The Isolation and Structure Elucidation of Fungal Metabolites [chapter]

Guy Harris
2004 Handbook of Industrial Mycology  
Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multistep protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream
more » ... he downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a "fraction bank" suitable for longterm laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversedphase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterization. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physicochemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as tetrahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried, and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency. M etabolic profiling of human biofluids by liquid chromatography−mass spectrometry (LC-MS) is widely used in clinical and epidemiological studies. Improvements in analytical technologies and automation of data processing have made it possible to increase both the number and throughput of sample analysis. In addition, technological advancements allowing greater analytical sensitivity, precision, and selectivity have led to the increase of the number of the detectable chemicals providing greater metabolome coverage. The extensive breadth of metabolic profiling data ensures that metabolite annotation and identification remain major bottlenecks in the metabolic phenotyping workflow, with low abundance metabolites, gut microbial cometabolites, secondary metabolites (glucuronides and sulfates), and chemically modified drug and diet related metabolites representing specific challenges. Growth of publicly available databases containing mass spectral reference data for thousands of chemical species 1−3 is limited by the availability of authentic chemical standards, and the value of establishing methodspecific in-house databases (e.g., complete with chromatographic retention time measurement) is further hampered by the lack of consistency in standard chromatographic profiling methods employed. Some in silico spectra prediction computational tools and pipelines 4,5 are being developed, 5−8 as well as retention time prediction, 9,10 chemical similarity, 11 and biological assumptions to extend the reach of generic metabolite annotation capabilities beyond the limitations of
doi:10.1201/9780203970553.ch8 fatcat:csjvlebv6jefrn6wwgs5vho234