Andreas Klitgaard

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association–based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

Aggressive dereplication using UHPLC-DAD-QTOF: screening extracts for up to 3000 fungal secondary metabolites

In natural-product drug discovery, finding new compounds is the main task, and thus fast dereplication of known compounds is essential. This is usually performed by manual liquid chromatography-ultraviolet (LC-UV) or visible light-mass spectroscopy (Vis-MS) interpretation of detected peaks, often assisted by automated identification of previously identified compounds. We used a 15 min high-performance liquid chromatography–diode array detection (UHPLC–DAD)–high-resolution MS method (electrospray ionization (ESI)+ or ESI−), followed by 10–60 s of automated data analysis for up to 3000 relevant elemental compositions. By overlaying automatically generated extracted-ion chromatograms from detected compounds on the base peak chromatogram, all major potentially novel peaks could be visualized. Peaks corresponding to compounds available as reference standards, previously identified compounds, and major contaminants from solvents, media, filters etc. were labeled to differentiate these from compounds only identified by elemental composition. This enabled fast manual evaluation of both known peaks and potential novel-compound peaks, by manual verification of: the adduct pattern, UV–Vis, retention time compared with log D, co-identified biosynthetic related compounds, and elution order. System performance, including adduct patterns, in-source fragmentation, and ion-cooler bias, was investigated on reference standards, and the overall method was used on extracts of Aspergillus carbonarius and Penicillium melanoconidium, revealing new nitrogen-containing biomarkers for both species.

Accurate Dereplication of Bioactive Secondary Metabolites from Marine-Derived Fungi by UHPLC-DAD-QTOFMS and a MS/HRMS Library

In drug discovery, reliable and fast dereplication of known compounds is essential for identification of novel bioactive compounds. Here, we show an integrated approach using ultra-high performance liquid chromatography-diode array detection-quadrupole time of flight mass spectrometry (UHPLC-DAD-QTOFMS) providing both accurate mass full-scan mass spectrometry (MS) and tandem high resolution MS (MS/HRMS) data. The methodology was demonstrated on compounds from bioactive marine-derived strains of Aspergillus, Penicillium, and Emericellopsis, including small polyketides, non-ribosomal peptides, terpenes, and meroterpenoids. The MS/HRMS data were then searched against an in-house MS/HRMS library of ~1300 compounds for unambiguous identification. The full scan MS data was used for dereplication of compounds not in the MS/HRMS library, combined with ultraviolet/visual (UV/Vis) and MS/HRMS data for faster exclusion of database search results. This led to the identification of four novel isomers of the known anticancer compound, asperphenamate. Except for very low intensity peaks, no false negatives were found using the MS/HRMS approach, which proved to be robust against poor data quality caused by system overload or loss of lock-mass. Only for small polyketides, like patulin, were both retention time and UV/Vis spectra necessary for unambiguous identification. For the ophiobolin family with many structurally similar analogues partly co-eluting, the peaks could be assigned correctly by combining MS/HRMS data and m/z of the [M + Na]+ ions.

Molecular and Chemical Characterization of the Biosynthesis of the 6-MSA-Derived Meroterpenoid Yanuthone D in Aspergillus niger

Secondary metabolites in filamentous fungi constitute a rich source of bioactive molecules. We have deduced the genetic and biosynthetic pathway of the antibiotic yanuthone D from Aspergillus niger. Our analyses show that yanuthone D is a meroterpenoid derived from the polyketide 6-methylsalicylic acid (6-MSA). Yanuthone D formation depends on a cluster composed of ten genes including yanA and yanI, which encode a 6-MSA polyketide synthase and a previously undescribed O-mevalon transferase, respectively. In addition, several branching points in the pathway were discovered, revealing five yanuthones (F, G, H, I, and J). Furthermore, we have identified another compound (yanuthone X1) that defines a class of yanuthones that depend on several enzymatic activities encoded by genes in the yan cluster but that are not derived from 6-MSA.

Combining Stable Isotope Labeling and Molecular Networking for Biosynthetic Pathway Characterization

Filamentous fungi are a rich source of bioactive compounds, ranging from statins over immunosuppressants to antibiotics. The coupling of genes to metabolites is of large commercial interest for production of the bioactives of the future. To this end, we have investigated the use of stable isotope labeled amino acids (SILAAs). SILAAs were added to the cultivation media of the filamentous fungus Aspergillus nidulans for the study of the cyclic tetrapeptide nidulanin A. Analysis by UHPLC-TOFMS confirmed that the SILAAs were incorporated into produced nidulanin A, and the change in observed m/z could be used to determine whether a compound (known or unknown) incorporated any of the added amino acids. Samples were then analyzed using MS/MS and the data used to perform molecular networking. The molecular network revealed several known and unknown compounds that were also labeled. Assisted by the isotope labeling, it was possible to determine the sequence of several of the compounds, one of which was the known metabolite fungisporin, not previously described in A. nidulans. Several novel analogues of nidulanin A and fungisporin were detected and tentatively identified, and it was determined that these metabolites were all produced by the same nonribosomal peptide synthase. The combination of stable isotope labeling and molecular network generation was shown to very effective for the automated detection of structurally related nonribosomal peptides, while the labeling was effective for determination of the peptide sequence, which could be used to provide information on biosynthesis of bioactive compounds.

An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria

We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient “mining” workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting. ABSTRACT Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P. luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient “mining” workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting.

Combining UHPLC-High Resolution MS and Feeding of Stable Isotope Labeled Polyketide Intermediates for Linking Precursors to End Products

We present the results from stable isotope labeled precursor feeding studies combined with ultrahigh performance liquid chromatography-high resolution mass spectrometry for the identification of labeled polyketide (PK) end-products. Feeding experiments were performed with (13)C8-6-methylsalicylic acid (6-MSA) and (13)C14-YWA1, both produced in-house, as well as commercial (13)C7-benzoic acid and (2)H7-cinnamic acid, in species of Fusarium, Byssochlamys, Aspergillus, and Penicillium. Incorporation of 6-MSA into terreic acid or patulin was not observed in any of six evaluated species covering three genera, because the 6-MSA was shunted into (2Z,4E)-2-methyl-2,4-hexadienedioic acid. This indicates that patulin and terreic acid may be produced in a closed compartment of the cell and that (2Z,4E)-2-methyl-2,4-hexadienedioic acid is a detoxification product toward terreic acid and patulin. In Fusarium spp., YWA1 was shown to be incorporated into aurofusarin, rubrofusarin, and antibiotic Y. In A. niger, benzoic acid was shown to be incorporated into asperrubrol. Incorporation levels of 0.7-20% into the end-products were detected in wild-type strains. Thus, stable isotope labeling is a promising technique for investigation of polyketide biosynthesis and possible compartmentalization of toxic metabolites.