Evaluation of freely available software tools for untargeted quantification of 13C isotopic enrichment in cellular metabolome from HR-LC/MS data

Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics

In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

An optimization method for untargeted MS-based isotopic tracing investigations of metabolism

INTRODUCTION

Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking.

OBJECTIVES

To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism.

METHODS

The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of ¹³C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism.

RESULTS

The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time.

CONCLUSION

The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.

Soil Salinity, a Serious Environmental Issue and Plant Responses: A Metabolomics Perspective

The effects of global warming have increasingly led to devastating environmental stresses, such as heat, salinity, and drought. Soil salinization is a serious environmental issue and results in detrimental abiotic stress, affecting 7% of land area and 33% of irrigated lands worldwide. The proportion of arable land facing salinity is expected to rise due to increasing climate change fuelled by anthropogenic activities, exacerbating the threat to global food security for the exponentially growing populace. As sessile organisms, plants have evolutionarily developed mechanisms that allow ad hoc responses to salinity stress. The orchestrated mechanisms include signalling cascades involving phytohormones, kinases, reactive oxygen species (ROS), and calcium regulatory networks. As a pillar in a systems biology approach, metabolomics allows for comprehensive interrogation of the biochemistry and a deconvolution of molecular mechanisms involved in plant responses to salinity. Thus, this review highlights soil salinization as a serious environmental issue and points to the negative impacts of salinity on plants. Furthermore, the review summarises mechanisms regulating salinity tolerance on molecular, cellular, and biochemical levels with a focus on metabolomics perspectives. This critical synthesis of current literature is an opportunity to revisit the current models regarding plant responses to salinity, with an invitation to further fundamental research for novel and actionable insights.

In vivo2H/13C flux analysis in metabolism research

Identifying the factors and mechanisms that regulate metabolism under normal and diseased states requires methods to quantify metabolic fluxes of live tissues within their physiological milieu. A number of recent developments have expanded the reach and depth of isotope-based in vivo flux analysis, which have in turn challenged existing dogmas in metabolism research. First, minimally invasive techniques of intravenous isotope infusion and sampling have advanced in vivo metabolic tracer studies in animal models and human subjects. Second, recent breakthroughs in analytical instrumentation have expanded the scope of isotope labeling measurements and reduced sample volume requirements. Third, innovative modeling approaches and publicly available software tools have facilitated rigorous analysis of sophisticated experimental designs involving multiple tracers and expansive metabolomics datasets. These developments have enabled comprehensive in vivo quantification of metabolic fluxes in specific tissues and have set the stage for integrated multi-tissue flux assays.

Photosynthetic Co-Production of Succinate and Ethylene in A Fast-Growing Cyanobacterium, Synechococcus elongatus PCC 11801

Cyanobacteria are emerging as hosts for photoautotrophic production of chemicals. Recent studies have attempted to stretch the limits of photosynthetic production, typically focusing on one product at a time, possibly to minimise the additional burden of product separation. Here, we explore the simultaneous production of two products that can be easily separated: ethylene, a gaseous product, and succinate, an organic acid that accumulates in the culture medium. This was achieved by expressing a single copy of the ethylene forming enzyme (efe) under the control of P_cpcB, the inducer-free super-strong promoter of phycocyanin β subunit. We chose the recently reported, fast-growing and robust cyanobacterium, Synechococcus elongatus PCC 11801, as the host strain. A stable recombinant strain was constructed using CRISPR-Cpf1 in a first report of markerless genome editing of this cyanobacterium. Under photoautotrophic conditions, the recombinant strain shows specific productivities of 338.26 and 1044.18 μmole/g dry cell weight/h for ethylene and succinate, respectively. These results compare favourably with the reported productivities for individual products in cyanobacteria that are highly engineered. Metabolome profiling and ¹³C labelling studies indicate carbon flux redistribution and suggest avenues for further improvement. Our results show that S. elongatus PCC 11801 is a promising candidate for metabolic engineering.