Susceptibility to false discovery in biomarker research using liquid chromatography-high resolution mass spectrometry based untargeted metabolomics profiling

Impact of Acupuncture on Human Metabolomic Profiles: A Systematic Review

BACKGROUND/OBJECTIVES

Metabolomics provides insights into the biological underpinnings of disease development and treatment. This systematic review investigated the impact of acupuncture on metabolite levels and associated metabolic pathways using a metabolomic approach.

METHODS

Five databases (i.e., PubMed, Embase, Scopus, CINAHL, and Cochrane Central) were searched using terms such as "acupuncture" and "metabolites" to retrieve relevant journal articles published through January 2024. Studies utilizing mass spectrometry or nuclear magnetic resonance were included. Risk of bias was evaluated using the Cochrane Risk of Bias tool and the Newcastle-Ottawa scale. Metabolic pathway analysis was conducted using MetaboAnalyst 6.0 to identify common significant pathways affected by acupuncture. Additionally, subgroup pathway enrichment analysis identified metabolites significantly altered in more than two studies.

RESULTS

Among 4019 articles, 22 studies met inclusion criteria, examining changes in metabolomic biomarkers before and after acupuncture for various diseases and symptoms. A total of 226 metabolites showed significant changes, with 14 common metabolites altered in more than two studies (glutamine, androsterone glucuronide, choline, citric acid, decanoylcarnitine, estrone, glutathione, glycine, hypoxanthine, lactic acid, pyruvic acid, serine, proline, and sn-glycero-3-phosphocholine). Common pathways affected by acupuncture were glycine, serine, and threonine metabolism, glutathione metabolism, arginine biosynthesis, and glyoxylate and dicarboxylate metabolism.

CONCLUSIONS

This review provides insights of the metabolomic mechanisms underlying acupuncture, highlighting its impact on specific metabolic pathways. Recognizing these changes can enhance acupuncture's effectiveness and support the development of personalized treatments. The findings underscore metabolomics as a valuable tool for understanding and optimizing acupuncture for various diseases and symptoms.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

The Use of Metabolomics as a Tool to Compare the Regulatory Mechanisms in the Cecum, Ileum, and Jejunum in Healthy Rabbits and with Diarrhea

Simple Summary

The problems caused by antibiotic abuse have swept the world, and the Chinese government has responded to calls for a comprehensive ban on antibiotics. However, not using antibiotics also challenges China’s existing livestock industry. Based on this, we carried out a nontargeted metabolomics analysis of the jejunum, ileum, and cecum of diarrhea rabbits and normal rabbits fed with antibiotic-free diets, respectively, to find out the mechanism of action of each intestinal segment group and between different intestinal segments. The screened differential metabolites were mostly related to intestinal barrier, intestinal inflammation, and autophagy after a KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. In this paper, we analyzed the metabolic pathways that were significantly different between different intestinal segments and illustrated the mechanism and potential connections of the screened differential metabolites in different intestinal segments in the form of charts.

Abstract

For many years, antibiotics in feed have been an effective and economical means to promote growth and disease resistance in livestock production. However, the rampant abuse of antibiotics has also brought very serious harm to human health and the environment. Therefore, the Chinese government promulgated laws and regulations on 1 July 2020, to prohibit the use of antibiotics in feed. To improve the effect of antibiotic-free feeding on China’s existing rabbit industry, we used the nontargeted metabolomics method to detect the differences between diarrhea rabbits (Dia) and normal rabbits (Con) on an antibiotic-free diet. A total of 1902 different metabolites were identified. A KEGG analysis showed that in the cecum, metabolites were mainly concentrated in bile secretion, antifolate resistance, aldosterone synthesis, and secretion pathways. The ileal metabolites were mainly concentrated in tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, steroid hormone biosynthesis, alanine, aspartate, and glutamate metabolism. The metabolites in the jejunum were mainly rich in panquinone and other terpenoid compound quinone biosynthesis, AMPK (adenosine 5′-monophosphate (AMP)-activated protein kinase) signal, inositol phosphate metabolism, and pentose phosphate pathway. After a deep excavation of the discovered differential metabolites and metabolic pathways with large differences between groups, it was found that these metabolic pathways mainly involved intestinal inflammation, intestinal barrier, and autophagy. The results showed that panquinone and other terpenoids could increase AMPK activity to promote cell metabolism and autophagy, thus trying to prevent inflammation and alleviate intestinal disease symptoms. In addition, we discussed the possible reasons for the changes in the levels of seven intestinal endogenous metabolites in rabbits in the diarrhea group. The possibility of improving diarrhea by adding amino acids to feed was discussed. In addition, the intermediate products produced by the pentose phosphate pathway and coenzyme Q had a positive effect on steroid hormone biosynthesis to combat intestinal inflammation.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.