Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Metabolic changes induced by Cuscuta campestris Yunck in the host species Artemisia campestris subsp. variabilis (Ten.) Greuter as a strategy for successful parasitisation

C. campestris parasitisation increases internal host defences at the expense of environmentally directed ones in the host species A. campestris, thus limiting plant defence against progressive parasitisation. Cuscuta campestris Yunck is a holoparasitic species that parasitises wild species and crops. Among their hosts, Artemisia campestris subsp. variabilis (Ten.) Greuter is significantly affected in natural ecosystems. Limited information is available on the host recognition mechanism and there are no data on the interactions between these species and the effects on the primary and specialised metabolism in response to parasitisation. The research aims at evaluating the effect of host-parasite interactions, through a GC-MS untargeted metabolomic analysis, chlorophyll a fluorescence, ionomic and δ¹³C measurements, as well as volatile organic compound (VOC) fingerprint in A. campestris leaves collected in natural environment. C. campestris parasitisation altered plant water status, forcing stomatal opening, stimulating plant transpiration, and inducing physical damages to the host antenna complex, thus reducing the efficiency of its photosynthetic machinery. Untargeted-metabolomics analysis highlighted that the parasitisation significantly perturbed the amino acids and sugar metabolism, inducing an increase in the production of osmoprotectants, which generally accumulate in plants as a protective strategy against oxidative stress. Notably, VOCs analysis highlighted a reduction in sesquiterpenoids and an increase in monoterpenoids levels; involved in plant defence and host recognition, respectively. Moreover, C. campestris induced in the host a reduction in 3-hexenyl-acetate, a metabolite with known repellent activity against Cuscuta spp. We offer evidences that C. campestris parasitisation increases internal host defences via primary metabolites at the expense of more effective defensive compounds (secondary metabolites), thus limiting A. campestris defence against progressive parasitisation.

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.

Untargeted metabolomics in primary murine bone marrow stromal cells reveals distinct profile throughout osteoblast differentiation

INTRODUCTION

Skeletal homeostasis is an exquisitely regulated process most directly influenced by bone resorbing osteoclasts, bone forming osteoblasts, and the mechano-sensing osteocytes. These cells work together to constantly remodel bone as a mechanism to prevent from skeletal fragility. As such, when an individual experiences a disconnect in these tightly coupled processes, fracture incidence increases, such as during ageing, gonadal hormone deficiency, weightlessness, and diabetes. While therapeutic options have significantly aided in the treatment of low bone mineral density (BMD) or osteoporosis, limited options remain for anabolic or bone forming agents. Therefore, it is of interest to continue to understand how osteoblasts regulate their metabolism to support the energy expensive process of bone formation.

OBJECTIVE

The current project sought to rigorously characterize the distinct metabolic processes and intracellular metabolite profiles in stromal cells throughout osteoblast differentiation using untargeted metabolomics.

METHODS

Primary, murine bone marrow stromal cells (BMSCs) were characterized throughout osteoblast differentiation using standard staining protocols, Seahorse XFe metabolic flux analyses, and untargeted metabolomics.

RESULTS

We demonstrate here that the metabolic footprint of stromal cells undergoing osteoblast differentiation are distinct, and while oxidative phosphorylation drives adenosine triphosphate (ATP) generation early in the differentiation process, mature osteoblasts depend on glycolysis. Importantly, the intracellular metabolite profile supports these findings while also suggesting additional pathways critical for proper osteoblast function.

CONCLUSION

These data are the first of their kind to characterize these metabolites in conjunction with the bioenergetic profile in primary, murine stromal cells throughout osteoblast differentiation and provide provocative targets for future investigation.

Loss of function of lysosomal acid lipase (LAL) profoundly impacts osteoblastogenesis and increases fracture risk in humans

Lysosomal acid lipase (LAL) is essential for cholesteryl ester (CE) and triacylglycerol (TAG) hydrolysis in the lysosome. Clinically, an autosomal recessive LIPA mutation causes LAL deficiency (LALD), previously described as Wolman Disease or Cholesteryl Ester Storage Disease (CESD). LAL-D is associated with ectopic lipid accumulation in the liver, small intestine, spleen, adrenal glands, and blood. Considering the importance of unesterified cholesterol and fatty acids in bone metabolism, we hypothesized that LAL is essential for bone formation, and ultimately, skeletal health. To investigate the role of LAL in skeletal homeostasis, we used LAL-deficient (^-/-) mice, in vitro osteoblast cultures, and novel clinical data from LAL-D patients. Both male and female LAL^-/- mice demonstarted lower trabecular and cortical bone parameters , which translated to reduced biomechanical properties. Further histological analyses revealed that LAL^-/- mice had fewer osteoblasts, with no change in osteoclast or marrow adipocyte numbers. In studying the cell-autonomous role of LAL, we observed impaired differentiation of LAL^-/- calvarial osteoblasts and in bone marrow stromal cells treated with the LAL inhibitor lalistat. Consistent with LAL's role in other tissues, lalistat resulted in profound lipid puncta accumulation and an altered intracellular lipid profile. Finally, we analyzed a large de-identified national insurance database (i.e. 2016/2017 Optum Clinformatics®) which revealed that adults (≥18 years) with CESD (n = 3076) had a higher odds ratio (OR = 1.21; 95% CI = 1.03-1.41) of all-cause fracture at any location compared to adults without CESD (n = 13.7 M) after adjusting for demographic variables and osteoporosis. These data demonstrate that alterations in LAL have significant clinical implications related to fracture risk and that LAL's modulation of lipid metabolism is a critical for osteoblast function.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Time-course analysis of Streptococcus sanguinis after manganese depletion reveals changes in glycolytic and nucleic acid metabolites

INTRODUCTION

Manganese is important for the endocarditis pathogen Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci.

OBJECTIVES

We applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion.

METHODS

EDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC-MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM). Recombinant enzymes were assayed for metal dependence.

RESULTS

We observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Glycolytic intermediates such as fructose-1,6-bisphosphate increased, suggesting that enzymes that utilize them require manganese for activity or expression. Recombinant enzymes were confirmed to utilize manganese in vitro. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases resulting from manganese-dependent regulation.

CONCLUSION

Differential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which are connected to carbon catabolite repression.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

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.

The Chemical Exposome of Human Aging

Aging is an inevitable biological phenomenon displayed by single cells and organs to entire organismal systems. Aging as a biological process is characterized as a progressive decline in intrinsic biological function. Understanding the causative mechanisms of aging has always captured the imagination of researchers since time immemorial. Although both biological and chronological aging are well defined and studied in terms of genetic, epigenetic, and lifestyle predispositions, the hallmarks of aging in terms of small molecules (i.e., endogenous metabolites to chemical exposures) are limited to obscure. On top of the endogenous metabolites leading to the onset and progression of healthy aging, human beings are constantly exposed to a natural and anthropogenic "chemical" environment round the clock, from conception till death, affecting one's physiology, health and well-being, and disease predisposition. The research community has started gaining sizeable insights into deciphering the aging factors such as immunosenescence, nutrition, frailty, inflamm-aging, and diseases till date, without much input from their interaction with exogenous chemical exposures. The "exposome" around us, mostly, accelerates the process of aging by affecting the internal biological pathways and signaling mechanisms that result in the deterioration of human health. However, the entirety of exposome on human aging is far from established. This review intends to catalog the known and established associations of the exposome from past studies focusing on aging in humans and other model organisms. Further discussed are the current technologies and informatics tools that enable the study of aging exposotypes, and thus, provide a window of opportunities and challenges to study the "aging exposome" in granular details.

Short-term effects of the allelochemical umbelliferone on Triticum durum L. metabolism through GC-MS based untargeted metabolomics

The present study used untargeted metabolomics to investigate the short-term metabolic changes induced in wheat seedlings by the specialized metabolite umbelliferone, an allelochemical. We used 10 day-old wheat seedlings treated with 104 μM umbelliferone over a time course experiment covering 6 time points (0 h, 6 h, 12 h, 24 h, 48 h, and 96 h), and compared the metabolomic changes to control (mock-treated) plants. Using gas chromatography mass spectrometry (GCMS)-based metabolomics, we obtained quantitative data on 177 metabolites that were derivatized (either derivatized singly or multiple times) or not, representing 139 non-redundant (unique) metabolites. Of these 139 metabolites, 118 were associated with a unique Human Metabolome Database (HMDB) identifier, while 113 were associated with a Kyoto Encyclopedia of Genes and Genomes (KEGG) identifier. Relative quantification of these metabolites across the time-course of umbelliferone treatment revealed 22 compounds (sugars, fatty acids, secondary metabolites, organic acids, and amino acids) that changed significantly (repeated measures ANOVA, P-value < 0.05) over time. Using multivariate partial least squares discriminant analysis (PLS-DA), we showed the grouping of samples based on time-course across the control and umbelliferone-treated plants, whereas the metabolite-metabolite Pearson correlations revealed tightly formed clusters of umbelliferone-derived metabolites, fatty acids, amino acids, and carbohydrates. Also, the time-course umbelliferone treatment revealed that phospho-l-serine, maltose, and dehydroquinic acid were the top three metabolites showing highest importance in discrimination among the time-points. Overall, the biochemical changes converge towards a mechanistic explanation of the plant metabolic responses induced by umbelliferone. In particular, the perturbation of metabolites involved in tryptophan metabolism, as well as the imbalance of the shikimate pathways, which are strictly interconnected, were significantly altered by the treatment, suggesting a possible mechanism of action of this natural compound.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Solute Carrier Family 37 Member 2 (SLC37A2) Negatively Regulates Murine Macrophage Inflammation by Controlling Glycolysis

Increased flux of glucose through glycolysis is a hallmark of inflammatory macrophages and is essential for optimal effector functions. Solute carrier (SLC) 37A2 is an endoplasmic reticulum-anchored phosphate-linked glucose-6-phosphate transporter that is highly expressed in macrophages and neutrophils. We demonstrate that SLC37A2 plays a pivotal role in murine macrophage inflammatory activation and cellular metabolic rewiring. Toll-like receptor (TLR) 4 stimulation by lipopolysaccharide (LPS) rapidly increases macrophage SLC37A2 protein expression. SLC37A2 deletion reprograms macrophages to a hyper-glycolytic process and accelerates LPS-induced inflammatory cytokine production, which partially depends on nicotinamide adenine dinucleotide (NAD⁺) biosynthesis. Blockade of glycolysis normalizes the differential expression of pro-inflammatory cytokines between control and SLC37A2 deficient macrophages. Conversely, overexpression of SLC37A2 lowers macrophage glycolysis and significantly reduces LPS-induced pro-inflammatory cytokine expression. In conclusion, our study suggests that SLC37A2 dampens murine macrophage inflammation by down-regulating glycolytic reprogramming as a part of macrophage negative feedback system to curtail acute innate activation.

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LPS treatment rapidly elevates macrophage SLC37A2 protein expression

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SLC37A2 dampens early glycolytic reprogramming in acute macrophage inflammation

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SLC37A2 suppresses macrophage cell-surface and endosomal TLR activation

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SLC37A2 attenuates macrophage cellular ROS production

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research

Big data-driven omics research has led to a steep rise in investigations involving two of the most functional omes, the metabolome and microbiome. The former is touted as the closest to the phenotype, and the latter is implicated in general well-being and a plethora of human diseases. Although some research publications have integrated the concepts of the two domains, most focus their analyses on evidence solely originating from one or the other. With a growing interest in connecting the microbiome and metabolome in the context of disease, researchers must also appreciate the disconnect between the two domains. In the present review, drawing examples from the current literature, tools, and resources, I discuss the connections between the microbiome and metabolome and highlight challenges and opportunities in linking them together for the basic, translational, clinical, and nursing research communities.

The chemical exposome of type 2 diabetes mellitus: Opportunities and challenges in the omics era

Type 2 diabetes mellitus (T2DM) is a global silent killer, with > 450 million affected adults worldwide. A diverse array of non-modifiable risk factors such as family history, age (> 45 yrs), race/ethnicity, genetics, and history of gestational diabetes and modifiable risk factors such as physical inactivity, high body fat, body weight, high blood pressure, and high cholesterol for progression of prediabetes to T2DM. Given, that the modern world human population is constantly exposed to multiple stressors in the form of physical (i.e., sound, weather etc.) and chemical environment (i.e., diet, pollutants etc.), industrialization, and modernization has led to form a basis for exposomal correlation with T2DM incidence. Over the past decade, there have been emerging reports on association of levels of persistent organic pollutants (POPs), phthalates, antibiotics, drugs, air pollution, pesticides, and heavy metals with T2DM. In this review, we discuss the well known chemical exposome that has been associated with T2DM; the tools and approaches to capture this chemical exposome, and future opportunities and challenges in this exciting area of research. We further provide a window of thoughts, whether omics technologies can help fill in the gaps to help provide high throughput exposomics datasets in an unbiased manner to help understand T2DM pathophysiology in the context of industrialization, drastic lifestyle changes, urbanization, and pollution. We also discuss and provide guidelines/call to action for future exposomics studies investigating the association of T2DM with exposomes in the context of both epidemiological and experimental approaches.

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer

Rationale: Colorectal cancer (CRC) is a malignant tumor with the third highest morbidity rate among all cancers. Driven by the host's genetic makeup and environmental exposures, the gut microbiome and its metabolites have been implicated as the causes and regulators of CRC pathogenesis. We assessed human fecal samples as noninvasive and unbiased surrogates to catalog the gut microbiota and metabolome in patients with CRC. Methods: Fecal samples collected from CRC patients (CRC group, n = 50) and healthy volunteers (H group, n = 50) were subjected to microbiome (16S rRNA gene sequencing) and metabolome (gas chromatography-mass spectrometry, GC-MS) analyses. The datasets were analyzed individually and integrated for combined analysis using various bioinformatics approaches. Results: Fecal metabolomic analysis led to the identification of 164 metabolites spread across 40 metabolic pathways in both groups. In addition, there were 42 and 17 metabolites specific to the H and CRC groups, respectively. Sequencing of microbial diversity revealed 1084 operational taxonomic units (OTUs) across the two groups, and there was less species diversity in the CRC group than in the H group. Seventy-six discriminatory OTUs were identified for the microbiota of H volunteers and CRC patients. Integrated analysis correlated CRC-associated microbes with metabolites, such as polyamines (cadaverine and putrescine). Conclusions: Our results provide substantial evidence of a novel interplay between the gut microbiome and metabolome (i.e., polyamines), which is drastically perturbed in CRC. Microbe-associated metabolites can be used as diagnostic biomarkers in therapeutic explorations.

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 < 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.

1H NMR metabolomic analysis of skin and blubber of bottlenose dolphins reveals a functional metabolic dichotomy

The common bottlenose dolphin (Tursiops truncatus) is a carnivorous cetacean that thrives in marine environments, one of the apex predators of the marine food web. They are found in coastal and estuarine ecosystems, which are known to be sensitive to environmental impacts. Dolphins are considered sentinel organisms for monitoring the health of coastal marine ecosystems due to their role as predators that can bioaccumulate contaminants. Although recent studies have focused on capturing the circulating metabolomes of these mammals, and in the context of pollutants and exposures in the marine environment, skin and blubber are important surface and protective tissues that have not been adequately probed for metabolism. Using a proton nuclear magnetic resonance spectroscopy (¹H NMR) based metabolomics approach, we quantified 51 metabolites belonging to 74 different metabolic pathways in the skin and blubber of stranded bottlenose dolphin (n = 4) samples collected at different localities in the Southern Zone coast of Yucatan Peninsula of Mexico. Results indicate that metabolism of skin and blubber are quantitatively very different. These metabolite abundances could help discriminate the tissue-types using supervised partial least square regression discriminant analysis (PLSDA). Further, using hierarchical clustering analysis and random forest analysis of the metabolite abundances, the results pointed to unique metabolites that are important classifiers of the tissue-type. On one hand, the differential metabolic patterns, mainly linking fatty acid metabolism and ketogenic amino acids, seem to constitute a characteristic of blubber, thus pointing to fat synthesis and deposition. On the other hand, the skin showed several metabolites involved in gluconeogenic pathways, pointing towards an active anabolic energy-generating metabolism. The most notable pathways found in both tissues included: urea cycle, nucleotide metabolism, amino acid metabolism, glutathione metabolism among others. Our ¹H NMR metabolomics analysis allowed the quantification of metabolites associated with these two organs, i.e., pyruvic acid, arginine, ornithine, 2-hydroxybutyric acid, 3-hydroxyisobutyric acid, and acetic acid, as discriminatory and classifying metabolites. These results would lead to further understanding of the functional and physiological roles of dolphin skin and blubber metabolism for better efforts in their conservation, as well as useful target biopsy tissues for monitoring of dolphin health conditions in marine pollution and ecotoxicology studies.

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.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

The guard cell ionome: Understanding the role of ions in guard cell functions

The ionome is critical for plant growth, productivity, defense, and it eventually affects human food quantity and quality. Located on the leaf surface, stomatal guard cells are critical gatekeepers for water, gas, and pathogens. Insights form ionomics (metallomics) is imperative as we enter an omics-driven systems biology era where an understanding of guard cell function and physiology is advanced through efforts in genomics, transcriptomics, proteomics, and metabolomics. While the roles of major cations (K, Ca) and anions (Cl) are well known in guard cell function, the related physiology, movement and regulation of trace elements, metal ions, and heavy metals are poorly understood. The majority of the information on the role of trace elements in guard cells emanates from classical feeding experiments, field or in vitro fortification, micropropagation, and microscopy studies, while novel insights are available from limited metal ion transporter and ion channel studies. Given the rejuvenated and recent interest in the constantly changing ionome in plant mineral balance and eventually in human nutrition and health, we looked into the far from established guard cell ionome in lieu of the modern omics era of high throughput research endeavors. Newer technologies and tools i.e., high resolution mass spectrometry, advanced imaging, and phenomics are now available to delve into the guard cell ionomes. In this review, research efforts on guard cell ionomes were collated and categorized, and we highlight the underlying role of the largely unknown ionome in guard cell function towards a systems physiology understanding of plant health and productivity.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites

INTRODUCTION

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.

Diversity of methanogenic archaea in freshwater sediments of lacustrine ecosystems

About half of the global methane (CH₄ ) emission is contributed by the methanogenic archaeal communities leading to a significant increase in global warming. This unprecedented situation has increased the ever growing necessity of evaluating the control measures for limiting CH₄ emission to the atmosphere. Unfortunately, research endeavors on the diversity and functional interactions of methanogens are not extensive till date. We anticipate that the study of the diversity of methanogenic community is paramount for understanding the metabolic processes in freshwater lake ecosystems. Although there are several disadvantages of conventional culture-based methods for determining the diversity of methanogenic archaeal communities, in order to understand their ecological roles in natural environments it is required to culture the microbes. Recently different molecular techniques have been developed for determining the structure of methanogenic archaeal communities thriving in freshwater lake ecosystem. The two gene based cloning techniques required for this purpose are 16S rRNA and methyl coenzyme M reductase (mcrA) in addition to the recently developed metagenomics approaches and high throughput next generation sequencing efforts. This review discusses the various methods of culture-dependent and -independent measures of determining the diversity of methanogen communities in lake sediments in lieu of the different molecular approaches and inter-relationships of diversity of methanogenic archaea.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Jasmonate-mediated stomatal closure under elevated CO2 revealed by time-resolved metabolomics

Foliar stomatal movements are critical for regulating plant water loss and gas exchange. Elevated carbon dioxide (CO₂ ) levels are known to induce stomatal closure. However, the current knowledge on CO₂ signal transduction in stomatal guard cells is limited. Here we report metabolomic responses of Brassica napus guard cells to elevated CO₂ using three hyphenated metabolomics platforms: gas chromatography-mass spectrometry (MS); liquid chromatography (LC)-multiple reaction monitoring-MS; and ultra-high-performance LC-quadrupole time-of-flight-MS. A total of 358 metabolites from guard cells were quantified in a time-course response to elevated CO₂ level. Most metabolites increased under elevated CO₂ , showing the most significant differences at 10 min. In addition, reactive oxygen species production increased and stomatal aperture decreased with time. Major alterations in flavonoid, organic acid, sugar, fatty acid, phenylpropanoid and amino acid metabolic pathways indicated changes in both primary and specialized metabolic pathways in guard cells. Most interestingly, the jasmonic acid (JA) biosynthesis pathway was significantly altered in the course of elevated CO₂ treatment. Together with results obtained from JA biosynthesis and signaling mutants as well as CO₂ signaling mutants, we discovered that CO₂ -induced stomatal closure is mediated by JA signaling.

Metabolomic Responses of Arabidopsis Suspension Cells to Bicarbonate under Light and Dark Conditions

Global CO₂ level presently recorded at 400 ppm is expected to reach 550 ppm in 2050, an increment likely to impact plant growth and productivity. Using targeted LC-MS and GC-MS platforms we quantified 229 and 29 metabolites, respectively in a time-course study to reveal short-term responses to different concentrations (1, 3, and 10 mM) of bicarbonate (HCO₃⁻) under light and dark conditions. Results indicate that HCO₃⁻ treatment responsive metabolomic changes depend on the HCO₃⁻ concentration, time of treatment, and light/dark. Interestingly, 3 mM HCO₃⁻ concentration treatment induced more significantly changed metabolites than either lower or higher concentrations used. Flavonoid biosynthesis and glutathione metabolism were common to both light and dark-mediated responses in addition to showing concentration-dependent changes. Our metabolomics results provide insights into short-term plant cellular responses to elevated HCO₃⁻ concentrations as a result of ambient increases in CO₂ under light and dark.

Polyploidy and the proteome

Although major advances have been made during the past 20 years in our understanding of the genetic and genomic consequences of polyploidy, our knowledge of polyploidy and the proteome is in its infancy. One of our goals is to stimulate additional study, particularly broad-scale proteomic analyses of polyploids and their progenitors. Although it may be too early to generalize regarding the extent to which transcriptomic data are predictive of the proteome of polyploids, it is clear that the proteome does not always reflect the transcriptome. Despite limited data, important observations on the proteomes of polyploids are emerging. In some cases, proteomic profiles show qualitatively and/or quantitatively non-additive patterns, and proteomic novelty has been observed. Allopolyploids generally combine the parental contributions, but there is evidence of parental dominance of one contributing genome in some allopolyploids. Autopolyploids are typically qualitatively identical to but quantitatively different from their parents. There is also evidence of parental legacy at the proteomic level. Proteomes clearly provide insights into the consequences of genomic merger and doubling beyond what is obtained from genomic and/or transcriptomic data. Translating proteomic changes in polyploids to differences in morphology and physiology remains the holy grail of polyploidy--this daunting task of linking genotype to proteome to phenotype should emerge as a focus of polyploidy research in the next decade. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

New nodes and edges in the glucosinolate molecular network revealed by proteomics and metabolomics of Arabidopsis myb28/29 and cyp79B2/B3 glucosinolate mutants

Glucosinolates present in Brassicales are important for human health and plant defense against insects and pathogens. Here we investigate the proteomes and metabolomes of Arabidopsis myb28/29 and cyp79B2/B3 mutants deficient in aliphatic glucosinolates and indolic glucosinolates, respectively. Quantitative proteomics of the myb28/29 and cyp79B2/B3 mutants led to the identification of 2785 proteins, of which 142 proteins showed significant changes in the two mutants compared to wild type (WT). By mapping the differential proteins using STRING, we detected 59 new edges in the glucosinolate metabolic network. These connections can be classified as primary with direct roles in glucosinolate metabolism, secondary related to plant stress responses, and tertiary involved in other biological processes. Gene Ontology analysis of the differential proteins showed high level of enrichment in the nodes belonging to metabolic process including glucosinolate biosynthesis and response to stimulus. Using metabolomics, we quantified 292 metabolites covering a broad spectrum of metabolic pathways, and 89 exhibited differential accumulation patterns between the mutants and WT. The changing metabolites (e.g., γ-glutamyl amino acids, auxins and glucosinolate hydrolysis products) complement our proteomics findings. This study contributes toward engineering and breeding of glucosinolate profiles in plants in efforts to improve human health, crop quality and productivity.

BIOLOGICAL SIGNIFICANCE

Glucosinolates in Brassicales constitute an important group of natural metabolites important for plant defense and human health. Its biosynthetic pathways and transcriptional regulation have been well-studied. Using Arabidopsis mutants of important genes in glucosinolate biosynthesis, quantitative proteomics and metabolomics led to identification of many proteins and metabolites that are potentially related to glucosinolate metabolism. This study provides a comprehensive insight into the molecular networks of glucosinolate metabolism, and will facilitate efforts toward engineering and breeding of glucosinolate profiles for enhanced crop defense, and nutritional value.

The guard cell metabolome: functions in stomatal movement and global food security

Guard cells represent a unique single cell-type system for the study of cellular responses to abiotic and biotic perturbations that affect stomatal movement. Decades of effort through both classical physiological and functional genomics approaches have generated an enormous amount of information on the roles of individual metabolites in stomatal guard cell function and physiology. Recent application of metabolomics methods has produced a substantial amount of new information on metabolome control of stomatal movement. In conjunction with other "omics" approaches, the knowledge-base is growing to reach a systems-level description of this single cell-type. Here we summarize current knowledge of the guard cell metabolome and highlight critical metabolites that bear significant impact on future engineering and breeding efforts to generate plants/crops that are resistant to environmental challenges and produce high yield and quality products for food and energy security.

Plant single-cell and single-cell-type metabolomics

In conjunction with genomics, transcriptomics, and proteomics, plant metabolomics is providing large data sets that are paving the way towards a comprehensive and holistic understanding of plant growth, development, defense, and productivity. However, dilution effects from organ- and tissue-based sampling of metabolomes have limited our understanding of the intricate regulation of metabolic pathways and networks at the cellular level. Recent advances in metabolomics methodologies, along with the post-genomic expansion of bioinformatics knowledge and functional genomics tools, have allowed the gathering of enriched information on individual cells and single cell types. Here we review progress, current status, opportunities, and challenges presented by single cell-based metabolomics research in plants.

Culture of East Indian sandalwood tree somatic embryos in air-lift bioreactors for production of santalols, phenolics and arabinogalactan proteins

The Indian Sandalwood tree is globally acclaimed for the precious essential oil and heartwood. Over-exploitation, diseases, and habitat loss have posed significant challenges to find an alternative bioresource for biomass production. Here, we report the successful growth of in vitro grown somatic embryos in 10 L air-lift bioreactors. Additionally, we characterized arabinogalactan proteins and small molecule constituents such as phenolics and terpenoids that are secreted by the suspended somatic embryos into the culture media. In parallel to the biochemical characterisation, we followed the entire developmental progression of proembryogenic masses into matured cotyledonary embryos during a single run of the bioreactor.

The East Indian sandalwood tree, Santalum album, yields one of the costliest heartwoods and precious essential oil. Unsurprisingly, this endangered forest species is severely affected due to unmet global demands, illegal trade and harvesting, overharvesting and an epidemic mycoplasmal spike disease. In vitro micropropagation endeavours have resulted in defined in vitro stages such as somatic embryos that are amenable to mass production in bioreactors. We report on somatic embryo production in a 10-L air-lift-type bioreactor, and compare the growth and biochemical parameters with those of a 2-L air-lift-type bioreactor. For the 10-L bioreactor with biomass (475.7 ± 18 g fresh weight; P < 0.01), concomitantly santalols (5.2 ± 0.15 mg L⁻¹; P < 0.05), phenolics (31 ± 1.6 mg L⁻¹) and arabinogalactan proteins (AGPs) (39 ± 3.1 mg L⁻¹; P < 0.05) are produced in 28 days. In addition, we identified and quantified several santalols and phenolics by means of high-performance thin-layer chromatography and reverse-phase high-pressure liquid chromatography analyses, respectively. Results indicate that 10-L-capacity air-lift bioreactors are capable of supporting somatic embryo cultures, while the extracellular medium provides opportunities for production of industrial raw materials such as santalols, phenolics and AGPs. This will prove useful for further optimization and scale-up studies of plant-produced metabolites.

Evaluation of in vivo anti-hyperglycemic and antioxidant potentials of α-santalol and sandalwood oil

Sandalwood finds numerous mentions across diverse traditional medicinal systems in use worldwide. The objective of this study was to evaluate the in vivo anti-hyperglycemic and antioxidant potential of sandalwood oil and its major constituent α-santalol. The in vivo anti-hyperglycemic experiment was conducted in alloxan-induced diabetic male Swiss albino mice models. The in vivo antioxidant experiment was performed in d-galactose mediated oxidative stress induced male Swiss albino mice models. Intraperitoneal administration of α-santalol (100mg/kg BW) and sandalwood oil (1g/kg BW) for an week modulated parameters such as body weight, blood glucose, serum bilirubin, liver glycogen, and lipid peroxides contents to normoglycemic levels in the alloxan-induced diabetic mice. Similarly, intraperitoneal administration of α-santalol (100mg/kg BW) and sandalwood oil (1g/kg BW) for two weeks modulated parameters such as serum aminotransferases, alkaline phosphatase, bilirubin, superoxide dismutase, catalase, free sulfhydryl, protein carbonyl, nitric oxide, liver lipid peroxide contents, and antioxidant capacity in d-galactose mediated oxidative stress induced mice. Besides, it was observed that the beneficial effects of α-santalol were well complimented, differentially by other constituents present in sandalwood oil, thus indicating synergism in biological activity of this traditionally used bioresource.

Draft genome sequence of the rubber tree Hevea brasiliensis

Background

Hevea brasiliensis, a member of the Euphorbiaceae family, is the major commercial source of natural rubber (NR). NR is a latex polymer with high elasticity, flexibility, and resilience that has played a critical role in the world economy since 1876.

Results

Here, we report the draft genome sequence of H. brasiliensis. The assembly spans ~1.1 Gb of the estimated 2.15 Gb haploid genome. Overall, ~78% of the genome was identified as repetitive DNA. Gene prediction shows 68,955 gene models, of which 12.7% are unique to Hevea. Most of the key genes associated with rubber biosynthesis, rubberwood formation, disease resistance, and allergenicity have been identified.

Conclusions

The knowledge gained from this genome sequence will aid in the future development of high-yielding clones to keep up with the ever increasing need for natural rubber.

TLC-Bioautographic Evaluation of In Vitro Anti-tyrosinase and Anti-cholinesterase Potentials of Sandalwood Oil

Sandalwood oil, rich in sesquiterpenoid alcohols, has been used in traditional medicinal systems as a relaxant and coolant. Besides, sandalwood oil is used as an ingredient in numerous skin fairness enhancing cosmetics. However, there is no available information on biological activities that relate to the above applications. Hence, the anti-tyrosinase and anti-cholinesterase potentials of sandalwood oil were probed by both TLC-bioautographic and colorimetric methods. Results obtained from colorimetric assays indicated that sandalwood oil is a potent inhibitor of tyrosinase (IC50=171 μg mL−1) and cholinesterases (IC50=4.8-58 μg mL−1), in comparison with the positive controls used in the assays, kojic acid and physostigmine, respectively. The TLC-bioautographic assays indicated that α-santalol, the major constituent of the oil, is a strong inhibitor of both tyrosinase and cholinesterase. These in vitro results indicate that there is a great potential of this essential oil for use in the treatment of Alzheimer's disease, as well as in skin-care.

TLC-bioautographic evaluation of in vitro anti-tyrosinase and anti-cholinesterase potentials of sandalwood oil

Sandalwood oil, rich in sesquiterpenoid alcohols, has been used in traditional medicinal systems as a relaxant and coolant. Besides, sandalwood oil is used as an ingredient in numerous skin fairness enhancing cosmetics. However, there is no available information on biological activities that relate to the above applications. Hence, the anti-tyrosinase and anti-cholinesterase potentials of sandalwood oil were probed by both TLC-bioautographic and colorimetric methods. Results obtained from colorimetric assays indicated that sandalwood oil is a potent inhibitor of tyrosinase (IC50 = 171 microg mL(-1)) and cholinesterases (IC50 = 4.8-58 microg mL(-1)), in comparison with the positive controls used in the assays, kojic acid and physostigmine, respectively. The TLC-bioautographic assays indicated that alpha-santalol, the major constituent of the oil, is a strong inhibitor of both tyrosinase and cholinesterase. These in vitro results indicate that there is a great potential of this essential oil for use in the treatment of Alzheimer's disease, as well as in skin-care.