Mycoplasma hyopneumoniae J elicits an antioxidant response and decreases the expression of ciliary genes in infected swine epithelial cells.

Mycoplasma hyopneumoniae is the most costly pathogen for swine production. Although several studies have focused on the host-bacterium association, little is known about the changes in gene expression of swine cells upon infection. To improve our understanding of this interaction, we infected swine epithelial NPTr cells with M. hyopneumoniae strain J to identify differentially expressed mRNAs and miRNAs. The levels of 1,268 genes and 170 miRNAs were significantly modified post-infection. Up-regulated mRNAs were enriched in genes related to redox homeostasis and antioxidant defense, known to be regulated by the transcription factor NRF2 in related species. Down-regulated mRNAs were enriched in genes associated with cytoskeleton and ciliary functions. Bioinformatic analyses suggested a correlation between changes in miRNA and mRNA levels, since we detected down-regulation of miRNAs predicted to target antioxidant genes and up-regulation of miRNAs targeting ciliary and cytoskeleton genes. Interestingly, most down-regulated miRNAs were detected in exosome-like vesicles suggesting that M. hyopneumoniae infection induced a modification of the composition of NPTr-released vesicles. Taken together, our data indicate that M. hyopneumoniae elicits an antioxidant response induced by NRF2 in infected cells. In addition, we propose that ciliostasis caused by this pathogen is partially explained by the down-regulation of ciliary genes.

DegreeCox - a network-based regularization method for survival analysis.

BACKGROUND: Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DEGREECOX, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes. RESULTS: We applied DEGREECOX to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with RIDGE and LASSO, DEGREECOX shows an improvement in the classification of high and low risk patients in a par with NET-COX. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DEGREECOX gives results that are similar to those of the best performing methods, in a few cases slightly better. CONCLUSIONS: Network-based regularization seems a promising framework to deal with the dimensionality problem. The centrality metrics proposed can be easily expanded to accommodate other topological properties of different biological networks.

Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure.

MOTIVATION: The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. RESULTS: We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. AVAILABILITY AND IMPLEMENTATION: The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr.

Predicting the proteins of Angomonas deanei, Strigomonas culicis and their respective endosymbionts reveals new aspects of the trypanosomatidae family.

Endosymbiont-bearing trypanosomatids have been considered excellent models for the study of cell evolution because the host protozoan co-evolves with an intracellular bacterium in a mutualistic relationship. Such protozoa inhabit a single invertebrate host during their entire life cycle and exhibit special characteristics that group them in a particular phylogenetic cluster of the Trypanosomatidae family, thus classified as monoxenics. In an effort to better understand such symbiotic association, we used DNA pyrosequencing and a reference-guided assembly to generate reads that predicted 16,960 and 12,162 open reading frames (ORFs) in two symbiont-bearing trypanosomatids, Angomonas deanei (previously named as Crithidia deanei) and Strigomonas culicis (first known as Blastocrithidia culicis), respectively. Identification of each ORF was based primarily on TriTrypDB using tblastn, and each ORF was confirmed by employing getorf from EMBOSS and Newbler 2.6 when necessary. The monoxenic organisms revealed conserved housekeeping functions when compared to other trypanosomatids, especially compared with Leishmania major. However, major differences were found in ORFs corresponding to the cytoskeleton, the kinetoplast, and the paraflagellar structure. The monoxenic organisms also contain a large number of genes for cytosolic calpain-like and surface gp63 metalloproteases and a reduced number of compartmentalized cysteine proteases in comparison to other TriTryp organisms, reflecting adaptations to the presence of the symbiont. The assembled bacterial endosymbiont sequences exhibit a high A+T content with a total of 787 and 769 ORFs for the Angomonas deanei and Strigomonas culicis endosymbionts, respectively, and indicate that these organisms hold a common ancestor related to the Alcaligenaceae family. Importantly, both symbionts contain enzymes that complement essential host cell biosynthetic pathways, such as those for amino acid, lipid and purine/pyrimidine metabolism. These findings increase our understanding of the intricate symbiotic relationship between the bacterium and the trypanosomatid host and provide clues to better understand eukaryotic cell evolution.

Efficient representation and P-value computation for high-order Markov motifs.

MOTIVATION: Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Markov chainbased models generalize the PWM model by allowing for interposition dependencies to be considered, at the cost of substantial computational overhead, which may limit their application. RESULTS: In this article, we consider two aspects regarding the use of higher order Markov models for biological sequence motifs, namely, the representation and the computation of P-values for motifs described by a set of occurrences. We propose an efficient representation based on the use of tries, from which empirical position-specific conditional base probabilities can be computed, and extend state-of-the-art PWM-based algorithms to allow for the computation of exact P-values for high-order Markov motif models. AVAILABILITY: The software is available in the form of a Java objectoriented library from http://www.cin.ufpe.br/approxiamtely paguso/kmarkov.