Guanosine Neuroprotective Action in Hippocampal Slices Subjected to Oxygen and Glucose Deprivation Restores ATP Levels,Lactate Release and Glutamate Uptake Impairment: Involvement of Nitric Oxide

Neurochemical Research - Stroke is a major cause of disability and death worldwide. Oxygen and glucose deprivation (OGD) in brain tissue preparations can reproduce several pathological features...     

Exogenous Tryptophan Promotes Cutaneous Wound Healing of Chronically Stressed Mice through Inhibition of TNF-α and IDO Activation

Stress prolongs the inflammatory response compromising the dermal reconstruction and wound closure. Acute stress-induced inflammation increases indoleamine 2, 3-dioxygenase-stimulated tryptophan catabolism. To investigate the role of indoleamine 2, 3-dioxygenase expression and tryptophan administration in adverse effects of stress on cutaneous wound healing, mice were submitted to chronic restraint stress and treated with tryptophan daily until euthanasia. Excisional lesions were created on each mouse and 5 or 7 days later, the lesions were analyzed. In addition, murine skin fibroblasts were exposed to elevated epinephrine levels plus tryptophan, and fibroblast activity was evaluated. Tryptophan administration reversed the reduction of the plasma tryptophan levels and the increase in the plasma normetanephrine levels induced by stress 5 and 7 days after wounding. Five days after wounding, stress-induced increase in the protein levels of tumor necrosis factor-α and indoleamine 2, 3-dioxygenase, and this was inhibited by tryptophan. Stress-induced increase in the lipid peroxidation and the amount of the neutrophils, macrophages and T cells number was reversed by tryptophan 5 days after wounding. Tryptophan administration inhibited the reduction of myofibroblast density, collagen deposition, re-epithelialization and wound contraction induced by stress 5 days after wounding. In dermal fibroblast culture, the tryptophan administration increased the cell migration and AKT phosphorylation in cells treated with high epinephrine levels. In conclusion, tryptophan-induced reduction of inflammatory response and indoleamine 2, 3-dioxygenase expression may have accelerated cutaneous wound healing of chronically stressed mice.     

Effects of forest degradation on the sand fly communities of northeast Brazil

The richness and abundance of sand fly species were studied in northeastern Brazil in areas of leishmaniasis transmission. The study was carried out in two forest areas with different deforestation times for agricultural and livestock activities: one modified by long‐term settlement (more than 50 years of occupation) and another less impacted by short‐term settlement (10 years). The sand flies were captured with CDC light traps from 18:00 to 06:00 for three consecutive nights, once a month, from May, 2012 to April, 2014. The study captured 21,708 specimens and also 33 species of Lutzomyia and two of Brumptomyia. Species richness and abundance were higher in the more conserved area of short‐term occupation (31 species; 61.7%) than in the more degraded area with long‐term occupation (17 species; 38.3%). In the most conserved area, the species richness was higher in the forest fragment than in the rural settlement, whereas in the degraded area the richness was higher in the peri‐domicile than in the forest. The diversity was higher in the degraded area forest. There were significant statistical differences when comparing the means of total abundance with the intra‐domicile, peri‐domicile, and forest environments. The average abundance was statistically higher in the peri‐domicile compared to the forest (p = 0.009), but there were no statistically significant differences between intra‐domicile‐peri‐domicile (p = 0.11) and forest‐intra‐domicile (p = 0.87). In conclusion, a change in vegetation cover negatively affects the richness and abundance of sand flies in the natural environment.     

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.     

Gapped spectral dictionaries and their applications for database searches of tandem mass spectra

Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. Our MS-Gapped-Dictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. MS-Gapped-Dictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches.     

Increased nitric oxide production in platelets from severe chronic renal failure patients

Nitric oxide (NO) production occurs through oxidation of the amino acid L-arginine by NO synthase (NOS). NO inhibits platelet activation by increasing the levels of cyclic guanosine monophosphate (cGMP), thus maintaining vascular homeostasis. Our group previously demonstrated (da Silva et al. 2005) an enhancement of the L-arginine-NO-cGMP pathway in platelets taken from chronic renal failure (CRF) patients on haemodialysis associated with reduced platelet aggregation. We investigate the platelet L-arginine-NO-cGMP pathway, platelet function, and inflammation from patients in CRF on conservative treatment. A total of 42 CRF patients and 42 controls (creatinine clearance = 27 ± 3 vs. 93 ± 1 mL per min per 1.73 m2, respectively) participated in this study. NOS activity and expression and cGMP concentration were measured in platelets. Platelet aggregation induced by collagen or ADP was evaluated and plasma levels of fibrinogen were determined by the Clauss method. A marked increase in basal NOS activity was seen in undialysed CRF patients compared with controls, accompanied by an elevation of fibrinogen plasma levels. There were no differences in expression of NOS and in cGMP levels. In this context, platelet aggregation was not affected. We provide the first evidence of increased intraplatelet NO biosynthesis in undialysed CRF patients, which can be an early marker of future haemostatic abnormalities during dialysis treatment.     

Peptide identification by tandem mass spectrometry with alternate fragmentation modes

The high-throughput nature of proteomics mass spectrometry is enabled by a productive combination of data acquisition protocols and the computational tools used to interpret the resulting spectra. One of the key components in mainstream protocols is the generation of tandem mass (MS/MS) spectra by peptide fragmentation using collision induced dissociation, the approach currently used in the large majority of proteomics experiments to routinely identify hundreds to thousands of proteins from single mass spectrometry runs. Complementary to these, alternative peptide fragmentation methods such as electron capture/transfer dissociation and higher-energy collision dissociation have consistently achieved significant improvements in the identification of certain classes of peptides, proteins, and post-translational modifications. Recognizing these advantages, mass spectrometry instruments now conveniently support fine-tuned methods that automatically alternate between peptide fragmentation modes for either different types of peptides or for acquisition of multiple MS/MS spectra from each peptide. But although these developments have the potential to substantially improve peptide identification, their routine application requires corresponding adjustments to the software tools and procedures used for automated downstream processing. This review discusses the computational implications of alternative and alternate modes of MS/MS peptide fragmentation and addresses some practical aspects of using such protocols for identification of peptides and post-translational modifications.     

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software.Microorganisms have evolved their cellular metabolism to generate energy for life in unusual environments (1), and their capabilities are of great interest in the production of renewable bioenergy and could contribute toward managing the world''s current energy and climate crisis (2). Genomics studies have increased the number of sequenced bioenergy-related microbial genomes and revealed the possible biological reactions involved in bioenergy production (3). Studies of photosynthetic microorganisms, for example, have yielded insights into how they harvest solar energy and use it to produce bioenergy products (4). Despite this importance of microorganisms, the characterization of diverse microbial phenotypes by proteomics tandem mass spectrometry (MS/MS) has been limited. The dominant approaches for MS/MS analysis heavily rely on the availability of completely annotated genomes (i.e. accurate protein databases) (5–7), yet most microorganisms populating the planet have unsequenced or poorly annotated genomes. Thus it remains challenging to identify proteins from environmental and unculturable organisms.One solution to protein identification in a species with no sequenced genome is to use the genomes of closely related species (8). This requires matching MS/MS data to slightly different peptides in amino acid sequences (polymorphic, orthologous peptides); but matching shifted masses of peptides and their fragment ions is computationally expensive and challenging. Moreover, different species-specific post-translational modifications (PTMs)¹ can make the cross-species identification more complex. The common computational approach is tolerantly matching de novo sequences derived from MS/MS data to the database while allowing for amino acid mutations and modifications (9–11). However, this approach critically depends on good de novo interpretations, which are nearly always partially incorrect and yield high-quality subsequences only for a small fraction of all spectra. The blind database search approach, developed to identify peptides with unexpected modifications, can also be used to directly match MS/MS data from unknown species to a database of closely related species, but its utilization is limited because of its exceptionally large search space (12–18). These spectrum-database matching approaches to cross-species identification pose significant challenges in its speed and sensitivity with a huge database, which leads to a much longer search time and more false positive identifications (19, 20).As a complementary approach to spectrum-database matching, spectral library searching is an emerging and promising approach (21). A spectral library is a large collection of identified MS/MS spectra, and an unknown query spectrum can then be identified by direct spectral matching to the library. The great advantage of this approach is the reduction of search space and the use of fragmentation patterns of peptides. The spectral networks approach expands this concept to the identification of modified peptides in MS/MS data sets (22, 23). Spectral networks do not directly search a database, but groups MS/MS spectra by computing the pairwise similarity between MS/MS spectra of peptide variants and then constructs networks where each spectrum defines a node and each significant spectral pair, highly correlated in the fragmentation pattern, defines an edge (Fig. 1). In spectral networks, identification of spectra belonging to the same subnetwork should be related and thus the peptide sequence for an identified spectrum can be propagated to neighboring unidentified spectra.Open in a separate windowFig. 1.Overview of multi-species spectral networks. Nodes represent individual spectra and edges between nodes represent significant pairwise alignment between spectra; edges are labeled with amino acid mutations (dotted edges) or parent mass differences (solid edges). In spectral networks, a peptide and its related variants are ideally grouped into a single subnetwork. If at least one spectrum in a subnetwork is annotated (filled node), all the neighboring spectra (unfilled nodes) can potentially become identified by propagating the annotation over network edges. For example, all spectra in the subnetwork of “peptide A” (top left, blue network) can be annotated via up to three iterative propagations, first from A to {A₁, A₂, A₃}, second from {A₂, A₃} to {A₄, A₅}, and third from {A₄, A₅} to A₆. This paradigm can be equally applied to cross-species data analysis, as “peptide L” identified in species 1 (top middle, olive-colored network) is propagated to a node unidentified in species 2, identifying its orthologous “peptide l”, with a serine to alanine polymorphism. Thus, spectral networks enable the detection of orthologous peptide pairs between different species.We recently reported that a vast number of polymorphic, orthologous peptides across species are present in MS/MS data sets (24). We propose a new approach in cross-species proteomics research that aggregates MS/MS of multiple related species followed by spectral networks analysis of the pooled data to capitalize on pairs of spectra from orthologous peptides, as shown in Fig. 1. This approach does not require advance knowledge of the genomes for all species, and enables the identification of novel, polymorphic peptides across species via interspecies propagation. Compared with previous approaches, cross-species spectral network analysis has two major advantages. First, by matching spectra to spectra instead of spectra to database sequences, spectral networks only consider the sequence variability of peptides present in the samples instead of considering all possible variability across the whole database of related species; thus the performance of spectral networks is independent of database size. Second, the analysis of the set of highly related spectra increases the reliability in identifying polymorphic peptides in that multiple different spectra can support the same novel identification. The utility of spectral networks can be also expanded to the proteomic analysis of microbial communities that often contain hundreds of distinct organisms (25, 26). But despite the success of spectral networks in low complexity data sets (22, 23), the analysis of large multi-species proteomics data requires significantly higher reliability in spectral similarity scores because the number of pairwise spectral comparisons grows quadratically with the number of spectra.In this work, we present algorithmic and statistical advances to spectral networks to improve its utility with large and diverse spectral data sets. To statistically assess the significance of spectral alignments in pairing millions of spectra, we propose Align-GF (generating function for spectral alignment) to compute rigorous p values of a spectral pair based on the complete score histogram of all possible alignments between two spectra. We show that Align-GF successfully addressed the reliability challenge in a large data set analysis and demonstrated its utility by leading to a 4-fold increase in the sensitivity of spectral pairs. Even with this dramatically improved accuracy, a very small number of incorrect pairs in a network can still complicate propagation of annotations. To further progress toward the ideal scenario where each subnetwork consists of only spectra from a single peptide family, we introduce new procedures to split mixed networks from different peptide families and show that these effectively eliminate many false spectral pairs. Finally, we propose the first approach to calculation of false discovery rate (FDR) for spectral networks propagation of identifications from unmodified to progressively more modified peptides. The proposed FDR estimation was conservative and was more rigorous for highly modified peptides, and thus now makes propagation results comparable to other peptide identification approaches.The cross-species spectral networks techniques proposed here enabled the proteomic analysis of three different Cyanothece species, including a strain where the genome sequence is not known. Cyanobacteria are one of the most diverse and widely distributed microorganisms and have received significant consideration as satisfying various demands required in bioenergy generation (27). We show that spectral networks can improve peptide identification by up to 38% compared with mainstream approaches, including many polymorphic and modified peptides. Spectral networks could identify peptides with highly divergent sequences (with 7 amino acid mutations) by leveraging networks of variant peptides, and one example subnetwork of species-specific variants of phycobilisome proteins reflects the diversity of photosynthetic light-harvesting strategies (28). Our approach thus demonstrates the potential gains in multi-species proteomics and sets the stage for related developments in higher-complexity metaproteomics samples. Finally, spectral networks revealed many unidentified subnetworks containing only unidentified spectra, thus strongly suggesting the presence of novel peptides that are missing from current protein databases. Although we illustrate the potential of our approach on a specific set of bioenergy-related species, we note that the proposed approach is generic and should be applicable to any other set of related species. The diversity of biologically important protein families could be studied by comparing closely and more remotely related species.     

Aroma Profile of Rubus ulmifolius Flowers and Fruits During Different Ontogenetic Phases

The chemical composition of spontaneous volatile emission from Rubus ulmifolius flowers and fruits during different stages of development was evaluated by HS‐SPME‐GC/MS. In total, 155 chemical compounds were identified accounting 84.6 – 99.4% of whole aroma profile of flowers samples and 92.4 – 96.6% for fruit samples. The main constituents were α‐copaene, β‐caryophyllene, germacrene D, (E,E)‐α‐farnesene, 1,7‐octadien‐3‐one,2‐methyl‐6‐methylene, tridecane, (E)‐2‐hexenol acetate, (E)‐3‐hexenol acetate and cyperene. The results give a chemotaxonomic contribution to the characterization of the VOCs emitted from flowers and fruits during their ontogenic development.