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

Expert Review of Proteomics, Год журнала: 2020, Номер 17(4), С. 243 - 255

Опубликована: Апрель 2, 2020

Introduction Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in reductionist way focusing on changes individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype an organism, intricate nature relationships may be better explored when considering whole system.Areas covered This review highlights multiple network strategies that can applied for metabolomics from different perspectives including: association networks based quantitative information, mass spectra similarity to assist metabolite annotation and biochemical systematic interpretation. We also highlight some organization obtained through exploration approaches.Expert opinion Network established method allows identification non-intuitive as well unknown compounds spectrometry. Additionally, representation within context intuitive use statistical summarize perspective.

Язык: Английский

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Toxicomicrobiomics: The Human Microbiome vs. Pharmaceutical, Dietary, and Environmental Xenobiotics

Frontiers in Pharmacology, Год журнала: 2020, Номер 11

Опубликована: Апрель 16, 2020

The harmful impact of xenobiotics on the environment and human health is being more widely recognized; yet, inter- intraindividual genetic variations among humans modulate extent harm, mostly through modulating outcome xenobiotic metabolism detoxification. As Human Genome Project revealed that host genetic, epigenetic, regulatory could not sufficiently explain complexity interindividual variability in metabolism, its sequel, Microbiome Project, investigating how this may be influenced by human-associated microbial communities. Xenobiotic–microbiome relationships are mutual dynamic. Not only does microbiome have a direct metabolizing potential xenobiotics, but it can also influence expression genes activity enzymes. On other hand, alter composition, leading to state dysbiosis, which linked multiple diseases adverse outcomes, including increased toxicity some xenobiotics. Toxicomicrobiomics studies these influences between ever-changing cloud various origins, with emphasis their fate toxicity, as well classes xenobiotic-modifying enzymes, This review article discusses classic recent findings toxicomicrobiomics, examples interactions gut, skin, urogenital, oral microbiomes pharmaceutical, food-derived, environmental current future prospects toxicomicrobiomic research discussed, tools strategies for performing such thoroughly critically compared.

Язык: Английский

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From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Metabolites, Год журнала: 2019, Номер 9(12), С. 308 - 308

Опубликована: Дек. 17, 2019

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset progression, response intervention. Each step of the analytical statistical pipeline crucial for generation high-quality, robust data. Metabolite identification remains bottleneck in these studies; therefore, confidence data produced paramount order maximize biological output. Here, we outline key steps workflow provide details on important parameters considerations. Studies should be designed carefully ensure appropriate power adequate controls. Subsequent sample handling preparation avoid introduction bias, which can significantly affect downstream interpretation. It not possible cover entire metabolome with single platform; platform reflect under investigation question(s) consideration. The large, complex datasets need pre-processed extract meaningful information. Finally, most time-consuming are metabolite identification, as well metabolic pathway network analysis. Here discuss some widely used tools pitfalls each workflow, ultimate aim guiding reader towards efficient their studies.

Язык: Английский

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Metabolomic Analysis of Defense-Related Reprogramming in Sorghum bicolor in Response to Colletotrichum sublineolum Infection Reveals a Functional Metabolic Web of Phenylpropanoid and Flavonoid Pathways

Frontiers in Plant Science, Год журнала: 2019, Номер 9

Опубликована: Янв. 4, 2019

The metabolome of a biological system provides functional readout the cellular state, thus serving as direct signatures biochemical events that define dynamic equilibrium metabolism and correlated phenotype. Hence, to elucidate processes involved in sorghum responses fungal infection, liquid chromatography-mass spectrometry-based untargeted metabolomic study was designed. Metabolic alterations three cultivars responding Colletotrichum sublineolum, were investigated. At 4-leaf growth stage, plants inoculated with spore suspensions infection monitored over time: 0, 3, 5, 7 9 days post inoculation. Non-infected used negative controls. metabolite composition aqueous-methanol extracts analysed on an ultra-high performance chromatography coupled high-definition mass spectrometry. acquired multidimensional data processed create matrices for multivariate statistical analysis chemometric modelling. computed models indicated time- cultivar-related metabolic changes reflect infection. pathway correlation-based network analyses revealed this multi-component defence response is characterised by web, containing defence-related molecular cues counterattack pathogen invasion. Components are metabolites from range interconnected pathways phenylpropanoid flavonoid being central hub web. One key features altered accumulation array phenolic compounds, particularly de novo biosynthesis antifungal 3-deoxyanthocynidin phytoalexins, apigeninidin, luteolinidin related conjugates. results complemented qRT-PCR gene expression showed upregulation marker genes. Unravelling characteristics mechanism underlying sorghum–C. sublineolum interactions, provided valuable insights potential applications breeding crop enhanced disease resistance. Furthermore, contributes ongoing efforts towards comprehensive understanding regulation reprogramming plant under biotic stress.

Язык: Английский

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Systems biology for crop improvement

The Plant Genome, Год журнала: 2021, Номер 14(2)

Опубликована: Май 5, 2021

Abstract In recent years, generation of large‐scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most these datasets different crop species, however, were studied independently as a result, full insight could not be gained on the molecular basis complex traits biological networks. A systems biology approach involving integration multiple omics data, modeling, prediction cellular functions is required to understand flow information that underlies traits. this context, with multiomics crucial allows holistic understanding dynamic system levels organization interacting external environment for phenotypic expression. Here, we present progress made area various studies—integrative approaches special focus application improvement. We have also discussed challenges opportunities integration, underpinning yield stress tolerance major cereals legumes.

Язык: Английский

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The Metabolome: a Key Measure for Exposome Research in Epidemiology

Current Epidemiology Reports, Год журнала: 2019, Номер 6(2), С. 93 - 103

Опубликована: Апрель 26, 2019

Язык: Английский

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Applications of Metabolomics in Forensic Toxicology and Forensic Medicine

International Journal of Molecular Sciences, Год журнала: 2021, Номер 22(6), С. 3010 - 3010

Опубликована: Март 16, 2021

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil criminal cases. New high-throughput technologies, borrowed from chemistry physics, have proven that metabolomics, the youngest “omics sciences”, could be one most powerful tools for monitoring changes disciplines. Metabolomics is a particular method allows measurement metabolic multicellular system using two different approaches: targeted untargeted. Targeted studies focused on known number defined metabolites. Untargeted metabolomics aims to capture metabolites present sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can applied extract useful important information both This review describe role medicine.

Язык: Английский

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Metabolomics in Exercise and Sports: A Systematic Review

Sports Medicine, Год журнала: 2021, Номер 52(3), С. 547 - 583

Опубликована: Окт. 30, 2021

Язык: Английский

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Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

Applied Microbiology and Biotechnology, Год журнала: 2022, Номер 106(9-10), С. 3465 - 3488

Опубликована: Май 1, 2022

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food nutrition, their pharmacological used for healthy aging. Their molecular properties validated through use recent high-throughput genomic, transcriptomic, metabolomic tools techniques. Together, these updated multi-omic have been to study fungal metabolites structure mode action on biological cellular processes. Diverse groups fungi different proteins secondary which possess tremendous biotechnological pharmaceutical applications. Furthermore, its acceptability can be accelerated by adopting multi-omics, bioinformatics, machine learning that generate a huge amount data. The integration artificial intelligence in era omics big data has opened up new outlook both basic applied researches area nutraceuticals nutrition. KEY POINTS: • Multi-omic tool helps identification novel Intra-omic from genomics bioinformatics Novel application human health.

Язык: Английский

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Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Frontiers in Molecular Biosciences, Год журнала: 2022, Номер 9

Опубликована: Март 8, 2022

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

Язык: Английский

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