Bacterial diversity using metagenomics of 16s rDNA in water kefir, an innovative source of probiotics for bee nutrition.

Water kefir is a sparkling, slightly acidic fermented beverage made from sugar, water, and water kefir grains, which are a mixture of yeast and bacteria. These grains produce a variety of fermentation compounds such as lactic acid, acetaldehyde, acetoin, ethanol and carbon dioxide. In this study, a high-throughput sequencing technique was used to characterize the bacterial composition of the original water kefir from which potential probiotics were obtained. We studied the bacterial diversity of both water kefir grains and beverages. DNA was extracted from three replicate samples of both grains and beverages using the Powerlyzer Microbial Kit. The hypervariable V1-V2 region of the bacterial 16S ribosomal RNA gene was amplified to prepare six DNA libraries. Between 1.4M and 2.4M base-pairs were sequenced for the library. In total, 28721971 raw reads were obtained from all the samples. Estimated species richness was higher in kefir beverage samples compared to grain samples. Moreover, a higher level of microbial alpha diversity was observed in the beverage samples. Particularly, the predominant bacteria in beverages were Anaerocolumna and Ralstonia, while in grains Liquorilactobacillus dominated, with lower levels of Leuconostoc and Oenococcus. Although the bacterial diversity in kefir grains was low because only three genera were the most represented, all of them are LAB bacteria with the potential to serve as probiotics in the artificial feeding of bees.

Differential Methylation Patterns in Apomictic vs. Sexual Genotypes of the Diplosporous Grass Eragrostis curvula.

DNA methylation is an epigenetic mechanism by which a methyl group is added to a cytosine or an adenine. When located in a gene/regulatory sequence it may repress or de-repress genes, depending on the context and species. Eragrostis curvula is an apomictic grass in which facultative genotypes increases the frequency of sexual pistils triggered by epigenetic mechanisms. The aim of the present study was to look for correlations between the reproductive mode and specific methylated genes or genomic regions. To do so, plants with contrasting reproductive modes were investigated through MCSeEd (Methylation Context Sensitive Enzyme ddRad) showing higher levels of DNA methylation in apomictic genotypes. Moreover, an increased proportion of differentially methylated positions over the regulatory regions were observed, suggesting its possible role in regulation of gene expression. Interestingly, the methylation pathway was also found to be self-regulated since two of the main genes (ROS1 and ROS4), involved in de-methylation, were found differentially methylated between genotypes with different reproductive behavior. Moreover, this work allowed us to detect several genes regulated by methylation that were previously found as differentially expressed in the comparisons between apomictic and sexual genotypes, linking DNA methylation to differences in reproductive mode.

A High-Density Linkage Map of the Forage Grass Eragrostis curvula and Localization of the Diplospory Locus.

Eragrostis curvula (Schrad.) Nees (weeping lovegrass) is an apomictic species native to Southern Africa that is used as forage grass in semiarid regions of Argentina. Apomixis is a mechanism for clonal propagation through seeds that involves the avoidance of meiosis to generate an unreduced embryo sac (apomeiosis), parthenogenesis, and viable endosperm formation in a fertilization-dependent or -independent manner. Here, we constructed the first saturated linkage map of tetraploid E. curvula using both traditional (AFLP and SSR) and high-throughput molecular markers (GBS-SNP) and identified the locus controlling diplospory. We also identified putative regulatory regions affecting the expressivity of this trait and syntenic relationships with genomes of other grass species. We obtained a tetraploid mapping population from a cross between a full sexual genotype (OTA-S) with a facultative apomictic individual of cv. Don Walter. Phenotypic characterization of F1 hybrids by cytoembryological analysis yielded a 1:1 ratio of apomictic vs. sexual plants (34:27, X 2 = 0.37), which agrees with the model of inheritance of a single dominant genetic factor. The final number of markers was 1,114 for OTA-S and 2,019 for Don Walter. These markers were distributed into 40 linkage groups per parental genotype, which is consistent with the number of E. curvula chromosomes (containing 2 to 123 markers per linkage group). The total length of the OTA-S map was 1,335 cM, with an average marker density of 1.22 cM per marker. The Don Walter map was 1,976.2 cM, with an average marker density of 0.98 cM/marker. The locus responsible for diplospory was mapped on Don Walter linkage group 3, with other 65 markers. QTL analyses of the expressivity of diplospory in the F1 hybrids revealed the presence of two main QTLs, located 3.27 and 15 cM from the diplospory locus. Both QTLs explained 28.6% of phenotypic variation. Syntenic analysis allowed us to establish the groups of homologs/homeologs for each linkage map. The genetic linkage map reported in this study, the first such map for E. curvula, is the most saturated map for the genus Eragrostis and one of the most saturated maps for a polyploid forage grass species.

Crosstalk pathway inference using topological information and biclustering of gene expression data.

Detection of crosstalks among pathways is a challenging task, which requires the identification of different types of interactions associated with cellular processes. A common strategy used in bioinformatics consists in extrapolating pathway associations from the pairwise analysis of some genes related to them, using gene expression data and topological information. PET, the method proposed in this paper, goes a step further by incorporating a strategy for the detection of correlation across conditions between differentially expressed genes based on biclustering analysis. In order to evaluate the performance of this new approach, a comparison with two recently published algorithms was carried out. The methods were contrasted in the inference of pathway associations from Alzheimer disease datasets, where the new proposal presents a higher crosstalk discoveries' rate. Finally, the analysis of the biological relevance of the pathway associations inferred by PET has shown the soundness of the extracted knowledge.

Discretization of gene expression data revised.

Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered. This study presents a revision of the current state-of-the-art discretization techniques, together with the key subjects that need to be considered when designing or selecting a discretization approach for gene expression data.

Discovering time-lagged rules from microarray data using gene profile classifiers.

BACKGROUND: Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. RESULTS: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. CONCLUSIONS: A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.