A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

T-bet suppresses proliferation of malignant B cells in chronic lymphocytic leukemia.

The T-box transcription factor T-bet is known as a master regulator of T-cell response but its role in malignant B cells is not sufficiently explored. Here, we conducted single-cell resolved multi-omics analyses of malignant B cells from patients with chronic lymphocytic leukemia (CLL) and studied a CLL mouse model with genetic knockout of TBX21. We found that T-bet acts as a tumor suppressor in malignant B cells by decreasing their proliferation rate. NF-κB activity induced by inflammatory signals provided by the microenvironment, triggered T-bet expression which impacted on promoter proximal and distal chromatin co-accessibility and controlled a specific gene signature by mainly suppressing transcription. Gene set enrichment analysis identified a positive regulation of interferon signaling, and a negative control of proliferation by T-bet. In line, we showed that T-bet represses cell cycling and is associated with longer overall survival of CLL patients. Our study uncovers a novel tumor suppressive role of T-bet in malignant B cells via its regulation of inflammatory processes and cell cycling which has implications for stratification and therapy of CLL patients. Linking T-bet activity to inflammation explains the good prognostic role of genetic alterations in inflammatory signaling pathways in CLL.

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis.

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

MsQuality: an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data.

MOTIVATION: Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows. RESULTS: The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets. AVAILABILITY AND IMPLEMENTATION: The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation.

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis.

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis.

Dark-induced senescence provokes profound metabolic shifts to recycle nutrients and to guarantee plant survival. To date, research on these processes has largely focused on characterizing mutants deficient in individual pathways. Here, we adopted a time-resolved genome-wide association-based approach to characterize dark-induced senescence by evaluating the photochemical efficiency and content of primary and lipid metabolites at the beginning, or after 3 or 6 days in darkness. We discovered six patterns of metabolic shifts and identified 215 associations with 81 candidate genes being involved in this process. Among these associations, we validated the roles of four genes associated with glycine, galactinol, threonine, and ornithine levels. We also demonstrated the function of threonine and galactinol catabolism during dark-induced senescence. Intriguingly, we determined that the association between tyrosine contents and TYROSINE AMINOTRANSFERASE 1 influences enzyme activity of the encoded protein and transcriptional activity of the gene under normal and dark conditions, respectively. Moreover, the single-nucleotide polymorphisms affecting the expression of THREONINE ALDOLASE 1 and the amino acid transporter gene AVT1B, respectively, only underlie the variation in threonine and glycine levels in the dark. Taken together, these results allow us to present a very detailed model of the metabolic aspects of dark-induced senescence, as well as the process itself.

MatrixQCvis: shiny-based interactive data quality exploration for omics data.

MOTIVATION: First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference. RESULTS: We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. AVAILABILITY AND IMPLEMENTATION: MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diversity of Chemical Structures and Biosynthesis of Polyphenols in Nut-Bearing Species.

Nuts, such as peanut, almond, and chestnut, are valuable food crops for humans being important sources of fatty acids, vitamins, minerals, and polyphenols. Polyphenols, such as flavonoids, stilbenoids, and hydroxycinnamates, represent a group of plant-specialized (secondary) metabolites which are characterized as health-beneficial antioxidants within the human diet as well as physiological stress protectants within the plant. In food chemistry research, a multitude of polyphenols contained in culinary nuts have been studied leading to the identification of their chemical properties and bioactivities. Although functional elucidation of the biosynthetic genes of polyphenols in nut species is crucially important for crop improvement in the creation of higher-quality nuts and stress-tolerant cultivars, the chemical diversity of nut polyphenols and the key biosynthetic genes responsible for their production are still largely uncharacterized. However, current technical advances in whole-genome sequencing have facilitated that nut plant species became model plants for omics-based approaches. Here, we review the chemical diversity of seed polyphenols in majorly consumed nut species coupled to insights into their biological activities. Furthermore, we present an example of the annotation of key genes involved in polyphenolic biosynthesis in peanut using comparative genomics as a case study outlining how we are approaching omics-based approaches of the nut plant species.

Kingdom-wide analysis of the evolution of the plant type III polyketide synthase superfamily.

The emergence of type III polyketide synthases (PKSs) was a prerequisite for the conquest of land by the green lineage. Within the PKS superfamily, chalcone synthases (CHSs) provide the entry point reaction to the flavonoid pathway, while LESS ADHESIVE POLLEN 5 and 6 (LAP5/6) provide constituents of the outer exine pollen wall. To study the deep evolutionary history of this key family, we conducted phylogenomic synteny network and phylogenetic analyses of whole-genome data from 126 species spanning the green lineage including Arabidopsis thaliana, tomato (Solanum lycopersicum), and maize (Zea mays). This study thereby combined study of genomic location and context with changes in gene sequences. We found that the two major clades, CHS and LAP5/6 homologs, evolved early by a segmental duplication event prior to the divergence of Bryophytes and Tracheophytes. We propose that the macroevolution of the type III PKS superfamily is governed by whole-genome duplications and triplications. The combined phylogenetic and synteny analyses in this study provide insights into changes in the genomic location and context that are retained for a longer time scale with more recent functional divergence captured by gene sequence alterations.

Coordinating Sulfur Pools under Sulfate Deprivation.

Plants display manifold metabolic changes on sulfate deficiency (S deficiency) with all sulfur-containing pools of primary and secondary metabolism affected. O-Acetylserine (OAS), whose levels are rapidly altered on S deficiency, is correlated tightly with novel regulators of plant sulfur metabolism that have key roles in balancing plant sulfur pools, including the Sulfur Deficiency Induced genes (SDI1 and SDI2), More Sulfur Accumulation1 (MSA1), and GGCT2;1. Despite the importance of OAS in the coordination of S pools under stress, mechanisms of OAS perception and signaling have remained elusive. Here, we put particular focus on the general OAS-responsive genes but also elaborate on the specific roles of SDI1 and SDI2 genes, which downregulate the glucosinolate (GSL) pool size. We also highlight the key open questions in sulfur partitioning.

Plant Mitochondrial Carriers: Molecular Gatekeepers That Help to Regulate Plant Central Carbon Metabolism.

The evolution of membrane-bound organelles among eukaryotes led to a highly compartmentalized metabolism. As a compartment of the central carbon metabolism, mitochondria must be connected to the cytosol by molecular gates that facilitate a myriad of cellular processes. Members of the mitochondrial carrier family function to mediate the transport of metabolites across the impermeable inner mitochondrial membrane and, thus, are potentially crucial for metabolic control and regulation. Here, we focus on members of this family that might impact intracellular central plant carbon metabolism. We summarize and review what is currently known about these transporters from in vitro transport assays and in planta physiological functions, whenever available. From the biochemical and molecular data, we hypothesize how these relevant transporters might play a role in the shuttling of organic acids in the various flux modes of the TCA cycle. Furthermore, we also review relevant mitochondrial carriers that may be vital in mitochondrial oxidative phosphorylation. Lastly, we survey novel experimental approaches that could possibly extend and/or complement the widely accepted proteoliposome reconstitution approach.

Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics.

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.

Mass Spectrometry-Based Untargeted Plant Metabolomics.

Metabolomics has grown into one of the major approaches for systems biology studies, in part driven by developments in mass spectrometry (MS), providing high sensitivity and coverage of the metabolome at high throughput. Untargeted metabolomics allows for the investigation of metabolic phenotypes involving several hundreds to thousands of metabolites. In this approach, all signals in a mass chromatogram are processed in an unbiased way, allowing for a deeper investigation of metabolic phenotypes, but also resulting in significantly more complex data processing and post-processing steps. In this article, we discuss all the intricacies involved in extracting and analyzing metabolites by chromatography coupled to MS, as well as the processing and analysis of such datasets. © 2019 The Authors. Basic Protocol 1: Metabolite extraction for LC-MS Alternate Protocol: Methyl tert-butyl ether (MTBE) extraction for multiple mass spectrometry platforms (GC-polar, LC-polar, LC-lipid) Basic Protocol 2: LC-MS analysis Support Protocol 1: GC-MS derivatization and analysis Support Protocol 2: Lipid analysis Basic Protocol 3: LC-MS data processing Basic Protocol 4: Data analysis Basic Protocol 5: Metabolite annotation Support Protocol 3: Molecular networking using MetNet Support Protocol 4: Co-injection of authentic standards.

The metaRbolomics Toolbox in Bioconductor and beyond.

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

MetNet: Metabolite Network Prediction from High-Resolution Mass Spectrometry Data in R Aiding Metabolite Annotation.

A major bottleneck of mass spectrometric metabolomic analysis is still the rapid detection and annotation of unknown m/ z features across biological matrices. This kind of analysis is especially cumbersome for complex samples with hundreds to thousands of unknown features. Traditionally, the annotation was done manually imposing constraints in reproducibility and automatization. Furthermore, different analysis tools are typically used at different steps which requires parsing of data and changing of environments. We present here MetNet, implemented in the R programming language and available as an open-source package via the Bioconductor project. MetNet, which is compatible with the output of the xcms/CAMERA suite, uses the data-rich output of mass spectrometry metabolomics to putatively link features on their relation to other features in the data set. MetNet uses both structural and quantitative information on metabolomics data for network inference and enables the annotation of unknown analytes.

The Intracellular Localization of the Vanillin Biosynthetic Machinery in Pods of Vanilla planifolia.

Vanillin is the most important flavor compound in the vanilla pod. Vanilla planifolia vanillin synthase (VpVAN) catalyzes the conversion of ferulic acid and ferulic acid glucoside into vanillin and vanillin glucoside, respectively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of vanilla pod sections demonstrates that vanillin glucoside is preferentially localized within the mesocarp and placental laminae whereas vanillin is preferentially localized within the mesocarp. VpVAN is present as the mature form (25 kDa) but, depending on the tissue and isolation procedure, small amounts of the immature unprocessed form (40 kDa) and putative oligomers (50, 75 and 100 kDa) may be observed by immunoblotting using an antibody specific to the C-terminal sequence of VpVAN. The VpVAN protein is localized within chloroplasts and re-differentiated chloroplasts termed phenyloplasts, as monitored during the process of pod development. Isolated chloroplasts were shown to convert [14C]phenylalanine and [14C]cinnamic acid into [14C]vanillin glucoside, indicating that the entire vanillin de novo biosynthetic machinery converting phenylalanine to vanillin glucoside is present in the chloroplast.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

MetCirc: navigating mass spectral similarity in high-resolution MS/MS metabolomics data.

SUMMARY: Among the main challenges in metabolomics are the rapid dereplication of previously characterized metabolites across a range of biological samples and the structural prediction of unknowns from MS/MS data. Here, we developed MetCirc to comprehensively align and calculate pairwise similarity scores among MS/MS spectral data and visualize these across a range of biological samples. MetCirc comprises functionalities to interactively organize these data according to compound familial groupings and to accelerate the discovery of shared metabolites and hypothesis formulation for unknowns. As such, MetCirc provides a significant advance to address biological questions in areas where chemodiversity plays a role. AVAILABILITY AND IMPLEMENTATION: MetCirc , implemented in the open-source R language, together with its vignette are available in the Bioconductor project and at https://github.com/PlantDefenseMetabolism/MetCirc . CONTACT: thomasnaake@googlemail.com or emmanuel.gaquerel@cos.uni-heidelberg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.