Identification of a Huperzine A-producing endophytic fungus from Phlegmariurus taxifolius

Molecular Biology Reports - Highly prized huperzine A (Hup A), a natural alkaloid formerly isolated from the Chinese medicinal plant Huperzia serrata, has been widely used for the treatment of...     

Loss of phytoplankton functional and taxonomic diversity induced by river regulation in a large tropical river

Hydrobiologia - Human activities have disrupted the functioning of river ecosystems around the world. In the Amazon basin, hydropower expansion has affected diversity patterns, but no study has...     

Colonization of Madagascar periwinkle (Catharanthus roseus), by endophytes encoding gfp marker

This study reports the introduction of gfp marker in two endophytic bacterial strains (Pantoea agglomerans C33.1, isolated from cocoa, and Enterobacter cloacae PR2/7, isolated from citrus) to monitor the colonization in Madagascar perinwinkle (Catharanthus roseus). Stability of the plasmid encoding gfp was confirmed in vitro for at least 72 h of bacterial growth and after the colonization of tissues, under non-selective conditions. The colonization was observed using fluorescence microscopy and enumeration of culturable endophytes in inoculated perinwinkle plants that grew for 10 and 20 days. Gfp-expressing strains were re-isolated from the inner tissues of surface-sterilized roots and stems of inoculated plants, and the survival of the P. agglomerans C33:1gfp in plants 20 days after inoculation, even in the absence of selective pressure, suggests that is good colonizer. These results indicated that both gfp-tagged strains, especially P. agglomerans C33.1, may be useful tools to deliver enzymes or other proteins in plant.     

Mathematical modeling of collagen turnover in biological tissue

We present a theoretical and computational model for collagen turnover in soft biological tissues. Driven by alterations in the mechanical environment, collagen fiber bundles may undergo important chronic changes, characterized primarily by alterations in collagen synthesis and degradation rates. In particular, hypertension triggers an increase in tropocollagen synthesis and a decrease in collagen degradation, which lead to the well-documented overall increase in collagen content. These changes are the result of a cascade of events, initiated mainly by the endothelial and smooth muscle cells. Here, we represent these events collectively in terms of two internal variables, the concentration of growth factor TGF- $\beta $ and tissue inhibitors of metalloproteinases TIMP. The upregulation of TGF- $\beta $ increases the collagen density. The upregulation of TIMP also increases the collagen density through decreasing matrix metalloproteinase MMP. We establish a mathematical theory for mechanically-induced collagen turnover and introduce a computational algorithm for its robust and efficient solution. We demonstrate that our model can accurately predict the experimentally observed collagen increase in response to hypertension reported in literature. Ultimately, the model can serve as a valuable tool to predict the chronic adaptation of collagen content to restore the homeostatic equilibrium state in vessels with arbitrary micro-structure and geometry.     

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.     

Sequence verification of synthetic DNA by assembly of sequencing reads

Gene synthesis attempts to assemble user-defined DNA sequences with base-level precision. Verifying the sequences of construction intermediates and the final product of a gene synthesis project is a critical part of the workflow, yet one that has received the least attention. Sequence validation is equally important for other kinds of curated clone collections. Ensuring that the physical sequence of a clone matches its published sequence is a common quality control step performed at least once over the course of a research project. GenoREAD is a web-based application that breaks the sequence verification process into two steps: the assembly of sequencing reads and the alignment of the resulting contig with a reference sequence. GenoREAD can determine if a clone matches its reference sequence. Its sophisticated reporting features help identify and troubleshoot problems that arise during the sequence verification process. GenoREAD has been experimentally validated on thousands of gene-sized constructs from an ORFeome project, and on longer sequences including whole plasmids and synthetic chromosomes. Comparing GenoREAD results with those from manual analysis of the sequencing data demonstrates that GenoREAD tends to be conservative in its diagnostic. GenoREAD is available at www.genoread.org.     

Analysis of genome instability in breast cancer

Breast cancer is a heterogeneous disease, previously associated with genomic instability. Our aim was to analyze microsatellite markers in order to determine patterns and levels of instability, as well as possible correlations with histopathological parameters. Polymerase chain reaction was used to characterize microsatellite instability (MSI) and loss of heterozygosity (LOH) in 107 breast carcinomas at twelve microsatellite loci. Some of the markers were selected because of their relation to steroid hormone metabolism, which seems to be related to sporadic breast cancer risk. D5S346 and D17S250 markers showed a statistically significant frequency of MSI. LOH in D3S1611, D17S250, AR and ER-β were associated with some parameters of worse prognosis. Marker group analysis showed that CYP19, AR and ER-β were related to histological grade III, ER-negative and PR-negative cases. Our results suggest that marker group analysis may be preferred to the single marker strategy, being predictive of worst prognosis when single markers are unable to provide such information. A further evaluation of steroid metabolism genes and their association with low penetrance genes in breast cancer may be useful.     

Reef fishes foraging facilitation behavior: increasing the access to a food resource

Fishes associated in schools acquire adaptive advantages by grouping together, e.g., access to a larger variety of food resources, foraging sites, and protection against potential predators. This work presents the first record of a feeding association between the bucktooth parrotfish, Sparisoma radians and the sailor’s grunt Haemulon parra, on Tamandaré reefs, Southwestern Atlantic. Through this association, S. radians gained access to otherwise unavailable food resources to be found inside territorial damselfish domain, thus characterizing an event of foraging facilitation.