Fungi population metabolomics and molecular network study reveal novel biomarkers for early detection of aflatoxigenic Aspergillus species

Xie, Huali; Wang, Xiupin; Hooft, Justin J. J. van der; Medema, Marnix H.; Chen, Zhi Yuan; Yue, Xiaofeng; Zhang, Qi; Li, Peiwu

doi:10.1016/j.jhazmat.2021.127173

Cited by 20 publications

(14 citation statements)

References 40 publications

(44 reference statements)

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“…The discovery of these phylogenetic patterns and the proposed mechanisms by which they originate enhances our understanding of how fungi adapt to geographic environments with chemical innovation. It can be used to infer genetic and metabolic 49 markers using arti cial intelligence algorithm to distinguish a atoxin-producing strains in the A. avus population for discerning early-stage mycotoxin contamination risk. The discovery also gives insight into the evolutionary trends of toxigenic fungal variation caused by global climate change and will inform rational design of 'personalized' geographical control agents, in order to achieve more accurate and long-term control of harmful fungi, mitigation of the adverse effects of mycotoxins, and then reducing global food loss for alleviating the food security crisis.…”

Section: Discussionmentioning

confidence: 99%

“…All isolates non-targeted metabolome raw data were collected via our optimized and standardized metabolome platform 49,50 . An C18 column (Hypersil Gold, 100 mm × 2.1 mm (i.d.))…”

Section: Metabolomics Data Acquisition By Uplc-hrmsmentioning

confidence: 99%

“…Eventually, a .csv quantitative le was exported and then was submitted to GNPS or MetDNA2 84 after modifying the data matrix format for metabolites identi cation or molecular networking analysis and structural annotation. We followed the metabolomics data quality control methods and data quality assessments as reported before 49 .…”

Section: Metabolome Features Extraction and Data Qualitymentioning

confidence: 99%

“…Longitude from 87.3 E (Changji, Xinjiang Uygur Autonomous Region) to 123.42 (Qiqihar, Heilongjiang Province) in the west. Altitudes range from 5.3 m (Lianyungang, Jiangsu Province) near sea level to 2330m (Zayü County, Tibet Autonomous Region) 49 . Detailed strain sampling site coordinates were described in Supplementary Material Table 1.…”

Section: Aspergillus Avus Strains Library Construction and Mycelium C...mentioning

confidence: 99%

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WITHDRAWN: Global multi-omics profiling reveals evolutionary drivers of phylogeographic diversity of fungal specialized metabolism

Zhang

Xie

Hu³

et al. 2023

Preprint

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Chemical innovation is essential for fungi to adapt to specific geographical environments and ecological niches. However, it remains unclear how genomic changes drive fungal specialized metabolic differentiation across the biosphere. Here, we studied the global genomic and metabolic diversity of the aflatoxin-producing Aspergillus flavus as a case. We generated paired genome and metabolome data for > 550 strains from across China, and supplemented this with 187 publicly available genomes from other countries. We found that the global genetic diversity of A. flavus shows strong phylogeographic patterns, with specific clades associating with different environments. These clades exhibit distinct specialized metabolic profiles, with low-aflatoxin-producing clades often producing other mycotoxins instead. Furthermore, clade-specific gene clusters do exist that could be linked to clade-specific metabolites, implying that different niches select for the production of different toxins. Our results reveal that these different mycotoxin profiles are generally not explained by loss or gain of biosynthetic gene clusters, but are likely driven by regulatory and primary metabolic variation. Altogether, our findings provide a framework to understand fungal environmental adaptation and pave the path towards enhanced mycotoxin early risk prediction and ‘personalized’ geographical control strategies.

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Section: Discussionmentioning

confidence: 99%

“…All isolates non-targeted metabolome raw data were collected via our optimized and standardized metabolome platform 49,50 . An C18 column (Hypersil Gold, 100 mm × 2.1 mm (i.d.))…”

Section: Metabolomics Data Acquisition By Uplc-hrmsmentioning

confidence: 99%

Section: Metabolome Features Extraction and Data Qualitymentioning

confidence: 99%

Section: Aspergillus Avus Strains Library Construction and Mycelium C...mentioning

confidence: 99%

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WITHDRAWN: Global multi-omics profiling reveals evolutionary drivers of phylogeographic diversity of fungal specialized metabolism

Zhang

Xie

Hu³

et al. 2023

Preprint

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“…Recently, Xie and co-worker introduced the XGBoost algorithm tool into the global food safety risk management. It will be used in the prevention and control of mycotoxins by analyzing novel biomarkers associated with aflatoxigenic Aspergillus species using population metabolomics and machine learning tools (Xie et al 2022 ).…”

Section: Multi-omic Tools In Basidiomycete Metabolite Regulation Studiesmentioning

confidence: 99%

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

Shankar

Sharma

2022

Appl Microbiol Biotechnol

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

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Protein biomarker for early diagnosis of microbial toxin contamination: Using Aspergillus flavus as an example

Yan

et al. 2023

Food Frontiers

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Microbial toxins are important factors that contribute to human diseases and food losses. Predicting the production of these toxins before they occur is a significant challenge. In this study, we innovatively developed a strategy called targeted acquisition of toxin‐related immunogen (TATRI) to identify early diagnostic biomarkers for microbial toxins. Taking Aspergillus flavus as an example, we employed an immunoassay‐based screening approach to identify nanobodies that exhibited significant response signals to different toxigenic strains. Subsequently, we isolated and purified the specific immunogen pre‐aflatoxin biomarker PAB‐01 that bound to the selected nanobody. The results showed that the level of PAB‐01 was positively correlated with the toxin‐producing ability of A. flavus strains and could be detected before the production of aflatoxins. It met the essential requirements as an early diagnostic biomarker for aflatoxin contamination. Protein identification and bioinformatics analysis revealed that PAB‐01 is a glycoprotein that can be secreted into the extracellular space. It is located on chromosome 5 of A. flavus and belongs to the 75 family of glycoside hydrolases. Furthermore, we used 7 g/kg as the critical concentration of PAB‐01 to diagnose the risk of aflatoxin contamination in stored peanuts, with an accuracy rate of 75.9% for high‐risk samples. In summary, this work provides a promising approach for the identification of protein biomarkers prior to microbial toxin production and the establishment of early diagnostic techniques for microbial toxins.

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Fungi population metabolomics and molecular network study reveal novel biomarkers for early detection of aflatoxigenic Aspergillus species

Cited by 20 publications

References 40 publications

WITHDRAWN: Global multi-omics profiling reveals evolutionary drivers of phylogeographic diversity of fungal specialized metabolism

WITHDRAWN: Global multi-omics profiling reveals evolutionary drivers of phylogeographic diversity of fungal specialized metabolism

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

Protein biomarker for early diagnosis of microbial toxin contamination: Using Aspergillus flavus as an example

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