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.

Identifying small RNAs derived from maternal- and somatic-type rRNAs in zebrafish development.

rRNAs are non-coding RNAs present in all prokaryotes and eukaryotes. In eukaryotes there are four rRNAs: 18S, 5.8S, 28S, originating from a common precursor (45S), and 5S. We have recently discovered the existence of two distinct developmental types of rRNA: a maternal-type, present in eggs and a somatic-type, expressed in adult tissues. Lately, next-generation sequencing has allowed the discovery of new small-RNAs deriving from longer non-coding RNAs, including small-RNAs from rRNAs (srRNAs). Here, we systemically investigated srRNAs of maternal- or somatic-type 18S, 5.8S, 28S, with small-RNAseq from many zebrafish developmental stages. We identified new srRNAs for each rRNA. For 5.8S, we found srRNA consisting of the 5' or 3' halves, with only the latter having different sequence for the maternal- and somatic-types. For 18S, we discovered 21 nt srRNA from the 5' end of the 18S rRNA with a striking resemblance to microRNAs; as it is likely processed from a stem-loop precursor and present in human and mouse Argonaute-complexed small-RNA. For 28S, an abundant 80 nt srRNA from the 3' end of the 28S rRNA was found. The expression levels during embryogenesis of these srRNA indicate they are not generated from rRNA degradation and might have a role in the zebrafish development.

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.

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.

Genome sequences of 10 new carnation mottle virus variants.

Here, we report the genome sequences of 10 Carnation mottle virus variants. Six variants originated from a single proprietary carnation cultivar, and four were derived from four different proprietary cultivars. All variants showed nucleotide differences, but the last four did not show any variation at the amino acid level.

Low energy nebulization preserves integrity of SARS-CoV-2 mRNA vaccines for respiratory delivery.

Nebulization of mRNA therapeutics can be used to directly target the respiratory tract. A promising prospect is that mucosal administration of lipid nanoparticle (LNP)-based mRNA vaccines may lead to a more efficient protection against respiratory viruses. However, the nebulization process can rupture the LNP vehicles and degrade the mRNA molecules inside. Here we present a novel nebulization method able to preserve substantially the integrity of vaccines, as tested with two SARS-CoV-2 mRNA vaccines. We compare the new method with well-known nebulization methods used for medical respiratory applications. We find that a lower energy level in generating LNP droplets using the new nebulization method helps safeguard the integrity of the LNP and vaccine. By comparing nebulization techniques with different energy dissipation levels we find that LNPs and mRNAs can be kept largely intact if the energy dissipation remains below a threshold value, for LNP integrity 5-10 J/g and for mRNA integrity 10-20 J/g for both vaccines.

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.

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.

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.