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.     

Do wood-based panels made with agro-industrial residues provide environmentally benign alternatives? An LCA case study of sugarcane bagasse addition to particle board manufacturing

Purpose

Sugarcane bagasse is one of the main agro-industrial residues which can be used to produce wood-based panels. However, more investigations related to its environmental performance assessment are needed, focusing on questions such as: Does it provide environmental benefits? What are its main environmental impacts? Could it substitute wood as raw material? Accordingly, this paper presents a life cycle assessment (LCA) study of particle board manufactured with sugarcane bagasse residues.

Methods

The cradle-to-gate assessment of 1 m³ of particle board made with sugarcane bagasse (PSB) considered three main subsystems: bagasse generation, bagasse distribution, and PSB production. For the inventory of PSB, dataset from two previous LCA studies related to the conventional particle board production and the ethanol life cycle for the Brazilian context were used. The allocation criterion for the bagasse generation subsystem was 9.08 % (economic base). The potential environmental impact phase was assessed by applying the CML and USEtox methods. PSB was compared with the conventional particle board manufactured in Brazil by the categories of the CML and USETox, and including land use indicators. Finally, two scenarios were analyzed to evaluate the influence of the allocation criteria and the consumption of sugarcane bagasse.

Results and discussion

All hotspots identified by CML and USETox methods are mainly related to the PSB production subsystem (24–100 % of impacts) due to heavy fuel oil, electricity, and urea-formaldehyde resin supply chain. The bagasse generation subsystem was more relevant to the eutrophication category (75 % of impacts). The bagasse distribution subsystem was not relevant because the impacts on all categories were lower than 1 %. PSB can substitute the conventional particle board mainly because of its lower contribution to abiotic depletion and ecotoxicity. Regarding land use impacts, PSB showed lower values according to all indicators (38–40 % of all impacts), which is explained by the lower demand for land occupation in comparison to that of the traditional particle board.

Conclusions

PSB can replace the traditional particle board due to its better environmental performance. The analysis of the economic allocation criterion was relevant only for the EP category, being important to reduce diesel and N-based fertilizers use during sugarcane cultivation. Regarding the influence of the sugarcane bagasse consumption, it is suggested that the sugarcane bagasse be mixed up to 75 % during particle board manufacturing so that good quality properties and environmental performance of panels can be provided.     

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).     

A new species of Lichtheimia (Mucoromycotina,Mucorales) isolated from Brazilian soil

During studies on Mucorales in semiarid and littoral dune areas in the northeast of Brazil, two cultures of an Absidia-like species were isolated from soil. They were characterized based on morphological, physiological and molecular data (5.8S and LSU rDNA sequences). The phylogenetic analyses of the isolates revealed that they belong to the Lichtheimiaceae and are closely related to species of Lichtheimia. The two isolates produced simple or branched, erect and circinate sporophores, occasionally with a septum under the sporangia, characteristics also common in Lichtheimia species. However, different from the described Lichtheimia species, the columellae of our isolates were mainly short hemispherical, never spatulate or elliptical and without projections. Sometimes, a long conical or bell shaped apophysis was found. Both isolates grew better at 30–35 °C, with no development at 42 °C, and giant cells were not observed. Based on the evidence of the analyzed datasets a new species of Lichtheimia is proposed.     

American Exceptionalism: Population Trends and Flight Initiation Distances in Birds from Three Continents

Background

All organisms may be affected by humans'' increasing impact on Earth, but there are many potential drivers of population trends and the relative importance of each remains largely unknown. The causes of spatial patterns in population trends and their relationship with animal responses to human proximity are even less known.

Methodology/Principal Finding

We investigated the relationship between population trends of 193 species of bird in North America, Australia and Europe and flight initiation distance (FID); the distance at which birds take flight when approached by a human. While there is an expected negative relationship between population trend and FID in Australia and Europe, we found the inverse relationship for North American birds; thus FID cannot be used as a universal predictor of vulnerability of birds. However, the analysis of the joint explanatory ability of multiple drivers (farmland breeding habitat, pole-most breeding latitude, migratory habit, FID) effects on population status replicated previously reported strong effects of farmland breeding habitat (an effect apparently driven mostly by European birds), as well as strong effects of FID, body size, migratory habit and continent. Farmland birds are generally declining.

Conclusions/Significance

Flight initiation distance is related to population trends in a way that differs among continents opening new research possibilities concerning the causes of geographic differences in patterns of anti-predator behavior.     

Neurolysin Knockout Mice Generation and Initial Phenotype Characterization

The oligopeptidase neurolysin (EC 3.4.24.16; Nln) was first identified in rat brain synaptic membranes and shown to ubiquitously participate in the catabolism of bioactive peptides such as neurotensin and bradykinin. Recently, it was suggested that Nln reduction could improve insulin sensitivity. Here, we have shown that Nln KO mice have increased glucose tolerance, insulin sensitivity, and gluconeogenesis. KO mice have increased liver mRNA for several genes related to gluconeogenesis. Isotopic label semiquantitative peptidomic analysis suggests an increase in specific intracellular peptides in gastrocnemius and epididymal adipose tissue, which likely is involved with the increased glucose tolerance and insulin sensitivity in the KO mice. These results suggest the exciting new possibility that Nln is a key enzyme for energy metabolism and could be a novel therapeutic target to improve glucose uptake and insulin sensitivity.     

Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate,Organize and Frame Massive Dataset from Multi-Omics Analyses

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (¹H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.     

Diversity of Bacteria Carried by Pinewood Nematode in USA and Phylogenetic Comparison with Isolates from Other Countries

Pine wilt disease (PWD) is native to North America and has spread to Asia and Europe. Lately, mutualistic relationship has been suggested between the pinewood nematode (PWN), Bursaphelenchus xylophilus the causal nematode agent of PWD, and bacteria. In countries where PWN occurs, nematodes from diseased trees were reported to carry bacteria from several genera. However no data exists for the United States. The objective of this study was to evaluate the diversity of the bacterial community carried by B. xylophilus, isolated from different Pinus spp. with PWD in Nebraska, United States. The bacteria carried by PWN belonged to Gammaproteobacteria (79.9%), Betaproteobacteria (11.7%), Bacilli (5.0%), Alphaproteobacteria (1.7%) and Flavobacteriia (1.7%). Strains from the genera Chryseobacterium and Pigmentiphaga were found associated with the nematode for the first time. These results were compared to results from similar studies conducted from other countries of three continents in order to assess the diversity of bacteria with associated with PWN. The isolates from the United States, Portugal and China belonged to 25 different genera and only strains from the genus Pseudomonas were found in nematodes from all countries. The strains from China were closely related to P. fluorescens and the strains isolated from Portugal and USA were phylogenetically related to P. mohnii and P. lutea. Nematodes from the different countries are associated with bacteria of different species, not supporting a relationship between PWN with a particular bacterial species. Moreover, the diversity of the bacteria carried by the pinewood nematode seems to be related to the geographic area and the Pinus species. The roles these bacteria play within the pine trees or when associated with the nematodes, might be independent of the presence of the nematode in the tree and only related on the bacteria''s relationship with the tree.