Cultivation of Melilotus officinalis as a source of bioactive compounds in association with soil recovery practices.

Introduction: Melilotus officinalis is a Leguminosae with relevant applications in medicine and soil recovery. This study reports the application of Melilotus officinalis plants in soil recovery and as a source of bioactive compounds. Methods: Plants were cultivated in semiarid soil under four different fertilizer treatments, urban waste compost at 10 t/ha and 20 t/ha, inorganic fertilizer and a control (no fertilizer). Agronomic properties of soil (pH, EC, soil respiration, C content, macro- and microelements) were analyzed before and after treatment. Also, germination, biomass, element contents, and physiological response were evaluated. Metabolite composition of plants was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Results and discussion: Results showed a significant enhancement of the soil microbial activity in planted soils amended with compost, though there were no other clear effects on the soil physicochemical and chemical characteristics during the short experimental period. An improvement in M. officinalis germination and growth was observed in soils with compost amendment. Metabolite composition of plants was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Principal Component and Agglomerative Hierarchical Clustering models suggest that there is a clear separation of the metabolome of four groups of plants grown under different soil treatments. The five most important discriminative metabolites (annotated) were oleamide, palmitic acid, stearic acid, 3-hydroxy-cis-5-octenoylcarnitine, and 6-hydroxynon-7- enoylcarnitine. This study provides information on how the metabolome of Melilotus might be altered by fertilizer application in poor soil regions. These metabolome changes might have repercussions for the application of this plant in medicine and pharmacology. The results support the profitability of Melilotus officinalis cultivation for bioactive compounds production in association with soil recovery practices.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics.

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

Binary Simplification as an Effective Tool in Metabolomics Data Analysis.

Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features' intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This "Binary Simplification" encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.

Cu2+-binding to S100B triggers polymerization of disulfide cross-linked tetramers with enhanced chaperone activity against amyloid-ß aggregation.

S100B is an extracellular protein implicated in Alzheimer's Disease and a suppressor of amyloid-ß aggregation. Herein we report a mechanism tying Cu2+ binding to a change in assembly state yielding disulfide cross-linked oligomers with higher anti-aggregation activity. This chemical control of chaperone function illustrates a regulatory process relevant under metal and proteostasis dysfunction as in neurodegeneration.

Integrating metabolomics and targeted gene expression to uncover potential biomarkers of fungal/oomycetes-associated disease susceptibility in grapevine.

Vitis vinifera, one of the most cultivated fruit crops, is susceptible to several diseases particularly caused by fungus and oomycete pathogens. In contrast, other Vitis species (American, Asian) display different degrees of tolerance/resistance to these pathogens, being widely used in breeding programs to introgress resistance traits in elite V. vinifera cultivars. Secondary metabolites are important players in plant defence responses. Therefore, the characterization of the metabolic profiles associated with disease resistance and susceptibility traits in grapevine is a promising approach to identify trait-related biomarkers. In this work, the leaf metabolic composition of eleven Vitis genotypes was analysed using an untargeted metabolomics approach. A total of 190 putative metabolites were found to discriminate resistant/partial resistant from susceptible genotypes. The biological relevance of discriminative compounds was assessed by pathway analysis. Several compounds were selected as promising biomarkers and the expression of genes coding for enzymes associated with their metabolic pathways was analysed. Reference genes for these grapevine genotypes were established for normalisation of candidate gene expression. The leucoanthocyanidin reductase 2 gene (LAR2) presented a significant increase of expression in susceptible genotypes, in accordance with catechin accumulation in this analysis group. Up to our knowledge this is the first time that metabolic constitutive biomarkers are proposed, opening new insights into plant selection on breeding programs.

Vitis vinifera 'Pinot noir' leaves as a source of bioactive nutraceutical compounds.

Agricultural by-products are often hidden sources of healthy plant ingredients. The investigation of the nutritional values of these by-products is essential towards sustainable agriculture and improved food systems. In the vine industry, grape leaves are a bulky side product which is strategically removed and treated as waste in the process of wine production. In this work we performed an untargeted metabolomic profiling of the methanol extract of the leaves of Vitis vinifera cultivar 'Pinot noir', analysed their fatty acid content, and estimated their antioxidative capacity, with the purpose of investigating its nutritional and medicinal value. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) analysis identified the presence of numerous compounds which are known to possess diverse nutritional and pharmacological properties, particularly polyphenols and phenolic compounds (e.g. caffeic acid, catechin, kaempferol and quercetin), several phytosterols and fatty acids. Fatty acids were the most represented lipids' secondary class, with the essential alpha-linolenic acid being the most abundant in 'Pinot noir' leaves, with a relative content of 42%. Also, we have found that 'Pinot noir' leaves present a high antioxidant capacity, putting grapevine leaves at the top of the list of foods with the highest antioxidative activity. Our findings scientifically confirmed that 'Pinot noir' leaves have a high content and diversity of biologically active phytochemical compounds which make it of exceptional interest for pharmaceutical and food industries.

Early stage metabolic events associated with the establishment of Vitis vinifera - Plasmopara viticola compatible interaction.

Grapevine (Vitis vinifera L.) is the most widely cultivated and economically important fruit crop in the world, with 7.5 million of production area in 2017. The domesticated varieties of grapevine are highly susceptible to many fungal infections, of which downy mildew, caused by the biotrophic oomycete Plasmopara viticola (Berk. et Curt.) Berl. et de Toni is one of the most threatening. In V. vinifera, several studies have shown that a weak and transient activation of a defense mechanism occurs, but it is easily overcome by the pathogen leading to the establishment of a compatible interaction. Major transcript, protein and physiologic changes were shown to occur at later infection time-points, but comprehensive data on the first hours of interaction is scarce. In the present work, we investigated the major physiologic and metabolic changes that occur in the first 24 h of interaction between V. vinifera cultivar Trincadeira and P. viticola. Our results show that there was a negative modulation of several metabolic classes associated to pathogen defense such as flavonoids or phenylpropanoids as well as an alteration of carbohydrate content after inoculation with the pathogen. We also found an accumulation of hydrogen peroxide and increase of lipid peroxidation but to a low extent, that seems to be insufficient to restrain pathogen growth during the initial biotrophic phase of the interaction.

Metabolomics for undergraduates: Identification and pathway assignment of mitochondrial metabolites.

Metabolomics is a key discipline in systems biology, together with genomics, transcriptomics, and proteomics. In this omics cascade, the metabolome represents the biochemical products that arise from cellular processes and is often regarded as the final response of a biological system to environmental or genetic changes. The overall screening approach to identify all the metabolites in a given biological system is called metabolic fingerprinting. Using high-resolution and high-mass accuracy mass spectrometry, large metabolome coverage, sensitivity, and specificity can be attained. Although the theoretical concepts of this methodology are usually provided in life-science programs, hands-on laboratory experiments are not usually accessible to undergraduate students. Even if the instruments are available, there are not simple laboratory protocols created specifically for teaching metabolomics. We designed a straightforward hands-on laboratory experiment to introduce students to this methodology, relating it to biochemical knowledge through metabolic pathway mapping of the identified metabolites. This study focuses on mitochondrial metabolomics since mitochondria have a well-known, medium-sized cellular sub-metabolome. These features facilitate both data processing and pathway mapping. In this experiment, students isolate mitochondria from potatoes, extract the metabolites, and analyze them by high-resolution mass spectrometry (using an FT-ICR mass spectrometer). The resulting mass list is submitted to an online program for metabolite identification, and compounds associated with mitochondrial pathways can be highlighted in a metabolic network map.

Metabolite extraction for high-throughput FTICR-MS-based metabolomics of grapevine leaves.

In metabolomics there is an ever-growing need for faster and more comprehensive analysis methods to cope with the increase of biological studies. Direct infusion Fourier-transform ion cyclotron-resonance mass spectrometry (DI-FTICR-MS) is used in non-targeted metabolomics to obtain high-resolution snapshots of the metabolic state of a system. In any metabolic profiling study, the establishment of an effective metabolite extraction protocol is paramount. We developed an improved metabolite extraction method, compatible with DI-FTICR-MS-based metabolomics, using grapevine leaves. This extraction protocol allowed the extraction of polar and non-polar compounds, covering all major classes found in plants and increasing metabolome coverage.

The glyoxalase pathway: the first hundred years... and beyond.

The discovery of the enzymatic formation of lactic acid from methylglyoxal dates back to 1913 and was believed to be associated with one enzyme termed ketonaldehydemutase or glyoxalase, the latter designation prevailed. However, in 1951 it was shown that two enzymes were needed and that glutathione was the required catalytic co-factor. The concept of a metabolic pathway defined by two enzymes emerged at this time. Its association to detoxification and anti-glycation defence are its presently accepted roles, since methylglyoxal exerts irreversible effects on protein structure and function, associated with misfolding. This functional defence role has been the rationale behind the possible use of the glyoxalase pathway as a therapeutic target, since its inhibition might lead to an increased methylglyoxal concentration and cellular damage. However, metabolic pathway analysis showed that glyoxalase effects on methylglyoxal concentration are likely to be negligible and several organisms, from mammals to yeast and protozoan parasites, show no phenotype in the absence of one or both glyoxalase enzymes. The aim of the present review is to show the evolution of thought regarding the glyoxalase pathway since its discovery 100 years ago, the current knowledge on the glyoxalase enzymes and their recognized role in the control of glycation processes.

Metabolism of biodiesel-derived glycerol in probiotic Lactobacillus strains.

Three probiotic Lactobacillus strains, Lactobacillus acidophilus, Lactobacillus plantarum, and Lactobacillus delbrueckii, were tested for their ability to assimilate and metabolize glycerol. Biodiesel-derived glycerol was used as the main carbon and energy source in batch microaerobic growth. Here, we show that the tested strains were able to assimilate glycerol, consuming between 38 and 48 % in approximately 24 h. L. acidophilus and L. delbrueckii showed a similar growth, higher than L. plantarum. The highest biomass reached was 2.11 g L⁻¹ for L. acidophilus, with a cell mass yield (Y (X/S)) of 0.37 g g⁻¹. L. delbrueckii and L. plantarum reached a biomass of 2.06 and 1.36 g L⁻¹. All strains catabolize glycerol mainly through glycerol kinase (EC 2.7.1.30). For these lactobacillus species, kinetic parameters for glycerol kinase showed Michaelis-Menten constant (K(m)) ranging from 1.2 to 3.8 mM. The specific activities for glycerol kinase in these strains were in the range of 0.18 to 0.58 U mg protein⁻¹, with L. acidophilus ATCC 4356 showing the maximum specific activity after 24 h of cultivation. Glycerol dehydrogenase activity was also detected in all strains studied but only for the reduction of glyceraldehyde with NADPH (K(m) for DL-glyceraldehyde ranging from 12.8 to 32.3 mM). This enzyme shows a very low oxidative activity with glycerol and NADP+ and, most likely, under physiological conditions, the oxidative reaction does not occur, supporting the assumption that the main metabolic flux concerning glycerol metabolism is through the glycerol kinase pathway.

Mining GO annotations for improving annotation consistency.

Despite the structure and objectivity provided by the Gene Ontology (GO), the annotation of proteins is a complex task that is subject to errors and inconsistencies. Electronically inferred annotations in particular are widely considered unreliable. However, given that manual curation of all GO annotations is unfeasible, it is imperative to improve the quality of electronically inferred annotations. In this work, we analyze the full GO molecular function annotation of UniProtKB proteins, and discuss some of the issues that affect their quality, focusing particularly on the lack of annotation consistency. Based on our analysis, we estimate that 64% of the UniProtKB proteins are incompletely annotated, and that inconsistent annotations affect 83% of the protein functions and at least 23% of the proteins. Additionally, we present and evaluate a data mining algorithm, based on the association rule learning methodology, for identifying implicit relationships between molecular function terms. The goal of this algorithm is to assist GO curators in updating GO and correcting and preventing inconsistent annotations. Our algorithm predicted 501 relationships with an estimated precision of 94%, whereas the basic association rule learning methodology predicted 12,352 relationships with a precision below 9%.

The glyoxalase pathway in protozoan parasites.

The glyoxalase system is the main catabolic route for methylglyoxal, a non-enzymatic glycolytic byproduct with toxic and mutagenic effects. This pathway includes two enzymes, glyoxalase I and glyoxalase II, which convert methylglyoxal to d-lactate by using glutathione as a catalytic cofactor. In protozoan parasites the glyoxalase system shows marked deviations from this model. For example, the functional replacement of glutathione by trypanothione (a spermidine-glutathione conjugate) is a characteristic of trypanosomatids. Also interesting are the lack of glyoxalase I and the presence of two glyoxalase II enzymes in Trypanosoma brucei. In Plasmodium falciparum the glyoxalase pathway is glutathione-dependent, and glyoxalase I is an atypical monomeric enzyme with two active sites. Although it is tempting to exploit these differences for their potential therapeutic value, they provide invaluable clues regarding methylglyoxal metabolism and the evolution of protozoan parasites. Glyoxalase enzymes have been characterized in only a few protozoan parasites, namely Plasmodium falciparum and the trypanosomatids Leishmania and Trypanosoma. In this review, we will focus on the key features of the glyoxalase pathway in major human protozoan parasites, with particular emphasis on the characterized systems in Plasmodium falciparum, Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp. We will also search for genes encoding glyoxalase I and II in Toxoplasma gondii, Entamoeba histolytica, and Giardia lamblia.

Optimization of time-course experiments for kinetic model discrimination.

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction.In this work, a method is proposed to optimize the design of enzyme kinetic assays with the goal of selecting a model among a set of candidates. We focus on models with systems of ordinary differential equations as the underlying mathematical description. The method provides a design where an extension of the Kullback-Leibler distance, computed over the time courses predicted by the models, is maximized. Given the asymmetric nature this measure, a generalized differential evolution algorithm for multi-objective optimization problems was used.The kinetics of yeast glyoxalase I (EC 4.4.1.5) was chosen as a difficult test case to evaluate the method. Although a single-substrate kinetic model is usually considered, a two-substrate mechanism has also been proposed for this enzyme. We designed an experiment capable of discriminating between the two models by optimizing the initial substrate concentrations of glyoxalase I, in the presence of the subsequent pathway enzyme, glyoxalase II (EC 3.1.2.6). This discriminatory experiment was conducted in the laboratory and the results indicate a two-substrate mechanism for the kinetics of yeast glyoxalase I.

Enlightening the molecular basis of trypanothione specificity in trypanosomatids: mutagenesis of Leishmania infantum glyoxalase II.

Leishmania infantum glyoxalase II shows absolute specificity towards its trypanothione thioester substrate. In the previous work, we performed a comparative analysis of glyoxalase II structures determined by X-ray crystallography which revealed that Tyr291 and Cys294, absent in the human homologue, are essential for substrate binding. To validate this trypanothione specificity hypothesis we produced a mutant L. infantum GLO2 enzyme by replacing Tyr291 and Cys294 by arginine and lysine, respectively. This new enzyme is capable to use the glutathione thioester substrate, with kinetic parameters similar to the ones from the human enzyme. Substrate specificity is likely to be mediated by spermidine moiety binding, providing a primer for understanding the molecular basis of trypanothione specificity.

Cloning, expression, purification, crystallization and preliminary X-ray diffraction analysis of glyoxalase I from Leishmania infantum.

Glyoxalase I (GLO1) is the first of the two glyoxalase-pathway enzymes. It catalyzes the formation of S-D-lactoyltrypanothione from the non-enzymatically formed hemithioacetal of methylglyoxal and reduced trypanothione. In order to understand its substrate binding and catalytic mechanism, GLO1 from Leishmania infantum was cloned, overexpressed in Escherichia coli, purified and crystallized. Two crystal forms were obtained: a cube-shaped form and a rod-shaped form. While the cube-shaped form did not diffract X-rays at all, the rod-shaped form exhibited diffraction to about 2.0 A resolution. The crystals belonged to space group P2(1)2(1)2, with unit-cell parameters a = 130.03, b = 148.51, c = 50.63 A and three dimers of the enzyme per asymmetric unit.

Enzyme classification with peptide programs: a comparative study.

BACKGROUND: Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length. RESULTS: We have developed a machine learning methodology, called peptide programs (PPs), to deal directly with protein sequences and compared its performance with that of Support Vector Machines (SVMs) and BLAST in detailed enzyme classification tasks. Overall, the PPs and SVMs had a similar performance in terms of Matthews Correlation Coefficient, but the PPs had generally a higher precision. BLAST performed globally better than both methodologies, but the PPs had better results than BLAST and SVMs for the smaller datasets. CONCLUSION: The higher precision of the PPs in comparison to the SVMs suggests that dealing with sequences is advantageous for detailed protein classification, as precision is essential to avoid annotation errors. The fact that the PPs performed better than BLAST for the smaller datasets demonstrates the potential of the methodology, but the drop in performance observed for the larger datasets indicates that further development is required.Possible strategies to address this issue include partitioning the datasets into smaller subsets and training individual PPs for each subset, or training several PPs for each dataset and combining them using a bagging strategy.

Protein glycation in vivo: functional and structural effects on yeast enolase.

Protein glycation is involved in structure and stability changes that impair protein functionality, which is associated with several human diseases, such as diabetes and amyloidotic neuropathies (Alzheimer's disease, Parkinson's disease and Andrade's syndrome). To understand the relationship of protein glycation with protein dysfunction, unfolding and beta-fibre formation, numerous studies have been carried out in vitro. All of these previous experiments were conducted in non-physiological or pseudo-physiological conditions that bear little to no resemblance to what may happen in a living cell. In vivo, glycation occurs in a crowded and organized environment, where proteins are exposed to a steady-state of glycation agents, namely methylglyoxal, whereas in vitro, a bolus of a suitable glycation agent is added to diluted protein samples. In the present study, yeast was shown to be an ideal model to investigate glycation in vivo since it shows different glycation phenotypes and presents specific protein glycation targets. A comparison between in vivo glycated enolase and purified enolase glycated in vitro revealed marked differences. All effects regarding structure and stability changes were enhanced when the protein was glycated in vitro. The same applies to enzyme activity loss, dimer dissociation and unfolding. However, the major difference lies in the nature and location of specific advanced glycation end-products. In vivo, glycation appears to be a specific process, where the same residues are consistently modified in the same way, whereas in vitro several residues are modified with different advanced glycation end-products.

Metrics for GO based protein semantic similarity: a systematic evaluation.

BACKGROUND: Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations. RESULTS: We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation. CONCLUSIONS: This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid simGIC was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.

Measuring intracellular enzyme concentrations: Assessing the effect of oxidative stress on the amount of glyoxalase I.

Enzymology is one of the fundamental areas of biochemistry and involves the study of the structure, kinetics, and regulation of enzyme activity. Research in this area is often conducted with purified enzymes and extrapolated to in vivo conditions. The specificity constant, k(S) , is the ratio between k(cat) (the catalytic constant) and K(m) (Michaelis-Menten constant), and expresses the efficiency of an enzyme as a catalyst. This parameter is usually determined for purified enzymes, and in this work, we propose a classroom experiment for its determination in situ, in permeabilized yeast cells, based on a method of external enzyme addition, which was previously reported. Under these conditions, which resemble the in vivo state, enzyme concentrations and protein interactions are preserved. The students are presented with a novel approach in enzymology, based on the titration methods that allow the measurement of the enzyme amount, and thus the k(cat) and k(S) . The method will also be used to investigate the effect of exposure to oxidative stress conditions on yeast glyoxalase I.