Phylogenomic and synteny analysis of BAHD and SCP/SCPL gene families reveal their evolutionary histories in plant specialized metabolism.

Plant chemical diversity is largely owing to a number of enzymes which catalyse reactions involved in the assembly, and in the subsequent chemical modifications, of the core structures of major classes of plant specialized metabolites. One such reaction is acylation. With this in mind, to study the deep evolutionary history of BAHD and the serine-carboxypeptidase-like (SCPL) acyltransferase genes, we assembled phylogenomic synteny networks based on a large-scale inference analysis of orthologues across whole-genome sequences of 126 species spanning Stramenopiles and Archaeplastida, including , tomato () and maize ().

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.

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 the T-cell response but its role in malignant B cells has not been 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 a genetic knockout of Tbx21. We found that T-bet acts as a tumor suppressor in malignant B cells by decreasing their proliferation rate.

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.

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).

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

A draft map of the mouse pluripotent stem cell spatial proteome.

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis.

Visualization of proteomics data using R and bioconductor.

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience.