Predominance of syntrophic bacteria, Methanosaeta and Methanoculleus in a two-stage up-flow anaerobic sludge blanket reactor treating coffee processing wastewater at high organic loading rate.

The effect of the organic loading rate (OLR) on the performance and microbial composition of a two-stage UASB system treating coffee processing wastewater was assessed. The system was operated with OLR up to 18.2 g COD (L d)-1 and effluent recirculation. Methane production and effluent characteristics were monitored. The microbial composition was examined through next-generation sequencing and qPCR from the anaerobic sludge of the first reactor (R1) operated at low and high OLR. The system showed operational stability, obtaining a maximum methane production of 2.2 L CH4 (L d)-1, with a removal efficiency of COD and phenolic compounds of 84 and 73%, respectively. The performance of R1 at high OLR in steady conditions was associated with an appropriate proportion of nutrients (particularly Fe) and a marked increase of the syntrophic bacteria Syntrophus and Candidatus Cloacimonas, and acetoclastic and hydrogenotrophic methanogens, mainly Methanosaeta, Methanoculleus, Methanobacterium and Methanomassiliicoccus.

Genome-wide copy number variation (CNV) detection in Nelore cattle reveals highly frequent variants in genome regions harboring QTLs affecting production traits.

BACKGROUND: Copy number variations (CNVs) have been shown to account for substantial portions of observed genomic variation and have been associated with qualitative and quantitative traits and the onset of disease in a number of species. Information from high-resolution studies to detect, characterize and estimate population-specific variant frequencies will facilitate the incorporation of CNVs in genomic studies to identify genes affecting traits of importance. RESULTS: Genome-wide CNVs were detected in high-density single nucleotide polymorphism (SNP) genotyping data from 1,717 Nelore (Bos indicus) cattle, and in NGS data from eight key ancestral bulls. A total of 68,007 and 12,786 distinct CNVs were observed, respectively. Cross-comparisons of results obtained for the eight resequenced animals revealed that 92 % of the CNVs were observed in both datasets, while 62 % of all detected CNVs were observed to overlap with previously validated cattle copy number variant regions (CNVRs). Observed CNVs were used for obtaining breed-specific CNV frequencies and identification of CNVRs, which were subsequently used for gene annotation. A total of 688 of the detected CNVRs were observed to overlap with 286 non-redundant QTLs associated with important production traits in cattle. All of 34 CNVs previously reported to be associated with milk production traits in Holsteins were also observed in Nelore cattle. Comparisons of estimated frequencies of these CNVs in the two breeds revealed 14, 13, 6 and 14 regions in high (>20 %), low (<20 %) and divergent (NEL > HOL, NEL < HOL) frequencies, respectively. CONCLUSIONS: Obtained results significantly enriched the bovine CNV map and enabled the identification of variants that are potentially associated with traits under selection in Nelore cattle, particularly in genome regions harboring QTLs affecting production traits.

Genomic Variants Revealed by Invariably Missing Genotypes in Nelore Cattle.

High density genotyping panels have been used in a wide range of applications. From population genetics to genome-wide association studies, this technology still offers the lowest cost and the most consistent solution for generating SNP data. However, in spite of the application, part of the generated data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. Some discarded data consists of markers that failed to generate genotypes, labeled as missing genotypes. A subset of missing genotypes that occur in the whole population under study may be caused by technical issues but can also be explained by the presence of genomic variations that are in the vicinity of the assayed SNP and that prevent genotyping probes from annealing. The latter case may contain relevant information because these missing genotypes might be used to identify population-specific genomic variants. In order to assess which case is more prevalent, we used Illumina HD Bovine chip genotypes from 1,709 Nelore (Bos indicus) samples. We found 3,200 missing genotypes among the whole population. NGS re-sequencing data from 8 sires were used to verify the presence of genomic variations within their flanking regions in 81.56% of these missing genotypes. Furthermore, we discovered 3,300 novel SNPs/Indels, 31% of which are located in genes that may affect traits of importance for the genetic improvement of cattle production.

Jararhagin, a snake venom metalloprotease-disintegrin, activates the Rac1 GTPase and stimulates neurite outgrowth in neuroblastoma cells.

It has been shown previously that the snake venom metalloprotease-disintegrin jararhagin stimulates cell migration and cytoskeletal rearrangement, independently of its effects on cellular adhesion but possibly associated with the activation of small GTP-binding proteins from the Rho family [Costa, E.P., Santos, M.F., 2004. Toxicon 44(8), 861-870.] Here we show that jararhagin stimulates spreading, actin dynamics and neurite outgrowth in neuroblastoma cells, and that this effect is accompanied by the translocation of the Rac1 small GTPase to the membrane fraction, suggesting its activation. Stimulation of neurite outgrowth was observed within minutes and was dependent on the proteolytic activity of the toxin. These results suggest that jararhagin may stimulate neuronal differentiation, being a potential tool for neuronal regeneration studies.

An integrated approach to the characterization of cell movement.

BACKGROUND: Most phenomena in developmental biology involve or depend upon cell migration. This article describes a comprehensive framework for the characterization and analysis of trajectories defined by cell movement. The following two perspectives are considered: (a) the behavior of each individual cell and (b) interactions between neighboring pairs of cells. METHODS: The measurements considered for individual trajectories include the velocity magnitude and orientation, maximum spatial dispersion, displacement effectiveness, and displacement entropies. Interactions between two trajectories are characterized by comparing the respective velocities. RESULTS: The potential of the overall framework is illustrated using data of moving cells in different biological environments. The work shows that it is possible to use the new algorithm presented here to characterize cell motility. CONCLUSIONS: The features of the algorithm were successful in determining the motility changes under different experimental conditions.