An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

Background: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results: The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: Http://synapticgenes.bnd.edu.uy Conclusions: We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability: Http://synapticgenes.bnd.edu.uyFil: Pazos Obregón, Flavio. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Palazzo, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Soto, Pablo. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Guerberoff, Gustavo. Universidad de la República; UruguayFil: Yankilevich, Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Cantera, Rafael. Instituto de Investigaciones Biológicas "Clemente Estable"; Urugua

Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene’s function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.ANII: FSDA_1_2017_1_1424

BOD1 Is Required for Cognitive Function in Humans and <i>Drosophila</i>

Here we report a stop-mutation in the BOD1 (Biorientation Defective 1) gene, which co-segregates with intellectual disability in a large consanguineous family, where individuals that are homozygous for the mutation have no detectable BOD1 mRNA or protein. The BOD1 protein is required for proper chromosome segregation, regulating phosphorylation of PLK1 substrates by modulating Protein Phosphatase 2A (PP2A) activity during mitosis. We report that fibroblast cell lines derived from homozygous BOD1 mutation carriers show aberrant localisation of the cell cycle kinase PLK1 and its phosphatase PP2A at mitotic kinetochores. However, in contrast to the mitotic arrest observed in BOD1-siRNA treated HeLa cells, patient-derived cells progressed through mitosis with no apparent segregation defects but at an accelerated rate compared to controls. The relatively normal cell cycle progression observed in cultured cells is in line with the absence of gross structural brain abnormalities in the affected individuals. Moreover, we found that in normal adult brain tissues BOD1 expression is maintained at considerable levels, in contrast to PLK1 expression, and provide evidence for synaptic localization of Bod1 in murine neurons. These observations suggest that BOD1 plays a cell cycle-independent role in the nervous system. To address this possibility, we established two Drosophila models, where neuron-specific knockdown of BOD1 caused pronounced learning deficits and significant abnormalities in synapse morphology. Together our results reveal novel postmitotic functions of BOD1 as well as pathogenic mechanisms that strongly support a causative role of BOD1 deficiency in the aetiology of intellectual disability. Moreover, by demonstrating its requirement for cognitive function in humans and Drosophila we provide evidence for a conserved role of BOD1 in the development and maintenance of cognitive features

Seasonal variation in glucosinolate content in Brassica oleracea crops grown in northwestern Spain

Brassica oleracea L. crops including kales, cabbages, and Tronchuda cabbages are widely grown in northwestern Spain and Portugal but little information is available on leaf glucosinolate content of these crops. The objectives were to determine the diversity for the total glucosinolate content and profile on leaves in a collection of 153 kales, 26 cabbages, and three Tronchuda cabbages varieties grown at two growing seasons and to determine the seasonal variation of glucosinolates in cabbages and Tronchuda cabbage varieties. Sinigrin, glucoiberin, and glucobrassicin were the major glucosinolates found in kales. Glucoiberin was the most common glucosinolate in Tronchuda cabbages in both planting seasons and in cabbages sown in fall season whereas glucobrassicin and glucoiberin were the most common glucosinolates in cabbages in spring season. In kales the total glucosinolate content ranged from 11.0 to 53 lmol g 1 dw, with a mean value of 26.3 lmol g 1 dw. Four kale varieties (MBG-BRS0468, MBG-BRS0476, MBG-BRS0060 and MBG-BRS0223) showed the highest total sinigrin or glucobrassicin contents. So, they could be good candidates for future breeding programs. In cabbages, the total glucosinolate content ranged from 10.9 to 27 g 1 dw. Total glucosinolate concentration during spring sowing (22 lm g 1 dw) was higher than those in fall sowing (13 lm g 1 dw). Regarding both high glucosinolate content and the agronomic value, MBGBRS0057 and MBG-BRS0074 could be good sources of beneficial glucosinolates. The presence of high concentrations of sinigrin, glucoiberin, and glucobrassicin warrant further search into their potential use to enhance the level of these important phytochemicals in these edible crops.Research supported by the National Plan for Research and Development (AGL2003-01366 and AGL2006-04055) and Excma. Diputación Provincial de Pontevedra.Peer reviewe

Glucosinolate Variation in Leaves of Brassica rapa Crops

Total and individual glucosinolate (GSL) content of leaves of vegetable turnip rape (Brassica rapa L. var. rapa) was determined in a set of 45 varieties consisting in early, medium and late types grown at two locations in northwestern Spain. The objectives were to determine the diversity among varieties in GSL content and to relate that variation with earliness and plant habit. Eight GSL were identified, being two aliphatic GSL, gluconapin (84. 4 % of the total GSL) and glucobrassicanapin (7.2 % of the total GSL) the most abundant. Indolic and aromatic GSL content were low but also showed significant differences among varieties. Differences in total and individual GSL content were found among varieties, plant habit groups, and earliness groups. Total GSL content ranged from 19 to 37.3 μmol g -1 dw in early and extra-late groups, respectively, and from 19.5 to 36.3 μmol g -1 dw for turnips and turnip greens groups, respectively. These differences were consistent to values found for gluconapin content where the turnip group had the highest values (31.8 μmol g -1 dw) and the turnip top group had the lowest (15. 7 μmol g -1 dw). Two varieties, MBG-BRS0429 and MBG-BRS0550 (from turnip greens and extra-late groups) and MBG-BRS0438 (from turnips and late groups), stood out as they had the highest total GSL content and could be used as a good source of these beneficial bioactive compounds. Elucidation of genetic diversity among crops can provide useful information to assist plant breeders to design improved breeding strategies in order to obtain varieties rich on GSL. © 2012 Springer Science+Business Media, Inc.This research was supported by funding from the National Plan for Research and Development (AGL2006-04455 and AGL2009-09922) and Excma. Diputación Provincial de Pontevedra.Peer Reviewe