Laura Kamenetzky

High polymorphism in genes encoding antigen B from human infecting strains of Echinococcus granulosus.

Echinococcus granulosus antigen B (AgB) is encoded by a gene family and is involved in the evasion of the host immune response. E. granulosus exists as a number of strains (G1-G10) that differ in biological characteristics. We used PCR-SSCP followed by DNA sequencing to evaluate sequence variation and transcription profile of AgB in 5 E. granulosus strains. Twenty-four genomic sequences were isolated and clustered in 3 groups related to 2 of the 5 reported AgB genes. AgB4 genes were present in almost all strains, whereas AgB2 were present as functional genes exclusively in G1/G2 cluster, and as non-functional genes in G5 and the G6/G7 cluster, suggesting inter-strain variation. The AgB transcription patterns, analysed by RT-PCR, showed that AgB2 and AgB4 genes were transcribed in G1, while only the AgB4 gene was transcribed in G7 strain. Cysts from the same strain or cluster shared more genomic and cDNA variants than cysts from different strain or cluster. The level of nucleotide and deduced amino acid sequence variation observed is higher than that reported so far for coding genes of other helminths. Neutrality was rejected for AgB2 genes. These data show the genetic polymorphism of antigen-coding genes among genetically characterized strains of E. granulosus.

Neural network model for integration and visualization of introgressed genome and metabolite data

The volume of information derived from postgenomic technologies is rapidly increasing. Due to the amount of data involved, novel computational models are needed for introducing order into the massive data sets produced by these new technologies. Data integration is also gaining increasing attention for merging signals in order to discover unknown pathways. These topics require the development of adequate soft computing tools. This work proposes a neural network model for discovering relationships between gene expression and metabolite profiles of introgressed lines. It also provides a simple visualization interface for identification of coordinated variations in mRNA and metabolites. This may be useful when the focus is on the easily identification of groups of different patterns, independently of the number of formed clusters. This kind of analysis may help for the inference of a-priori unknown metabolic pathways involving the grouped data. The model has been used on a case study involving data from tomato fruits.

Echinococcus granulosus : intraspecific genetic variation assessed by a DNA repetitive element

A 186 bp Echinococcus granulosus-specific repetitive element, TREg, was used to assess genetic variation between strains. In G7 genotype (pig strain) it has the characteristics of a satellite DNA element with a copy number of 23000 per haploid genome. Analysis, by sequencing of TREg monomers, showed a great degree of identity within them. In the G1 genotype (common sheep strain) TREg-like repetitive elements were found in an interspersed distribution throughout the genome and in only 120 copies. The sequences of these monomers showed a great degree of variation between them and with TREg of G7 origin. The G6 genotype (camel strain) showed a pattern of distribution and copy number similar to the G7 genotype, and the G2 genotype (Tasmanian sheep strain) similar to the G1 genotype. Isolates from the G5 (cattle strain) and G4 (horse strain) genotypes also showed unique hybridization patterns in Southern blot experiments. The genomic plasticity of E. granulosus, which may have important consequences in the epidemiology and control of cystic hydatid disease is reflected in the results of this work.

miRNAfe: A comprehensive tool for feature extraction in microRNA prediction

miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software. The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from software of the original authors. The source code is freely available for academic use under GPL license at http://sourceforge.net/projects/sourcesinc/files/mirnafe/0.90/. A user-friendly access is provided as web interface at http://fich.unl.edu.ar/sinc/web-demo/mirnafe/. A more configurable web interface can be accessed at http://fich.unl.edu.ar/sinc/web-demo/mirnafe-full/.

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM

The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high class-imbalance classification problem. The classical way of approaching it has been training a binary classifier in a supervised manner, using well-known pre-miRNAs as positive class and artificially defining the negative class. However, although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this work, we propose a novel and effective way of approaching this problem using machine learning, without the definition of negative examples. The proposal is based on clustering unlabeled sequences of a genome together with well-known miRNA precursors for the organism under study, which allows for the quick identification of the best candidates to miRNA as those sequences clustered with known precursors. Furthermore, we propose a deep model to overcome the problem of having very few positive class labels. They are always maintained in the deep levels as positive class while less likely pre-miRNA sequences are filtered level after level. Our approach has been compared with other methods for pre-miRNAs prediction in several species, showing effective predictivity of novel miRNAs. Additionally, we will show that our approach has a lower training time and allows for a better graphical navegability and interpretation of the results. A web-demo interface to try deepSOM is available at http://fich.unl.edu.ar/sinc/web-demo/deepsom/.

Allelic differences in a vacuolar invertase affect Arabidopsis growth at early plant development

Improving carbon fixation in order to enhance crop yield is a major goal in plant sciences. By quantitative trait locus (QTL) mapping, it has been demonstrated that a vacuolar invertase (vac-Inv) plays a key role in determining the radical length in Arabidopsis. In this model, variation in vac-Inv activity was detected in a near isogenic line (NIL) population derived from a cross between two divergent accessions: Landsberg erecta (Ler) and Cape Verde Island (CVI), with the CVI allele conferring both higher Inv activity and longer radicles. The aim of the current work is to understand the mechanism(s) underlying this QTL by analyzing structural and functional differences of vac-Inv from both accessions. Relative transcript abundance analyzed by quantitative real-time PCR (qRT-PCR) showed similar expression patterns in both accessions; however, DNA sequence analyses revealed several polymorphisms that lead to changes in the corresponding protein sequence. Moreover, activity assays revealed higher vac-Inv activity in genotypes carrying the CVI allele than in those carrying the Ler allele. Analyses of purified recombinant proteins showed a similar K m for both alleles and a slightly higher V max for that of Ler. Treatment of plant extracts with foaming to release possible interacting Inv inhibitory protein(s) led to a large increase in activity for the Ler allele, but no changes for genotypes carrying the CVI allele. qRT-PCR analyses of two vac-Inv inhibitors in seedlings from parental and NIL genotypes revealed different expression patterns. Taken together, these results demonstrate that the vac-Inv QTL affects root biomass accumulation and also carbon partitioning through a differential regulation of vac-Inv inhibitors at the mRNA level.

MicroRNA discovery in the human parasite Echinococcus multilocularis from genome-wide data

Abstract The cestode parasite Echinococcus multilocularis is the aetiological agent of alveolar echinococcosis, responsible for considerable human morbidity and mortality. This disease is a worldwide zoonosis of major public health concern and is considered a neglected disease by the World Health Organization. The complete genome of E. multilocularis has been recently sequenced and assembled in a collaborative effort between the Wellcome Trust Sanger Institute and our group, with the main aim of analyzing protein-coding genes. These analyses suggested that approximately 10% of E. multilocularis genome is composed of protein-coding regions. This shows there is still a vast proportion of the genome that needs to be explored, including non-coding RNAs such as small RNAs (sRNAs). Within this class of small regulatory RNAs, microRNAs (miRNAs) can be found, which have been identified in many different organisms ranging from viruses to higher eukaryotes. MiRNAs are a key regulation mechanism of gene expression at post-transcriptional level and play important roles in biological processes such as development, proliferation, cell differentiation and metabolism in animals and plants. In spite of this, identification of miRNAs directly from genom e -wide data only is still a very challenging task. There are many miRNAs that remain unidentified due to the lack of either sequence information of particular phylums or appropriate algorithms to identify novel miRNAs. The motivation for this work is the discovery of new miRNAs in E. multilocularis based on non-target genomic data only, in order to obtain useful information from the currently available unexplored data. In this work, we present the discovery of new pre-miRNAs in the E. multilocularis genome through a novel approach based on machine learning. We have extracted the most commonly used structural features from the folded sequences of the parasite genome: triplets, minimum free energy and sequence length. These features have been used to train a novel deep architecture of self-organizing maps (SOMs). This model can be trained with a high class imbalance and without the artificial definition of a negative class. We discovered 886 pre-miRNA candidates within the E. multilocularis genome-wide data. After that, experimental validation by small RNA-seq analysis clearly showed 23 pre-miRNA candidates with a pattern compatible with miRNA biogenesis, indicating them as high confidence miRNAs. We discovered new pre-miRNA candidates in E. multilocularis using non-target genomic data only. Predictions were meaningful using only sequence data, with no need of RNA-seq data or target analysis for prediction. Furthermore, the methodology employed can be easily adapted and applied on any draft genomes, which are actually the most interesting ones since most non-model organisms have this kind of status and carry real biological and sanitary relevance. Availability Web demo: http://fich.unl.edu.ar/sinc/web-demo/mirna-som/ Source code: http://sourceforge.net/projects/sourcesinc/files/mirnasom/

Improving clustering with metabolic pathway data

BackgroundIt is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters.ResultsA novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view.ConclusionsAnalyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

Molecular diagnosis of natural fasciolosis by DNA detection in sheep faeces.

Fasciolosis is an important parasitic zoonosis considered the most important helminth infection of ruminants in tropical countries. The aim of this study was to develop a PCR assay for the sensitive and specific detection of F. hepatica in formalin preserved sheep faeces. A 405-bp fragment of the cytochrome c oxidase subunit 1 gene of F. hepatica was amplified from stool samples of infected sheep. The PCR assay showed a detection limit of 20 pg of F. hepatica DNA. No cross-reactions were observed with samples containing coccidian oocysts or gastrointestinal nematodes eggs. Our PCR technique showed to be effective for specific detection of F. hepatica infections in sheep.

First genetic characterization of Fasciola hepatica in Argentina by nuclear and mitochondrial gene markers

Fasciola hepatica is a trematode showing genetic variation among isolates from different regions of the world. The objective of this work was to characterize for the first time F. hepatica isolates circulating in different regions of Argentina. Twenty-two adult flukes were collected from naturally infected bovine livers in different areas from Argentina and used for DNA extraction. We carried out PCR amplification and sequence analysis of the ribosomal internal transcribed spacer 1 (ITS1), mitochondrial nicotinamide adenine dinucleotide dehydrogenase subunits 4 and 5 (nad4 and nad5) and mitochondrial cytochrome c oxidase subunit I (cox1) genes as genetic markers. Phylogenies were reconstructed using maximum parsimony algorithm. A total of 6 haplotypes were found for cox1, 4 haplotypes for nad4 and 3 haplotypes for nad5. The sequenced ITS1 fragment was identical in all samples. The analyzed cox1 gene fragment is the most variable marker and is recommended for future analyses. No geographic association was found in the Argentinean samples.