Comprehensive Improvement of Sample Preparation Methodologies Facilitates Dynamic Metabolomics of Aspergillus niger.
Xiaomei Z ZhengJiandong YuTimothy C CairnsLihui ZhangZhidan ZhangQiongqiong ZhangPing ZhengJibin SunYanhe MaPublished in: Biotechnology journal (2018)
Metabolomics is an essential discipline in industrial biotechnology. Sample preparation approaches dramatically influence data quality and, ultimately, interpretation and conclusions from metabolomic experiments. However, standardized protocols for highly reproducible metabolic datasets are limited, especially for the fungal cell factory Aspergillus niger. Here, an improved liquid chromatography-tandem mass spectrometry-based pipeline for A. niger metabolomics is developed. It is found that fast filtration with liquid nitrogen is more suitable for cell quenching, causing minimal disruption to cell integrity, and improved intracellular metabolite recovery when compared to cold methanol quenching approaches. Seven solutions are evaluated for intracellular metabolite extraction, and found acetonitrile/water (1:1, v/v) at -20 °C, combined with boiling ethanol extraction protocols, showed unbiased metabolite profiling. This improved methodology is applied to unveil the dynamic metabolite profile of one citrate over-producing A. niger isolate under citrate fermentation. Citrate precursors, especially pyruvate, oxaloacetate, and malate, are maintained at a relatively high intracellular level, which can be necessary for high citrate synthesis flux. Glutamine shows a similar trend compared to citrate production, suggesting glutamine may be involved in intracellular pH homeostasis. Taken together, this study delivers a highly standardized and improved metabolomics methodology and paves the way for systems metabolic engineering in biotechnologically important fungi.
Keyphrases
- single cell
- liquid chromatography tandem mass spectrometry
- mass spectrometry
- cell therapy
- reactive oxygen species
- rna seq
- simultaneous determination
- machine learning
- ms ms
- risk assessment
- molecularly imprinted
- wastewater treatment
- solid phase extraction
- quality improvement
- deep learning
- artificial intelligence
- ionic liquid
- liquid chromatography