Feeding activity of Callinectes ornatus Ordway, 1863 and Callinectes danae Smith, 1869 (Crustacea, Brachyura, Portunidae) in Ubatuba, SP, Brazil

The feeding activity along the day cycle and the time consumed for extracellular digestion were evaluated in the portunids C. ornatus and C. danae. Swimming crabs were obtained from trawling in Ubatuba bay, São Paulo, Brazil, during both the rainy and dry seasons. In each season, daily scheduled samples were taken at dawn (±6 h), noon (±12 h), dusk (±18 h) and midnight (±24 h). All individuals were dissected and the degree of stomach replenishment was recorded. In order to estimate the time elapsed for extracellular digestion, crabs were fed, and groups were dissected at 30 min intervals to check the conditions of their stomachs. In general, both species show a higher feeding activity during periods of lower light intensity, as evidenced by an increased percentage of full stomachs in dusk and midnight samples. The obtained results support higher feeding activity at night in these species and indicate short time for extracellular digestion, not exceeding 8 h. Nevertheless, full stomachs were recorded in all sampling schedules. In this case, it should be considered that elimination of certain food items such as fish bones, mollusk shells and carapace fragments of crustaceans could take more time than other items. Additionally, some crab species could require a cycle of cell replacement in the midgut gland epithelium until they can take their next meal.     

Saccharomyces cerevisiae: a potential host for carboxylic acid production from lignocellulosic feedstock?

Carboxylic acids are important bulk chemicals that can be used as building blocks for the production of polymers, as acidulants, preservatives and flavour compound or as precursors for the synthesis of pharmaceuticals. Today, their production mainly takes place through catalytic processing of petroleum-based precursors. An appealing alternative would be to produce these compounds from renewable resources, using tailor-made microorganisms. Saccharomyces cerevisiae has already demonstrated its value for bioethanol production from renewable resources. In this review, we discuss Saccharomyces cerevisiae engineering potential, current strategies for carboxylic acid production as well as the specific challenges linked to the use of lignocellulosic biomass as carbon source.     

PepExplorer: A Similarity-driven Tool for Analyzing de Novo Sequencing Results

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith–Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.Very often, groundbreaking discoveries with a significant impact on the biotechnological and biomedical fields have emerged from studying “non-canonical” organisms. For example, the study of Thermus aquaticus allowed us to ultimately pave the way to modern molecular biology with the characterization of that organism''s thermostable DNA polymerase (1). The characterization of the green fluorescent protein in Aequoria victoria led to a revolution in cellular biology and to a Nobel Prize being awarded to Osamu Shimomura, Martin Chalfie, and Roger Tsien. In Brazil, Sergio Ferreira''s work on the venom of the Brazilian poisonous snake Bothrops jararaca enabled the development of the first angiotensin-converting enzyme inhibitor drug (Captopril) for the treatment of hypertension (2).In scenarios such as these, proteomics has the potential to allow a better understanding of the complexity of biological systems and the process of evolution than the study of the genetic code alone. It enables the characterization of molecular processes according to their protein content, facilitating new discoveries. In proteomics, the most frequently used strategy for protein identification is so-called peptide spectrum matching (PSM),¹ or the comparison of experimental mass spectra obtained by fragmenting peptides in a mass spectrometer to theoretical spectra generated from a sequence database. In general, the identification process follows from the sequence whose theoretical spectrum yields the highest matching score according to some empirical or probabilistic function. Examples of search engines adopting this strategy are SEQUEST (3), X!Tandem (4), and Mascot (5).Back in the 1990s, establishment of a cutoff score for confident identification relied mostly on user experience; for example, given a specific charge state, Washburn et al. established cross-correlation and deltaCn cutoff values for SEQUEST in order to allow the selection of a subset of confident identifications from LCQ data. This has since been termed “the Washburn criterion.” In what followed, target-decoy databases were implemented to allow for more sophisticated refinements in filtering the data (6). In 2007, Elias and Gygi published a seminal paper on the target-decoy approach to shotgun proteomics (7) that ultimately firmed this approach as a standard and motivated the development of several statistical filters capable of converging to a list of confident identifications satisfying a user-specified false-discovery rate (FDR) with significantly more sensitivity than the conservative Washburn criterion. Such statistical filters include mixtures of probabilities (8), quadratic discriminant analysis (9), semi-supervised learning with support vector machines (10), and Bayesian logic (11) using a semi-labeled decoy analysis to account for overfitting (12). With so many advances, the PSM workflow has become the gold standard, as it is very sensitive and the least error-prone method when a database is available with the corresponding proteins. The latter factor limits the application of PSM to those organisms for which accurate sequence databases have been established. If a peptide''s sequence is not contained within the sequence database, it cannot be identified via the PSM method. However, efforts in developing error-tolerant PSM approaches such as implemented in Mascot have made it possible to handle minor sequence modifications constrained by a simple set of rules. Nevertheless, increasing the search space in the PSM approach leads to decreased sensitivity (13).Even though the concept of computer-aided de novo sequencing predates that of PSM (14), advances in the quality of mass spectrometry data and the power of computer hardware have allowed it to reemerge at the heart of a highly active field. De novo sequencing is unbiased insofar as it is not constrained by a sequence database, and it is therefore complementary to PSM. However, it has remained the most error prone of the two methods (15). The challenges of de novo sequencing notwithstanding, a few recent and notable improvements in computer-aided de novo analysis are PepNovo (16), which combines graph theory with machine learning; pNovo+ (17), which is optimized for high-resolution HCD data; NovoHMM (18), relying on hidden Markov models for increased sensitivity; and PEAKS (19), which creates a spectrum graph model by performing dynamic programming on the mass values regardless of the presence of an observed fragment ion. By considering the complementarities of different fragmentation strategies (e.g. collision induced dissociation, electron transfer dissociation (20), and electron capture dissociation (21)), computational proteomics scientists have also demonstrated significant advances in de novo accuracy (22). In particular, the Bandeira group has continually pushed the limits and redefined the notion of what de novo sequencing can do by introducing the spectral networks paradigm (23–25). Briefly, this strategy can assemble mass spectra into spectral pairs by joining overlapping spectra obtained from sample aliquots digested by different enzymes. As a consequence, it reduces noise and significantly improves protein coverage. Its latest version also combines data from different fragmentation techniques.These algorithm developments have improved de novo sequencing, shifting the bottleneck to post-sequence processing of data. This is because the output of de novo software is a long list of highly similar full and partial peptide sequence and scores. An initial attempt to overcome these limitations consisted of a tag approach that was a hybrid of de novo sequencing and database searching: short sequence tags were derived from tandem mass spectra and used to search a sequence database (26). In what followed, a modified version based on the FASTA homology search tool was proposed for homology-driven proteomics (27). This strategy was implemented as part of the CIDentify tool, whose novelty was to account, in the alignment score, for limitations of mass spectrometry sequencing such as switching between leucine and isoleucine or other combinations of amino acids having the same mass. The next steps were taken mainly by the Shevchenko group through the introduction of the MS-Blast algorithm, which relies on a different set of scores and uses the PAM30MS substitution matrix, itself tailored for mass-spectrometry-based proteomics (28, 29). For a complete review of de novo sequencing and homology searching, we suggest Ref. 30.The current de novo post-processing paradigm presents several limitations that are similar to those of the early PSM workflow. Output files generally consist of a peptide list with corresponding scores, demanding an experienced user to assess trustworthy identifications. If the same peptide is analyzed by different mass spectrometers, different scores might be generated, which makes data comparison between different groups a challenging task. In a sense, problems are similar to those encountered when adopting the early Washburn criterion. Assembling protein information from a list of peptides is not a simple task, and usually it is not performed using state-of-the-art de novo tools. Although there are great tools for doing this at the PSM level, there is still a lack of similar tools for de novo sequencing.To tackle the aforementioned shortcomings, and in line with our strong interest in diversity-driven proteomics (29), we present a methodology for post-processing de novo sequencing data that allows inference of protein identification through statistical mapping of de novo sequencing results to a protein sequence database. Our approach begins with the use of Gotoh''s version of the Smith–Waterman algorithm, based on affine gap scoring (31) for increased scalability, to align de novo sequences against those in a target-decoy database. Then a radial basis function neural network (RBF-NN) is used to rank results according to alignment score, de novo score, precursor charge state, and peptide length. Finally, a heuristic method is used to present protein identification results in a user-friendly, interactive report. The resulting algorithm was implemented as the software PepExplorer. In essence, its goal is somewhat similar to that of post-processing tools such as DTASelect (9), Percolator (10), and SEPro (11), but with an extra layer of complexity inherent from de novo sequencing. PepExplorer can handle the output of several widely adopted de novo tools, such as PepNovo, pNovo+, and PEAKS, and accepts a generic format to enable result analysis from a broader range of tools once results are run through simple parsers. Similarly, the software accepts a series of database formats for input analysis. These features are not found in other tools. PepExplorer is freely available to the scientific community and is provided with the necessary documentation.The effectiveness of our methodology has been verified in two distinct scenarios, the first a real but controlled experiment and the other pertaining to comprehensive profiling of the plasma components of Bothrops jararaca, a venomous viper endemic to Brazil, southern Paraguay, and northern Argentina. The first scenario''s purpose was to validate the effectiveness of the tool in analyzing a published Pyrococcus furiosus dataset (11). We note that this organism is recognized by the proteomics community as well suited for benchmarking, because it allows for the rigorous testing of identification algorithms at the peptide and protein levels (32, 33). We modified the P. furiosus sequence database in such a way that no more peptides were identified via the PSM approach or another widely adopted error-tolerant search tool, Mod-A (34). We then found that we could recover protein identifications using our tool. The B. jararaca scenario has allowed us to explore uncharted territory, as this organism has an incomplete sequence database and we were therefore required to rely on those of orthologous organisms. In particular, B. jararaca plasma was chosen because it is a main research model studied at the Laboratory of Toxinology (FIOCRUZ, Brazil), and several natural inhibitors of snake toxins have already been identified/characterized from this biological matrix (35–37).     

Evaluating Purifying Selection in the Mitochondrial DNA of Various Mammalian Species

Mitochondrial DNA (mtDNA), the circular DNA molecule inside the mitochondria of all eukaryotic cells, has been shown to be under the effect of purifying selection in several species. Traditional testing of purifying selection has been based simply on ratios of nonsynonymous to synonymous mutations, without considering the relative age of each mutation, which can be determined by phylogenetic analysis of this non-recombining molecule. The incorporation of a mutation time-ordering from phylogeny and of predicted pathogenicity scores for nonsynonymous mutations allow a quantitative evaluation of the effects of purifying selection in human mtDNA. Here, by using this additional information, we show that purifying selection undoubtedly acts upon the mtDNA of other mammalian species/genera, namely Bos sp., Canis lupus, Mus musculus, Orcinus orca, Pan sp. and Sus scrofa. The effects of purifying selection were comparable in all species, leading to a significant major proportion of nonsynonymous variants with higher pathogenicity scores in the younger branches of the tree. We also derive recalibrated mutation rates for age estimates of ancestors of these various species and proposed a correction curve in order to take into account the effects of selection. Understanding this selection is fundamental to evolutionary studies and to the identification of deleterious mutations.     

Consideration of multiple load cases is critical in modelling orthotropic bone adaptation in the femur

Functional adaptation of the femur has been investigated in several studies by embedding bone remodelling algorithms in finite element (FE) models, with simplifications often made to the representation of bone’s material symmetry and mechanical environment. An orthotropic strain-driven adaptation algorithm is proposed in order to predict the femur’s volumetric material property distribution and directionality of its internal structures within a continuum. The algorithm was applied to a FE model of the femur, with muscles, ligaments and joints included explicitly. Multiple load cases representing distinct frames of two activities of daily living (walking and stair climbing) were considered. It is hypothesised that low shear moduli occur in areas of bone that are simply loaded and high shear moduli in areas subjected to complex loading conditions. In addition, it is investigated whether material properties of different femoral regions are stimulated by different activities. The loading and boundary conditions were considered to provide a physiological mechanical environment. The resulting volumetric material property distribution and directionalities agreed with ex vivo imaging data for the whole femur. Regions where non-orthogonal trabecular crossing has been documented coincided with higher values of predicted shear moduli. The topological influence of the different activities modelled was analysed. The influence of stair climbing on the properties of the femoral neck region is highlighted. It is recommended that multiple load cases should be considered when modelling bone adaptation. The orthotropic model of the complete femur is released with this study.     

Next-generation sequencing analyses of the emergence and maintenance of mutations in CTL epitopes in HIV controllers with differential viremia control

Background

Despite the low level of viral replication in HIV controllers (HICs), studies have reported viral mutations related to escape from cytotoxic T-lymphocyte (CTL) response in HIV-1 plasma sequences. Thus, evaluating the dynamics of the emergence of CTL-escape mutants in HICs reservoirs is important for understanding viremia control. To analyze the HIV-1 mutational profile and dynamics of CTL-escape mutants in HICs, we selected 11 long-term non-progressor individuals and divided them into the following groups: (1) viremic controllers (VCs; n?=?5) and (2) elite controllers (ECs; n?=?6). For each individual, we used HIV-1 proviral DNA from PBMCs related to earliest (V_E) and latest (V_L) visits to obtain gag and nef sequences using the Illumina HiSeq system. The consensus of each mapped gene was used to assess viral divergence, and next-generation sequencing data were employed to identify SNPs and variations within and flanking CTL epitopes.

Results

Divergence analysis showed higher values for nef compared to gag among the HICs. EC and VC groups showed similar divergence rates for both genes. Analysis of the number of SNPs showed that VCs present more variability in both genes. Synonymous/non-synonymous mutation ratios were?<?1 for gag among ECs and for nef among ECs and VCs, exhibiting a predominance of non-synonymous mutations. Such mutations were observed in regions encoding CTL-restricted epitopes in all individuals. All ECs presented non-synonymous mutations in CTL epitopes but generally at low frequency (<?1%); all VCs showed a high number of mutations, with significant frequency changes between V_E and V_L visits. A higher frequency of internal mutations was observed for gag epitopes, with significant changes across visits compared to Nef epitopes, indicating a pattern associated with differential genetic pressure.

Conclusions

The high genetic conservation of HIV-1 gag and nef among ECs indicates that the higher level of viremia control restricts the evolution of both genes. Although viral replication levels in HICs are low or undetectable, all individuals exhibited CTL epitope mutations in proviral gag and nef variants, indicating that potential CTL escape mutants are present in HIC reservoirs and that situations leading to a disequilibrium of the host-virus relationship can result in the spread of CTL-escape variants.

     

Proneural genes and the specification of neural cell types

Certain morphological, physiological and molecular characteristics are shared by all neurons. However, despite these similarities, neurons constitute the most diverse cell population of any organism. Recently, considerable attention has been focused on identifying the molecular mechanisms that underlie this cellular diversity. Parallel studies in Drosophila and vertebrates have revealed that proneural genes are key regulators of neurogenesis, coordinating the acquisition of a generic neuronal fate and of specific subtype identities that are appropriate for the location and time of neuronal generation. These studies reveal that, in spite of differences between invertebrate and vertebrate neural lineages, Drosophila and vertebrate proneural genes have remarkably similar roles.     

Glutaric acid stimulates glutamate binding and astrocytic uptake and inhibits vesicular glutamate uptake in forebrain from young rats

Glutaric acidemia type I (GA I) is an inherited neurometabolic disorder caused by glutaryl-CoA dehydrogenase deficiency, which leads to accumulation in body fluids and in brain of predominantly glutaric acid (GA), and to a lesser extent of 3-hydroxyglutaric and glutaconic acids. Neurological presentation is common in patients with GA I. Although the mechanisms underlying brain damage in this disorder are not yet well established, there is growing evidence that excitotoxicity may play a central role in the neuropathogenesis of this disease. In the present study, preparations of synaptosomes, synaptic plasma membranes and synaptic vesicles, as well as cultured astrocytes from rat forebrain were exposed to various concentrations of GA for the determination of the basal and potassium-induced release of [(3)H]glutamate by synaptosomes, Na(+)-independent glutamate binding to synaptic membranes and vesicular glutamate uptake and Na(+)-dependent glutamate uptake into astrocytes, respectively. GA (1-100 nM) significantly stimulated [(3)H]glutamate binding to brain plasma membranes (40-70%) in the absence of extracellular Na(+) concentrations, reflecting glutamate binding to receptors. Furthermore, this stimulatory effect was totally abolished by the metabotropic glutamate ligands DHPG, DCG-IV and l-AP4, attenuated by the ionotropic non-NMDA glutamate receptor agonist AMPA and had no interference of the NMDA receptor antagonist MK-801. Moreover, [(3)H]glutamate uptake into synaptic vesicles was inhibited by approximately 50% by 10 and 100 nM GA and Na(+)-dependent [(3)H]glutamate uptake by astrocytes was significantly increased (up to 50%) in a dose-dependent manner (maximal stimulation at 100 microM GA). In contrast, synaptosomal glutamate release was not affected by the acid at concentrations as high as 1 mM. These results indicate that the inhibition of glutamate uptake into synaptic vesicles by low concentrations GA may result in elevated concentrations of the excitatory neurotransmitter in the cytosol and the stimulatory effect of this organic acid on glutamate binding may potentially cause excitotoxicity to neural cells. Finally, taken together these results and previous findings showing that GA markedly decreases synaptosomal glutamate uptake, it is possible that the stimulatory effect of GA on astrocyte glutamate uptake might indicate that astrocytes may protect neurons from excitotoxic damage caused by GA by increasing glutamate uptake and therefore reducing the concentration of this excitatory neurotransmitter in the synaptic cleft.