Precise Metabolomics Reveals a Diversity of Aging‐Associated Metabolic Features DOI
He Tian,

Ni Zhen,

Sin Man Lam

et al.

Small Methods, Journal Year: 2022, Volume and Issue: 6(7)

Published: May 8, 2022

Mass spectrometry-based metabolomics has emerged as a powerful technique for biomedical research, although technical issues with its analytical precision and structural characterization remain. Herein, robust non-targeted strategy accurate quantitation precise profiling of metabolomes is developed applied to investigate plasma metabolic features associated human aging. A comprehensive set isotope-labeled standards (ISs) covering major pathways incorporated quantify polar metabolites. Matching rules select ISs calibration follow primary criterion minimal coefficients variations (COVs). If COVs between specific particular metabolite fall within 5% window, further selection conducted based on similarities proximity in retention time. The introduction refined appropriate reduces the 480 identified metabolites quality control samples from 14.3% 9.8% facilitates identification additional metabolite. Finally, approach reveals perturbations diverse array across aging that principally implicate steroid metabolism, amino acid lipid purine which allows authors draw correlates pathology various age-related diseases. These findings provide clues prevention treatment these

Language: Английский

Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data DOI Open Access
Zhiqiang Pang,

Guangyan Zhou,

Jessica Ewald

et al.

Nature Protocols, Journal Year: 2022, Volume and Issue: 17(8), P. 1735 - 1761

Published: June 17, 2022

Language: Английский

Citations

1056
Small molecule metabolites: discovery of biomarkers and therapeutic targets DOI Creative Commons
Shi Qiu, Ying Cai, Hong Yao

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: March 20, 2023

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks diseases. Metabolite signatures that have close proximity subject's phenotypic informative dimension, are useful for predicting diagnosis prognosis diseases as well monitoring treatments. The lack early biomarkers could poor serious outcomes. Therefore, noninvasive methods with high specificity selectivity desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool biomarker pathway analysis, revealing possible mechanisms human various deciphering therapeutic potentials. It help identify functional related variation delineate biochemical changes indicators pathological damage prior disease development. Recently, scientists established large number profiles reveal underlying networks target exploration in biomedicine. This review summarized analysis on potential value small-molecule candidate metabolites clinical events, may better diagnosis, prognosis, drug screening treatment. We also discuss challenges need be addressed fuel next wave breakthroughs.

Language: Английский

Citations

365
Applications of Multi-Omics Technologies for Crop Improvement DOI Creative Commons
Yaodong Yang, Mumtaz Ali Saand,

Liyun Huang

et al.

Frontiers in Plant Science, Journal Year: 2021, Volume and Issue: 12

Published: Sept. 3, 2021

Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) paved a way new generation of different omics, such genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, phenomics also been well-documented crop science. Multi-omics with high throughput techniques played an important role elucidating growth, senescence, yield, responses to biotic abiotic stress numerous crops. These omics implemented some crops including wheat ( Triticum aestivum L.), soybean Glycine max ), tomato Solanum lycopersicum barley Hordeum vulgare maize Zea mays millet Setaria italica cotton Gossypium hirsutum Medicago truncatula , rice Oryza sativa L.). The integration functional genomics other highlights relationships between genomes phenotypes under specific physiological environmental conditions. purpose this review is dissect multi-omics breeding We highlight applications various approaches, proteomics, phenomics, implementation robust methods improve genetics Potential challenges that confront regard analysis genes their networks well development potential traits improvement are discussed. panomics platform allows complex construct models can be used predict traits. Systems biology datasets enhance our understanding molecular regulator improvement. In context, we suggest entire by employing “phenotype genotype” “genotype phenotype” concept. Hence, top-down (phenotype genotype) bottom-up (genotype phenotype) model through may beneficial conditions stresses.

Language: Английский

Citations

242
Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning DOI
Daniil A. Boiko,

Konstantin S. Kozlov,

Julia V. Burykina

et al.

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(32), P. 14590 - 14606

Published: Aug. 8, 2022

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which vital materials science, life sciences fields such as metabolomics proteomics, mechanistic research in chemistry. Although it one most powerful methods individual compound detection, complete signal assignment mixtures still great challenge. The unconstrained formula-generating algorithm, covering entire spectra revealing components, "dream tool" researchers. We present framework efficient MS data interpretation, describing novel approach detailed based on deisotoping performed by gradient-boosted decision trees neural network that generates molecular formulas from fine isotopic structure, approaching long-standing inverse spectral problem. were successfully tested three examples: fragment ion protein sequencing natural samples sciences, study cross-coupling catalytic system

Language: Английский

Citations

184
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine DOI Open Access
Xiujing He, Xiaowei Liu,

Fengli Zuo

et al.

Seminars in Cancer Biology, Journal Year: 2022, Volume and Issue: 88, P. 187 - 200

Published: Dec. 31, 2022

Language: Английский

Citations

151
Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking DOI Creative Commons
Zhiwei Zhou,

Mingdu Luo,

Haosong Zhang

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 4, 2022

Abstract Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, metabolite annotation is a major challenge metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), enable global from knowns unknowns The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, MS/MS similarity peak correlation network. To demonstrate principle, apply vitro enzymatic system different biological samples, with ~100–300 putative annotated each data set. Among them, >80% are corroborated silico tools. Finally, validate 5 that absent common libraries through repository mining synthesis of chemical standards. Together, enables efficient annotations, substantially advances discovery recurrent for samples model organisms, towards deciphering dark matter

Language: Английский

Citations

140
The Potential of Metabolomics in Biomedical Applications DOI Creative Commons
Vanessa González-Covarrubias, Eduardo Martínez‐Martínez, Laura del Bosque‐Plata

et al.

Metabolites, Journal Year: 2022, Volume and Issue: 12(2), P. 194 - 194

Published: Feb. 19, 2022

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed discovery relevant metabolites that characterize wide variety human phenotypes with respect to health, disease, drug monitoring, even aging. Metabolomics, parallel genomics, has led biomarkers aided in understanding diversity molecular mechanisms, highlighting its application precision medicine. This review focuses on metabolomics can be applied improve as well trends impacts metabolic neurodegenerative diseases, cancer, longevity, exposome, liquid biopsy development, pharmacometabolomics. identification distinct metabolomic profiles will help improvement clinical strategies treat disease. In years come, become tool routinely diagnose monitor health aging, or development. Biomedical applications already foreseen progression such obesity diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, genomics; these assess disease severity predict potential treatment. Future endeavors should focus determining applicability utility metabolomic-derived markers their appropriate implementation large-scale settings.

Language: Английский

Citations

128
To metabolomics and beyond: a technological portfolio to investigate cancer metabolism DOI Creative Commons
Federica Danzi,

Raffaella Pacchiana,

Andrea Mafficini

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: March 22, 2023

Abstract Tumour cells have exquisite flexibility in reprogramming their metabolism order to support tumour initiation, progression, metastasis and resistance therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic redox status sustain increased energetic demand cells. Over last decades, cancer field has seen an explosion new biochemical technologies giving more tools than ever before navigate this complexity. Within cell or tissue, metabolites constitute direct signature molecular phenotype thus profiling concrete clinical applications oncology. Metabolomics fluxomics, are key technological approaches that mainly revolutionized enabling researchers both qualitative mechanistic model cancer. Furthermore, upgrade from bulk single-cell analysis provided unprecedented opportunity investigate biology at cellular resolution allowing depth quantitative complex heterogenous diseases. More recently, advent functional genomic screening allowed identification pathways, processes, biomarkers novel therapeutic targets concert with other allow patient stratification treatment regimens. This review is intended be guide for metabolism, highlighting current emerging technologies, emphasizing advantages, disadvantages potential leading development innovative anti-cancer

Language: Английский

Citations

108
Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine DOI Creative Commons
Javier Plou, Pablo S. Valera, Isabel Garcı́a

et al.

ACS Photonics, Journal Year: 2022, Volume and Issue: 9(2), P. 333 - 350

Published: Feb. 2, 2022

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification patients based on their predicted disease risk, prognosis, and response to specific treatment. Up now, genomics, transcriptomics, immunohistochemistry have been main clinically amenable tools at hand for identifying key diagnostic, prognostic, predictive biomarkers. However, other molecular strategies, including metabolomics, are still in infancy require development new biomarker technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has recognized as promising technology monitoring thanks its high sensitivity label-free operation, which should help accelerate discovery corresponding screening simpler, faster, less-expensive manner. Many studies demonstrated excellent performance SERS biomedical applications. such also revealed several variables considered accurate monitoring, particular, when signal is collected from biological sources (tissues, cells or biofluids). This Perspective aimed piecing together puzzle with view future challenges implications. We address most relevant requirements plasmonic substrates applications, well artificial intelligence biotechnology guide highly versatile sensors.

Language: Английский

Citations

103
Emerging therapies in cancer metabolism DOI Creative Commons
Yi Xiao, Tian‐Jian Yu, Ying Xu

et al.

Cell Metabolism, Journal Year: 2023, Volume and Issue: 35(8), P. 1283 - 1303

Published: Aug. 1, 2023

Language: Английский

Citations

91