The AAP gene family for amino acid permeases contributes to development of the cyst nematode Heterodera schachtii in roots of Arabidopsis

The beet cyst nematode Heterodera schachtii is able to infect Arabidopsis plants and induce feeding sites in the root. These syncytia are the only source of nutrients for the nematodes throughout their life and are a nutrient sink for the host plant. We have studied here the role of amino acid transporters for nematode development. Arabidopsis contains a large number of different amino acid transporters in several gene families but those of the AAP family were found to be especially expressed in syncytia. Arabidopsis contains 8 AAP genes and they were all strongly expressed in syncytia with the exception of AAP5 and AAP7, which were slightly downregulated. We used promoter::GUS lines and in situ RT-PCR to confirm the expression of several AAP genes and LHT1, a lysine- and histidine-specific amino acid transporter, in syncytia. The strong expression of AAP genes in syncytia indicated that these transporters are important for the transport of amino acids into syncytia and we used T-DNA mutants for several AAP genes to test for their influence on nematode development. We found that mutants of AAP1, AAP2, and AAP8 significantly reduced the number of female nematodes developing on these plants. Our study showed that amino acid transport into syncytia is important for the development of the nematodes

Improving RNA-Seq Precision with MapAl

With currently available RNA-Seq pipelines, expression estimates for most genes are very noisy. We here introduce MapAl, a tool for RNA-Seq expression profiling that builds on the established programs Bowtie and Cufflinks. In the post-processing of RNA-Seq reads, it incorporates gene models already at the stage of read alignment, increasing the number of reliably measured known transcripts consistently by 50%. Adding genes identified de novo then allows a reliable assessment of double the total number of transcripts compared to other available pipelines. This substantial improvement is of general relevance: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not

Reproducibility assessment of independent component analysis of expression ratios from DNA microarrays.

DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next to a neutral control, examining relative changes (ratios). Independent component analysis (ICA) is a promising modern method for the analysis of such experiments. The condition of ICA algorithms can, however, depend on the characteristics of the data examined, making algorithm properties such as robustness specific to the given application domain. To address the lack of studies examining the robustness of ICA applied to microarray measurements, we report on the stability of variational Bayesian ICA in this domain. Microarray data are usually preprocessed and transformed. Hence we first examined alternative transforms and data selections for the smallest modelling reconstruction errors. Log-ratio data are reconstructed better than non-transformed ratio data by our linear model with a Gaussian error term. To compare ICA results we must allow for ICA invariance under rescaling and permutation of the extracted signatures, which hold the loadings of the original variables (gene transcript ratios) on particular latent variables. We introduced a method to optimally match corresponding signatures between sets of results. The stability of signatures was then examined after (1) repetition of the same analysis run with different random number generator seeds, and (2) repetition of the analysis with partial data sets. The effects of both dropping a proportion of the gene transcript ratios and dropping measurements for several samples have been studied. In summary, signatures with a high relative data power were very likely to be retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wildtype vs. wild-type experiments, moreover, yielded 10 reliably identified signatures, demonstrating that the variance observed is not just noise.Peer Reviewe

CHO microRNA engineering is growing up : recent successes and future challenges

microRNAs with their ability to regulate complex pathways that control cellular behavior and phenotype have been proposed as potential targets for cell engineering in the context of optimization of biopharmaceutical production cell lines, specifically of Chinese Hamster Ovary cells. However, until recently, research was limited by a lack of genomic sequence information on this industrially important cell line. With the publication of the genomic sequence and other relevant data sets for CHO cells since 2011, the doors have been opened for an improved understanding of CHO cell physiology and for the development of the necessary tools for novel engineering strategies. In the present review we discuss both knowledge on the regulatory mechanisms of microRNAs obtained from other biological models and proof of concepts already performed on CHO cells, thus providing an outlook of potential applications of microRNA engineering in production cell lines

Effects of Aneuploidy on Genome Structure, Expression, and Interphase Organization in Arabidopsis thaliana

Aneuploidy refers to losses and/or gains of individual chromosomes from the normal chromosome set. The resulting gene dosage imbalance has a noticeable affect on the phenotype, as illustrated by aneuploid syndromes, including Down syndrome in humans, and by human solid tumor cells, which are highly aneuploid. Although the phenotypic manifestations of aneuploidy are usually apparent, information about the underlying alterations in structure, expression, and interphase organization of unbalanced chromosome sets is still sparse. Plants generally tolerate aneuploidy better than animals, and, through colchicine treatment and breeding strategies, it is possible to obtain inbred sibling plants with different numbers of chromosomes. This possibility, combined with the genetic and genomics tools available for Arabidopsis thaliana, provides a powerful means to assess systematically the molecular and cytological consequences of aberrant numbers of specific chromosomes. Here, we report on the generation of Arabidopsis plants in which chromosome 5 is present in triplicate. We compare the global transcript profiles of normal diploids and chromosome 5 trisomics, and assess genome integrity using array comparative genome hybridization. We use live cell imaging to determine the interphase 3D arrangement of transgene-encoded fluorescent tags on chromosome 5 in trisomic and triploid plants. The results indicate that trisomy 5 disrupts gene expression throughout the genome and supports the production and/or retention of truncated copies of chromosome 5. Although trisomy 5 does not grossly distort the interphase arrangement of fluorescent-tagged sites on chromosome 5, it may somewhat enhance associations between transgene alleles. Our analysis reveals the complex genomic changes that can occur in aneuploids and underscores the importance of using multiple experimental approaches to investigate how chromosome numerical changes condition abnormal phenotypes and progressive genome instability

Transcriptomic characterization of two major Fusarium resistance quantitative trait loci (QTLs), Fhb1 and Qfhs.ifa-5A, identifies novel candidate genes

Fusarium head blight, caused by Fusarium graminearum, is a devastating disease of wheat. We developed near-isogenic lines (NILs) differing in the two strongest known F. graminearum resistance quantitative trait loci (QTLs), Qfhs.ndsu-3BS (also known as resistance gene Fhb1) and Qfhs.ifa-5A, which are located on the short arm of chromosome 3B and on chromosome 5A, respectively. These NILs showing different levels of resistance were used to identify transcripts that are changed significantly in a QTL-specific manner in response to the pathogen and between mock-inoculated samples. After inoculation with F. graminearum spores, 16 transcripts showed a significantly different response for Fhb1 and 352 for Qfhs.ifa-5A. Notably, we identified a lipid transfer protein which is constitutively at least 50-fold more abundant in plants carrying the resistant allele of Qfhs.ifa-5A. In addition to this candidate gene associated with Qfhs.ifa-5A, we identified a uridine diphosphate (UDP)-glycosyltransferase gene, designated TaUGT12887, exhibiting a positive difference in response to the pathogen in lines harbouring both QTLs relative to lines carrying only the Qfhs.ifa-5A resistance allele, suggesting Fhb1 dependence of this transcript. Yet, this dependence was observed only in the NIL with already higher basal resistance. The complete cDNA of TaUGT12887 was reconstituted from available wheat genomic sequences, and a synthetic recoded gene was expressed in a toxin-sensitive strain of Saccharomyces cerevisiae. This gene conferred deoxynivalenol resistance, albeit much weaker than that observed with the previously characterized barley HvUGT13248

The impact of quantitative optimization of hybridization conditions on gene expression analysis.

BACKGROUND: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. RESULTS: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays.For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions. CONCLUSIONS: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Reproducibility Assessment of Independent Component Analysis of Expression Ratios From DNA Microarrays

DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next to a neutral control, examining relative changes (ratios). Independent component analysis (ICA) is a promising modern method for the analysis of such experiments. The condition of ICA algorithms can, however, depend on the characteristics of the data examined, making algorithm properties such as robustness specific to the given application domain. To address the lack of studies examining the robustness of ICA applied to microarray measurements, we report on the stability of variational Bayesian ICA in this domain. Microarray data are usually preprocessed and transformed. Hence we first examined alternative transforms and data selections for the smallest modelling reconstruction errors. Log-ratio data are reconstructed better than non-transformed ratio data by our linear model with a Gaussian error term. To compare ICA results we must allow for ICA invariance under rescaling and permutation of the extracted signatures, which hold the loadings of the original variables (gene transcript ratios) on particular latent variables. We introduced a method to optimally match corresponding signatures between sets of results. The stability of signatures was then examined after (1) repetition of the same analysis run with different random number generator seeds, and (2) repetition of the analysis with partial data sets. The effects of both dropping a proportion of the gene transcript ratios and dropping measurements for several samples have been studied. In summary, signatures with a high relative data power were very likely to be retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wildtype vs. wild-type experiments, moreover, yielded 10 reliably identified signatures, demonstrating that the variance observed is not just noise