Rob J. Dekker

Endothelial KLF2 links local arterial shear stress levels to the expression of vascular tone-regulating genes.

Lung Krüppel-like factor (LKLF/KLF2) is an endothelial transcription factor that is crucially involved in murine vasculogenesis and is specifically regulated by flow in vitro. We now show a relation to local flow variations in the adult human vasculature: decreased LKLF expression was noted at the aorta bifurcations to the iliac and carotid arteries, coinciding with neointima formation. The direct involvement of shear stress in the in vivo expression of LKLF was determined independently by in situ hybridization and laser microbeam microdissection/reverse transcriptase-polymerase chain reaction in a murine carotid artery collar model, in which a 4- to 30-fold induction of LKLF occurred at the high-shear sites. Dissection of the biomechanics of LKLF regulation in vitro demonstrated that steady flow and pulsatile flow induced basal LKLF expression 15- and 36-fold at shear stresses greater than approximately 5 dyne/cm2, whereas cyclic stretch had no effect. Prolonged LKLF induction in the absence of flow changed the expression of angiotensin-converting enzyme, endothelin-1, adrenomedullin, and endothelial nitric oxide synthase to levels similar to those observed under prolonged flow. LKLF repression by siRNA suppressed the flow response of endothelin-1, adrenomedullin, and endothelial nitric oxide synthase (P < 0.05). Thus, we demonstrate that endothelial LKLF is regulated by flow in vivo and is a transcriptional regulator of several endothelial genes that control vascular tone in response to flow.

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 218. 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.

Mother-Specific Signature in the Maternal Transcriptome Composition of Mature, Unfertilized Zebrafish Eggs.

Maternal mRNA present in mature oocytes plays an important role in the proper development of the early embryo. As the composition of the maternal transcriptome in general has been studied with pooled mature eggs, potential differences between individual eggs are unknown. Here we present a transcriptome study on individual zebrafish eggs from clutches of five mothers in which we focus on the differences in maternal mRNA abundance per gene between and within clutches. To minimize technical interference, we used mature, unfertilized eggs from siblings. About half of the number of analyzed genes was found to be expressed as maternal RNA. The expressed and non-expressed genes showed that maternal mRNA accumulation is a non-random process, as it is related to specific biological pathways and processes relevant in early embryogenesis. Moreover, it turned out that overall the composition of the maternal transcriptome is tightly regulated as about half of the expressed genes display a less than twofold expression range between the observed minimum and maximum expression values of a gene in the experiment. Even more, the maximum gene-expression difference within clutches is for 88% of the expressed genes lower than twofold. This means that expression differences observed in maternally expressed genes are primarily caused by differences between mothers, with only limited variability between eggs from the same mother. This was underlined by the fact that 99% of the expressed genes were found to be differentially expressed between any of the mothers in an ANOVA test. Furthermore, linking chromosome location, transcription factor binding sites, and miRNA target sites of the genes in clusters of distinct and unique mother-specific gene-expression, suggest biological relevance of the mother-specific signatures in the maternal transcriptome composition. Altogether, the maternal transcriptome composition of mature zebrafish oocytes seems to be tightly regulated with a distinct mother-specific signature.

Expression of Distinct Maternal and Somatic 5.8S, 18S, and 28S rRNA Types during Zebrafish Development

There is mounting evidence that the ribosome is not a static translation machinery, but a cell-specific, adaptive system. Ribosomal variations have mostly been studied at the protein level, even though the essential transcriptional functions are primarily performed by rRNAs. At the RNA level, oocyte-specific 5S rRNAs are long known for Xenopus. Recently, we described for zebrafish a similar system in which the sole maternal-type 5S rRNA present in eggs is replaced completely during embryonic development by a somatic-type. Here, we report the discovery of an analogous system for the 45S rDNA elements: 5.8S, 18S, and 28S. The maternal-type 5.8S, 18S, and 28S rRNA sequences differ substantially from those of the somatic-type, plus the maternal-type rRNAs are also replaced by the somatic-type rRNAs during embryogenesis. We discuss the structural and functional implications of the observed sequence differences with respect to the translational functions of the 5.8S, 18S, and 28S rRNA elements. Finally, in silico evidence suggests that expansion segments (ES) in 18S rRNA, previously implicated in ribosome-mRNA interaction, may have a preference for interacting with specific mRNA genes. Taken together, our findings indicate that two distinct types of ribosomes exist in zebrafish during development, each likely conducting the translation machinery in a unique way.

Linking Maternal and Somatic 5S rRNA types with Different Sequence-Specific Non-LTR Retrotransposons

5S rRNA is a ribosomal core component, transcribed from many gene copies organized in genomic repeats. Some eukaryotic species have two 5S rRNA types defined by their predominant expression in oogenesis or adult tissue. Our next-generation sequencing study on zebrafish egg, embryo, and adult tissue identified maternal-type 5S rRNA that is exclusively accumulated during oogenesis, replaced throughout the embryogenesis by a somatic-type, and thus virtually absent in adult somatic tissue. The maternal-type 5S rDNA contains several thousands of gene copies on chromosome 4 in tandem repeats with small intergenic regions, whereas the somatic-type is present in only 12 gene copies on chromosome 18 with large intergenic regions. The nine-nucleotide variation between the two 5S rRNA types likely affects TFIII binding and riboprotein L5 binding, probably leading to storage of maternal-type rRNA. Remarkably, these sequence differences are located exactly at the sequence-specific target site for genome integration by the 5S rRNA-specific Mutsu retrotransposon family. Thus, we could define maternal- and somatic-type MutsuDr subfamilies. Furthermore, we identified four additional maternal-type and two new somatic-type MutsuDr subfamilies, each with their own target sequence. This target-site specificity, frequently intact maternal-type retrotransposon elements, plus specific presence of Mutsu retrotransposon RNA and piRNA in egg and adult tissue, suggest an involvement of retrotransposons in achieving the differential copy number of the two types of 5S rDNA loci.

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.

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.

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.

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.

In Vitro-In Vivo Gene Expression Analysis in Atherosclerosis

Atherosclerosis, the pathologic inflammatory response to injury. of the human vessel wall, has been long recognized for its complexity of initiation, progression and ultimate appearance of clinical symptoms [1]. Many proteins and other compounds have been implicated in atherogenesis, and this list is now growing exponentially with the recent advances in high-throughput gene expression profiling [1 2 3 4 5 6 7 8 9].Indeed, a plethora of individual genes show altered expression during atherosclerosis, but the development of intervention strategies based on such individual genes in animal models has been rather challenging. The translation into treatment of atherosclerosis in man has proven even more difficult. A clear gene-environment interaction, most notably Western-type diet and life-style, lies at the basis of disease development. This indicates that disturbed patterns of gene-expression rather than single culprit genes form the basis for the widespread penetrance of the disease in the elderly Western population. We are applying functional Genomics to the study of atherosclerosis, with the goal of characterizing healthy and diseased gene expression profiles. While our immediate objective is to characterize those genes that are differentially expressed during atherogenesis, our long-term goal is to determine how a healthy gene expression profile can be induced in the cells of the vascular wall. This implies not only to identify differentially expressed genes but also to determine their function and, most importantly, to analyze the integrated pathways and mechanisms through which their expression is regulated. In this report we will describe the use of differential display RT-PCR and cDNA microarray expression analysis to determine changes in gene expression profiles in cultured vascular endothelial cells in response to pro-and antiatherogenic stimuli. We will briefly explore the computational analysis of such gene expression profiles as detected by a custom cardiovascular microarray. Finally, we show that insights that were gained in vitro can be extended to the in vivo (atherosclerotic) vascular wall.