The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Undisclosed, unmet and neglected challenges in multi-omics studies

[EN] Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.This work has been funded by the Spanish Ministry of Science and Innovation with grant number BES-2016-076994 to A.A.-L.Tarazona, S.; Arzalluz-Luque, Á.; Conesa, A. (2021). Undisclosed, unmet and neglected challenges in multi-omics studies. Nature Computational Science. 1(6):395-402. https://doi.org/10.1038/s43588-021-00086-z3954021

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Time-dependent metabolic phenotyping of inflammatory dysregulation

A rich and functional description of a patient health status is the fundamental basis for the personalisation of treatment and the targeting of interventions. The function of inflammation in the healing process as well as its involvement in most major diseases is well established, yet the specific mechanism by which it contributes to the pathogenesis is still not fully understood. If conditions arising from a dysregulation of the inflammatory process are to be treated before they become irreversible, a novel understanding of these pathologies must be achieved and a stratification of patients based on their inflammatory status undertaken. The work presented in this thesis aims to deliver new analytical and statistical approaches to support the investigation of the time-dependent dysregulation of inflammation. Lipid mediators have been described as exerting a major role in the initiation and regulation of the inflammatory response, yet analytical platforms for their large-scale characterisation in human biofluids are lacking. This thesis reports the validation of an assay for the simultaneous quantification of pro- and anti-inflammatory signalling molecules in multiple human biofluids. The coverage of the assay in each biofluid is subsequently established, characterising inflammatory signalling across biological compartments. A second study explores the assay’s applicability in a clinical context; investigating the relationship between lipid mediators, current clinical markers of inflammation and post-operative complications. Characterising the interplay between signalling and regulatory networks is key to understanding a living system’s response to perturbations, yet few statistical approaches are suited for the detection of time-dependent patterns in short and irregularly sampled longitudinal datasets. This thesis reports the development of a statistical approach to support the identification of altered time-trajectories in such studies. The method’s wide applicability is subsequently demonstrated on two investigations covering the diversity of metabolic phenotyping data generation platforms. This thesis is a proof of concept for the characterisation of patient-specific inflammatory status in a clinical context and the identification of altered time-dependent patterns. Both analytical and statistical developments have been motivated by the needs of real world applications and provide a template for the characterisation and analysis of the molecular basis for treatment.Open Acces

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Characterisation of xenometabolome signatures in complex biomatrices for enhanced human population phenotyping

Metabolic phenotyping facilitates the analysis of low molecular weight compounds in complex biological samples, with resulting metabolite profiles providing a window on endogenous processes and xenobiotic exposures. Accurate characterisation of the xenobiotic component of the metabolome (the xenometabolome) is particularly valuable when metabolic phenotyping is used for epidemiological and clinical population studies where exposure of participants to xenobiotics is unknown or difficult to control/estimate. Additionally, as metabolic phenotyping has increasingly been incorporated into toxicology and drug metabolism research, phenotyping datasets may be exploited to study xenobiotic metabolism at the population level. This thesis describes novel analytical and data-driven strategies for broadening xenometabolome coverage to allow effective partitioning of endogenous and xenobiotic metabolome signatures. The data driven strategy was multi-faceted, involving the generation of a reference database and the application of statistical methodologies. The database contains over 100 common xenobiotics profiles - generated using established liquid chromatography-mass-spectrometry methods – and provided the basis for an empirically derived screen for human urine and blood samples. The prevalence of these xenobiotics was explored in an exemplar phenotyping dataset (ALZ; n = 650; urine), with 31 xenobiotics detected in an initial screen. Statistical based methods were tailored to extract xenobiotic-related signatures and evaluated using drugs with well-characterised human metabolism. To complement the data-driven strategies for xenometabolome coverage, a more analytical based strategy was additionally developed. A dispersive solid phase extraction sample preparation protocol for blood products was optimised, permitting efficient removal of lipids and proteins, with minimal effect on low molecular weight metabolites. The suitability and reproducibility of this method was evaluated in two independent blood sample sets (AZstudy12; n=171, MARS; n=285). Finally, these analytical and statistical strategies were applied to two existing large-scale phenotyping study datasets: AIRWAVE (n = 3000 urine, n=3000 plasma samples) and ALZ (n= 650 urine, n= 449 serum) and used to explore both xenobiotic and endogenous responses to triclosan and polyethylene glycol exposure. Exposure to triclosan highlighted affected pathways relating to sulfation, whilst exposure to PEG highlighted a possible perturbation in the glutathione cycle. The analytical and statistical strategies described in this thesis allow for a more comprehensive xenometabolome characterisation and have been used to uncover previously unreported relationships between xenobiotic and endogenous metabolism.Open Acces