A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs

BACKGROUND: In recent years, intensive computational efforts have been directed towards the discovery of promoter motifs that correlate with mRNA expression profiles. Nevertheless, it is still not always possible to predict steady-state mRNA expression levels based on promoter signals alone, suggesting that other factors may be involved. Other genic regions, in particular 3' UTRs, which are known to exert regulatory effects especially through controlling RNA stability and localization, were less comprehensively investigated, and deciphering regulatory motifs within them is thus crucial. RESULTS: By analyzing 3' UTR sequences and mRNA decay profiles of Saccharomyces cerevisiae genes, we derived a catalog of 53 sequence motifs that may be implicated in stabilization or destabilization of mRNAs. Some of the motifs correspond to known RNA-binding protein sites, and one of them may act in destabilization of ribosome biogenesis genes during stress response. In addition, we present for the first time a catalog of 23 motifs associated with subcellular localization. A significant proportion of the 3' UTR motifs is highly conserved in orthologous yeast genes, and some of the motifs are strikingly similar to recently published mammalian 3' UTR motifs. We classified all genes into those regulated only at transcription initiation level, only at degradation level, and those regulated by a combination of both. Interestingly, different biological functionalities and expression patterns correspond to such classification. CONCLUSION: The present motif catalogs are a first step towards the understanding of the regulation of mRNA degradation and subcellular localization, two important processes which - together with transcription regulation - determine the cell transcriptome

Extraction of transcription regulatory signals from genome-wide DNA–protein interaction data

Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike

Messenger RNA Fluctuations and Regulatory RNAs Shape the Dynamics of Negative Feedback Loop

Single cell experiments of simple regulatory networks can markedly differ from cell population experiments. Such differences arise from stochastic events in individual cells that are averaged out in cell populations. For instance, while individual cells may show sustained oscillations in the concentrations of some proteins, such oscillations may appear damped in the population average. In this paper we investigate the role of RNA stochastic fluctuations as a leading force to produce a sustained excitatory behavior at the single cell level. Opposed to some previous models, we build a fully stochastic model of a negative feedback loop that explicitly takes into account the RNA stochastic dynamics. We find that messenger RNA random fluctuations can be amplified during translation and produce sustained pulses of protein expression. Motivated by the recent appreciation of the importance of non--coding regulatory RNAs in post--transcription regulation, we also consider the possibility that a regulatory RNA transcript could bind to the messenger RNA and repress translation. Our findings show that the regulatory transcript helps reduce gene expression variability both at the single cell level and at the cell population level.Comment: 87.18.Vf --> Systems biology 87.10.Mn --> Stochastic models in biological systems 87.18.Tt --> Noise in biological systems http://www.ncbi.nlm.nih.gov/pubmed/20365787 http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevE2010.pd

Editorial overview:Systems biology for biotechnology

About 15 years ago, systems biology was introduced as a novel approach to biological research. On the one side, its introduction was a result of the recognition that through solely the reductionist approach, we would ulti- mately not be able to understand how biological systems function as a whole. On the other side, new high-throughput technologies for large-scale experi- mental assessment and perturbation of biological systems, which emerged at around the same time, were embraced by this new field, or gave it additional momentum. Although specially earmarked funding opportunities for systems biology, having boosted the early systems biology research, have largely vanished, systems biology has found its place next to the more classical biological research approaches. Today, new experimental and computational systems biology approaches are still being developed, indicating that the field’s toolbox continues to grow. Despite being a relatively young field, systems biology has also already greatly contributed in advancing biotechnology, for instance by generating insights about complex systems, or by providing system-level perturbation and analysis tools of either experimental or computational nature. This systems biology issue of Current Opinion of Biotechnology highlights this fact. In this issue, we focus on two important areas, where biotechnology aims to contribute in solving important societal issues. The first area is the area of biomedicine, where ultimately new medical treatments or prevention measures are the goal, and the second area is the one of industrial biotech- nology, where the ambition is to develop cell-factories, through which our current economy, still largely relying on fossil resources, could ultimately be transformed into a more sustainable one. With regards to the area of biomedicine, Lukacisinova and Bollenbach covered the topic of antimicrobial resistance. In their article, they argue that key towards solving this problem is a deeper understanding of the underly- ing dynamics of resistance evolution. They demonstrate that a combination of experimental and theoretical approaches from different disciplines, for instance new technology for studying evolution in the laboratory, can yield insights that might be crucial to develop effective strategies for combating resistance. A related topic was covered in an article by Radzikowski et al., namely the topic of bacterial persistence, a phenotype that is characterized by temporal tolerance against antibiotics, without any genetic resistance. While most research in the persistence field has adopted a reductionist approach, Radzikowski et al. sketched a novel systems-level perspective of bacterial persistence, integrating the current knowledge and recent findings generated by high-throughput experimental methods. Interestingly, both reviews provide some initial indication that eventually both areas — the one of antimicrobial resistance and the one of persistence — could eventually even be connected. In another biomedically oriented article, Moor and Itzkovitz covered the topic of tissue organization, considering tissues as complex systems com- posed of diverse cell types that interact to yield anatomical units. The authors highlight recent advances in spatial transcriptomics. They show how this approach opens the way for tissue-level systems biology towards unraveling the principles that govern the division of labor between the diverse cell of the tissue. Finally, in the last article in the biomedical area of this issue, Zhang et al. reviewed exciting advances on host–microbiota interactions, having important roles in human health as well as in mitigating disease. Through highlighting recent large-scale and high-throughput genetic screening studies, the authors show that the nematode Caenorhab- ditis elegans and its bacterial diet has turned out to be an excellent model for investigations on host–microbiota interactions. Together, these contribu- tions on the one hand highlight the scientific challenges at hand, and on the other side also demonstrate the power of systems biology to further advance our understanding of these complex systems. Such improved understanding will surely lead to biomedical exploitation at some point. In the second set of articles, four reviews highlight advances that have the potential to fuel the necessary transition to a more sustainable economy. These articles do not cover specific industrial applications, but rather highlight advances in the development of large-scale experimental and modeling tools. CRISPR/Cas9 is currently revolutionizing the biosciences. Jakociunas et al. review how the CRISPR/Cas9 system can be used as a tool for system-level perturbations of cell metabolism, indicating the power of this tool for metabolic engineering. Next to being able to generate genetic diversity — system-wide and in a targeted manner — screening and selec- tion of genotypes with desired phenotypes is also necessary for industrial biotechnology. Vervoort et al. demonstrate in their article how lab-on-chip strategies miniaturize the screening and selection process to the nanoliter scale and the single-cell level, allowing for massive parallelization of this important process in strain development. Next to expanding our capabilities for high-throughput experimentation, which is exploited for industrial biotechnology, systems-biology has also contributed approaches for rational strain development and optimization, for instance by means of modeling approaches. Covering different methodolo- gies, Chen et al. reviewed the recent progress in modeling approaches for improvement of cell factories ranging from stoichiometric approaches to approaches also considering enzyme kinetics, through which different issues in metabolic systems, such as pathway robustness, can be addressed. To build kinetic models on cellular metabolism, which could be used for the design of cell factories, information about the kinetics of enzymes is required. In their contribution, Davidi and Milo show how recent quantita- tive proteomics can be leveraged to gain novel insight into in vivo enzyme kinetics. Further, they demonstrate how recently gained understanding about the use of enzymes can explain metabolic strategies. From the collection of these reviews, it is clear that systems biology greatly contributes to the advances of biotechnology in generating novel system- level insights and as well as tools for system analysis and system-level experimental perturbation

Composition and regulation of maternal and zygotic transcriptomes reflects species-specific reproductive mode

Background Early embryos contain mRNA transcripts expressed from two distinct origins; those expressed from the mother's genome and deposited in the oocyte (maternal) and those expressed from the embryo's genome after fertilization (zygotic). The transition from maternal to zygotic control occurs at different times in different animals according to the extent and form of maternal contributions, which likely reflect evolutionary and ecological forces. Maternally deposited transcripts rely on post-transcriptional regulatory mechanisms for precise spatial and temporal expression in the embryo, whereas zygotic transcripts can use both transcriptional and post-transcriptional regulatory mechanisms. The differences in maternal contributions between animals may be associated with gene regulatory changes detectable by the size and complexity of the associated regulatory regions. Results We have used genomic data to identify and compare maternal and/or zygotic expressed genes from six different animals and find evidence for selection acting to shape gene regulatory architecture in thousands of genes. We find that mammalian maternal genes are enriched for complex regulatory regions, suggesting an increase in expression specificity, while egg-laying animals are enriched for maternal genes that lack transcriptional specificity. Conclusions We propose that this lack of specificity for maternal expression in egg-laying animals indicates that a large fraction of maternal genes are expressed non-functionally, providing only supplemental nutritional content to the developing embryo. These results provide clear predictive criteria for analysis of additional genomes.Molecular and Cellular Biolog

Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network

microRNAs (miRs) are small RNAs that regulate gene expression at the posttranscriptional level. It is anticipated that, in combination with transcription factors (TFs), they span a regulatory network that controls thousands of mammalian genes. Here we set out to uncover local and global architectural features of the mammalian miR regulatory network. Using evolutionarily conserved potential binding sites of miRs in human targets, and conserved binding sites of TFs in promoters, we uncovered two regulation networks. The first depicts combinatorial interactions between pairs of miRs with many shared targets. The network reveals several levels of hierarchy, whereby a few miRs interact with many other lowly connected miR partners. We revealed hundreds of “target hubs” genes, each potentially subject to massive regulation by dozens of miRs. Interestingly, many of these target hub genes are transcription regulators and they are often related to various developmental processes. The second network consists of miR–TF pairs that coregulate large sets of common targets. We discovered that the network consists of several recurring motifs. Most notably, in a significant fraction of the miR–TF coregulators the TF appears to regulate the miR, or to be regulated by the miR, forming a diversity of feed-forward loops. Together these findings provide new insights on the architecture of the combined transcriptional–post transcriptional regulatory network

Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation

The state of the transcriptome reflects a balance between mRNA production and degradation. Yet how these two regulatory arms interact in shaping the kinetics of the transcriptome in response to environmental changes is not known. We subjected yeast to two stresses, one that induces a fast and transient response, and another that triggers a slow enduring response. We then used microarrays following transcriptional arrest to measure genome-wide decay profiles under each condition. We found condition-specific changes in mRNA decay rates and coordination between mRNA production and degradation. In the transient response, most induced genes were surprisingly destabilized, whereas repressed genes were somewhat stabilized, exhibiting counteraction between production and degradation. This strategy can reconcile high steady-state level with short response time among induced genes. In contrast, the stress that induces the slow response displays the more expected behavior, whereby most induced genes are stabilized, and repressed genes are destabilized. Our results show genome-wide interplay between mRNA production and degradation, and that alternative modes of such interplay determine the kinetics of the transcriptome in response to stress