Genome Sequence of a New Carnation Small Viroid-Like RNA, CarSV-1.

Here, we report the genome sequence of a new circular viroid-like RNA (CarSV-1) derived from Dianthus caryophyllus (carnation) leaves. The CarSV-1 genome has notable sequence similarity (62%) to the well-studied CarSV viroid-like RNA and comprises the complete hammerhead consensus sequences involved in self-cleavage. CarSV-1 co-occurs with carnation viruses, such as CarMV.

The dynamics of early-state transcriptional changes and aggregate formation in a Huntington's disease cell model.

BACKGROUND: Huntington's disease (HD) is a fatal neurodegenerative disorder caused by a CAG expansion in the Huntingtin (HTT) gene. Proteolytic cleavage of mutant huntingtin (Htt) protein with an expanded polyglutamine (polyQ) stretch results in production of Htt fragments that aggregate and induce impaired ubiquitin proteasome, mitochondrial functioning and transcriptional dysregulation. To understand the time-resolved relationship between aggregate formation and transcriptional changes at early disease stages, we performed temporal transcriptome profiling and quantification of aggregate formation in living cells in an inducible HD cell model. RESULTS: Rat pheochromocytoma (PC12) cells containing a stably integrated, doxycycline-inducible, eGFP-tagged N-terminal human Htt fragment with an expanded polyQ domain were used to analyse gene expression changes at different stages of mutant Htt aggregation. At earliest time points after doxycycline induction no detectable aggregates and few changes in gene expression were observed. Aggregates started to appear at intermediate time points. Aggregate formation and subsequent enlargement of aggregates coincided with a rapid increase in the number of differentially expressed (DE) genes. The increase in number of large aggregates coincided with a decrease in the number of smaller aggregates whereas the transcription profile reverted towards the profile observed before mutant Htt induction. Cluster-based analysis of the 2,176 differentially expressed genes revealed fourteen distinct clusters responding differently over time. Functional enrichment analysis of the two major gene clusters revealed that genes in the up-regulated cluster were mainly involved in metabolic (antioxidant activity and cellular ketone metabolic processes) and genes in the down-regulated cluster in developmental processes, respectively. Promoter-based analysis of the identified gene clusters resulted in identification of a transcription factor network of which several previously have been linked to HD. CONCLUSIONS: We demonstrate a time-resolved relationship between Htt aggregation and changes in the transcriptional profile. We identified two major gene clusters showing involvement of (i) mitochondrial dysfunction and (ii) developmental processes implying cellular homeostasis defects. We identified novel and known HD-linked transcription factors and show their interaction with known and predicted regulatory proteins. Our data provide a novel resource for hypothesis building on the role of transcriptional key regulators in early stages of HD and possibly other polyQ-dependent diseases.

Transcriptome dynamics in early zebrafish embryogenesis determined by high-resolution time course analysis of 180 successive, individual zebrafish embryos.

BACKGROUND: Recently, much progress has been made in the field of gene-expression in early embryogenesis. However, the dynamic behaviour of transcriptomes in individual embryos has hardly been studied yet and the time points at which pools of embryos are collected are usually still quite far apart. Here, we present a high-resolution gene-expression time series with 180 individual zebrafish embryos, obtained from nine different spawns, developmentally ordered and profiled from late blastula to mid-gastrula stage. On average one embryo per minute was analysed. The focus was on identification and description of the transcriptome dynamics of the expressed genes in this embryonic stage, rather than to biologically interpret profiles in cellular processes and pathways. RESULTS: In the late blastula to mid-gastrula stage, we found 6,734 genes being expressed with low variability and rather gradual changes. Ten types of dynamic behaviour were defined, such as genes with continuously increasing or decreasing expression, and all expressed genes were grouped into these types. Also, the exact expression starting and stopping points of several hundred genes during this developmental period could be pinpointed. Although the resolution of the experiment was so high, that we were able to clearly identify four known oscillating genes, no genes were observed with a peaking expression. Additionally, several genes showed expression at two or three distinct levels that strongly related to the spawn an embryo originated from. CONCLUSION: Our unique experimental set-up of whole-transcriptome analysis of 180 individual embryos, provided an unparalleled in-depth insight into the dynamics of early zebrafish embryogenesis. The existence of a tightly regulated embryonic transcriptome program, even between individuals from different spawns is shown. We have made the expression profile of all genes available for domain experts. The fact that we were able to separate the different spawns by their gene-expression variance over all expressed genes, underlines the importance of spawn specificity, as well as the unexpectedly tight gene-expression regulation in early zebrafish embryogenesis.

Transcriptome data on maternal RNA of 24 individual zebrafish eggs from five sibling mothers.

Maternal mRNA that is present in the mature oocyte plays an important role in the proper development of the early embryo. To elucidate the role of the maternal transcriptome we recently reported a microarray study on individual zebrafish eggs from five different clutches from sibling mothers and showed differences in maternal RNA abundance between and within clutches, "Mother-specific signature in the maternal transcriptome composition of mature, unfertilized Eggs" [1]. Here we provide in detail the applied preprocessing method as well as the R-code to identify expressed and non-expressed genes in the associated transcriptome dataset. Additionally, we provide a website that allows a researcher to search for the expression of their gene of interest in this experiment.

Valuable lessons-learned in transcriptomics experimentation.

We have collected several valuable lessons that will help improve transcriptomics experimentation. These lessons relate to experiment design, execution, and analysis. The cautions, but also the pointers, may help biologists avoid common pitfalls in transcriptomics experimentation and achieve better results with their transcriptome studies.

Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization.

There is an increasing interest in complementing RNA-seq experiments with small-RNA (sRNA) expression data to obtain a comprehensive view of a transcriptome. Currently, two main experimental challenges concerning sRNA-seq exist: how to check the size distribution of isolated sRNAs, given the sensitive size-selection steps in the protocol; and how to normalize data between samples, given the low complexity of sRNA types. We here present two separate sets of synthetic RNA spike-ins for monitoring size-selection and for performing data normalization in sRNA-seq. The size-range quality control (SRQC) spike-in set, consisting of 11 oligoribonucleotides (10-70 nucleotides), was tested by intentionally altering the size-selection protocol and verified via several comparative experiments. We demonstrate that the SRQC set is useful to reproducibly track down biases in the size-selection in sRNA-seq. The external reference for data-normalization (ERDN) spike-in set, consisting of 19 oligoribonucleotides, was developed for sample-to-sample normalization in differential-expression analysis of sRNA-seq data. Testing and applying the ERDN set showed that it can reproducibly detect differential expression over a dynamic range of 2(18). Hence, biological variation in sRNA composition and content between samples is preserved while technical variation is effectively minimized. Together, both spike-in sets can significantly improve the technical reproducibility of sRNA-seq.

Absence/presence calling in microarray-based CGH experiments with non-model organisms.

Structural variations in genomes are commonly studied by (micro)array-based comparative genomic hybridization. The data analysis methods to infer copy number variation in model organisms (human, mouse) are established. In principle, the procedures are based on signal ratios between test and reference samples and the order of the probe targets in the genome. These procedures are less applicable to experiments with non-model organisms, which frequently comprise non-sequenced genomes with an unknown order of probe targets. We therefore present an additional analysis approach, which does not depend on the structural information of a reference genome, and quantifies the presence or absence of a probe target in an unknown genome. The principle is that intensity values of target probes are compared with the intensities of negative-control probes and positive-control probes from a control hybridization, to determine if a probe target is absent or present. In a test, analyzing the genome content of a known bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful, demonstrated by receiver operating characteristic area under the curve values larger than 0.9995. We show its usability in various applications, such as comparing genome content and validating next-generation sequencing reads from eukaryotic non-model organisms.

Gene expression patterns and life cycle responses of toxicant-exposed chironomids.

Cellular stress responses are frequently presumed to be more sensitive than traditional ecotoxicological life cycle end points such as survival and growth. Yet, the focus to reduce test duration and to generate more sensitive end points has caused transcriptomics studies to be performed at low doses during short exposures, separately and independently from traditional ecotoxicity tests, making comparisons with life cycle end points indirect. Therefore we aimed to directly compare the effects on growth, survival, and gene expression of the nonbiting midge Chironomus riparius. To this purpose, we simultaneously analyzed life cycle and transcriptomics responses of chironomid larvae exposed to four model toxicants. We observed that already at the lowest test concentrations many transcripts were significantly differentially expressed, while the life cycle end points of C. riparius were hardly affected. Analysis of the differentially expressed transcripts showed that at the lowest test concentrations substantial and biologically relevant cellular stress was induced and that many transcripts responded already maximally at these lowest test concentrations. The direct comparison between molecular end life cycle responses after fourteen days of exposure revealed that gene expression is more sensitive to toxicant exposure than life cycle end points, underlining the potential of transcriptomics for ecotoxicity testing and environmental risk assessment.

Combining next-generation sequencing and microarray technology into a transcriptomics approach for the non-model organism Chironomus riparius.

Whole-transcriptome gene-expression analyses are commonly performed in species that have a sequenced genome and for which microarrays are commercially available. To do such analyses in species with no or limited genome data, i.e. non-model organisms, necessary transcriptomics resources, i.e. an annotated transcriptome and a validated gene-expression microarray, must first be developed. The aim of the present study was to establish an advanced approach for developing transcriptomics resources for non-model organisms by combining next-generation sequencing (NGS) and microarray technology. We applied our approach to the non-biting midge Chironomus riparius, an ecologically relevant species that is widely used in sediment ecotoxicity testing. We sampled extensively covering all C. riparius developmental stages as well as toxicant exposed larvae and obtained from a normalized cDNA library 1.5 M NGS reads totalling 501 Mbp. Using the NGS data we developed transcriptomics resources in several steps. First, we designed 844 k probes directly on the NGS reads, as well as 76 k probes targeting expressed sequence tags of related species. These probes were tested for their affinity to C. riparius DNA and mRNA, by performing two biological experiments with a 1 M probe-selection microarray that contained the entire probe-library. Subsequently, the 1.5 M NGS reads were assembled into 23,709 isotigs and 135,082 singletons, which were associated to ~55 k, respectively, ~61 k gene ontology terms and which corresponded together to 22,593 unique protein accessions. An algorithm was developed that took the assembly and the probe affinities to DNA and mRNA into account, what resulted in 59 k highly-reliable probes that targeted uniquely 95% of the isotigs and 18% of the singletons. Concluding, our approach allowed the development of high-quality transcriptomics resources for C. riparius, and is applicable to any non-model organism. It is expected, that these resources will advance ecotoxicity testing with C. riparius as whole-transcriptome gene-expression analysis are now possible with this species.

Integrating heterogeneous sequence information for transcriptome-wide microarray design; a Zebrafish example.

BACKGROUND: A complete gene-expression microarray should preferably detect all genomic sequences that can be expressed as RNA in an organism, i.e. the transcriptome. However, our knowledge of a transcriptome of any organism still is incomplete and transcriptome information is continuously being updated. Here, we present a strategy to integrate heterogeneous sequence information that can be used as input for an up-to-date microarray design. FINDINGS: Our algorithm consists of four steps. In the first step transcripts from different resources are grouped into Transcription Clusters (TCs) by looking at the similarity of all transcripts. TCs are groups of transcripts with a similar length. If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence. Secondly, all TCs are mapped to a genome assembly and gene information is added to the design. Thirdly TC members are ranked according to their trustworthiness and the most reliable sequence is used for the probe design. The last step is the actual array design. We have used this strategy to build an up-to-date zebrafish microarray. CONCLUSIONS: With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy. By changing the parameters in the procedure it is possible to control the similarity within the TCs and thus the amount of candidate sequences for the design. The annotation of the microarray is carried out simultaneously with the design.

SigWinR; the SigWin-detector updated and ported to R.

BACKGROUND: Our SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (life) scientists with access to the grid can use this tool. Therefore and on request, we have developed the SigWinR package which makes the SigWin-detector available to a much wider audience. At the same time, we have introduced several improvements to its algorithm as well as its functionality, based on the feedback of SigWin-detector end users. FINDINGS: To allow usage of the SigWin-detector on a desktop computer, we have rewritten it as a package for R: SigWinR. R is a free and widely used multi platform software environment for statistical computing and graphics. The package can be installed and used on all platforms for which R is available. The improvements involve: a visualization of the input-sequence values supporting the interpretation of Ridgeograms; a visualization allowing for an easy interpretation of enriched or depleted regions in the sequence using windows of pre-defined size; an option that allows the analysis of circular sequences, which results in rectangular Ridgeograms; an application to identify regions of co-altered gene expression (ROCAGEs) with a real-life biological use-case; adaptation of the algorithm to allow analysis of non-regularly sampled data using a constant window size in physical space without resampling the data. To achieve this, support for analysis of windows with an even number of elements was added. CONCLUSION: By porting the SigWin-detector as an R package, SigWinR, improving its algorithm and functionality combined with adequate performance, we have made SigWin-detector more useful as well as more easily accessible to scientists without a grid infrastructure.

Salvaging Affymetrix probes after probe-level re-annotation.

BACKGROUND: Affymetrix GeneChips can be re-annotated at the probe-level by breaking up the original probe-sets and recomposing new probe-sets based on up-to-date genomic knowledge, such as available in Entrez Gene. This results in custom Chip Description Files (CDF). Using these custom CDFs improves the quality of the data and thus the results of related gene expression studies. However, 44-71% of the probes on a GeneChip are lost in this re-annotation process. Although generally aimed at less known genes, losing these probes obviously means a substantial loss of expensive experiment data. Biologists are therefore very reluctant to adopt this approach. FINDINGS: We aimed to re-introduce the non-affected Affymetrix probe-sets after these re-annotation procedures. For this, we developed an algorithm (CDF-Merger) and applied it to standard Affymetrix CDFs and custom Brainarray CDFs to obtain Hybrid CDFs. Thus, salvaging lost Affymetrix probes with our CDF-Merger restored probe content up to 94%. Because the salvaged probes (up to 54% of the probe content on the arrays) represent less-reliable probe-sets, we made the origin of all probe-set definitions traceable, so biologists can choose at any time in their analyses, which subset of probe-sets they want to use. CONCLUSION: The availability of up-to-date Hybrid CDFs plus R environment allows for easy implementation of our approach.