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Filamentøse svampe udfylder en meget vigtig rolle i naturen hvor de nedbryder organisk materiale og derved frigiver næringsstoffer, som kan udnyttes af andre organismer. Svampe og andre mikroorganismer producerer derudover en bred vifte af bioaktive stoffer, de såkaldte sekundære metabolitter. Disse udfylder forskellige rolle såsom signalering, forsvar eller pigmentering. Produkter fra mikroorganismer har en todelt indflydelse på det menneskelige samfund: de er blevet benyttet som lægemidler eller som inspiration til udviklingen af lægemidler i århundreder. Samtidigt udgør svampeinfektioner i afgrøder, og den efterfølgende kontaminering med mykotoxiner, en fortsat trussel mod menneskers helbred. På grund af dette er metoder til at detektere og analysere disse stoffer vitale. Det er blevet skønnet at der eksisterer omkring 1,5 millioner forskellige svampe arter på Jorden, hvilket langt overstiger det skønnede antal af planter på 300.000, og dette betyder at der potentielt stadig findes mange uopdagede biologisk interessante stoffer. Den primære analyseteknik der benyttes til at undersøge produktionen af stoffer fra disse forskellige organismer er væskekromatografi kombineret med massespektrometri (LC-MS). Med udviklingen af nye og forbedrede analyseinstrumenter til kemisk analyse er selve analysetiden blevet reduceret mens mængden af information, der opnås fra hver af disse analytiske undersøgelser, er steget drastisk. Som en konsekvens af dette er det begrænsende trin i analysen af mikroorganismer ikke længere selve den kemiske analyse, men i stedet analyse af data. Klassiske metoder, hvor datafiler analyseres enkeltvis og manuelt, er ikke længere tilstrækkelige til at håndtere de stigende mængder data. Der er derfor nødvendigt at udvikle nye metoder til at udvinde værdifuld information fra data, samt at øge hastigheden hvormed data analyseres. Et af hovedmålene med mit PhD studium var at udvikle metoder der tillader analyse af store mængder data fra metabolit-ekstrakter fra filamentøse svampe og andre mikroorganismer, samt at reducere den tid der bruges på manuel tolkning af LC-MS data. Dette ledte til udvikling af en metode, kaldet aggressiv dereplikering, der benytter sig af metabolit-biblioteker til at screene LC-MS, for derved at annotere kendte stoffer. Ved at overlejre base peak kromatogrammer kunne potentielt nye toppe derved visualiseres, hvilket tillod hurtig dereplikering af data. Metoden blev yderligere udviklet til at benytte tandem MS data (MS/MS), hvilket øgede tilliden til identifikationen af fundne stoffer. Et andet mål med mit studie har været at udvikle metoder der gør det muligt at koble sekundære metabolitter til de biosyntetiske gener der er ansvarlige for deres produktion. Denne kobling af metabolitter til gener er af stor kommerciel interesse med henblik på fremtidig produktion af bioaktive stoffer. En del af mit studie var fokuseret på identifikation samt udredning af biosyntesen af det ikke-ribosomale peptid (NRP) nidulanin A fra Aspergillus nidulans. Flere analoger til nidulanin A blev også fundet, men disse kunne ikke strukturopklares da blev produceret i meget små mængder. I stedet blev en ny fremgangsmåde udviklet til at undersøge NRPer ved hjælp af stabile isotopmærkede (SIL) aminosyrer og efterfølgende MS/MS analyse. Optagne MS/MS spektre blev analyseret ved at danne et molekylært netværk, som grupperede stoffer der udviste samme MS/MS spektre. Kombinationen af SIL og molekylære netværk viste sig at være meget effektivt til detektion af strukturelt relaterede NRPer. Ved at udnytte mærkning alene var det muligt at bestemme viii sekvensen af det cykliske peptid, og metoden kan benyttes til at undersøge biosyntesen af andre bioaktive stoffer. I et andet studie blev målrettet analyse kombineret med SIL udgangsstoffer brugt til at bestemme biosyntesen af stoffet yanuthone D, som produceres af A. niger. Metoden blev ydermere anvendt til at identificere andre stoffer, som bliver biosyntetiseret fra samme udgangsstof. Yderligere studier af polyketider blev foretaget, igen med brug af SIL udgangsstoffer for at undersøge fordele og ulemper ved fremgangsmåden. Disse studier ledte til bestemmelse af det biosyntetiske ophav af flere stoffer fra Fusarium, blandt andet antibiotic Y, samt til en tentativ identifikation af et intermediat i antibiotic Ys biosyntese. Ydermere blev benzoesyre bestemt til at være udgangsstoffet for stoffet asperrubrol i A. niger. Afslutningsvist blev en fremgangsmåde udviklet til at evaluere det biosyntetiske potentiale i bakterier samt undersøge den kemiske diversitet. Til dette studie blev 13 forskellige stammer, relateret til den marine bakterie Pseudoalteromonas luteoviolacea, undersøgt i et ikke-målrettet (untargeted) metabolomics eksperiment, hvorefter de kemiske data blev korrelerede med fuld-genom sekvenser fra stammerne. Vi fandt derved at 30 % af de kemiske egenskaber samt 24 % af de biosyntetiske gener var unikke for den enkelte stamme, mens kun 2 % af kemiske detaljer (features) samt 7 % af generne var fælles mellem alle stammerne. Den oprindelige liste af 2.000 kemiske features blev reduceret til 50 særligt beskrivende kemiske detaljer ved hjælp af en genetisk algoritme som blev evalueret ved hjælp af en support vector machine. Disse kemiske detaljer blev effektivt dereplikeret ved brug af et molekylært netværk, og ledte til identifikation af flere kendte antibakterielle stoffer, flere af hvilke ikke tidligere var bestemt fra denne organisme. Ved at kombinere metabolomics samt genom-data var det da muligt at koble metabolitter til deres biosyntese på et meget tidligt tidspunkt i opdagelsesprocessen. På basis af de opnåede resultater, må det konkluderes af de udviklede metoder og metodikker er meget effektive samt anvendelige til analyse af metabolitter fra en bred vifte af mikroorganismer. De udviklede metoder har ledt til ny indsigt i mikrobielle sekundære metabolitter, og det står klart at stadig flere opdagelser kan gøres ved brug af disse metoder. Filamentous fungi serve a very important role in Nature where they break down organic matter, releasing nutrients that can be used by other organisms. Fungi and other microorganisms also produce a wide array of bioactive compounds, the secondary metabolites( SMs), used for such diverse roles as signaling, defense, or pigmentation. Compounds from microorganisms have a dual impact on human society: they have been used as drugs, or as inspiration for the development of drugs for centuries. However, fungal infection of crops and the subsequent contamination by mycotoxins, continue to pose a threat to human health. Because of this, methods for detection and analysis of these compounds are vital. Estimates suggest that there are around 1.5 million different fungal species on Earth, dwarfing the number of plants estimated to 300,000, meaning that there potentially are many more interesting compounds are still to be discovered. The main analytical technique used to investigate production of products from these diverse organisms is liquid-chromatography coupled to mass spectrometry (LC-MS). With the development of new and improved analytical instrumentation for chemical analysis, the time needed to perform a single analytical run has decreased, while the amount of information obtained from each of these analytical runs has increased drastically. Consequently, the limiting step in chemical analysis of a microorganism is no longer the analytical run itself, but rather analysis of the resulting data. Classical methods for manual interpretation of one single data file at a time are not sufficient to cope with this influx of data. Hence, there is a need for development of new methods for data analysis to extract valuable information in the data, and also speeding up the data analysis itself. A prime goal of my PhD study was to develop methods that allow for high-throughput analysis of metabolite extracts from filamentous fungi and other microorganisms, and to reduce the time spent on manual interpretation of LC-MS data. This lead to development of a method that utilizes compound libraries to screen the recorded LC-MS data and annotate known compounds, a process we have named aggressive dereplication. By overlaying automatically generated extracted-ion chromatograms from detected compounds on the base peak chromatogram, all major potentially novel peaks can be visualized, allowing for fast dereplication of samples. This was further developed to include the use of recorded MS/MS data, allowing for greater confidence in matched compounds. Another goal of the present study has been to develop methods that allow for faster coupling of SMs to their biosynthetic genes, as coupling of genes to metabolites is of large commercial interest for production of the bioactive compounds of the future. One part of my study focused on identification and elucidation of the biosynthesis of a nonribosomal peptide (NRP) nidulanin A from Aspergillus nidulans. Although the study was successful several analogs were not structure elucidated due to very low production titers. Instead a novel approach was developed for probing the biosynthesis of NRPs using stable isotope labeled (SIL) amino acids and subsequent analysis by MS/MS. Recorded MS/MS data were analyzed using molecular networking, coupling together compounds that exhibit similar MS/MS spectra. The combination of stable isotope labeling and molecular networking proved very effective for detection of structurally related NRPs. Labeling alone aided in determining the cyclic-peptide sequence, and may be used to provide information on biosynthesis of bioactive compounds. In another study, the combined approach of targeted analysis methods and SIL precursors was used to elucidate the biosynthesis of yanuthone D in A. niger, and to determine compounds biosynthesized from the same precursor. Further studies on the biosynthesis of polyketides were conducted using feeding studies with SIL precursor in order to determine advantages and disadvantages of the approach. This led to determination of the biosynthetic origin of several compounds in Fusarium including antibiotic Y, and tentative identification of an intermediate in its biosynthetic pathway. Last, benzoic acid was identified as the precursor to asperrubrol in A. niger. Finally, I have developed an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Here, 13 strains related to the marine bacterial species Pseudoalteromonas luteoviolacea were investigated in an untargeted metabolomics experiment and the results were correlated to whole-genome sequences of the strains. We found that 30 % of all chemical features and 24 % of the biosynthetic genes were unique to a single strain, while only 2 % of the features and 7 % of the genes were shared between all. The list of chemical features, originally comprising 2,000 features, was reduced to 50 discriminating features using a genetic algorithm combined with support vector machine evaluation. These features were efficiently dereplicated by molecular networking, which lead to tentative identification of several known antibacterial compounds, some of which had not previously been described from this organism. By combining metabolomics and genomics data, it was possible to link metabolites to chemical pathways at a very early stage in the discovery process. Based on these results, the data analysis methods and methodologies developed during these studies have shown to be very effective and applicable to metabolite analysis of a wide range of microorganisms, and not restricted to fungi. The developed methods have revealed new insights into microbial SMs, and it is clear that even more discoveries can be made using these methods.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.