VANTED: A system for advanced data analysis and visualization in the context of biological networks

BACKGROUND: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes. RESULTS: We present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature. CONCLUSION: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at

Unraveling the metabolome of grapevine through FT-ICR-MS : from nutritional value to pathogen resistance

Grapevine (Vitis vinifera L.) is one of most important fruit crops in the world due to its numerous food products, namely fresh and dried table grapes, wine and intermediate products, with a high economic importance worldwide. Concerning nutritional value, grapes are highly studied and a great diversity of secondary bioactive metabolites has already been identified. However, an important grapevine by-product, also containing a high nutritional value, but sometimes disregarded is grapevine leaves. They are an abundant source of compounds with interest in human health and are already included in human diet in several countries. The study of the nutritional values of this by-product is essential towards the improvement of food systems. Hence, in this PhD dissertation an untargeted metabolomic profiling of the leaves of Vitis vinifera cultivar ‘Pinot noir’ was performed by Fourier-transform ion cyclotron-resonance mass spectrometry (FT-ICR-MS), (CHAPTER II). Numerous compounds with diverse nutritional and pharmacological properties, particularly polyphenols and phenolic compounds, several phytosterols and fatty acids (the most represented lipids’ secondary class), were identified. Grapevine leaves were also evaluated for their antioxidant capacity. It was found that leaves present a high antioxidant capacity, similar to berries, putting grapevine leaves at the top of the list of foods with the highest antioxidant activity. Traditional premium cultivars of wine and table grapes are highly susceptible to various diseases. Grapevine downy mildew, powdery mildew and gray mold are caused, respectively, by the biotrophic oomycete Plasmopara viticola (Berk. & Curt.) Berl. & de Toni) Beri, et de Toni], by the biotrophic fungus Erysiphe necator (Schweinf.) Burrill) and by the necrotrophic fungus Botrytis cinerea Pers.). In Europe, disease management became one of the main tasks for viticulture, being the current strategy, for disease control, the massive use of fungicides and pesticides in each growing season. This practice has several associated problems, from the environmental impact to the economical level, and even in human health. The alternative approach to the application of pesticides is breeding for resistance, clearly the most effective and sustainable approach, particularly if coupled to the selection of desirable traits from local grapevine cultivars. However, a successful breeding program of grape plants with increased resistance traits against pathogens requires not only an understanding of the innate resistance mechanisms of cultivars against fungi/oomycetes, but also the identification of biomarkers of tolerance or susceptibility. Among these, metabolic biomarkers may prove particularly useful, not only because they can be determined in a high throughput way but, above all, because metabolites provide an accurate image of the metabolic state of the plant. To better understand the metabolic differences associated with intrinsic defence mechanisms of grapevine to pathogens, the metabolome of several genotypes with different tolerance degrees to fungal/oomycete pathogens was compared through an untargeted metabolomics approach by FT-ICR-MS (CHAPTERS III, IV and V). First, a comparison of two Vitis vinifera (V. vinifera cv. Trincadeira e V. vinifera cv. Regent, susceptible and tolerant, respectively, to pathogens) was performed and discriminatory compounds between these two cultivars, were identified (CHAPTER III). Also, through the comparison of the metabolome of one Vitis vinifera (V. vinifera cv. Cabernet Sauvignon, susceptible to pathogens) and one Vitis species (Vitis rotundifolia, tolerant), was possible to distinguish both genotypes and determine that Vitis rotundifolia metabolome appeared to be more complex according to the chemical formulas analysed (CHAPTER IV). Albeit grapevine metabolome is complex, it is possible to distinguish Vitis species and different genotypes within the same species. Ultimately, to identify compounds that contribute to the segregation between susceptible and tolerant grapevines, eleven Vitis genotypes, were compared at the metabolite level (CHAPTER V). From all the metabolites identified, seven compounds with a higher accumulation on susceptible genotypes were selected. Their metabolic pathways were analysed and the expression profile of biosynthesis and/or degradation enzymes coding genes was evaluated by Real-time Polymerase Chain Reaction (qPCR). qPCR studies require as internal controls one or more reference genes. Hence, in this study, ten possible reference genes were tested and the three most stable reference genes (ubiquitin-conjugating enzyme – UBQ, SAND family protein - SAND and elongation factor 1-alpha - EF1α) were established for our analysis and selected for qPCR data normalization. Our data revealed that the leucoanthocyanidin reductase 2 gene (LAR2) presented a significant increase of expression in susceptible genotypes, in accordance with catechin accumulation in this analysis group, being a possible metabolic constitutive biomarker, associated to susceptibility. The interaction of grapevine-P.viticola was also analysed by FT-ICR-MS (CHAPTERS VI and VII). The metabolome of Vitis vinifera cv. Trincadeira after 24 hours post-infection (hpi) was analysed and, based only on the chemical profile and representation plots, the discrimination between infected and non-infected grapevine leaves was possible (CHAPTER VI). A further analysis of Vitis vinifera cv. Trincadeira infected with P. viticola was performed through Matrix-assisted laser desorption/ionization (MALDI) FT-ICR-MS imaging, to identify leaf surface compounds related to the grapevine-pathogen interaction (CHAPTER VII). Putatively identified sucrose ions were more abundant on P. viticola infected leaves when compared to control ones. Also, sucrose was mainly located around the veins, which is an indicator of the correlation of putatively identified sucrose at P. viticola infection sites, leading to the hypothesis that the pathogen is extracting sucrose from grapevine to reproduce. Each chapter was written as a scientific article and has its own abstract, introduction, materials and methods, results and discussion, conclusion, acknowledgments and references. The results obtained in this PhD thesis are a starting point on the elucidation of the molecular mechanisms related to the intrinsic tolerance/susceptibility to different pathogens. Also, these results can be used for the development of new approaches and help to improve breeding and introgression line programs

Evolving paradigms in biological carbon cycling in the ocean

Carbon is a keystone element in global biogeochemical cycles. It plays a fundamental role in biotic and abiotic processes in the ocean, which intertwine to mediate the chemistry and redox status of carbon in the ocean and the atmosphere. The interactions between abiotic and biogenic carbon (e.g., CO2, CaCO3, organic matter) in the ocean are complex, and there is a half-century-old enigma about the existence of a huge reservoir of recalcitrant dissolved organic carbon (RDOC) that equates to the magnitude of the pool of atmospheric CO2. The concepts of the biological carbon pump (BCP) and the microbial loop (ML) shaped our understanding of the marine carbon cycle. The more recent concept of the microbial carbon pump (MCP), which is closely connected to those of the BCP and the ML, explicitly considers the significance of the ocean's RDOC reservoir and provides a mechanistic framework for the exploration of its formation and persistence. Understanding of the MCP has benefited from advanced “omics”, and novel research in biological oceanography and microbial biogeochemistry. The need to predict the ocean’s response to climate change makes an integrative understanding of the MCP, BCP and ML a high priority. In this review, we summarize and discuss progress since the proposal of the MCP in 2010 and formulate research questions for the future

Digging deeper: Development and application of an untargeted exometabolomics approach to identify biogeochemical hotspots of dissolved organic matter vulnerability in Arctic soils

Arctic soils contain vast reserves of carbon (C) that, with rising temperatures, may become a significant source of greenhouse gases (GHGs) (i.e. CO2, CH4, N2O) due to increased microbial decomposition of soil organic matter (SOM). However, there are significant spatial variations in GHG production that lead to hotspots of C release across the landscape, creating significant uncertainty in climate models. Reliably predicting the magnitude of C loss via microbial production of GHGs, and the proportion lost as either CO2 or CH4, depends on many factors, including soil temperature and moisture, microbial community structure and function, as well as the composition and availability of the most labile SOM pool—low molecular weight dissolved organic matter (LMW DOM). While the effects of temperature and moisture on GHG production in Arctic soils have been studied extensively, there is a dearth of information on the effects of LMW DOM chemistry and its potential to be a predictive chemical signal of biological hotspots of C release, in large part due to unique analytical challenges. LMW DOM is an incredibly complex and dynamic mixture of small molecules from both biotic and abiotic origin that turnover on the order of days or even hours and are obscured by countless other interfering signals in the soil, each a complicating factor in isolation, detection, and quantitation. Recent advancements in liquid chromatography mass spectrometry (LC/MS) have provided a means for sensitive, robust, and high-throughput measurements of LMW DOM composition and availability but have not yet been applied in Arctic soils. In this dissertation, an untargeted LC/MS approach for characterizing LMW DOM availability was developed and evaluated, benchmarking its analytical performance in Arctic soils for the first time. The optimized approach was then applied to soils from two Arctic ecosystems to measure variations in LMW DOM across the landscape, due to soil depth, aboveground vegetation, topography, or level of degradation due to thaw. In addition to establishing the LC/MS measurements and data interpretation, this dissertation also had several key interdisciplinary components including remote-location field sample collection, establishing an accessible data analysis pipeline, and examining this work from a public policy perspective

Biological Networks

Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems

Application of omic approaches on the mechanisms of pollutants using Daphnia magna as model species

[eng] Environmental toxicology is undergoing a paradigm shift due to the new concerning environmental reality. Nowadays, to manage subtler and chronic effects of chemicals, either single or mixtures, is an imperative need, especially at low and environmentally relevant concentrations. Not least important is to deal with emerging contaminants (ECs), whose harmful effects in ecosystems and toxicity mechanisms are still unknown. Therefore, new strategies for assessing the toxicity of pollutants with greater environmental relevance must be developed, what requires the application of integrative approaches combining tools from different disciplines. Omic technologies allow the holistic measurement of effects at low levels of biological organization in high throughput platforms, and provide mechanistic data which may become essential in the development and application of more efficient and effective testing strategies. Overall, this thesis aimed to prove the importance of integrating omic and conventional toxicological approaches in order to obtain significant information that helps to unravel any new toxicity mechanism triggered by ECs on the aquatic environment using Daphnia magna as model species. The ECs studied included suspected lipid disruptors (endocrine disrupting compounds, EDCs) and ECs that are known to affect the central nervous system (i.e. neuroactive pharmaceuticals and other chemicals). Different integrative approaches have been developed to assess the toxicity of these compounds by linking effects on reproduction and behavior (individual organism responses), with gene expression changes and its subsequent metabolomic (and thus lipidomic) disruption in D. magna. The ability of EDCs and neuroactive pharmaceuticals to affect reproduction and disrupt lipid homeostasis, as well as the molecular signaling pathways that modulate this disruption, has been addressed throughout the thesis. Within the chapter 2, microarray transcriptomic analysis of D. magna adult females exposed to some EDCs during reproduction was performed, together with the effects on their lipidome by a lipidomic analysis using UHPLC-TOF MS. Common transcriptional mechanisms were identified as energy-related categories, molting and reproduction, and different lipid functional categories. The obtained results allowed to link reproductive effects with changes in lipid profiles and disrupted transference of lipids to eggs in D. magna females. Lipidomic effects of neuroactive pharmaceuticals at environmental concentrations and the driven molecular mechanisms behind them were studied in chapter 3. The hypothesis that serotonin may be involved in regulating lipid dynamics and fecundity responses in D. magna was confirmed by the analysis of the lipidome of genetically tryptophan hydrolase gene knockout clones. Finally, in chapter 4 a targeted metabolomic approached was developed to analyze neurotransmitters in D. magna samples and employed in the study of the effects of neuroactive pharmaceuticals that affected Daphnias’ cognitive behavior. Metabolomic results were linked to the associated transcriptional disruption studied through RNAseq, probing the suitability of these organisms for environmental neurotoxicity studies. Overall, the results obtained throughout this thesis allowed to link transcriptomic signaling pathways with metabolomic effects (lipidomic and neurotransmitter profiles) and with apical responses (reproduction and behavior).[spa] La toxicología ambiental está experimentando un cambio de paradigma debido a la preocupante nueva realidad medioambiental. Hoy en día, gestionar los efectos más sutiles y crónicos de los compuestos químicos, ya sean individualizados o en mezclas, es una necesidad imperiosa, especialmente en concentraciones bajas y relevantes para el medio ambiente. No menos importante es ocuparse de los contaminantes emergentes (EC), cuyos efectos nocivos en los ecosistemas y sus mecanismos de toxicidad aún se desconocen. Por lo tanto, deben elaborarse nuevas estrategias con mayor relevancia ambiental para evaluar la toxicidad de los contaminantes, lo que requiere la aplicación de enfoques integradores que combinen herramientas de distintas disciplinas. Las tecnologías ómicas permiten medidas holística de efectos producidos a bajos niveles de organización biológica en plataformas de alto rendimiento, y proporcionan datos mecanicistas que pueden resultar esenciales para el desarrollo y la aplicación de estrategias de ensayo más eficientes y eficaces. En general, el objetivo de esta tesis ha sido demostrar la importancia de integrar enfoques toxicológicos ómicos con ensayos toxicológicos convencionales a fin de obtener información significativa que ayude a desentrañar cualquier nuevo mecanismo de toxicidad desencadenado por ECs en el medio acuático utilizando Daphnia magna como especie modelo. Los contaminantes estudiados en esta tesis incluyeron aquellos sospechosos de ser disruptores de lípidos (compuestos disruptores endocrinos, EDC) y ECs que se sabe que afectan al sistema nervioso central (es decir, fármacos neuroactivos y otros productos químicos). Se han desarrollado diferentes enfoques integradores para evaluar la toxicidad de estos compuestos vinculando los efectos sobre la reproducción y el comportamiento (respuestas individuales del organismo), con los cambios en la expresión de los genes y su posterior alteración metabolómica (y por tanto lipidómica) en D. magna. A lo largo de esta tesis se ha abordado la capacidad de los EDCs y de los fármacos neuroactivos de afectar a la reproducción y perturbar la homeostasis lipídica, así como a las vías de señalización molecular que modulan esta perturbación. En el capítulo 2, se realizó un análisis transcriptómico mediante microarrays de hembras adultas de D. magna expuestas a algunos EDCs durante su etapa reporductora, y se estudiaron los efectos producidos en su lipidoma mediante un análisis lipidómico utilizando UHPLC-TOF MS. Se identificaron mecanismos transcripcionales comunes descritos con categorías funcionales relacionadas con la energía, la muda y la reproducción, así como diferentes categorías funcionales de lípidos. Los resultados obtenidos permitieron vincular los efectos reproductivos con cambios en los perfiles de lípidos, así como con una alterada transferencia de lípidos de las hembras de D. magna a sus huevos. En el capítulo 3 se estudiaron los efectos lipidómicos de productos farmacéuticos neuroactivos en concentraciones ambientalmente relevantes y los mecanismos moleculares asociados a ellos. La hipótesis de que la serotonina puede participar en la regulación de la dinámica de los lípidos y las respuestas de la fecundidad en D. magna se confirmó mediante el análisis del lipidoma de clones con el gen triptófano hidrolasa silenciado. Por último, en el capítulo 4 se desarrolló un enfoque metabolómico dirigido para analizar neurotransmisores en D. magna y se empleó en el estudio de los efectos de fármacos neuroactivos que afectaban a su comportamiento cognitivo. Los resultados metabólicos se vincularon a la alteración transcripcional asociada estudiada a través del RNAseq, probando la idoneidad de estos organismos para estudios de neurotoxicidad ambiental. En general, los resultados obtenidos a lo largo de esta tesis permitieron vincular las vías de señalización transcriptómica con efectos metabolómicos (perfiles lipidómicos y de neurotransmisores) y con respuestas apicales (reproducción y comportamiento)

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub