Linking secondary metabolites to gene clusters through genome sequencing of six diverse Aspergillus species.

The fungal genus of Aspergillus is highly interesting, containing everything from industrial cell factories, model organisms, and human pathogens. In particular, this group has a prolific production of bioactive secondary metabolites (SMs). In this work, four diverse Aspergillus species (A. campestris, A. novofumigatus, A. ochraceoroseus, and A. steynii) have been whole-genome PacBio sequenced to provide genetic references in three Aspergillus sections. A. taichungensis and A. candidus also were sequenced for SM elucidation. Thirteen Aspergillus genomes were analyzed with comparative genomics to determine phylogeny and genetic diversity, showing that each presented genome contains 15-27% genes not found in other sequenced Aspergilli. In particular, A. novofumigatus was compared with the pathogenic species A. fumigatus This suggests that A. novofumigatus can produce most of the same allergens, virulence, and pathogenicity factors as A. fumigatus, suggesting that A. novofumigatus could be as pathogenic as A. fumigatus Furthermore, SMs were linked to gene clusters based on biological and chemical knowledge and analysis, genome sequences, and predictive algorithms. We thus identify putative SM clusters for aflatoxin, chlorflavonin, and ochrindol in A. ochraceoroseus, A. campestris, and A. steynii, respectively, and novofumigatonin, ent-cycloechinulin, and epi-aszonalenins in A. novofumigatus Our study delivers six fungal genomes, showing the large diversity found in the Aspergillus genus; highlights the potential for discovery of beneficial or harmful SMs; and supports reports of A. novofumigatus pathogenicity. It also shows how biological, biochemical, and genomic information can be combined to identify genes involved in the biosynthesis of specific SMs.

Genus level analysis of PKS-NRPS and NRPS-PKS hybrids reveals their origin in Aspergilli.

BACKGROUND: Filamentous fungi produce a vast amount of bioactive secondary metabolites (SMs) synthesized by e.g. hybrid polyketide synthase-nonribosomal peptide synthetase enzymes (PKS-NRPS; NRPS-PKS). While their domain structure suggests a common ancestor with other SM proteins, their evolutionary origin and dynamics in fungi are still unclear. Recent rational engineering approaches highlighted the possibility to reassemble hybrids into chimeras - suggesting molecular recombination as diversifying mechanism. RESULTS: Phylogenetic analysis of hybrids in 37 species - spanning 9 sections of Aspergillus and Penicillium chrysogenum - let us describe their dynamics throughout the genus Aspergillus. The tree topology indicates that three groups of PKS-NRPS as well as one group of NRPS-PKS hybrids developed independently from each other. Comparison to other SM genes lead to the conclusion that hybrids in Aspergilli have several PKS ancestors; in contrast, hybrids are monophyletic when compared to available NRPS genes - with the exception of a small group of NRPSs. Our analysis also revealed that certain NRPS-likes are derived from NRPSs, suggesting that the NRPS/NRPS-like relationship is dynamic and proteins can diverge from one function to another. An extended phylogenetic analysis including bacterial and fungal taxa revealed multiple ancestors of hybrids. Homologous hybrids are present in all sections which suggests frequent horizontal gene transfer between genera and a finite number of hybrids in fungi. CONCLUSION: Phylogenetic distances between hybrids provide us with evidence for their evolution: Large inter-group distances indicate multiple independent events leading to the generation of hybrids, while short intra-group distances of hybrids from different taxonomic sections indicate frequent horizontal gene transfer. Our results are further supported by adding bacterial and fungal genera. Presence of related hybrid genes in all Ascomycetes suggests a frequent horizontal gene transfer between genera and a finite diversity of hybrids - also explaining their scarcity. The provided insights into relations of hybrids and other SM genes will serve in rational design of new hybrid enzymes.

Bayesian prediction of bacterial growth temperature range based on genome sequences.

BACKGROUND: The preferred habitat of a given bacterium can provide a hint of which types of enzymes of potential industrial interest it might produce. These might include enzymes that are stable and active at very high or very low temperatures. Being able to accurately predict this based on a genomic sequence, would thus allow for an efficient and targeted search for production organisms, reducing the need for culturing experiments. RESULTS: This study found a total of 40 protein families useful for distinction between three thermophilicity classes (thermophiles, mesophiles and psychrophiles). The predictive performance of these protein families were compared to those of 87 basic sequence features (relative use of amino acids and codons, genomic and 16S rDNA AT content and genome size). When using naïve Bayesian inference, it was possible to correctly predict the optimal temperature range with a Matthews correlation coefficient of up to 0.68. The best predictive performance was always achieved by including protein families as well as structural features, compared to either of these alone. A dedicated computer program was created to perform these predictions. CONCLUSIONS: This study shows that protein families associated with specific thermophilicity classes can provide effective input data for thermophilicity prediction, and that the naïve Bayesian approach is effective for such a task. The program created for this study is able to efficiently distinguish between thermophilic, mesophilic and psychrophilic adapted bacterial genomes.

Novofumigatonin biosynthesis involves a non-heme iron-dependent endoperoxide isomerase for orthoester formation.

Novofumigatonin (1), isolated from the fungus Aspergillus novofumigatus, is a heavily oxygenated meroterpenoid containing a unique orthoester moiety. Despite the wide distribution of orthoesters in nature and their biological importance, little is known about the biogenesis of orthoesters. Here we show the elucidation of the biosynthetic pathway of 1 and the identification of key enzymes for the orthoester formation by a series of CRISPR-Cas9-based gene-deletion experiments and in vivo and in vitro reconstitutions of the biosynthesis. The novofumigatonin pathway involves endoperoxy compounds as key precursors for the orthoester synthesis, in which the Fe(II)/α-ketoglutarate-dependent enzyme NvfI performs the endoperoxidation. NvfE, the enzyme catalyzing the orthoester synthesis, is an Fe(II)-dependent, but cosubstrate-free, endoperoxide isomerase, despite the fact that NvfE shares sequence homology with the known Fe(II)/α-ketoglutarate-dependent dioxygenases. NvfE thus belongs to a class of enzymes that gained an isomerase activity by losing the α-ketoglutarate-binding ability.

Uncovering secondary metabolite evolution and biosynthesis using gene cluster networks and genetic dereplication.

The increased interest in secondary metabolites (SMs) has driven a number of genome sequencing projects to elucidate their biosynthetic pathways. As a result, studies revealed that the number of secondary metabolite gene clusters (SMGCs) greatly outnumbers detected compounds, challenging current methods to dereplicate and categorize this amount of gene clusters on a larger scale. Here, we present an automated workflow for the genetic dereplication and analysis of secondary metabolism genes in fungi. Focusing on the secondary metabolite rich genus Aspergillus, we categorize SMGCs across genomes into SMGC families using network analysis. Our method elucidates the diversity and dynamics of secondary metabolism in section Nigri, showing that SMGC diversity within the section has the same magnitude as within the genus. Using our genome analysis we were able to predict the gene cluster responsible for biosynthesis of malformin, a potentiator of anti-cancer drugs, in 18 strains. To proof the general validity of our predictions, we developed genetic engineering tools in Aspergillus brasiliensis and subsequently verified the genes for biosynthesis of malformin.

Investigation of inter- and intraspecies variation through genome sequencing of Aspergillus section Nigri.

Aspergillus section Nigri comprises filamentous fungi relevant to biomedicine, bioenergy, health, and biotechnology. To learn more about what genetically sets these species apart, as well as about potential applications in biotechnology and biomedicine, we sequenced 23 genomes de novo, forming a full genome compendium for the section (26 species), as well as 6 Aspergillus niger isolates. This allowed us to quantify both inter- and intraspecies genomic variation. We further predicted 17,903 carbohydrate-active enzymes and 2,717 secondary metabolite gene clusters, which we condensed into 455 distinct families corresponding to compound classes, 49% of which are only found in single species. We performed metabolomics and genetic engineering to correlate genotypes to phenotypes, as demonstrated for the metabolite aurasperone, and by heterologous transfer of citrate production to Aspergillus nidulans. Experimental and computational analyses showed that both secondary metabolism and regulation are key factors that are significant in the delineation of Aspergillus species.

FunGeneClusterS: Predicting fungal gene clusters from genome and transcriptome data.

INTRODUCTION: Secondary metabolites of fungi are receiving an increasing amount of interest due to their prolific bioactivities and the fact that fungal biosynthesis of secondary metabolites often occurs from co-regulated and co-located gene clusters. This makes the gene clusters attractive for synthetic biology and industrial biotechnology applications. We have previously published a method for accurate prediction of clusters from genome and transcriptome data, which could also suggest cross-chemistry, however, this method was limited both in the number of parameters which could be adjusted as well as in user-friendliness. Furthermore, sensitivity to the transcriptome data required manual curation of the predictions. In the present work, we have aimed at improving these features. RESULTS: FunGeneClusterS is an improved implementation of our previous method with a graphical user interface for off- and on-line use. The new method adds options to adjust the size of the gene cluster(s) being sought as well as an option for the algorithm to be flexible with genes in the cluster which may not seem to be co-regulated with the remainder of the cluster. We have benchmarked the method using data from the well-studied Aspergillus nidulans and found that the method is an improvement over the previous one. In particular, it makes it possible to predict clusters with more than 10 genes more accurately, and allows identification of co-regulated gene clusters irrespective of the function of the genes. It also greatly reduces the need for manual curation of the prediction results. We furthermore applied the method to transcriptome data from A. niger. Using the identified best set of parameters, we were able to identify clusters for 31 out of 76 previously predicted secondary metabolite synthases/synthetases. Furthermore, we identified additional putative secondary metabolite gene clusters. In total, we predicted 432 co-transcribed gene clusters in A. niger (spanning 1.323 genes, 12% of the genome). Some of these had functions related to primary metabolism, e.g. we have identified a cluster for biosynthesis of biotin, as well as several for degradation of aromatic compounds. The data identifies that suggests that larger parts of the fungal genome than previously anticipated operates as gene clusters. This includes both primary and secondary metabolism as well as other cellular maintenance functions. CONCLUSION: We have developed FunGeneClusterS in a graphical implementation and made the method capable of adjustments to different datasets and target clusters. The method is versatile in that it can predict co-regulated clusters not limited to secondary metabolism. Our analysis of data has shown not only the validity of the method, but also strongly suggests that large parts of fungal primary metabolism and cellular functions are both co-regulated and co-located.