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Sampling strategies for sugarcane using either clonal replicates or diverse genotypes can bias the conclusions of RNA-Seq studies.

ABSTRACT: A key procedure for ensuring statistical confidence in differential gene expression analyses is to use biological replicates to compare distinct groups. Biological replicates allow the estimation of the residual variation in the gene expression levels among samples of a given experimental condition. In sugarcane, it is possible to obtain an estimate of residual variability at two levels: among samples of distinct genotypes of the same experimental treatment, or clonal replicates of the same genotype. The sequencing costs are often a limitation to leveraging both these levels in the same study, stressing the relevance of efforts to determine an appropriate experimental design. We aim to investigate this question by comparing the transcriptional profiles of young sugarcane culms with different sucrose levels using both sampling strategies. Our results show that clonal replicates provided enough statistical power to identify nearly three times more deferentially expressed genes than the more diverse strategy. However, it resulted in potentially less meaningful biological results, because many of the significant genes were likely related to the particular genotype of choice, rather than representing a common expression profile for the compared groups. This study supports the development of sound experimental designs in new studies regarding differential expression for sugarcane.

SUBMITTER: Mello VH

PROVIDER: S-EPMC10075064 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Sampling strategies for sugarcane using either clonal replicates or diverse genotypes can bias the conclusions of RNA-Seq studies.

Mello Victor Hugo VH Garcia Ana Letycia Basso ALB Correr Fernando Henrique FH Hosaka Guilherme Kenichi GK Carneiro Monalisa Sampaio MS Margarido Gabriel Rodrigues Alves GRA

Genetics and molecular biology 20230403 1

A key procedure for ensuring statistical confidence in differential gene expression analyses is to use biological replicates to compare distinct groups. Biological replicates allow the estimation of the residual variation in the gene expression levels among samples of a given experimental condition. In sugarcane, it is possible to obtain an estimate of residual variability at two levels: among samples of distinct genotypes of the same experimental treatment, or clonal replicates of the same geno ...[more]

PMID: 37017730

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Project description:Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

Project description:Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Project description:BackgroundDrought stress is the major environmental stress that affects plant growth and productivity. It triggers a wide range of responses detectable at molecular, biochemical and physiological levels. At the molecular level the response to drought stress results in the differential expression of several metabolic pathways. For this reason, exploring the subtle differences in gene expression of drought sensitive and drought tolerant genotypes enables the identification of drought-related genes that could be used for selection of drought tolerance traits. Genome-wide RNA-Seq technology was used to compare the drought response of two sorghum genotypes characterized by contrasting water use efficiency.ResultsThe physiological measurements carried out confirmed the drought sensitivity of IS20351 and the drought tolerance of IS22330 genotypes, as previously studied. The expression of drought-related genes was more abundant in the drought sensitive genotype IS20351 compared to the tolerant genotype IS22330. Under drought stress Gene Ontology enrichment highlighted a massive increase in transcript abundance in the sensitive genotype IS20351 in "response to stress" and "abiotic stimulus", as well as for "oxidation-reduction reaction". "Antioxidant" and "secondary metabolism", "photosynthesis and carbon fixation process", "lipids" and "carbon metabolism" were the pathways most affected by drought in the sensitive genotype IS20351. In addition, genotype IS20351 showed a lower constitutive expression level of "secondary metabolic process" (GO:0019748) and "glutathione transferase activity" (GO:000004364) under well-watered conditions.ConclusionsRNA-Seq analysis proved to be a very useful tool to explore differences between sensitive and tolerant sorghum genotypes. Transcriptomics analysis results supported all the physiological measurements and were essential to clarify the tolerance of the two genotypes studied. The connection between differential gene expression and physiological response to drought unequivocally revealed the drought tolerance of genotype IS22330 and the strategy adopted to cope with drought stress.

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Sampling strategies for sugarcane using either clonal replicates or diverse genotypes can bias the conclusions of RNA-Seq studies.

Publications

Sampling strategies for sugarcane using either clonal replicates or diverse genotypes can bias the conclusions of RNA-Seq studies.

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