Dmitri D. Pervouchine

Engineered riboregulators enable post-transcriptional control of gene expression

Recent studies have demonstrated the important enzymatic, structural and regulatory roles of RNA in the cell. Here we present a post-transcriptional regulation system in Escherichia coli that uses RNA to both silence and activate gene expression. We inserted a complementary cis sequence directly upstream of the ribosome binding site in a target gene. Upon transcription, this cis-repressive sequence causes a stem-loop structure to form at the 5′–untranslated region of the mRNA. The stem-loop structure interferes with ribosome binding, silencing gene expression. A small noncoding RNA that is expressed in trans targets the cis-repressed RNA with high specificity, causing an alteration in the stem-loop structure that activates expression. Such engineered riboregulators may lend insight into mechanistic actions of endogenous RNA-based processes and could serve as scalable components of biological networks, able to function with any promoter or gene to directly control gene expression.

Human genomics. The human transcriptome across tissues and individuals.

Expression, genetic variation, and tissues Human genomes show extensive genetic variation across individuals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within individuals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem samples covering 54 body sites from 175 individuals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science, this issue p. 648, p. 660, p. 666; see also p. 640 RNA expression documents patterns of human transcriptome variation across individuals and tissues. [Also see Perspective by Gibson] Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

Comparative analysis of the transcriptome across distant species.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.

RNAKinetics: a web server that models secondary structure kinetics of an elongating RNA.

The RNAKinetics server (http://www.ig-msk.ru/RNA/kinetics) is a web interface for the newly developed RNAKinetics software. The software models the dynamics of RNA secondary structure by the means of kinetic analysis of folding transitions of a growing RNA molecule. The result of the modeling is a kinetic ensemble, i.e. a collection of RNA structures that are endowed with probabilities, which depend on time. This approach gives comprehensive probabilistic description of RNA folding pathways, revealing important kinetic details that are not captured by the traditional structure prediction methods. The access to the RNAKinetics server is free.

Modulation of alternative splicing by long-range RNA structures in Drosophila

Accurate and efficient recognition of splice sites during pre-mRNA splicing is essential for proper transcriptome expression. Splice site usage can be modulated by secondary structures, but it is unclear if this type of modulation is commonly used or occurs to a significant degree with secondary structures forming over long distances. Using phlyogenetic comparisons of intronic sequences among 12 Drosophila genomes, we elucidated a group of 202 highly conserved pairs of sequences, each at least nine nucleotides long, capable of forming stable stem structures. This set was highly enriched in alternatively spliced introns and introns with weak acceptor sites and long introns, and most occurred over long distances (>150 nucleotides). Experimentally, we analyzed the splicing of several of these introns using mini-genes in Drosophila S2 cells. Wild-type splicing patterns were changed by mutations that opened the stem structure, and restored by compensatory mutations that re-established the base-pairing potential, demonstrating that these secondary structures were indeed implicated in the splice site choice. Mechanistically, the RNA structures masked splice sites, brought together distant splice sites and/or looped out introns. Thus, base-pairing interactions within introns, even those occurring over long distances, are more frequent modulators of alternative splicing than is currently assumed.

Gamma and Theta Rhythms in Biophysical Models of Hippocampal Circuits

The neural circuits of the hippocampus are extremely complex, with many classes of interneurons whose contributions to network dynamics and function are still unknown. Nevertheless, reduced models can provide insight into aspects of the dynamics and associated function. In this chapter, we discuss models at a variety of levels of complexity, all simple enough to probe the reasons for the behavior of the model. The chapter focuses on the main rhythms displayed by the hippocampus, the gamma (30–90 Hz) and theta (4–12 Hz) rhythms. We concentrate on modeling in vitro experiments, but with an eye toward possible in vivo implications.

A benchmark for RNA-seq quantification pipelines

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.

Low-Dimensional Maps Encoding Dynamics in Entorhinal Cortex and Hippocampus

Cells that produce intrinsic theta oscillations often contain the hyperpolarization-activated current Ih. In this article, we use models and dynamic clamp experiments to investigate the synchronization properties of two such cells (stellate cells of the entorhinal cortex and O-LM cells of the hippocampus) in networks with fast-spiking (FS) interneurons. The model we use for stellate cells and O-LM cells is the same, but the stellate cells are excitatory and the O-LM cells are inhibitory, with inhibitory postsynaptic potential considerably longer than those from FS interneurons. We use spike time response curve methods (STRC), expanding that technique to three-cell networks and giving two different ways in which the analysis of the three-cell network reduces to that of a two-cell network. We show that adding FS cells to a network of stellate cells can desynchronize the stellate cells, while adding them to a network of O-LM cells can synchronize the O-LM cells. These synchronization and desynchronization properties critically depend on Ih. The analysis of the deterministic system allows us to understand some effects of noise on the phase relationships in the stellate networks. The dynamic clamp experiments use biophysical stellate cells and in silico FS cells, with connections that mimic excitation or inhibition, the latter with decay times associated with FS cells or O-LM cells. The results obtained in the dynamic clamp experiments are in a good agreement with the analytical framework.

Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression

Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles.

Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction

BackgroundA vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based.ResultsWe describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome.ConclusionsThe reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.