Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica development.

Abstract Striga is a root parasitic weed that attacks many of the staple crops in Africa, India and Southeast Asia, inflicting tremendous losses in yield and for which there are few effective control measures. Studies of parasitic plant virulence and host resistance will be greatly facilitated by the recent emergence of genomic resources that include extensive transcriptome sequence datasets spanning all life stages of S. hermonthica. Functional characterization of Striga genes will require detailed analyses of gene expression patterns. Quantitative real-time PCR is a powerful tool for quantifying gene expression, but correct normalization of expression levels requires identification of control genes that have stable expression across tissues and life stages. Since no S. hermonthica housekeeping genes have been established for this purpose, we evaluated the suitability of six candidate housekeeping genes across key life stages of S. hermonthica from seed conditioning to flower initiation using qRT-PCR and high-throughput cDNA sequencing. Based on gene expression analysis by qRT-PCR and RNA-Seq across heterogeneous Striga life stages, we determined that using the combination of three genes, UBQ1, PP2A and TUB1 provides the best normalization for gene expression throughout the parasitic life cycle. The housekeeping genes characterized here provide robust standards that will facilitate powerful descriptions of parasite gene expression patterns

Gene expression atlas of fruit ripening and transcriptome assembly from RNA-seq data in octoploid strawberry (Fragaria × ananassa)

RNA-seq has been used to perform global expression analysis of the achene and the receptacle at four stages of fruit ripening, and of the roots and leaves of strawberry (Fragaria × ananassa). About 967 million reads and 191 Gb of sequence were produced, using Illumina sequencing. Mapping the reads in the related genome of the wild diploid Fragaria vesca revealed differences between the achene and receptacle development program, and reinforced the role played by ethylene in the ripening receptacle. For the strawberry transcriptome assembly, a de novo strategy was followed, generating separate assemblies for each of the ten tissues and stages sampled. The Trinity program was used for these assemblies, resulting in over 1.4 M isoforms. Filtering by a threshold of 0.3 FPKM, and doing Blastx (E-value < 1 e-30) against the UniProt database of plants reduced the number to 472,476 isoforms. Their assembly with the MIRA program (90% homology) resulted in 26,087 contigs. From these, 91.34 percent showed high homology to Fragaria vesca genes and 87.30 percent Fragaria iinumae (BlastN E-value < 1 e-100). Mapping back the reads on the MIRA contigs identified polymorphisms at nucleotide level, using FREEBAYES, as well as estimate their relative abundance in each sample

Transcriptomics of host-specific interactions in natural populations of the parasitic plant purple witchweed (Striga hermonthica)

Published online: 30 Jul 2019Host-specific interactions can maintain genetic and phenotypic diversity in parasites that attack multiple host species. Host diversity, in turn, may promote parasite diversity by selection for genetic divergence or plastic responses to host type. The parasitic weed purple witchweed [Striga hermonthica (Delile) Benth.] causes devastating crop losses in sub-Saharan Africa and is capable of infesting a wide range of grass hosts. Despite some evidence for host adaptation and host-by-Striga genotype interactions, little is known about intraspecific Striga genomic diversity. Here we present a study of transcriptomic diversity in populations of S. hermonthica growing on different hosts (maize [Zea mays L.] vs. grain sorghum [Sorghum bicolor (L.) Moench]). We examined gene expression variation and differences in allelic frequency in expressed genes of aboveground tissues from populations in western Nigeria parasitizing each host. Despite low levels of host-based genome-wide differentiation, we identified a set of parasite transcripts specifically associated with each host. Parasite genes in several different functional categories implicated as important in host–parasite interactions differed in expression level and allele on different hosts, including genes involved in nutrient transport, defense and pathogenesis, and plant hormone response. Overall, we provide a set of candidate transcripts that demonstrate host-specific interactions in vegetative tissues of the emerged parasite S. hermonthica. Our study shows how signals of host-specific processes can be detected aboveground, expanding the focus of host–parasite interactions beyond the haustorial connection

Overall average hardness after a 7d simulated supply chain (in air at 20°C).

Overall average hardness after a 7d simulated supply chain (in air at 20°C).</p

Fig 3 -

PCA 1 and 2 of TPM transcriptomic data counts colored by A) treatment and B) days post harvest. PC1 primarily separates fruit based on temperature and days post-harvest of the warmer fruit whereas PC2 separates based on days post harvest of fruit treated with 1-MCP and CA.</p

Elastic net top 15 gene expression.

The expression level (y-axis) of genes using TPM normalized counts over time (x-axis) for the top 15 genes identified using the elastic net model. (PNG)</p

Fig 6 -

Model performance of a single random forest reduced model (RF-rm) and elastic net reduced model (EN-rm) training A and testing B. training C and testing D. Data for replicated 100 runs of these model is presented in Table 1. Reported r2 and m_rmse values in this figure represent a single run of a representative model, whereas data reported in Table 1 represents the average of 100 replicates.</p

DOI 10.1007/s11033-012-2417-y

Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica developmen

Fig 5 -

Model stability of A) Random Forest (RF) Full Model (FM) and B) Elastic Net (EN) FM. The top 15 genes of each model are shown along the y-axis. Each point represents a single bootstrap re-run of the model and its position along the x-axis is the gene’s rank in importance from the re-run model. A rank of 1 is given to a gene if it is the most important in the respective model. A point is present for a re-run model only if the gene occurred in the top 500 important genes. Numbers on the right side y-axis of the graph indicate how many times the gene was selected by the model.</p