Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data

Variational autoencoders learn transferrable representations of metabolomics data

Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.

Stoichiometric ratios for biotics and xenobiotics capture effective metabolic coupling to re(de)fine biodegradation

Preserving human and environmental health requires anthropogenic pollutants to be biologically degradable. Depending on concentration, both nutrients and pollutants induce and activate metabolic capacity in the endemic bacterial consortium, which in turn aids their degradation. Knowledge on such 'acclimation' is rarely implemented in risk assessment cost-effectively. As a result, an accurate description of the mechanisms and kinetics of biodegradation remains problematic. In this study, we defined a yield 'effectivity', comprising the effectiveness at which a pollutant (substrate) enhances its own degradation by inducing (biomass) cofactors involved therein. Our architecture for calculation represents the interplay between concentration and metabolism via both stoichiometric and thermodynamic concepts. The calculus for yield 'effectivity' is biochemically intuitive, implicitly embeds co-metabolism and distinguishes 'endogenic' from 'exogenic' substances' reflecting various phenomena in biodegradation and bio-transformation studies. We combined data on half-lives of pollutants/nutrients in wastewater and surface water with transition-state rate theory to obtain also experimental values for effective yields. These quantify the state of acclimation: the portion of biodegradation kinetics attributable to (contributed by) 'natural metabolism', in view of similarity to natural substances. Calculated and experimental values showed statistically significant correspondence. Particularly, carbohydrate metabolism and nucleic acid metabolism appeared relevant for acclimation (R² = 0.11-0.42), affecting rates up to 10^4.9(±0.7) times: under steady-state acclimation, a compound stoichiometrically identical to carbohydrates or nucleic acids, is 10^3.2 to 10^4.9 times faster aerobically degraded than a compound marginally similar. Our new method, simulating (contribution by) the state of acclimation, supplements existing structure-biodegradation and kinetic models for predicting biodegradation in wastewater and surface water. The accuracy of prediction may increase when characterizing nutrients/co-metabolites in terms of, e.g., elemental analysis. We discuss strengths and limitations of our approach by comparison to empirical and mechanism-based methods.

A study in analysing the role of labour enactment towards employment protection and labour productivity

The current business environment is highly challenging and top management are looking for enhancing the productivity and output to achieve sustainable growth and development. The application of labour enactment is being explored extensively by government, regulators, management and employees so as to support the individuals to possess better employment protection, working condition and support in realising performance and efficiency. The changes in the labour enactment are focused in supporting the employees to possess better job security, enhance working environment conditions, salary and wages, supportive management and other related aspects so that they can enhance their performance and wellbeing. This study is poised to analyse the role of labour enactment towards employment protection and labour productivity. The study intends to support the existing research on the aspect that the enactment tend to support the employees to perform better in their work-related area, it also enables the management to implement clear and collaborative policies so as to achieve the growth and development of the organisation. 

A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions

The ongoing unpredictability of climate changes is exponentially exerting a negative impact on crop production, further aggravating detrimental abiotic stress effects. Several research studies have been focused on the genetic modification of crop plants to achieve more crop resilience against such stress factors; however, there has been a paradigm shift in modern agriculture focusing on more organic, eco-friendly and long-lasting systems to improve crop yield. As such, extensive research into the use of microbial and nonmicrobial biostimulants has been at the core of agricultural studies to improve crop growth and development, as well as to attain tolerance against several biotic and abiotic stresses. However, the molecular mechanisms underlying the biostimulant activity remain enigmatic. Thus, this study is a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach to unravel the hypothetical biochemical framework underlying effects of a nonmicrobial biostimulant (a silicon-based formulation) on tomato plants (Solanum lycopersium) under salinity stress conditions. This metabolomics study postulates that Si-based biostimulants could alleviate salinity stress in tomato plants through modulation of the primary metabolism involving changes in the tricarboxylic acid cycle, fatty acid and numerous amino acid biosynthesis pathways, with further reprogramming of several secondary metabolism pathways such as the phenylpropanoid pathway, flavonoid biosynthesis pathways including flavone and flavanol biosynthesis. Thus, the postulated hypothetical framework, describing biostimulant-induced metabolic events in tomato plants, provides actionable knowledge necessary for industries and farmers to, confidently and innovatively, explore, design, and fully implement Si-based formulations and strategies into agronomic practices for sustainable agriculture and food production.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Mass Spectrometry-Based Microbial Metabolomics: Techniques, Analysis, and Applications

The demand for understanding the roles genes play in biological systems has steered the biosciences into the direction the metabolome, as it closely reflects the metabolic activities within a cell. The importance of the metabolome is further highlighted by its ability to influence the genome, transcriptome, and proteome. Consequently, metabolomic information is being used to understand microbial metabolic networks. At the forefront of this work is mass spectrometry, the most popular metabolomics measurement technique. Mass spectrometry-based metabolomic analyses have made significant contributions to microbiological research in the environment and human disease. In this chapter, we break down the technical aspects of mass spectrometry-based metabolomics and discuss its application to microbiological research.