell biogenesis and metabolism in agave species are still poorly understood. Here, through RNAseq data mining, we reconstructed the cellulose biosynthesis pathway and the phenylpropanoid route producing lignin monomers in A. tequilana, and evaluated their expression patterns in silico and experimentally. Most of the orthologs retrieved showed differential expression levels when they were analyzed in different tissues with contrasting cellulose and lignin accumulation. Phylogenetic and structural motif analyses of putative CESA and CAD proteins allowed to identify those potentially involved with secondary cell wall formation. RT-qPCR assays revealed enhanced expression levels of AtqCAD5 and AtqCESA7 in parenchyma cells associated with extraxylary fibers, suggesting a mechanism of formation of sclerenchyma fibers in Agave similar to that reported for xylem cells in model eudicots. Overall, our results provide a framework for understanding molecular bases underlying cell wall biogenesis in Agave species studying mechanisms involving in leaf fiber development in monocots.

Selection and stability validation of reference gene candidates for transcriptional analysis in Rousettus aegyptiacus

Bats are the only mammals capable of powered flight and their body temperature can reach up to 42 °C during flight. Additionally, bats display robust type I IFN interferon (IFN-I) responses and some species constitutively express IFN-α. Reference genes with stable expression under temperature oscillations and IFN-I release are therefore critical for normalization of quantitative reverse-transcription polymerase chain reaction (qRT-PCR) data in bats. The expression stability of reference genes in Rousettus aegyptiacus remains elusive, although this species is frequently used in the infection research. We selected ACTB, EEF1A1, GAPDH and PGK1 as candidate reference genes and evaluated their expression stability in various tissues and cells from this model bat species upon IFN-I treatment at 35 °C, 37 °C and 40 °C by qRT-PCR. We employed two statistical algorithms, BestKeeper and NormFinder, and found that EEF1A1 exhibited the highest expression stability under all tested conditions. ACTB and GAPDH displayed unstable expression upon temperature change and IFN-I treatment, respectively. By normalizing to EEF1A1, we uncovered that GAPDH expression was significantly induced by IFN-I in R. aegyptiacus. Our study identifies EEF1A1 as the most suitable reference gene for qRT-PCR studies upon temperature changes and IFN-I treatment and unveils the induction of GAPDH expression by IFN-I in R. aegyptiacus. These findings are pertinent to other bat species and may be relevant for non-volant mammals that show physiological fluctuations of core body temperature.

Combining transcriptome analysis and GWAS for identification and validation of marker genes in the Physalis peruviana-Fusarium oxysporum pathosystem

Vascular wilt, caused by the pathogen Fusarium oxysporum f. sp. physali (Foph), is a major disease of cape gooseberry (Physalis peruviana L.) in Andean countries. Despite the economic losses caused by this disease, there are few studies related to molecular mechanisms in the P. peruviana—Foph pathosystem as a useful tool for crop improvement. This study evaluates eight candidate genes associated with this pathosystem, using real-time quantitative PCR (RT-qPCR). The genes were identified and selected from 1,653 differentially expressed genes (DEGs) derived from RNA-Seq analysis and from a previous genome-wide association study (GWAS) of this plant-pathogen interaction. Based on the RT-qPCR analysis, the tubuline (TUB) reference gene was selected for its highly stable expression in cape gooseberry. The RT-qPCR validation of the candidate genes revealed the biological variation in their expression according to their known biological function. Three genes related to the first line of resistance/defense responses were highly expressed earlier during infection in a susceptible genotype, while three others were overexpressed later, mostly in the tolerant genotype. These genes are mainly involved in signaling pathways after pathogen recognition, mediated by hormones such as ethylene and salicylic acid. This study provided the first insight to uncover the molecular mechanism from the P. peruviana—Foph pathosystem. The genes validated here have important implications in the disease progress and allow a better understanding of the defense response in cape gooseberry at the molecular level. Derived molecular markers from these genes could facilitate the identification of tolerant/susceptible genotypes for use in breeding schemes.