Evolution, structure and function of the putative biosynthetic gene cluster of the fungal secondary metabolite myriocin, a potent inhibitory sphingolipid.
This work identifies the putative myriocin biosynthesis gene cluster (BGC) through de novo sequencing of two producing fungi, Isaria sinclairii and Mycelia sterilia, yielding genomes of 27 and 20 secondary metabolite BGCs, respectively.
Abstract
Myriocin is a fungal secondary metabolite exploited worldwide as a powerful inhibitor of sphingolipid biosynthesis through its structural similarity to sphingosine. We identify the putative myriocin biosynthesis gene cluster (BGC) through de novo sequencing of two producing fungi, Isaria sinclairii and Mycelia sterilia, yielding genomes of 25.2 Mb and 34.2 Mb encoding 27 and 20 secondary metabolite BGCs, respectively. BGCs #5 in I. sinclairii and #18 in M. sterilia both shared and expressed the polyketide synthase (PKS) and alpha oxo-amine synthase (AOS) predicted for myriocin biosynthesis, with 74% and 79% sequence similarity, respectively. Analysis of a 2,236-fungal-genome database suggests the pathway originated in the Sordariomycete ancestor, presenting in two major clades distinguished by PKS gene orientation. The placement of thermophilic M. sterilia suggests myriocin BGC acquisition through horizontal gene transfer, but its origin in I. sinclairii is ambiguous. Heterologously-expressed IsMyrA bound aminomalonate, and a protein-protein docking interface was identified between the acyl carrier protein and IsMyrA. A model of PKS domain function, the roles of the PKS and AOS genes and the synteny of the putative myriocin biosynthetic gene cluster across 34 carrier species of ascomycetes is presented.
Understanding of the metabolic capabilities and genomic landscape of the P. fluorescens species is enhanced, providing a foundation for natural product discovery using bioinformatic approaches.
Sajid Iqbal, Farida Begum· Discover Genetics and Evolut...· 0 citations
The genus
Streptomyces
is one of the richest sources of bioactive natural products; however, a substantial proportion of its biosynthetic gene clusters (BGCs) remain cryptic and their metabolic products are unresolved. Advances in genome mining and computational prediction now enable comprehensive exploration of this hidden biosynthetic repertoire. In this study, whole-genome sequencing and comparative genomic analyses were performed on three three newly isolated
Streptomyces
strains to evaluate their specialized metabolic potential. Genome assemblies were annotated and systematically analyzed using antiSMASH, DeepBGC, GECCO, and PRISM to identify, cross-validate, and functionally characterize BGCs while predicting their associated secondary metabolite scaffolds. Taxonomic analyses based on Average Nucleotide Identity (ANI), phylogenomics, and BLAST identified the isolates as
Streptomyces thinghirensis, Streptomyces novocaesareae
, and
Streptomyces griseorubens
. Applying the consensus framework across the three
Streptomyces
genomes yielded 43 cryptic BGCs, lacking close similarity to reference BGCs in the MIBiG database, of which 26 were classified as HIGH, 10 as MEDIUM, and 7 as LOW confidence. Notably, numerous BGCs exhibited low abundance to characterized reference clusters, indicating a high potential for previously undescribed biosynthetic pathways and novel metabolite scaffolds. Comparative analyses further revealed strain-specific biosynthetic architectures together with putative metal-responsive regulatory systems;
Fur, Zur
, and
Nur
, which were frequently associated with specialized metabolite biosynthetic loci. Collectively, these findings demonstrate the effectiveness of integrated genome-mining strategies for prioritizing cryptic biosynthetic gene clusters and highlight the remarkable biosynthetic potential of newly identified
Streptomyces
isolates as a source of novel natural products.
Nada S. Al-Theyab, Haila M. Alnassar, Mohanad A. Ibrahim et al.· Frontiers in Microbiology· 0 citations
Abstract Many Streptomyces species have a signaling-molecule/receptor system for induction of secondary metabolite biosynthetic gene clusters (BGCs). Signaling molecules hitherto discovered and studied contain five-membered heterocycles, and are classified into three groups, including γ-butyrolactones, γ-butenolides, and furans. These molecules except for avenolide-type are biosynthesized by enzymes harboring an AfsA tandem repeat domain. These enzymes (AfsA homologs) catalyze a transfer of β-ketoacyl moiety to the hydroxyl group of dihydroxyacetone phosphate. Alignment of 59 afsA homolog genes showed that 86% (51/59) of them located adjacent to their possible signaling-molecule receptor genes, which reminds us to readily predict their signaling-molecule/receptor system for expression of BGCs. Apparent exception is the case of afsA-arpA system in Streptomyces griseus, whose distance was around 3.91 Mb. Understanding of the signaling-molecule/receptor system may lead to a practical genome mining strategy to awaken silent BGCs through derepression of transcription using the cognate ligands, signaling molecules.
Microbes produce bioactive secondary metabolites as toxins, pigments, or virulence factors. These specialized compounds are produced by nonribosomal peptide synthetases (NRPS), polyketide synthases (PKS), or hybrid NRPS/PKS pathways. The genes encoding NRPS and PKS reside in biosynthetic gene clusters (BGCs), some of which have no identified metabolite associated with them. Characterization of these orphan BGCs could provide insights into potential bioactive compounds that have yet to be discovered. Here, we characterize PA1216, a putative methyltransferase embedded within an NRPS BGC in Pseudomonas aeruginosa strain PAO1. We cloned, expressed, and purified PA1216, and developed an optimized differential scanning fluorimetry assay to measure its thermal stability, demonstrating concentration‐dependent stabilization in the presence of established methyltransferase cofactors and inhibitors. We then adapted this assay for high‐throughput screening of potential PA1216 substrates, identifying destabilizing compounds, including glycyl‐glycine dipeptides, amino esters with aromatic or basic side chains, and N‐Boc‐protected amino acids. In contrast, sodium salts of organic acids stabilized PA1216. Lastly, we employed AlphaFold to construct a predictive model, revealing that PA1216 contains a Rossmann‐like fold and a glycine‐rich loop, typical of class I methyltransferases, and we corroborated these secondary structural elements using circular dichroism spectroscopy. Overall, these studies illuminate PA1216 function and establish a platform for characterizing cryptic gene clusters within secondary metabolic pathways.
Amaan Fruitwala, Jade X Tiszler, Daniel Watson et al.· Protein Science· 0 citations
Filamentous fungi are major contributors to diverse secondary metabolites with broad applications to medicine, agriculture, and biotechnology. Advances in genome sequencing and bioinformatic tools have revealed that fungal genomes encode far more biosynthetic gene clusters (BGCs) than are expressed under normal laboratory conditions, leaving much biosynthetic potential transcriptionally silent. Overcoming this gap between predicted and observed secondary metabolism has become a major challenge in fungal natural product discovery. In this review, we summarize current strategies for activating silent or weakly expressed fungal BGCs through regulatory engineering, with an emphasis on approaches validated in Aspergillus, Penicillium, Monascus, and related filamentous fungi. We focus on genetic and chemical manipulations that enable coordinated activation of multiple biosynthetic pathways through chromatin-level modifiers, global transcriptional regulators, and developmental regulators. By framing these regulators as practical tools rather than solely biological components, we demonstrate their strengths, limitations, and applications in Aspergillus and related filamentous fungi. We further discuss emerging combinatorial and integrative approaches that use regulatory engineering alongside omics technologies and predictive tools, outlining alternatives and future directions for improving the interpretability of silent pathway activation.
Jennifer Shyong, Clay C C Wang· RSC Chemical Biology· 0 citations
The evolution of plant defensive specialized metabolites often involves repurposing existing primary metabolic pathways for novel roles. However, the relative contribution of changes in protein function versus gene regulation and localization in driving such divergency remains less well understood. Here, we investigate the evolutionary origin of long-branched-chain acylsugars—a class of insecticidal metabolites found in wild tomatoes but absent from their domesticated counterpart. We show that this chemical divergency is enabled by a single enzyme, an acyl-CoA synthetase (SpBACS1), that was repurposed to bridge two distinct primary metabolic pathways. We demonstrate that SpBACS1 activates products of amino acid catabolism and primes them as non-canonical starters for elongation by the plastidial fatty acid synthase machinery for acylsugar assembly. Strikingly, the loss of this trait during domestication was not due to impaired enzyme function. Instead, we describe two distinct regulatory mechanisms. First, the cultivated ortholog, SlBACS1, lacks expression in acylsugar-producing trichomes due to promoter sequence divergence, despite retaining enzyme catalytic activity. Concurrently, their paralogs, BACS2, were neolocalized to the mitochondria, functionally isolating them from the chloroplast-based primary fatty acid metabolic elongation pathway. These findings demonstrate how divergence in cis-regulatory elements and subcellular targeting, in the absence of protein function modification, were potent drivers of metabolic evolution, providing a strategy for re-engineering valuable chemical diversity into cultivated crops. Long-branched-chain acylsugars are insecticidal metabolites found in wild tomatoes but not their domesticated counterpart. Here the authors show changes in expression and localization of a key enzyme is responsible for this difference.
