Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment.

CONFOUNDING FACTORS: In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64). ANALYSIS APPROACH: Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes. ANOVA: ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors. CONCLUSION: Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Evaluation of ribonucleic acid amplification protocols for human oocyte transcriptome analysis.

OBJECTIVE: To develop a reliable, reproducible, and sensitive method for investigating gene-expression profiles from individual human oocytes. DESIGN: Five commercially available protocols were investigated for their efficiency to amplify messenger RNA (mRNA) from 54 single human oocytes. Protocols resulting in sufficient yields were further validated using microarray technology. For the validation, mRNA was isolated from 25 human oocytes. To eliminate biological variation, RNA from 13 human oocytes was pooled together and split into 12 identical samples for further mRNA amplification. From 12 oocytes, mRNA was individually isolated. SETTING: University medical center and university microarray laboratory. PATIENT(S): Couples undergoing intracytoplasmic sperm injection treatment were asked to donate their immature oocytes for research, and written informed consent was obtained in all cases. Seventy-nine human oocytes were used in total. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Amplification efficiency and microarray profiles. RESULT(S): Two of the five protocols (WT-Ovation One-Direct and Arcturus RiboAMp HS Plus) resulted in sufficient yields and high success rates and were further validated for their performance in obtaining reliable, reproducible, and sensitive expression profiles from individual human oocytes. Evaluation of these two protocols demonstrated that they both displayed low technical variation and produced highly reproducible profiles (r ≥ 0.95). One of them identified significantly more transcripts but also had a higher number of false discoveries. CONCLUSION(S): Two protocols generated ample amounts of mRNA for (quantitative) polymerase chain reaction, microarray, and sequencing techniques. Further validation using a design that discriminates between biological and technical variation showed that both protocols can be used for gene-expression profiling of individual human oocytes.

The effector repertoire of Fusarium oxysporum determines the tomato xylem proteome composition following infection.

Plant pathogens secrete small proteins, of which some are effectors that promote infection. During colonization of the tomato xylem vessels the fungus Fusarium oxysporum f.sp. lycopersici (Fol) secretes small proteins that are referred to as SIX (Secreted In Xylem) proteins. Of these, Six1 (Avr3), Six3 (Avr2), Six5, and Six6 are required for full virulence, denoting them as effectors. To investigate their activities in the plant, the xylem sap proteome of plants inoculated with Fol wild-type or either AVR2, AVR3, SIX2, SIX5, or SIX6 knockout strains was analyzed with nano-Liquid Chromatography-Mass Spectrometry (nLC-MSMS). Compared to mock-inoculated sap 12 additional plant proteins appeared while 45 proteins were no longer detectable in the xylem sap of Fol-infected plants. Of the 285 proteins found in both uninfected and infected plants the abundance of 258 proteins changed significantly following infection. The xylem sap proteome of plants infected with four Fol effector knockout strains differed significantly from plants infected with wild-type Fol, while that of the SIX2-knockout inoculated plants remained unchanged. Besides an altered abundance of a core set of 24 differentially accumulated proteins (DAPs), each of the four effector knockout strains affected specifically the abundance of a subset of DAPs. Hence, Fol effectors have both unique and shared effects on the composition of the tomato xylem sap proteome.

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.

A range finding protocol to support design for transcriptomics experimentation: examples of in-vitro and in-vivo murine UV exposure.

In transcriptomics research, design for experimentation by carefully considering biological, technological, practical and statistical aspects is very important, because the experimental design space is essentially limitless. Usually, the ranges of variable biological parameters of the design space are based on common practices and in turn on phenotypic endpoints. However, specific sub-cellular processes might only be partially reflected by phenotypic endpoints or outside the associated parameter range. Here, we provide a generic protocol for range finding in design for transcriptomics experimentation based on small-scale gene-expression experiments to help in the search for the right location in the design space by analyzing the activity of already known genes of relevant molecular mechanisms. Two examples illustrate the applicability: in-vitro UV-C exposure of mouse embryonic fibroblasts and in-vivo UV-B exposure of mouse skin. Our pragmatic approach is based on: framing a specific biological question and associated gene-set, performing a wide-ranged experiment without replication, eliminating potentially non-relevant genes, and determining the experimental 'sweet spot' by gene-set enrichment plus dose-response correlation analysis. Examination of many cellular processes that are related to UV response, such as DNA repair and cell-cycle arrest, revealed that basically each cellular (sub-) process is active at its own specific spot(s) in the experimental design space. Hence, the use of range finding, based on an affordable protocol like this, enables researchers to conveniently identify the 'sweet spot' for their cellular process of interest in an experimental design space and might have far-reaching implications for experimental standardization.

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.

Rifampicin-induced transcriptome response in rifampicin-resistant Mycobacterium tuberculosis.

Tuberculosis (TB) is still a major life-threatening infectious disease, within which especially the rise of multidrug resistant TB (MDR-TB) is currently worrying. This study focuses on mechanisms of development of rifampicin resistance, since rifampicin seems to play an important role in the development of MDR-TB. To provide further insight in rifampicin resistance, we performed a genome-wide transcriptional profile analysis for Mycobacterium tuberculosis (M. tuberculosis) using microarray technology and qRT-PCR analysis. We exposed a rifampicin-susceptible H37Rv wild type (H37Rv-WT) and a rifampicin-resistant progeny H37Rv strain with a H526Y mutation in the rpoB gene (H37Rv-H526Y) to several concentrations of rifampicin, to define the effect of rifampicin on the transcription profile. Our study showed that there are resistance-dependant differences in response between both M. tuberculosis strains. Gene clusters associated with efflux, transport and virulence were altered in the rifampicin-resistant H37Rv mutant compared to the rifampicin-susceptible H37Rv-WT strain after exposure to rifampicin. We conclude that the small gene cluster Rv0559c-Rv0560c in the H37Rv-H526Y strain was remarkably up-regulated in the microarray analysis and qRT-PCR results and appeared to be dependent on rifampicin concentration and time of exposure. Therefore this study suggests that Rv0559c and Rv0560c play a pivotal role in rifampicin resistance of M. tuberculosis. Further investigation of Rv0559c and Rv0560c is needed to reveal function and mechanism of both genes that were triggered upon rifampicin exposure.

RNA isolation for transcriptomics of human and mouse small skin biopsies.

BACKGROUND: Isolation of RNA from skin biopsies presents a challenge, due to the tough nature of skin tissue and a high presence of RNases. As we lacked the dedicated equipment, i.e. homogenizer or bead-beater, needed for the available RNA from skin isolation methods, we adapted and tested our zebrafish single-embryo RNA-isolation protocol for RNA isolation from skin punch biopsies. FINDINGS: We tested our new RNA-isolation protocol in two experiments: a large-scale study with 97 human skin samples, and a small study with 16 mouse skin samples. Human skin was sampled with 4.0 mm biopsy punches and for the mouse skin different punch diameter sizes were tested; 1.0, 1.5, 2.0, and 2.5 mm. The average RNA yield in human samples was 1.5 µg with an average RNA quality RIN value of 8.1. For the mouse biopsies, the average RNA yield was 2.4 µg with an average RIN value of 7.5. For 96% of the human biopsies and 100% of the mouse biopsies we obtained enough high-quality RNA. The RNA samples were successfully tested in a transcriptomics analysis using the Affymetrix and Roche NimbleGen platforms. CONCLUSIONS: Using our new RNA-isolation protocol, we were able to consistently isolate high-quality RNA, which is apt for further transcriptomics analysis. Furthermore, this method is already useable on biopsy material obtained with a punch diameter as small as 1.5 mm.

Single cell transcriptomics of neighboring hyphae of Aspergillus niger.

Single cell profiling was performed to assess differences in RNA accumulation in neighboring hyphae of the fungus Aspergillus niger. A protocol was developed to isolate and amplify RNA from single hyphae or parts thereof. Microarray analysis resulted in a present call for 4 to 7% of the A. niger genes, of which 12% showed heterogeneous RNA levels. These genes belonged to a wide range of gene categories.

RNA isolation method for single embryo transcriptome analysis in zebrafish.

BACKGROUND: Transcriptome analysis during embryogenesis usually requires pooling of embryos to obtain sufficient RNA. Hence, the measured levels of gene-expression represent the average mRNA levels of pooled samples and the biological variation among individuals is confounded. This can irreversibly reduce the robustness, resolution, or expressiveness of the experiment. Therefore, we developed a robust method to isolate abundant high-quality RNA from individual embryos to perform single embryo transcriptome analyses using zebrafish as a model organism. Available methods for embryonic zebrafish RNA isolation minimally utilize ten embryos. Further downscaling of these methods to one embryo is practically not feasible. FINDINGS: We developed a single embryo RNA extraction method based on sample homogenization in liquid nitrogen, RNA extraction with phenol and column purification. Evaluation of this method showed that: the quality of the RNA was very good with an average RIN value of 8.3-8.9; the yield was always >/= 200 ng RNA per embryo; the method was applicable to all stages of zebrafish embryogenesis; the success rate was almost 100%; and the extracted RNA performed excellent in microarray experiments in that the technical variation was much lower than the biological variation. CONCLUSIONS: Presented is a high-quality, robust RNA isolation method. Obtaining sufficient RNA from single embryos eliminates the necessity of sample pooling and its associated drawbacks. Although our RNA isolation method has been setup for transcriptome analysis in zebrafish, it can also be used for other model systems and other applications like (q)PCR and transcriptome sequencing.

Operon structure of Staphylococcus aureus.

In bacteria, gene regulation is one of the fundamental characteristics of survival, colonization and pathogenesis. Operons play a key role in regulating expression of diverse genes involved in metabolism and virulence. However, operon structures in pathogenic bacteria have been determined only by in silico approaches that are dependent on factors such as intergenic distances and terminator/promoter sequences. Knowledge of operon structures is crucial to fully understand the pathophysiology of infections. Presently, transcriptome data obtained from growth curves in a defined medium were used to predict operons in Staphylococcus aureus. This unbiased approach and the use of five highly reproducible biological replicates resulted in 93.5% significantly regulated genes. These data, combined with Pearson's correlation coefficients of the transcriptional profiles, enabled us to accurately compile 93% of the genome in operon structures. A total of 1640 genes of different functional classes were identified in operons. Interestingly, we found several operons containing virulence genes and showed synergistic effects for two complement convertase inhibitors transcribed in one operon. This is the first experimental approach to fully identify operon structures in S. aureus. It forms the basis for further in vitro regulation studies that will profoundly advance the understanding of bacterial pathophysiology in vivo.

Serious complications in gene-expression studies with stress perturbation: An example of UV-exposed p53-mutant mouse embryonic fibroblasts.

Reanalysis of our UV study of p53-mutant mouse embryonic fibroblasts revealed an intriguing orchestration of massive transcriptome responses. However, close scrutiny of the data uncovered an affected mRNA/rRNA ratio, effectively inhibiting valid data analysis. UV-dose range-finding showed low-dose UV specific- and high-dose stress-related responses, which represent a plea for UV dose range-finding in experimental 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.

Finding transcriptomics biomarkers for in vivo identification of (non-)genotoxic carcinogens using wild-type and Xpa/p53 mutant mouse models.

The carcinogenic potential of chemicals and pharmaceuticals is traditionally tested in the chronic, 2 year rodent bioassay. This assay is not only time consuming, expensive and often with a limited sensitivity and specificity but it also causes major distress to the experimental animals. A major improvement in carcinogenicity testing, especially regarding reduction and refinement of animal experimentation, could be the application of toxicogenomics. The ultimate aim of this study is to demonstrate a proof-of-principle for transcriptomics biomarkers in various tissues for identification of (subclasses of) carcinogenic compounds after short-term in vivo exposure studies. Both wild-type and DNA repair-deficient Xpa(-/-)/p53(+/-) (Xpa/p53) mice were exposed up to 14 days to compounds of three distinct classes: genotoxic carcinogens (GTXC), non-genotoxic carcinogens (NGTXC) and non-carcinogens. Subsequently, extensive transcriptomics analyses were performed on several tissues, and transcriptomics data were screened for potential biomarkers using advanced statistical learning techniques. For all tissues analyzed, we identified multigene gene-expression signatures that are, with a high confidence, predictive for GTXC and NGTXC exposures in both mouse genotypes. Xpa/p53 mice did not perform better in the short-term bioassay. We were able to achieve a proof-of-principle for the identification and use of transcriptomics biomarkers for GTXC or NGTXC. This supports the view that toxicogenomics with short-term in vivo exposure provides a viable tool for classifying (geno)toxic compounds.

The absence of Ser389 phosphorylation in p53 affects the basal gene expression level of many p53-dependent genes and alters the biphasic response to UV exposure in mouse embryonic fibroblasts.

Phosphorylation is important in p53-mediated DNA damage responses. After UV irradiation, p53 is phosphorylated specifically at murine residue Ser389. Phosphorylation mutant p53.S389A cells and mice show reduced apoptosis and compromised tumor suppression after UV irradiation. We investigated the underlying cellular processes by time-series analysis of UV-induced gene expression responses in wild-type, p53.S389A, and p53(-/-) mouse embryonic fibroblasts. The absence of p53.S389 phosphorylation already causes small endogenous gene expression changes for 2,253, mostly p53-dependent, genes. These genes showed basal gene expression levels intermediate to the wild type and p53(-/-), possibly to readjust the p53 network. Overall, the p53.S389A mutation lifts p53-dependent gene repression to a level similar to that of p53(-/-) but has lesser effect on p53-dependently induced genes. In the wild type, the response of 6,058 genes to UV irradiation was strictly biphasic. The early stress response, from 0 to 3 h, results in the activation of processes to prevent the accumulation of DNA damage in cells, whereas the late response, from 12 to 24 h, relates more to reentering the cell cycle. Although the p53.S389A UV gene response was only subtly changed, many cellular processes were significantly affected. The early response was affected the most, and many cellular processes were phase-specifically lost, gained, or altered, e.g., induction of apoptosis, cell division, and DNA repair, respectively. Altogether, p53.S389 phosphorylation seems essential for many p53 target genes and p53-dependent processes.

Delayed expression of apoptotic and cell-cycle control genes in carcinogen-exposed bladders of mice lacking p53.S389 phosphorylation.

Mice with non-phosphorylated serine 389 in p53 are susceptible for bladder tumors induced by 2-acetylaminofluorene (2-AAF). Since p53 is a transcription factor, this might well be preceded by differences in the regulation of gene expression. Microarray analysis was used to determine early transcriptional changes that might underlie this cancer-prone phenotype. Interestingly, lack of Ser389 phosphorylation led to endogenously different gene expression levels. The number of genes affected was, however, rather small. Conversely, after short-term exposure to 2-AAF, wild-type and p53.S389A bladders demonstrated a significant number of differentially expressed genes. Differences between wild-type and p53.S389A could mainly be attributed to a delayed, rather than complete absence of, transcriptional response of a group of genes, including well-known p53 target genes involved in apoptosis and cell-cycle control like Bax, Perp and P21. An analysis of differentially expressed genes in non-tumorigenic tissue and bladder tumors of p53.S389A after long-term exposure to 2-AAF revealed 319 genes. Comparison of these with those found after short-term exposure resulted in 23 transcripts. These possible marker genes might be useful for the early prediction of bladder tumor development. In conclusion, our data indicate that lack of Ser389 phosphorylation results in aberrant expression of genes needed to execute vital responses to DNA damage. Post-translational modifications, like Ser389 phosphorylation, seem crucial for fine-tuning the transcription of a specific set of genes and do not appear to give rise to major changes in transcription patterns. As such, Ser389 phosphorylation is needed for some, but certainly not all, p53 functions.