Effects of a commercial extract of the brown alga Ascophyllum nodosum on the biomass production of Kappaphycus alvarezii (Doty) Doty ex P. C. Silva and its carrageenan yield and gel quality cultivated in Brazil

Despite of its success, the carrageenan industry has had to cope with difficulties due to epiphyte infestations and diseases known as ice-ice. Some promising results in respect of biomass production, carrageenan yield, and protection epiphytes were obtained using a powdered extract of the brown alga Ascophyllum nodosum in Kappaphycus alvarezii seedling production. This study focused on the effects of the A. nodosum extract on the treatment of K. alvarezii cultivated on commercial floating rafts not only to evaluate improvements demonstrated in vitro but also the effects on the quality of the carrageenan. The seedlings were treated in an A. nodosum extract solution and placed alongside their controls on commercial floating rafts using the tubular net technique. Daily growth rate, carrageenan yield, gel strength, and gel viscosity were obtained over 20 and/or 40 days. After 20 days, daily growth rates showed no significant difference (p?=?0.44), while the carrageenan yield was higher in samples that were treated with the A. nodosum solution (p?<?0.001). After 40 days, both daily growth rate (p?=?000.7) and carrageenan yield (p?=?0.009) were higher in treated samples; however, gel strength was higher in control samples (p?=?0.03) as viscosity was also highest in the samples which had not been treated (p?<?0.001). The use of the A. nodosum extract treatment on the cultivation in situ of K. alvarezii was positive since after 40 days when the daily growth rate and carrageenan yield increased. In spite of the negative effect on the quality of the semi-refined carrageenan, the values were within commercial standards.     

Involvement of substance P and the NK-1 receptor in human pathology

The peptide substance P (SP) shows a widespread distribution in both the central and peripheral nervous systems, but it is also present in cells not belonging to the nervous system (immune cells, liver, lung, placenta, etc.). SP is located in all body fluids, such as blood, cerebrospinal fluid, breast milk, etc. i.e. it is ubiquitous in human body. After binding to the neurokinin-1 (NK-1) receptor, SP regulates many pathophysiological functions in the central nervous system, such as emotional behavior, stress, depression, anxiety, emesis, vomiting, migraine, alcohol addiction, seizures and neurodegeneration. SP has been also implicated in pain, inflammation, hepatitis, hepatotoxicity, cholestasis, pruritus, myocarditis, bronchiolitis, abortus, bacteria and viral infection (e.g., HIV infection) and it plays an important role in cancer (e.g., tumor cell proliferation, antiapoptotic effects in tumor cells, angiogenesis, migration of tumor cells for invasion, infiltration and metastasis). This means that the SP/NK-1 receptor system is involved in the molecular bases of many human pathologies. Thus, knowledge of this system is the key for a better understanding and hence a better management of many human diseases. In this review, we update the involvement of the SP/NK-1 receptor system in the physiopathology of the above-mentioned pathologies and we suggest valuable future therapeutic interventions involving the use of NK-1 receptor antagonists, particularly in the treatment of emesis, depression, cancer, neural degeneration, inflammatory bowel disease, viral infection and pruritus, in which that system is upregulated.     

A preliminary LCA case study: comparison of different pathways to produce purified terephthalic acid suitable for synthesis of 100 % bio-based PET

Purpose

This study provides a preliminary comparison of the environmental burdens of three different pathways for production of bio-based purified terephthalic acid (PTA), suitable for the production of 100 % bio-based poly(ethylene terephthalate), PET. These pathways are through (1) muconic acid originating in wheat stover; (2) isobutanol originating in corn; and (3) benzene, toluene, and xylene (BTX) originating in poplar. The goal is to point out what areas of these processes are the largest environmental contributors and hence are the most critical for development of accurate primary data, as well as to indicate which of these pathways looks most promising, from an environmental viewpoint, for production of 100 % bio-based PET.

Methods

Because much of the needed life cycle information to produce PTA is currently not available, inventory data for each scenario for the production of PTA were estimated based on the chemistry involved. In the impact analysis stage, the inventory data were classified and characterized with a focus on several environmental midpoint categories. SimaPro 7.3.3 software was used as the main computational software and Impact 2002+ v2.1 was used as the life cycle impact assessment methodology in this attributional life cycle assessment.

Results and discussion

Valuable preliminary environmental impact data including identification of critical steps in the process were obtained. The global warming value of PET synthesized through the muconic acid scenario was 1.6 times larger than that of the scenario of PET synthesized through BTX even after a limited Monte Carlo simulation of 1,000 runs.

Conclusions

Among the three scenarios for producing PET, PET synthesized through BTX looked the most promising to pursue for production of bio-based PET with lower environmental burdens. This work also indicated that the first production steps of producing PET through any of the evaluated scenarios (from biomass to the first intermediate) are responsible for the largest environmental burden and should be further characterized since they were the dominant processes in many impact categories.     

In Vitro Effect of H2O2, Some Transition Metals and Hydroxyl Radical Produced Via Fenton and Fenton-Like Reactions,on the Catalytic Activity of AChE and the Hydrolysis of ACh

It is well known that the principal biomolecules involved in Alzheimer’s disease (AD) are acetylcholinesterase (AChE), acetylcholine (ACh) and the amyloid beta peptide of 42 amino acid residues (Aβ42). ACh plays an important role in human memory and learning, but it is susceptible to hydrolysis by AChE, while the aggregation of Aβ42 forms oligomers and fibrils, which form senile plaques in the brain. The Aβ42 oligomers are able to produce hydrogen peroxide (H₂O₂), which reacts with metals (Fe²⁺, Cu²⁺, Cr³⁺, Zn²⁺, and Cd²⁺) present at high concentrations in the brain of AD patients, generating the hydroxyl radical (^·OH) via Fenton (FR) and Fenton-like (FLR) reactions. This mechanism generates high levels of free radicals and, hence, oxidative stress, which has been correlated with the generation and progression of AD. Therefore, we have studied in vitro how AChE catalytic activity and ACh levels are affected by the presence of metals (Fe³⁺, Cu²⁺, Cr³⁺, Zn²⁺, and Cd²⁺), H₂O₂ (without Aβ42), and ^· OH radicals produced from FR and FLR. The results showed that the H₂O₂ and the metals do not modify the AChE catalytic activity, but the ^·OH radical causes a decrease in it. On the other hand, metals, H₂O₂ and ^·OH radicals, increase the ACh hydrolysis. This finding suggests that when H₂O₂, the metals and the ^·OH radicals are present, both, the AChE catalytic activity and ACh levels diminish. Furthermore, in the future it may be interesting to study whether these effects are observed when H₂O₂ is produced directly from Aβ42.     

Comparative study of the effect of distance on the daily and hourly pollen counts in a city in the south-western Iberian Peninsula

The airborne pollen concentration in the city of Badajoz was measured in two locations 2.9 km apart. The measurements were taken from January to June between 2009 and 2012 using Hirst-type volumetric aerobiological samplers. One sampler was placed at the Faculty of Science (FS) and the other at the Agricultural Engineering School (AES) of the University of Extremadura, Spain, on terraces located 16 and 6 m above ground, respectively. The two sets of hourly and daily pollen concentrations were compared regarding the meteorological parameters and the distribution of local pollen sources. A total of 46 pollen types were counted, with a mean total concentration of 43 pollen grains/m³ in the winter and 336 pollen grains/m³ in the spring. In the winter, pollen grains from (in decreasing order) Cupressaceae, Fraxinus–Phillyrea, Urticaceae spp., Alnus glutinosa and Urtica membranacea types represented 77 % of the total. In the spring, 89 % of the total was represented by pollen grains from (in decreasing order) Quercus, Poaceae, Olea, Pinaceae and Plantago. The FS/AES ratio was 0.57 in the winter and 1.31 in the spring. While a Wilcoxon test applied to the daily total concentration data showed statistically significant differences between the two sites, a correlation study based on the Spearman coefficient showed statistically significant correlations in both the winter and spring. The results were similar when comparing the separate pollen types, except for Urticaceae spp., which showed no statistically significant correlation. The meteorological data studied showed a statistically significant correlation with the daily concentrations. A comparison of the hourly concentration data showed no correspondence with the time of maximum concentration. The local distribution of pollen sources explained some of the differences found between the two sites. Overall, the results indicate that a single aerobiological sampler may be sufficiently representative to register the daily pollen grain data of an urban area of approximately 3 km or greater in diameter, although it underestimates the influence of heterogeneity in the distribution of the local flora.     

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).     

Limb‐body wall defect: Experience of a reference service of fetal medicine from Southern Brazil

Background: Limb‐body wall defect is a rare condition characterized by a combination of large and complex defects of the ventral thorax and abdominal wall with craniofacial and limb anomalies. Methods: The aim of this study was to describe the experience of our fetal medicine service, a reference from Southern Brazil, with prenatally diagnosed patients with a limb‐body wall defect in a 3 years period. Only patients who fulfilled the criteria suggested by Hunter et al. (2011) were included in the study. Clinical data and results of radiological and cytogenetic evaluation were collected from their medical records. Results: Our sample was composed of 8 patients. Many of their mothers were younger than 25 years (50%) and in their first pregnancy (62.5%). It is noteworthy that one patient was referred due to suspected anencephaly and another due to a twin pregnancy with an embryonic sac. Craniofacial defects were verified in three patients (37.5%), thoracic/abdominal abnormalities in 6 (75%) and limb defects in eight (100%). Congenital heart defects were observed in five patients (62.5%). One of them presented a previously undescribed complex heart defect. Conclusion: The results disclosed that complementary exams, such as MRI and echocardiography, are important to better define the observed defects. Some of them, such as congenital heart defects, may be more common than previously reported. This definition is essential for the proper management of the pregnancy and genetic counseling of the family. The birth of these children must be planned with caution and for the prognosis a long survival possibility, despite unlikely and rare, must be considered. Birth Defects Research (Part A) 100:739–749, 2014. © 2014 Wiley Periodicals, Inc.     

Large-Scale Determination of Sequence,Structure, and Function Relationships in Cytosolic Glutathione Transferases across the Biosphere

The cytosolic glutathione transferase (cytGST) superfamily comprises more than 13,000 nonredundant sequences found throughout the biosphere. Their key roles in metabolism and defense against oxidative damage have led to thousands of studies over several decades. Despite this attention, little is known about the physiological reactions they catalyze and most of the substrates used to assay cytGSTs are synthetic compounds. A deeper understanding of relationships across the superfamily could provide new clues about their functions. To establish a foundation for expanded classification of cytGSTs, we generated similarity-based subgroupings for the entire superfamily. Using the resulting sequence similarity networks, we chose targets that broadly covered unknown functions and report here experimental results confirming GST-like activity for 82 of them, along with 37 new 3D structures determined for 27 targets. These new data, along with experimentally known GST reactions and structures reported in the literature, were painted onto the networks to generate a global view of their sequence-structure-function relationships. The results show how proteins of both known and unknown function relate to each other across the entire superfamily and reveal that the great majority of cytGSTs have not been experimentally characterized or annotated by canonical class. A mapping of taxonomic classes across the superfamily indicates that many taxa are represented in each subgroup and highlights challenges for classification of superfamily sequences into functionally relevant classes. Experimental determination of disulfide bond reductase activity in many diverse subgroups illustrate a theme common for many reaction types. Finally, sequence comparison between an enzyme that catalyzes a reductive dechlorination reaction relevant to bioremediation efforts with some of its closest homologs reveals differences among them likely to be associated with evolution of this unusual reaction. Interactive versions of the networks, associated with functional and other types of information, can be downloaded from the Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu).     

Artificial nest predation by brown howler monkeys (Alouatta guariba clamitans)

Howler monkeys (Alouatta spp.) have long been considered strongly vegetarian primates. Their occasional ingestion of invertebrates has largely been interpreted as unintentional. Recent observations of the consumption of bird eggs by Alouatta caraya living in small and resource-impoverished habitat patches in the state of Rio Grande do Sul (RS), Brazil help to confirm that such behavior by howler monkeys is at times intentional. We report the findings of an experimental study on artificial nest predation by free-ranging Alouatta guariba clamitans in RS and third-party unpublished observations of intentional feeding on animal matter by Alouatta arctoidea in Venezuela and Alouatta palliata in Mexico. A nest station composed of ten artificial nests baited daily with two quail eggs each was placed at six study sites. Each site was monitored from dawn to dusk during 10–12 consecutive days. Individuals (juvenile males and an adult female) from two of the six study groups inspected the nests and ate eggs once. Study subjects from these two groups were the only ones to be supplemented with food (basically fruit) by local inhabitants, a habit that may have decreased their level of neophobia and facilitated their visit to the artificial nests. We suggest that faunivory is an opportunistic and infrequent, but intentional howler monkey feeding behavior.     

Evaluating mortality rates and causalities in a critically endangered felid across its whole distribution range

The conservation of endangered species requires accurate data, and knowledge of cause-specific mortality rates is one of the most important issues. In recent years, conservation programs for the critically endangered Iberian lynx Lynx pardinus have been developed on the basis of mortality data derived 30 years ago from the small Doñana population. Thus, there is an urgent need for an update of mortality rates and causes in both populations (Sierra Morena and Doñana). Here we use radio-tracking information from the whole range of the Iberian lynx to quantify mortality rates and identify their causes. Between 2006 and 2011, we radio-tagged 78 Iberian lynxes from its two remaining populations (39 from Sierra Morena and 39 from Doñana). Mortality events were evaluated to identify causes, and cause-specific annual mortality rates (AMR) were obtained using the nonparametric cumulative incidence function estimator. Overall, AMR was estimated at 0.16?±?0.05 (0.19?±?0.09 in Sierra Morena and 0.12?±?0.07 in Doñana). Disease was the main cause of mortality both for the whole population and the Doñana population. Poaching was the main cause of mortality in Sierra Morena. Our results suggest that the best strategy for conserving this species is to focus action on decreasing the fatal effect of disease and poaching. Given the possible existence of an underlying inbreeding-mediated immunosuppression, genetic management aimed at increasing the genetic diversity of this population is also recommended.