Induction of laccases in Trametes versicolor by aqueous wood extracts

The induction of laccase isoforms in Trametes versicolor HEMIM-9 by aqueous extracts (AE) from softwood and hardwood was studied. Samples of sawdust of Pinus sp., Cedrela sp., and Quercus sp. were boiled in water to obtain AE. Different volumes of each AE were added to fungal cultures to determine the amount of AE needed for the induction experiments. Laccase activity was assayed every 24 h for 15 days. The addition of each AE (50 to 150 μl) to the fungal cultures increased laccase production compared to the control (0.42 ± 0.01 U ml^?1). The highest laccase activities detected were 1.92 ± 0.15 U ml^?1 (pine), 1.87 ± 0.26 U ml^?1 (cedar), and 1.56 ± 0.34 U ml^?1 (oak); laccase productivities were also significantly increased. Larger volumes of any AE inhibited mycelial growth. Electrophoretic analysis revealed two laccase bands (lcc1 and lcc2) for all the treatments. However, when lcc2 was analyzed by isoelectric focusing, inducer-dependent isoform patterns composed of three (pine AE), four (oak AE), and six laccase bands (cedar AE) were observed. Thus, AE from softwood and hardwood had induction effects in T. versicolor HEMIM-9, as indicated by the increase in laccase activity and different isoform patterns. All of the enzymatic extracts were able to decolorize the dye Orange II. Dye decolorization was mainly influenced by pH. The optimum pH for decolorization was pH 5 (85 %), followed by pH 7 (50 %) and pH 3 (15 %). No significant differences in the dye decolorizing capacity were detected between the control and the differentially induced laccase extracts (oak, pine and cedar). This could be due to the catalytic activities of isoforms with pI 5.4 and 5.8, which were detected under all induction conditions.     

Characterization of the interaction between protein Snu13p/15.5K and the Rsa1p/NUFIP factor and demonstration of its functional importance for snoRNP assembly

The yeast Snu13p protein and its 15.5K human homolog both bind U4 snRNA and box C/D snoRNAs. They also bind the Rsa1p/NUFIP assembly factor, proposed to scaffold immature snoRNPs and to recruit the Hsp90-R2TP chaperone complex. However, the nature of the Snu13p/15.5K–Rsa1p/NUFIP interaction and its exact role in snoRNP assembly remained to be elucidated. By using biophysical, molecular and imaging approaches, here, we identify residues needed for Snu13p/15.5K–Rsa1p/NUFIP interaction. By NMR structure determination and docking approaches, we built a 3D model of the Snup13p–Rsa1p interface, suggesting that residues R₂₄₉, R₂₄₆ and K₂₅₀ in Rsa1p and E₇₂ and D₇₃ in Snu13p form a network of electrostatic interactions shielded from the solvent by hydrophobic residues from both proteins and that residue W₂₅₃ of Rsa1p is inserted in a hydrophobic cavity of Snu13p. Individual mutations of residues in yeast demonstrate the functional importance of the predicted interactions for both cell growth and snoRNP formation. Using archaeal box C/D sRNP 3D structures as templates, the association of Snu13p with Rsa1p is predicted to be exclusive of interactions in active snoRNPs. Rsa1p and NUFIP may thus prevent premature activity of pre-snoRNPs, and their removal may be a key step for active snoRNP production.     

Floral structure and development in the monoecious palm Gaussia attenuata (Arecaceae; Arecoideae)

Background and Aims

Sexual dimorphism, at both the flower and plant level, is widespread in the palm family (Arecaceae), in contrast to the situation in angiosperms as a whole. The tribe Chamaedoreeae is of special interest for studies of the evolution of sexual expression since dioecy appears to have evolved independently twice in this group from a monoecious ancestor. In order to understand the underlying evolutionary pathways, it is important to obtain detailed information on flower structure and development in each of the main clades.

Methods

Dissection and light and scanning electron microscopy were performed on developing flowers of Gaussia attenuata, a neotropical species belonging to one of the three monoecious genera of the tribe.

Key Results

Like species of the other monoecious genera of the Chamaedoreeae (namely Hyophorbe and Synechanthus), G. attenuata produces a bisexual flower cluster known as an acervulus, consisting of a row of male flowers with a basal female flower. Whereas the sterile androecium of female flowers terminated its development at an early stage of floral ontogeny, the pistillode of male flowers was large in size but with no recognizable ovule, developing for a longer period of time. Conspicuous nectary differentiation in the pistillode suggested a possible role in pollinator attraction.

Conclusions

Gaussia attenuata displays a number of floral characters that are likely to be ancestral to the tribe, notably the acervulus flower cluster, which is conserved in the other monoecious genera and also (albeit in a unisexual male form) in the dioecious genera (Wendlandiella and a few species of Chamaedorea). Comparison with earlier data from other genera suggests that large nectariferous pistillodes and early arrest in staminode development might also be regarded as ancestral characters in this tribe.     

The effects of encoding data in diversity studies and the applicability of the weighting index approach for data analysis from different molecular markers

The use of molecular markers to study genetic diversity represents a breakthrough in this area, because of the increase in polymorphism levels and phenotypic neutrality. Codominant markers, such as microsatellites (SSR), are sensitive enough to distinguish the heterozygotes in genetic studies. Despite this advantage, there are some studies that ignore this feature and work with encoded data because of the simplicity of the evaluation, existence of polyploids and need for the combined analysis of different types of molecular markers. Thus, our study aims to investigate the consequences of these encodings on simulated and real data. In addition, we suggest an alternative analysis for genetic evaluations using different molecular markers. For the simulated data, we proposed the following two scenarios: the first uses SNP markers, and the second SSR markers. For real data, we used the SSR genotyping data from Coffea canephora accessions maintained in the Embrapa Germplasm Collection. The genetic diversity was studied using cluster analysis, the dissimilarity index, and the Bayesian approach implemented in the STRUCTURE software. For the simulated data, we observed a loss of genetic information to the encoded data in both scenarios. The same result was observed in the coffee studies. This loss of information was discussed in the context of a plant-breeding program, and the consequences were weighted to germplasm evaluations and the selection of parents for hybridization. In the studies that involved different types of markers, an alternative to the combined analysis is discussed, where the informativeness, coverage and quality of markers are weighted in the genetic diversity studies.     

QTL detection for coccidiosis (Eimeria tenella) resistance in a Fayoumi?×?Leghorn F2 cross,using a medium-density SNP panel

Background

Coccidiosis is a major parasitic disease that causes huge economic losses to the poultry industry. Its pathogenicity leads to depression of body weight gain, lesions and, in the most serious cases, death in affected animals. Genetic variability for resistance to coccidiosis in the chicken has been demonstrated and if this natural resistance could be exploited, it would reduce the costs of the disease. Previously, a design to characterize the genetic regulation of Eimeria tenella resistance was set up in a Fayoumi × Leghorn F₂ cross. The 860 F₂ animals of this design were phenotyped for weight gain, plasma coloration, hematocrit level, intestinal lesion score and body temperature. In the work reported here, the 860 animals were genotyped for a panel of 1393 (157 microsatellites and 1236 single nucleotide polymorphism (SNP) markers that cover the sequenced genome (i.e. the 28 first autosomes and the Z chromosome). In addition, with the aim of finding an index capable of explaining a large amount of the variance associated with resistance to coccidiosis, a composite factor was derived by combining the variables of all these traits in a single variable. QTL detection was performed by linkage analysis using GridQTL and QTLMap. Single and multi-QTL models were applied.

Results

Thirty-one QTL were identified i.e. 27 with the single-QTL model and four with the multi-QTL model and the average confidence interval was 5.9 cM. Only a few QTL were common with the previous study that used the same design but focused on the 260 more extreme animals that were genotyped with the 157 microsatellites only. Major differences were also found between results obtained with QTLMap and GridQTL.

Conclusions

The medium-density SNP panel made it possible to genotype new regions of the chicken genome (including micro-chromosomes) that were involved in the genetic control of the traits investigated. This study also highlights the strong variations in QTL detection between different models and marker densities.     

Detection of QTL controlling digestive efficiency and anatomy of the digestive tract in chicken fed a wheat-based diet

Background

Improving digestive efficiency is a major goal in poultry production, to reduce production costs, make possible the use of alternative feedstuffs and decrease the volume of manure produced. Since measuring digestive efficiency is difficult, identifying molecular markers associated with genes controlling this trait would be a valuable tool for selection. Detection of QTL (quantitative trait loci) was undertaken on 820 meat-type chickens in a F2 cross between D- and D+ lines divergently selected on low or high AMEn (apparent metabolizable energy value of diet corrected to 0 nitrogen balance) measured at three weeks in animals fed a low-quality diet. Birds were measured for 13 traits characterizing digestive efficiency (AMEn, coefficients of digestive utilization of starch, lipids, proteins and dry matter (CDUS, CDUL, CDUP, CDUDM)), anatomy of the digestive tract (relative weights of the proventriculus, gizzard and intestine and proventriculus plus gizzard (RPW, RGW, RIW, RPGW), relative length and density of the intestine (RIL, ID), ratio of proventriculus and gizzard to intestine weight (PG/I); and body weight at 23 days of age. Animals were genotyped for 6000 SNPs (single nucleotide polymorphisms) distributed on 28 autosomes, the Z chromosome and one unassigned linkage group.

Results

Nine QTL for digestive efficiency traits, 11 QTL for anatomy-related traits and two QTL for body weight at 23 days of age were detected. On chromosome 20, two significant QTL at the genome level co-localized for CDUS and CDUDM, i.e. two traits that are highly correlated genetically. Moreover, on chromosome 16, chromosome-wide QTL for AMEn, CDUS, CDUDM and CDUP, on chromosomes 23 and 26, chromosome-wide QTL for CDUS, on chromosomes 16 and 26, co-localized QTL for digestive efficiency and the ratio of intestine length to body weight and on chromosome 27 a chromosome-wide QTL for CDUDM were identified.

Conclusions

This study identified several regions of the chicken genome involved in the control of digestive efficiency. Further studies are necessary to identify the underlying genes and to validate these in commercial populations and breeding environments.     

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).