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.

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

MAIN CONCLUSIONS: 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.

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.

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.

High Resolution GC-Orbitrap-MS Metabolomics Using Both Electron Ionization and Chemical Ionization for Analysis of Human Plasma.

Gas chromatography-mass spectrometry (GC-MS) platforms are typically run in electron ionization (EI) mode for mass spectral matching and metabolite annotation. With the advent of high resolution mass spectrometry (HRMS), soft ionization techniques such as chemical ionization (CI) may provide additional coverage for compound identification. We evaluated NIST SRM 1950 pooled plasma reference sample using a HRGC-MS instrument [GC-Orbitrap-MS with electron ionization (EI), positive chemical ionization (PCI), and negative CI (NCI) capabilities] for metabolite annotation and quantification to assess the suitability of the platform for routine discovery metabolomics. Using both open source and vendor workflows, we validated the spectral matches with an in-house spectral library (Wake Forest CPM GC-MS spectral and retention time libraries) of EI-MS and CI-MS/MS spectra obtained from chemical standards. We confidently [metabolomics standards initiative (MSI) confidence level 2] identified 263, 93, and 65 metabolites using EI, PCI, and NCI modes, respectively, of which 270 metabolites (64%) were validated using our Wake Forest CPM GC-MS spectral libraries. When compared to published LC-MS-based efforts using the same NIST SRM 1950 plasma sample, there was only 17% overlap between the two platforms. In addition, the metabolomics analysis of community approved standard human plasma demonstrated the ability of EI- and CI-MS modes of analysis using a HRGC-MS platform to enable reproducible and interoperable spectral matching.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.