Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.Comment: 18 pages, 3 figures, 1 tabl

Inference of miRNA targets using evolutionary conservation and pathway analysis

BACKGROUND: MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially. RESULTS: We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development. CONCLUSION: We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server

Regulation of gene expression by micrornas : targeting specificity, kinetics and function

Summary: Understanding gene regulation is a central question of molecular biology. For decades, gene expression was thought to be controlled by a complex network of proteins called transcription factors. But ten years ago, microRNAs (miRNAs), a distinct class of short, evolutionarily-conserved non-coding RNAs were found to regulate gene expression. Hundreds of miRNAs have since then been discovered in species ranging from plants to nematodes to mammals, where they regulate diverse biological processes such as development, metabolism, immunity, cell cycle. MicroRNAs load into the Argonaute protein of the RNA-Induced Silencing Complex (RISC) and provide binding specificity to it. Upon guiding the RISC to a complementary motif in the 3' untranslated transcribed region (UTR) of a mRNA, miRNAs inhibit the translation and increase the decay rate of the target mRNA. While the molecular machinery required for miRNA action is well characterized, the biological function of the miRNAs identified so far remains unknown. Neither do we know through what target genes miRNAs achieve their biological function. The most common approach to this question consists in identifying genes that are differentially expressed following the experimental perturbation of the expression of a given miRNA by means of genetic knock-out or transfection. Perturbing the expression of a single miRNA has important side-effects on gene expression, but this problem can be partly addressed by crossing the genes responding to the miRNA perturbation with computational miRNA target predictions. In this thesis, we first illustrate how such a combined experimental and computational approach can be used to understand how the miR-375 miRNA controls glucose homeostasis. However, in practice, extracting direct, functional miRNA targets from miRNA perturbation experiments and computational predictions is a difficult task because state-of-the-art computational predictions yield large amounts of false-positives. We therefore set to improve the accuracy of computational predictions by inferring what sequence and structure properties characterize functional miRNA binding sites in a large number of miRNA perturbation experiments. We then combined these properties into an algorithm that is most accurate at miRNA target prediction. Also, we show that miRNA binding sites carried by mRNAs that respond to miRNA perturbation share the same properties as miRNA binding sites that are under evolutionary selective pressure, suggesting that miRNA binding sites may have been shaped by evolution to favor mRNA degradation. Further analyses also lead to the view that the temporal aspects of miRNA regulation may be far more important to the miRNA target identification problem than previously thought, especially for experiments measuring the effects of miRNA perturbation at the protein level, where taking the temporal aspects of miRNA regulation into account appears necessary both during experimental design and subsequent data analysis. While measurements from combined miRNA perturbation experiments and omics assays are crucial to determining what genes are regulated by a given miRNA, they are contaminated by side-effects and do not provide information on the precise location of the miRNA binding site within the 3' UTR of the target genes. To address these problems, we introduce PAR-CLIP, a combination of biochemical and computational methods to identify miRNA binding sites in high-throughput. The mRNA-miRNA-Argonaute ternary complex are first cross-linked. The ternary complex is then immuno-precipitated and the unprotected RNA eliminated by enzymatic digestion. Finally, ultra high-throughput sequencing of the remaining RNA and computational processing of the resulting sequencing libraries reveals the precise mRNA regions bound by miRNAs. PAR-CLIP does not require miRNA perturbation and makes it possible to identify thousands of miRNA binding sites in one experiment, with nucleotide resolution. In summary, the present thesis establishes methods that make it possible to map miRNA-mRNA interactions with high accuracy in the spatial domain, and paves the way for future investigation of miRNA-mediated gene regulation in the temporal domain. These methods will be useful in understanding the miRNA-mRNA interactions underlying the implication of miRNAs in the regulation of biological processes. ---------- Zusammenfassung: Die Regulation der Genexpression ist eine zentrale Frage der molekularen Biologie. Während Jahrzehnten wurde angenommen, dass die Expression der Genen von komplexen Netzwerken kontrolliert wird, die aus Proteinen, so genannten Transkriptionsfaktoren bestehen. Vor zehn Jahren wurde entdeckt, dass microRNAs (miRNAs) eine eigene Klasse kleiner, in der Evolution konservierter, nicht-codierender RNA bilden, die Genexpression regulieren. Seitdem wurden hunderte von miRNAs in Organismen, unter ihnen Pflanzen, Nematoden und Säugetieren entdeckt, wo sie diverse biologische Prozesse wie Entwicklung, Metabolismus, Immunität, Zellzyklus regulieren. MicroRNAs binden an die Argonaute Protein vom RNA-Induced Silencing Complex (RISC) und bestimmen so die Bindungsspezifität der Argonaute. MiRNAs führen dann den RISC zu einem komplementären Motif der 3' untranslatierten Region (UTR) einer mRNA, was zur Inhibition der Translation und zur Erhöhung der Zerfallsrate der gebundenen mRNA führt. Während die molekularen Mechanismen der Genexpressionsregulation durch miRNAs identifiziert wurden, bleibt die biologische Funktion einer grossen Mehrheit der miRNAs, die so weit entdeckt wurden, unbekannt. Es ist zudem unklar, durch welche Gene die miRNA ihre Funktion ausüben. Die häufigste Herangehensweise, diese Frage zu beantworten ist die Identifikation von Genen, deren Expression durch eine gegebene miRNA gestört wird. Genetische Knock-Outs oder Transfektionen sind experimentelle Mittel um die Expression zu stören. Die Expression einer einzelnen miRNAs zu stören kann erhebliche sekundäre Effekte auf die Expression von Genen haben. Durch die Kreuzung von miRNA abhängigen, differentiel exprimierten Genen mit rechnergeschützten miRNA Bindundungsstellenvorhersagen (rmBV) kann dieses Problem teilweise gelöst werden. In dieser Dissertation wurde diese Strategie eingesetzt um zu untersuchen, wie miRNA-375 die Glukosehomeostase kontrolliert. In der Praxis ist es jedoch eine anspruchsvolle Arbeit, direkte, funktionelle miRNA Zielgene aus miRNA-Störungsexperimenten und rmBV zu extrahieren da rmBV in der Regel einen hohen Anteil an falsch Positiven liefern. Wir verbesserten die Genauigkeit der rmBV indem wir die Sequenz- und Struktureigenschaften von funktionellen miRNA Bindungsstellen aus einer grossen Anzahl von miRNA Störungsexperimenten charakterisierten. Die identifizierten Eigenschaften wurden dann mit dem Algorithmus zur Vorhersage der miRNA-Bindungsstellen kombiniert, der bei der Identifikation von Ziel-miRNA am genauesten ist. Zudem zeigen wir, dass miRNA Bindungsstellen von miRNA-abhängigen mRNAs dieselben Eigenschaften aufweisen wie Bindungsstellen, welche unter evolutionärem Selektionsdruck stehen. Das führt zur Hypothese, dass miRNA Bindungsstellen durch die Evolution umgeformt wurden, um den mRNA Zerfall zu bevorzugen. Weitere Analysen führten zur Auffassung, dass die zeitlichen Aspekte der miRNA Regulation viel wichtiger sein könnten als bisher angenommen. Dies speziell für Experimente, die den Effekt der miRNA Störung auf der Ebene der Proteine messen. Bei diesen Experimenten scheint es unentbehrlich zu sein, während der Planung und Datenanalyse Rücksicht auf die zeitlichen Aspekte der miRNA Regulation zu nehmen. Messungen aus kombinierten miRNA Störungsexperimenten und Omics-Versuchen sind ausschlaggebend um festzustellen welche Gene von einer bestimmten miRNA reguliert werden. Sie leiden jedoch darunter, dass sie von sekundären Effekten gestört werden und dass sie keine Information über die genaue Lokalisation der miRNA Bindungsstellen liefern. Um diese Probleme zu lösen wurde die PAR-CLIP Methode entwickelt. Dies ist eine Kombination aus biochemischen und rechnergestützten Methoden um miRNA Bindungsstellen in hohen Datendurchsätzen zu identifizieren. Die ternären mRNA-miRNA-Argonaute Komplexe werden erst kovalent gebunden, dann immuno-prezipitiert. Danach wird die ungeschützte RNA in einem enzymatischen Verdau eliminiert. Schlussendlich wird die verbleibende RNA sequenziert und durch rechnergestützte Verarbeitung der Sequenzierdaten wird festgestellt, welche spezifischen mRNA Regionen von miRNAs gebunden werden. PAR-CLIP benötigt keine miRNA Störung und ermöglicht die Identifizierung tausender miRNA Bindungsstellen Nukleotid-Auflösend in einem einzigen Versuch. Zusammengefasst führt diese Dissertation Methoden ein, mit denen sich miRNA-mRNA Wechselwirkungen mit hoher räumlicher Genauigkeit kartografisieren lassen. Zudem öffnet sie den Weg für zukünftige Untersuchungen von zeitlichen Domänen in der miRNA vermittelten Genregulation. Diese Methoden werden entscheidend zum Verständnis der miRNA-mRNA Wechselwirkungen beitragen und den Einfluss der miRNA in der Regulation biologischer Prozesse betonen

MirZ: an integrated microRNA expression atlas and target prediction resource

MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at www.mirz.unibas.ch. The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapien

Tumor diversity and the trade-off between universal cancer tasks

Funder: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation); doi: https://doi.org/10.13039/501100001711Funder: Abisch-Frenkel-Stiftung (Abisch-Frenkel Foundation); doi: https://doi.org/10.13039/501100007362Abstract: Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory

Kaposi's Sarcoma Herpesvirus microRNAs Target Caspase 3 and Regulate Apoptosis

Kaposi's sarcoma herpesvirus (KSHV) encodes a cluster of twelve micro (mi)RNAs, which are abundantly expressed during both latent and lytic infection. Previous studies reported that KSHV is able to inhibit apoptosis during latent infection; we thus tested the involvement of viral miRNAs in this process. We found that both HEK293 epithelial cells and DG75 cells stably expressing KSHV miRNAs were protected from apoptosis. Potential cellular targets that were significantly down-regulated upon KSHV miRNAs expression were identified by microarray profiling. Among them, we validated by luciferase reporter assays, quantitative PCR and western blotting caspase 3 (Casp3), a critical factor for the control of apoptosis. Using site-directed mutagenesis, we found that three KSHV miRNAs, miR-K12-1, 3 and 4-3p, were responsible for the targeting of Casp3. Specific inhibition of these miRNAs in KSHV-infected cells resulted in increased expression levels of endogenous Casp3 and enhanced apoptosis. Altogether, our results suggest that KSHV miRNAs directly participate in the previously reported inhibition of apoptosis by the virus, and are thus likely to play a role in KSHV-induced oncogenesis

A standardized and reproducible method to measure decision-making in mice.

Abstract Progress in neuroscience is hindered by poor reproducibility of mouse behavior. Here we show that in a visual decision making task, reproducibility can be achieved by automating the training protocol and by standardizing experimental hardware, software, and procedures. We trained 101 mice in this task across seven laboratories at six different research institutions in three countries, and obtained 3 million mouse choices. In trained mice, variability in behavior between labs was indistinguishable from variability within labs. Psychometric curves showed no significant differences in visual threshold, bias, or lapse rates across labs. Moreover, mice across laboratories adopted similar strategies when stimulus location had asymmetrical probability that changed over time. We provide detailed instructions and open-source tools to set up and implement our method in other laboratories. These results establish a new standard for reproducibility of rodent behavior and provide accessible tools for the study of decision making in mice

Baromètre des civic tech 2019 - 1e édition

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