Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Differential Extraction and GC-MS based Quantification of Sesquiterpenoids from Immature Heartwood of East Indian Sandalwood Tree

The East Indian sandalwood tree yields the costliest heartwood and essential oil that are used in traditional medicine, aromatherapy and in cosmetic and fragrance industries. Steam distillation is the traditional method employed for extraction of the sesquiterpenoid rich essential oil from chips of matured heartwood. However, there is no information available on the comparative extractability of sesquiterpenoids when different solvents are employed. Thus we used four different solvents to extract, detect and quantify fourteen major sesquiterpenoids from immature heartwood, by gas chromatography- mass spectrometry (GC- MS) method employing an ion trap quadruple (ITQ) mass analyzer. Results suggest that, with increasing solvent polarity the diversity of sesquiterpenoids decreased, but the quantities of santalols increased. Moreover, n-hexane remained the best extraction solvent for santalols, i.e., yielding up to 92.6 % of total sesquiterpenoids quantified. Furthermore, Z-?-trans-bergamotol, Z-epi-?-santalol and Z-?-santalols were found to be the most abundant constituents of immature heartwood. Keywords: GC-MS, heartwood, Santalum album, sesquiterpenoid, solven

State of the field in multi-omics research: from computational needs to data mining and sharing

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow

DIMEdb::an integrated database and web service for metabolite identification in direct infusion mass spectrometery

AbstractMotivationMetabolomics involves the characterisation, identification, and quantification of small molecules (metabolites) that act as the reaction intermediates of biological processes. Over the past few years, we have seen wide scale improvements in data processing, database, and statistical analysis tools. Direct infusion mass spectrometery (DIMS) is a widely used platform that is able to produce a global fingerprint of the metabolome, without the requirement of a prior chromatographic step - making it ideal for wide scale high-throughput metabolomics analysis. In spite of these developments, metabolite identification still remains a key bottleneck in untargeted mass spectrometry-based metabolomics studies. The first step of the metabolite identification task is to query masses against a metaboite database to get putative metabolite annotations. Each existing metabolite database differs in a number of aspects including coverage, format, and accessibility - often limiting the user to a rudimentary web interface. Manually combining multiple search results for a single experiment where there may be potentially hundreds of masses to investigate becomes an incredibly arduous task.ResultsTo facilitate unified access to metabolite information we have created the Direct Infusion MEtabolite database (DIMEdb), a comprehensive web-based metabolite database that contains over 80,000 metabolites sourced from a number of renowned metabolite databases of which can be utilised in the analysis and annotation of DIMS data. To demostrate the efficacy of DIMEdb, a simple use case for metabolic identification is presented. DIMEdb aims to provide a single point of access to metabolite information, and hopefully facilitate the development of much needed bioinformatic tools.AvailabilityDIMEdb is freely available at https://[email protected] informationSupplementary data are available at Bioinformatics online.</jats:sec

Chemodiversity of the Glucosinolate-Myrosinase System at the Single Cell Type Resolution

Glucosinolates (GLSs) are a well-defined group of specialized metabolites, and like any other plant specialized metabolites, their presence does not directly affect the plant survival in terms of growth and development. However, specialized metabolites are essential to combat environmental stresses, such as pathogens and herbivores. GLSs naturally occur in many pungent plants in the order of Brassicales. To date, more than 200 different GLS structures have been characterized and their distribution differs from species to species. GLSs co-exist with classical and atypical myrosinases, which can hydrolyze GLS into an unstable aglycone thiohydroximate-O-sulfonate, which rearranges to produce different degradation products. GLSs, myrosinases, myrosinase interacting proteins, and GLS degradation products constitute the GLS-myrosinase (GM) system (“mustard oil bomb”). This review discusses the cellular and subcellular organization of the GM system, its chemodiversity, and functions in different cell types. Although there are many studies on the functions of GLSs and/or myrosinases at the tissue and whole plant levels, very few studies have focused on different single cell types. Single cell type studies will help to reveal specific functions that are missed at the tissue and organismal level. This review aims to highlight (1) recent progress in cellular and subcellular compartmentation of GLSs, myrosinases, and myrosinase interacting proteins; (2) molecular and biochemical diversity of GLSs and myrosinases; and (3) myrosinase interaction with its interacting proteins, and how it regulates the degradation of GLSs and thus the biological functions (e.g., plant defense against pathogens). Future prospects may include targeted approaches for engineering/breeding of plants and crops in the cell type-specific manner toward enhanced plant defense and nutrition

Analysis of serum changes in response to a high fat high cholesterol diet challenge reveals metabolic biomarkers of atherosclerosis

Atherosclerotic plaques are characterized by an accumulation of macrophages, lipids, smooth muscle cells, and fibroblasts, and, in advanced stages, necrotic debris within the arterial walls. Dietary habits such as high fat and high cholesterol (HFHC) consumption are known risk factors for atherosclerosis. However, the key metabolic contributors to diet-induced atherosclerosis are far from established. Herein, we investigate the role of a 2-year HFHC diet challenge in the metabolic changes of development and progression of atherosclerosis. We used a non-human primate (NHP) model (baboons, n = 60) fed a HFHC diet for two years and compared metabolomic profiles in serum from animals on baseline chow with serum collected after the challenge diet using two-dimensional gas chromatography time-of-flight mass-spectrometry (2D GC-ToF-MS) for untargeted metabolomic analysis, to quantify metabolites that contribute to atherosclerotic lesion formation. Further, clinical biomarkers associated with atherosclerosis, lipoprotein measures, fat indices, and arterial plaque formation (lesions) were quantified. Using two chemical derivatization (i.e., silylation) approaches, we quantified 321 metabolites belonging to 66 different metabolic pathways, which revealed significantly different metabolic profiles of HFHC diet and chow diet fed baboon sera. We found heritability of two important metabolites, lactic acid and asparagine, in the context of diet-induced metabolic changes. In addition, abundance of cholesterol, lactic acid, and asparagine were sex-dependent. Finally, 35 metabolites correlated (R2, 0.068-0.271, P \u3c 0.05) with total lesion burden assessed in three arteries (aortic arch, common iliac artery, and descending aorta) which could serve as potential biomarkers pending further validation. This study demonstrates the feasibility of detecting sex-specific and heritable metabolites in NHPs with diet-induced atherosclerosis using untargeted metabolomics allowing understanding of atherosclerotic disease progression in humans

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.DDF, YG, AP, CWD, BBM, DH, JR, and VC have been funded by Enveda Biosciences. This work has been funded by Enveda Biosciences (https://www.envedabio.com/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SM and DRB received no specific funding for this work.Peer ReviewedPostprint (author's final draft

Biological activities of East Indian sandalwood tree, Santalum album. PeerJ PrePrints 2013; e96v1

Abstract 21 The East Indian Sandalwood tree, Santalum album L. has been widely used in folk medicine 22 for treatment of common colds, bronchitis, skin disorders, heart ailments, general weakness, 23 fever, infection of the urinary tract, inflammation of the mouth and pharynx, liver and 24 gallbladder complaints and other maladies. With more than 200 constituents, the essential oil 25 is emergent as an interesting and biologically valuable active source of phytochemicals

Accumulation patterns of phenylpropanoids and enzymes in East Indian sandalwood tree undergoing developmental progression in vitro

Abstract The East Indian Sandalwood tree, Santalum album L., is sought for its fragrant essential oil and heartwood. The prolonged harvestable economic phase, over-exploitation, and poaching have contributed to extensive micro propagation endeavors in this woody tropical species. However, till date there is no information available regarding the metabolic changes associated with its development in vitro. Established in vitro cultures (i.e. callus, somatic embryo, and somatic seedlings) were examined extensively for phenylpropanoid pathway enzymes and metabolite accumulation patterns. Two to twelve fold increases in critical enzymatic activities across the three stages suggested a progressive developmental organization. Phenylpropanoid analysis by reverse phasehigh performance liquid chromatography (RP-HPLC) and liquid chromatography-electro spray ionization-mass spectrometry (LC-ESI-MS/MS) revealed changes and distribution patterns of 18 phenolics and 46 phenylpropanoids, respectively. Moreover, anatomical studies yielded insights into the vasculature&apos;s progressive organization and enhanced complexity. This study constitutes the first ever report on a comprehensive phenylpropanoid profiling analysis in East Indian sandalwood tree

Immunolocalization of α-santalol in sandalwood

<div><p>Alpha-santalol is a key constituent of sandalwood essential oil and is responsible for most of its biological activities. The heartwood of a mature East Indian sandalwood tree accumulates this sesquiterpenoid-rich oil. Although gas chromatography (GC) and GC–mass spectrometry (GC–MS)-based technologies are used to detect and quantify santalols from heartwoods and the essential oil, information on the sites of deposition of these molecules remains obscure. Recently, <i>in vitro</i> cells of sandalwood were shown to accumulate sandalwood oil constituents. However, no reports are available on the visualization of these small molecules <i>in planta</i>. Immunization of rabbits with a bovine serum albumin (BSA)–α-santalol conjugate resulted in the production of anti-α-santalol polyclonal antibody in six weeks, which showed high affinity and specificity. The success and extent of cross-linking of α-santalol with BSA was further confirmed by photometric, fluorometric and chromatographic methods. These polyclonal rabbit antibodies were used to immunolocalize α-santalol in sandalwood plant materials for the first time. Results indicate the localization of α-santalol to the vascular bundles of somatic embryos and leaves, whereas distribution was evident in secondary xylem, cortical parenchyma and epidermis of the mature stem. Furthermore, the polyclonal antibody is shown to be a useful tool in detection of both free and immobilized α-santalol for screening purposes.</p></div