Alain Sewer

A Mammalian microRNA Expression Atlas Based on Small RNA Library Sequencing

MicroRNAs (miRNAs) are small noncoding regulatory RNAs that reduce stability and/or translation of fully or partially sequence-complementary target mRNAs. In order to identify miRNAs and to assess their expression patterns, we sequenced over 250 small RNA libraries from 26 different organ systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis. Unexpectedly, a relatively small set of miRNAs, many of which are ubiquitously expressed, account for most of the differences in miRNA profiles between cell lineages and tissues. This broad survey also provides detailed and accurate information about mature sequences, precursors, genome locations, maturation processes, inferred transcriptional units, and conservation patterns. We also propose a subclassification scheme for miRNAs for assisting future experimental and computational functional analyses.

Identification of microRNAs of the herpesvirus family

Epstein-Barr virus (EBV or HHV4), a member of the human herpesvirus (HHV) family, has recently been shown to encode microRNAs (miRNAs). In contrast to most eukaryotic miRNAs, these viral miRNAs do not have close homologs in other viral genomes or in the genome of the human host. To identify other miRNA genes in pathogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from several virus-infected cell types. We cloned ten miRNAs in the Kaposi sarcoma–associated virus (KSHV or HHV8), nine miRNAs in the mouse gammaherpesvirus 68 (MHV68) and nine miRNAs in the human cytomegalovirus (HCMV or HHV5). These miRNA genes are expressed individually or in clusters from either polymerase (pol) II or pol III promoters, and share no substantial sequence homology with one another or with the known human miRNAs. Generally, we predicted miRNAs in several large DNA viruses, and we could neither predict nor experimentally identify miRNAs in the genomes of small RNA viruses or retroviruses.

Identification of clustered microRNAs using an ab initio prediction method

BackgroundMicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations.ResultsIn this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our ab initio method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web.ConclusionOur results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution.

Marek's Disease Virus Type 2 (MDV-2)-Encoded MicroRNAs Show No Sequence Conservation with Those Encoded by MDV-1

ABSTRACT MicroRNAs (miRNAs) are increasingly being recognized as major regulators of gene expression in many organisms, including viruses. Among viruses, members of the family Herpesviridae account for the majority of the currently known virus-encoded miRNAs. The highly oncogenic Mareks disease virus type 1 (MDV-1), an avian herpesvirus, has recently been shown to encode eight miRNAs clustered in the MEQ and LAT regions of the viral genome. The genus Mardivirus, to which MDV-1 belongs, also includes the nononcogenic but antigenically related MDV-2. As MDV-1 and MDV-2 are evolutionarily very close, we sought to determine if MDV-2 also encodes miRNAs. For this, we cloned, sequenced, and analyzed a library of small RNAs from the lymphoblastoid cell line MSB-1, previously shown to be coinfected with both MDV-1 and MDV-2. Among the 5,099 small RNA sequences determined from the library, we identified 17 novel MDV-2-specific miRNAs. Out of these, 16 were clustered in a 4.2-kb long repeat region that encodes R-LORF2 to R-LORF5. The single miRNA outside the cluster was located in the short repeat region, within the C-terminal region of the ICP4 homolog. The expression of these miRNAs in MSB-1 cells and infected chicken embryo fibroblasts was further confirmed by Northern blotting analysis. The identification of miRNA clusters within the repeat regions of MDV-2 demonstrates conservation of the relative genomic positions of miRNA clusters in MDV-1 and MDV-2, despite the lack of sequence homology among the miRNAs of the two viruses. The identification of these novel miRNAs adds to the growing list of virus-encoded miRNAs.

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

BackgroundHigh-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.ResultsFour complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.ConclusionsThe NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

A network-based approach to quantifying the impact of biologically active substances

Fe at u re s P E R S P E C T IV E Society increasingly demands close scrutiny of the potential health risks of long-term exposure to biologically active substances, such as therapeutic drugs or environmental toxins. Such risks are typically assessed a posteriori through clinical epidemiology studies. However, disease might take decades to manifest, at a point where changes in therapeutic regime, life style or exposure would not prevent disease onset. Moreover, disease risk as assessed correlatively in epidemiological studies is not intended to elucidate the mechanisms that link perturbations in molecular signaling to disease and, thus, provides fewer options for intervention. Here, we propose that network-based approaches to pharmacology are a valuable way to not only quantify biological network perturbations caused by active substances, but also identify mechanisms and biomarkers modulated in response to exposure and related to disease onset. We also discuss progress towards a generalizable approach for a mechanistic biological impact assessment. Novel computational methods that derive the quantitative biological impact [defined as a biological impact factor (BIF)] from underlying system-wide data using defined causal biological (i.e. molecular) network models as the substrate for data analysis are currently under

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models

BackgroundHigh-throughput measurement technologies such as microarrays provide complex datasets reflecting mechanisms perturbed in an experiment, typically a treatment vs. control design. Analysis of these information rich data can be guided based on a priori knowledge, such as networks or set of related proteins or genes. Among those, cause-and-effect network models are becoming increasingly popular and more than eighty such models, describing processes involved in cell proliferation, cell fate, cell stress, and inflammation have already been published. A meaningful systems toxicology approach to study the response of a cell system, or organism, exposed to bio-active substances requires a quantitative measure of dose-response at network level, to go beyond the differential expression of single genes.ResultsWe developed a method that quantifies network response in an interpretable manner. It fully exploits the (signed graph) structure of cause-and-effect networks models to integrate and mine transcriptomics measurements. The presented approach also enables the extraction of network-based signatures for predicting a phenotype of interest. The obtained signatures are coherent with the underlying network perturbation and can lead to more robust predictions across independent studies. The value of the various components of our mathematically coherent approach is substantiated using several in vivo and in vitro transcriptomics datasets. As a proof-of-principle, our methodology was applied to unravel mechanisms related to the efficacy of a specific anti-inflammatory drug in patients suffering from ulcerative colitis. A plausible mechanistic explanation of the unequal efficacy of the drug is provided. Moreover, by utilizing the underlying mechanisms, an accurate and robust network-based diagnosis was built to predict the response to the treatment.ConclusionThe presented framework efficiently integrates transcriptomics data and “cause and effect” network models to enable a mathematically coherent framework from quantitative impact assessment and data interpretation to patient stratification for diagnosis purposes.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology.

Case study: the role of mechanistic network models in systems toxicology

Twenty first century systems toxicology approaches enable the discovery of biological pathways affected in response to active substances. Here, we briefly summarize current network approaches that facilitate the detailed mechanistic understanding of the impact of a given stimulus on a biological system. We also introduce our network-based method with two use cases and show how causal biological network models combined with computational methods provide quantitative mechanistic insights. Our approach provides a robust comparison of the transcriptional responses in different experimental systems and enables the identification of network-based biomarkers modulated in response to exposure. These advances can also be applied to pharmacology, where the understanding of disease mechanisms and adverse drug effects is imperative for the development of efficient and safe treatment options.