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Genome and Metabolome MS-Based Mining of a Marine Strain of Aspergillus affinis.

ABSTRACT: Aspergillus section Circumdati encompasses several species that express both beneficial (e.g., biochemical transformation of steroids and alkaloids, enzymes and metabolites) and harmful compounds (e.g., production of ochratoxin A (OTA)). Given their relevance, it is important to analyze the genetic and metabolic diversity of the species of this section. We sequenced the genome of Aspergillus affinis CMG 70, isolated from sea water, and compared it with the genomes of species from section Circumdati, including A. affinis's strain type. The A. affinis genome was characterized considering secondary metabolites biosynthetic gene clusters (BGCs), carbohydrate-active enzymes (CAZymes), and transporters. To uncover the biosynthetic potential of A. affinis CMG 70, an untargeted metabolomics (LC-MS/MS) approach was used. Cultivating the fungus in the presence and absence of sea salt showed that A. affinis CMG 70 metabolite profiles are salt dependent. Analyses of the methanolic crude extract revealed the presence of both unknown and well-known Aspergillus compounds, such as ochratoxin A, anti-viral (e.g., 3,5-Di-tert-butyl-4-hydroxybenzoic acid and epigallocatechin), anti-bacterial (e.g., 3-Hydroxybenzyl alcohol, l-pyroglutamic acid, lecanoric acid), antifungal (e.g., lpyroglutamic acid, 9,12,13-Trihydroxyoctadec-10-enoic acid, hydroxyferulic acid), and chemotherapeutic (e.g., daunomycinone, mitoxantrone) related metabolites. Comparative analysis of 17 genomes from 16 Aspergillus species revealed abundant CAZymes (568 per species), secondary metabolite BGCs (73 per species), and transporters (1359 per species). Some BGCs are highly conserved in this section (e.g., pyranonigrin E and UNII-YC2Q1O94PT (ACR toxin I)), while others are incomplete or completely lost among species (e.g., bikaverin and chaetoglobosins were found exclusively in series Sclerotiorum, while asperlactone seemed completely lost). The results of this study, including genome analysis and metabolome characterization, emphasize the molecular diversity of A. affinis CMG 70, as well as of other species in the section Circumdati.

SUBMITTER: Goncalves MFM

PROVIDER: S-EPMC8709101 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Co-culture is known as an efficient way to explore the metabolic potential of fungal strains for new antibiotics and other therapeutic agents that could counter emerging health issues. To study the effect of co-culture on the secondary metabolites and bioactivities of two marine strains, Aspergillus terreus C23-3 and Aspergillus. unguis DLEP2008001, they were co-cultured in live or inactivated forms successively or simultaneously. The mycelial morphology and high-performance thin layer chromatography (HPTLC) including bioautography of the fermentation extracts were recorded. Furthermore, the agar cup-plate method was used to compare the antimicrobial activity of the extracts. Based on the above, liquid chromatography-photodiode array-tandem mass spectrometry (LC-PDA-MS/MS) together with Global Natural Products Social molecular networking (GNPS) and multiple natural products database mining were used to further analyze their secondary metabolite variations. The comprehensive results showed the following trends: (1) The strain first inoculated will strongly inhibit the growth and metabolism of the latter inoculated one; (2) Autoclaved A. unguis exerted a strong inducing effect on later inoculated A. terreus, while the autoclaved A. terreus showed high stability of its metabolites and still potently suppressed the growth and metabolism of A. unguis; (3) When the two strains are inoculated simultaneously, they both grow and produce metabolites; however, the A. terreus seemed to be more strongly induced by live A. unguis and this inducing effect surpassed that of the autoclaved A. unguis. Under some of the conditions, the extracts showed higher antimicrobial activity than the axenic cultures. Totally, A. unguis was negative in response but potent in stimulating its rival while A. terreus had the opposite effect. Fifteen MS detectable and/or UV active peaks showed different yields in co-cultures vs. the corresponding axenic culture. GNPS analysis assisted by multiple natural products databases mining (PubChem, Dictionary of Natural Products, NPASS, etc.) gave reasonable annotations for some of these peaks, including antimicrobial compounds such as unguisin A, lovastatin, and nidulin. However, some of the peaks were correlated with antagonistic properties and remain as possible novel compounds without mass or UV matching hits from any database. It is intriguing that the two strains both synthesize chemical 'weapons' for antagonism, and that these are upregulated when needed in competitive co-culture environment. At the same time, compounds not useful in this antagonistic setting are downregulated in their expression. Some of the natural products produced during antagonism are unknown chlorinated metabolites and deserve further study for their antimicrobial properties. In summary, this study disclosed the different responses of two Aspergillus strains in co-culture, revealed their metabolic variation, and displayed new opportunities for antibiotic discovery.

Project description:Fungal secondary metabolites are a rich source of valuable natural products, and genome sequencing has revealed a proliferation of predicted biosynthetic gene clusters in the genomes. However, it is currently an unfeasible task to characterize all biosynthetic gene clusters and to identify possible uses of the compounds. Therefore, a rational approach is needed to identify a short list of gene clusters responsible for producing valuable compounds. To this end, several bioactive clusters include a resistance gene, which is a paralog of the target gene inhibited by the compound. This mechanism can be used to identify these clusters. We have developed the FRIGG (fungal resistance gene-directed genome mining) pipeline for identifying this type of biosynthetic gene cluster based on homology patterns of the cluster genes. In this work, the FRIGG pipeline was run using 51 Aspergillus and Penicillium genomes, identifying 72 unique families of putative resistance genes. The pipeline also identified the previously characterized resistance gene inpE from the fellutamide B cluster, thereby validating the approach. We have successfully developed an approach to identify putative valuable bioactive clusters based on a specific resistance mechanism. This approach will be highly useful as an ever-increasing amount of genomic data becomes available; the art of identifying and selecting the right clusters producing novel valuable compounds will only become more crucial. IMPORTANCE Species belonging to the Aspergillus genus are known to produce a large number of secondary metabolites; some of these compounds are used as pharmaceuticals, such as penicillin, cyclosporine, and statin. With whole-genome sequencing, it became apparent that the genetic potential for secondary metabolite production is much larger than expected. As an increasing number of species are whole-genome sequenced, thousands of secondary metabolite genes are predicted, and the question of how to selectively identify novel bioactive compounds from this information arises. To address this question, we have created a pipeline to predict genes involved in the production of bioactive compounds based on a resistance gene hypothesis approach.

Project description:BackgroundDiagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis.MethodsTo identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites.Results12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites.ConclusionOur analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment.

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Genome and Metabolome MS-Based Mining of a Marine Strain of Aspergillus affinis.

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