Mónica Chagoyen

Integrated analysis of gene expression by association rules discovery

BackgroundMicroarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.ResultsIn this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.ConclusionThe integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Enge ne software package.

Genetic polymorphisms among C57BL/6 mouse inbred strains

Mice from the inbred C57BL/6 strain have been commonly used for the generation and analysis of transgenic and knockout animal models. However, several C57BL/6 substrains exist, and these are genetically and phenotypically different. In addition, each of these substrains can be purchased from different animal providers and, in some cases, they have maintained their breeding stocks separated for a long time, allowing genetic differences to accumulate due to individual variability and genetic drift. With the aim of describing the differences in the genotype of several C57BL/6 substrains, we applied the Illumina® Mouse Medium Density Linkage Mapping panel, with 1,449 single nucleotide polymorphisms (SNPs), to individuals from ten C57BL/6-related strains: C57BL/6JArc, C57BL/6J from The Jackson Lab, C57BL/6J from Crl, C57BL6/JRccHsd, C57BL/6JOlaHsd, C57BL/6JBomTac, B6(Cg)-Tyrc−2j/J, C57BL/6NCrl, C57BL/6NHsd and C57BL/6NTac. Twelve SNPs were found informative to discriminate among the mouse strains considered. Mice derived from the original C57BL/6J: C57BL/6JArc, C57BL/6J from The Jackson Lab and C57BL/6J from Crl, were indistinguishable. Similarly, all C57BL/6N substrains displayed the same genotype, whereas the additional substrains showed intermediate cases with substrain-specific polymorphisms. These results will be instrumental for the correct genetic monitoring and appropriate mouse colony handling of different transgenic and knockout mice produced in distinct C57BL/6 inbred substrains.

MBRole: enrichment analysis of metabolomic data

UNLABELLED While many tools exist for performing enrichment analysis of transcriptomic and proteomic data in order to interpret them in biological terms, almost no equivalent tools exist for metabolomic data. We present Metabolite Biological Role (MBRole), a web server for carrying out over-representation analysis of biological and chemical annotations in arbitrary sets of metabolites (small chemical compounds) coming from metabolomic data of any organism or sample. AVAILABILITY AND IMPLEMENTATION The web server is freely available at http://csbg.cnb.csic.es/mbrole. It was tested in the main web browsers.

Strigolactone Promotes Degradation of DWARF14, an α/β Hydrolase Essential for Strigolactone Signaling in Arabidopsis

A mechanism was identified by which degradation of D14, the putative receptor of the plant hormone strigolactone, is promoted by this hormone. This mechanism, which is mediated by protein degradation via the proteasome machinery and requires the activity of the F-box protein MAX2, should cause a substantial drop in strigolactone perception, thereby limiting hormone signaling duration and intensity. Strigolactones (SLs) are phytohormones that play a central role in regulating shoot branching. SL perception and signaling involves the F-box protein MAX2 and the hydrolase DWARF14 (D14), proposed to act as an SL receptor. We used strong loss-of-function alleles of the Arabidopsis thaliana D14 gene to characterize D14 function from early axillary bud development through to lateral shoot outgrowth and demonstrated a role of this gene in the control of flowering time. Our data show that D14 distribution in vivo overlaps with that reported for MAX2 at both the tissue and subcellular levels, allowing physical interactions between these proteins. Our grafting studies indicate that neither D14 mRNA nor the protein move over a long range upwards in the plant. Like MAX2, D14 is required locally in the aerial part of the plant to suppress shoot branching. We also identified a mechanism of SL-induced, MAX2-dependent proteasome-mediated degradation of D14. This negative feedback loop would cause a substantial drop in SL perception, which would effectively limit SL signaling duration and intensity.

New electron microscopy database and deposition system

To manage, organize and disseminate data on the structure of biological macromolecules solved by 3D electron microscopy, an electron microscopy database has been set up at the European Bioinformatics Institute.

Discovering semantic features in the literature: a foundation for building functional associations

BackgroundExperimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.ResultsWe present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes.ConclusionThe presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.

A variant to the “random approximation” of the reference-free alignment algorithm

Abstract Single-particle averaging from electron microscope images strongly depends on alignment. Most alignment procedures are based on cross-correlation of an initial reference image with the rest of the population, leading to a clear pattern dependence. Among the different approaches that have been proposed to minimize this problem, one of the most widely used is the so-called iterative reference-free alignment algorithm (RFAA), proposed by Penczek et al. [Ultramicroscopy 40 (1992) 33]. To avoid the pattern dependence shown by the initial “random approximation” step of this method, we propose a variant of the algorithm that is more independent of the input order of the initial images and which could substitute the random initialization of the RFAA.

Nuclear but Not Cytosolic Phosphoinositide 3-Kinase Beta Has an Essential Function in Cell Survival

ABSTRACT Class IA phosphoinositide 3-kinases (PI3Ks) are heterodimeric enzymes composed of a p85 regulatory and a p110 catalytic subunit that induce the formation of 3-polyphosphoinositides, which mediate cell survival, division, and migration. There are two ubiquitous PI3K isoforms p110α and p110β that have nonredundant functions in embryonic development and cell division. However, whereas p110α concentrates in the cytoplasm, p110β localizes to the nucleus and modulates nuclear processes such as DNA replication and repair. At present, the structural features that determine p110β nuclear localization remain unknown. We describe here that association with the p85β regulatory subunit controls p110β nuclear localization. We identified a nuclear localization signal (NLS) in p110β C2 domain that mediates its nuclear entry, as well as a nuclear export sequence (NES) in p85β. Deletion of p110β induced apoptosis, and complementation with the cytoplasmic C2-NLS p110β mutant was unable to restore cell survival. These studies show that p110β NLS and p85β NES regulate p85β/p110β nuclear localization, supporting the idea that nuclear, but not cytoplasmic, p110β controls cell survival.

MBROLE 2.0-functional enrichment of chemical compounds.

Metabolites Biological Role (MBROLE) is a server that performs functional enrichment analysis of a list of chemical compounds derived from a metabolomics experiment, which allows this list to be interpreted in biological terms. Since its release in 2011, MBROLE has been used by different groups worldwide to analyse metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE2, accessible at http://csbg.cnb.csic.es/mbrole2. MBROLE2 has been supplemented with 10 databases not available in the previous version, which allow analysis over a larger, richer set of vocabularies including metabolite–protein and drug–protein interactions. This new version performs automatic conversion of compound identifiers from different databases, thus simplifying usage. In addition, the user interface has been redesigned to generate an interactive, more intuitive representation of the results.

Structural characterization of the bacteriophage t7 tail machinery.

Background: T7 tail is involved in host recognition, DNA securing, and delivery. Results: The tail is formed by a tubular structure (proteins gp11 and gp12) surrounded by six fibers. Conclusion: gp11 is a gatekeeper-adaptor protein, and gp12 closes the ejection channel. Significance: Tailed bacteriophages may share a common molecular mechanism to coordinate the switch between DNA packaging and tail assembly. Most bacterial viruses need a specialized machinery, called “tail,” to inject their genomes inside the bacterial cytoplasm without disrupting the cellular integrity. Bacteriophage T7 is a well characterized member of the Podoviridae family infecting Escherichia coli, and it has a short noncontractile tail that assembles sequentially on the viral head after DNA packaging. The T7 tail is a complex of around 2.7 MDa composed of at least four proteins as follows: the connector (gene product 8, gp8), the tail tubular proteins gp11 and gp12, and the fibers (gp17). Using cryo-electron microscopy and single particle image reconstruction techniques, we have determined the precise topology of the tail proteins by comparing the structure of the T7 tail extracted from viruses and a complex formed by recombinant gp8, gp11, and gp12 proteins. Furthermore, the order of assembly of the structural components within the complex was deduced from interaction assays with cloned and purified tail proteins. The existence of common folds among similar tail proteins allowed us to obtain pseudo-atomic threaded models of gp8 (connector) and gp11 (gatekeeper) proteins, which were docked into the corresponding cryo-EM volumes of the tail complex. This pseudo-atomic model of the connector-gatekeeper interaction revealed the existence of a common molecular architecture among viruses belonging to the three tailed bacteriophage families, strongly suggesting that a common molecular mechanism has been favored during evolution to coordinate the transition between DNA packaging and tail assembly.