https://orcid.org/0000-0003-0341-1561
Figures
Abstract
Bacteria produce a plethora of natural products that are in clinical, agricultural and biotechnological use. Genome mining has uncovered millions of biosynthetic gene clusters (BGCs) that encode their biosynthesis, the vast majority of them lacking a clear product or function. Thus, a major challenge is to predict the bioactivities of the molecules these BGCs specify, and how to elicit their expression. Here, we present an innovative strategy whereby we harness the power of regulatory networks combined with global gene expression patterns to predict BGC functions. Bioinformatic analysis of all genes predicted to be controlled by the iron master regulator DmdR1 combined with co-expression data, led to identification of the novel operon desJGH that plays a key role in the biosynthesis of the iron overload drug desferrioxamine (DFO) B in Streptomyces coelicolor. Deletion of either desG or desH strongly reduces the biosynthesis of DFO B, while that of DFO E is enhanced. DesJGH most likely act by changing the balance between the DFO precursors. Our work shows the power of harnessing regulation-based genome mining to functionally prioritize BGCs, accelerating the discovery of novel bioactive molecules.
Citation: Augustijn HE, Reitz ZL, Zhang L, Boot JA, Elsayed SS, Challis GL, et al. (2025) Genome mining based on transcriptional regulatory networks uncovers a novel locus involved in desferrioxamine biosynthesis. PLoS Biol 23(6): e3003183. https://doi.org/10.1371/journal.pbio.3003183
Academic Editor: Tobias Bollenbach, Universitat zu Koln, GERMANY
Received: February 16, 2025; Accepted: April 29, 2025; Published: June 12, 2025
Copyright: © 2025 Augustijn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Default software parameters were used unless otherwise noted. Scripts are available at: https://github.com/zreitz/dmdR. All data and code are available on Zenodo at https://zenodo.org/records/15106944 (doi: 10.5281/zenodo.15106943).
Funding: This study was supported by the European Union ERC Advanced Grant 101055020-COMMUNITY to G.P.v.W and the European Union ERC Starting Grant 948770-DECIPHER to M.H.M. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: BGCs, biosynthetic gene clusters; DAP-seq, DNA Affinity Purification Sequencing; DFO, desferrioxamine; HAC, N-hydroxy-N-acetylcadaverine; HSC, succinylcadaverine; IC, information content; LC–MS, liquid chromatography–mass spectrometry; MS/MS, tandem mass spectrometry; NRPS, non-ribosomal peptide synthetase; PCC, Pearson correlation coefficient; PWMs, position weight matrices; ssDNA, single-strand DNA; TFs, transcription factors; TFBS, transcription factor binding site
Introduction
Within the genetic blueprint of microorganisms lies an immense reservoir of chemical potential, which likely constitutes the mechanistic basis for numerous phenotypes and offers a rich source of raw materials for discovery and development of among others antibiotics, anticancer agents, immunosuppressants, crop protection agents, and industrial ingredients [1,2]. Genome mining efforts have led to the identification of millions of biosynthetic gene clusters (BGCs) predicted to encode the biosynthesis of many thousands of natural product scaffolds [3]. However, only an estimated 3% of these specialized metabolites have undergone experimental characterization thus far, leaving a vast amount of untapped chemical diversity yet to be explored [4].
Identifying the diverse roles of specialized metabolites in microbiome interactions is highly challenging, primarily due to the dynamic nature of the host environment and the difficulties in replicating such conditions in laboratory settings. Moreover, while these molecules exhibit a wide range of functions, only a small fraction of metabolites will directly contribute towards microbiome-associated phenotypes such as disease suppression or growth promotion, or have the necessary properties to yield the next generation of crop protection agents, antibiotics, or food additives [5–7]. As a result, there is a pressing need for generalized strategies to predict the functions of specialized metabolites, enabling us to understand their mechanistic roles in inter-organismal interactions and to gauge their usefulness for industrial and clinical applications.
A major aim in current natural product discovery is to identify ways to reduce the genetic space of sequenced BGCs to manageable numbers, to inform scientists on which BGCs to prioritize in the search for novel bioactivity. Historically, scientists have investigated two dimensions, namely the molecular space via high-throughput screening of compound and strain libraries, followed by the genomic space in the 21st century, by investigating BGCs in sequenced genomes, based on the identification of enzyme-coding genes [8]. Perhaps the most advanced strategy for the latter has thus far been target-based genome mining, which uses self-resistance genes inside BGCs as beacons for recognizing the macromolecular targets of their products. However, the presence of recognizable self-resistance genes seems to be limited to a mere 5%–10% of BGCs, necessitating complementary methods to predict the functions of the remaining specialized metabolic diversity [9,10].
We anticipate that an attractive alternative would be regulation-guided approaches, given that the regulatory system plays a pivotal role in the transcription of BGCs. Overexpression or inactivation of cluster-situated regulatory genes have been used to activate their expression [11–13]. For example, targeting BGCs containing Streptomyces antibiotic regulatory protein family regulators enabled the discovery of BGCs encoding the biosynthesis of novel antibiotics [14,15]. Also, the Identification of Natural compound Biosynthesis pathways by Exploiting Knowledge of Transcriptional regulation strategy was able to unveil a previously undetectable BGC by identifying regulatory binding sites of the zinc-dependent regulator ZuR [16]. These early successes at the single-gene or single-BGC level indicate that genome-wide analysis of regulatory networks may be even more successful at unveiling BGC functions.
Here, we introduce a computational omics strategy that leverages genome-wide gene regulation information to provide functional predictions of BGCs in microbes. This novel approach connects genome-wide regulatory information derived from transcription factor binding site (TFBS) prediction to gene co-expression networks, thereby associating genes to functions. Genome-wide regulatory analysis of BGCs of Streptomyces coelicolor M145 in combination with co-expression patterns unveiled a novel BGC that had escaped detection by current genome mining software tools. Subsequent mutational analysis and metabolic profiling experiments showed that this BGC plays an important role in the biosynthesis of the well-studied siderophore desferrioxamine (DFO) B. These results illustrate the potential of our method to infer BGC function, facilitate the detection and prioritization of novel BGCs and ultimately pave the way for identifying genes responsible for the biosynthesis of novel bioactive molecules.
Results and discussion
1. Identifying functional associations through gene regulatory networks
A major challenge in genome-mining-based drug discovery lies in prioritizing BGCs within the vast unexplored biosynthetic space, and in particular finding novel ways to predict their function. We hypothesized that regulatory networks that control BGC expression might form a new, third, dimension for screening for potential functions, complementing phenotypic and genomic screening. The concept we propose is that if an unknown BGC (or any cluster of genes) is predicted to be controlled by a transcriptional regulator that responds to a known signal and is connected to a specific physiological response, that BGC may functionally relate to known BGCs controlled in a similar manner. From this point onward, when we refer to a BGC’s “function,” we mean the physiological, ecological role or classification of the metabolite(s) specified by a certain BGC as inferred through shared regulatory context.
To develop such a regulation-based genome mining strategy and assess its validity, we chose to focus on the model organism S. coelicolor M145. This microbe, belonging to the phylum Actinomycetota, is renowned for its exceptional ability to produce a wide array of bioactive compounds, making it an interesting target for natural product discovery [17–20]. Moreover, it is the bacterial species with currently the largest number of functionally characterized BGCs, with 17 out of its 27 BGCs having been connected to the production of a known metabolite, making it an ideal organism to assess how well regulation connects to function [21]. To investigate the functional relationships between this microbe’s regulatory machinery and specialized metabolite biosynthesis, we investigated the binding of transcription factors (TFs) to their corresponding binding sites (TFBSs). For this purpose, we used the regulatory data of the LogoMotif database [22]. Seventeen precalculated and manually curated position weight matrices (PWMs) associated with TFs in this database were used for genome-wide predictions of 730 TFBSs, using automated computational matching. The resulting matches can be classified as low, medium, or high confidence based on the prediction scores from the PWM pattern matching. The lower bound is set by a p-value threshold, which is further refined using an information content score to differentiate medium/higher-confidence matches, following the detection principles outlined in LogoMotif. Low-confidence matches are included as they may represent low-affinity binding sites capable of driving gene expression [23]. However, they may also increase the likelihood of false positives and should therefore be interpreted with caution.
Based on these predictions, a gene regulatory network was constructed to map out genome-wide regulation of BGCs (Fig 1a). This revealed both direct and indirect regulatory interactions between regulators. Direct regulation, for example, includes the N-acetylglucosamine-responsive regulator DasR controlling the iron-dependent regulator DmdR1 [24], and the ribonucleotide reductase-specific repressor NrdR that is predicted to directly regulate the DNA damage response regulator LexA. Indirectly, both DasR and AfsQ1, a regulator of antibiotic biosynthesis and morphological development, appear to control genes involved in transport and phosphorylation. Additionally, AbrC3, a response regulator that transactivates antibiotic production, and OsdR, a regulator of development and stress response, were both predicted to regulate an operon of unknown function, spanning from SCO0173 to SCO0178. Furthermore, AbrC3 and the zinc-responsive regulator ZuR appear to co-regulate the expression of the malic enzyme SCO2951, which plays a role in the production of the antibiotic Actinorhodin [25]. To further target specialized metabolism, TFBSs within BGC regions predicted by antiSMASH were highlighted (Fig 1a). A total of 81 TFBSs were found within antiSMASH BGC regions; 55 of these were at the region peripheries and putatively unrelated to specialized metabolite biosynthesis. To identify which TFBSs were truly linked to biosynthetic pathways, we then refined the boundaries of the BGCs (S1 Table) using literature evidence and gene co-expression patterns (see below). This resulted in the identification of 17 low-confidence and 9 medium/high-confidence BGC-TFBS associations each matching the physiological or ecological functions associated with the corresponding regulon (Fig 1a). These findings agree with existing experimental analyses, thus reinforcing the utility of our approach in accurately identifying BGC-TFBS connections (Fig 1b). For example, there is a clear correlation between TFBSs of the zinc uptake regulator (Zur) and the zinc-regulated coelibactin locus [26], as well as between the pleiotropic antibiotic biosynthesis regulator AfsQ1 and the antibiotic coelimycin P1 [27]. Additionally, we observed a connection between the iron-dependent regulator DmdR1 and the biosynthesis of two iron-chelating compound families that function as siderophores: the DFOs and coelichelin [28,29]. The novel interactions include a regulatory connection between AfsQ1 and the ectoine gene cluster, as well as between AfsR, an antibiotic production regulator, and 5-dimethylallylindole-3-acetonitrile (5-DMAIAN).
2. Co-expression analysis and operon-level expansion of the predicted DmdR1 regulon
Next, we aimed to go beyond antiSMASH-detectable BGCs and assess if we could infer the function of any uncharacterized operons and gene clusters using regulatory predictions. Expectedly, the predicted DmdR1 regulon exhibited a clear functional association with siderophores, as evidenced by the connection between its binding sites and known siderophore BGCs [30]. Therefore, we focused on exploring the functional connection between DmdR1 binding sites (iron boxes) and iron metabolic genes. A critical issue when using PWMs is the large number of false positive TFBS hits. To address this, we refined the general LogoMotif detection threshold for DmdR1 to be more accurate for S. coelicolor by applying the principles previously described for the calibration of the PREDetector algorithm [31]. This approach involves an analysis of the distribution of hits and the ratio of hits in non-coding versus coding regions (S1 Fig). The results demonstrated that higher PWM match scores correlated with a greater frequency of hits detected in non-coding regions, where iron boxes are typically found. By calculating the median score of the non-coding to coding ratio, we established a refined threshold of 22.875, leading to the identification of a total of 39 predicted DmdR1 binding sites (S2 Table). Among these 39 predicted binding sites, we identified 25 unique binding site locations, 22 of which corresponded to previously reported DmdR1 target genes. Based on these predictions, we identified three novel putative DmdR1 target genes: SCO2114, SCO2275, and SCO5998.
Bacterial regulons consist not only of genes with TFBSs in their regulatory region, but also any downstream co-operonic genes. DmdR1-controlled operons were predicted using a co-expression analysis of a previously published transcriptome. The RNA-Seq dataset of Lee and colleagues [32] was chosen for its relatively high sample count (22 for S. coelicolor) and the study’s focus on iron restriction. Reads were retrieved from NCBI SRA and mapped to the S. coelicolor M145 genome, and gene count data were processed using previously reported techniques to generate a pairwise gene co-expression matrix [33,34]. Of the 30 predicted DmdR1 target genes with a significant PWM match score, 26 were anti-correlated with transcription of dmdR1 (Pearson correlation coefficient [PCC] <−0.43, p < 0.05, Fig 2a). The co-expression data support the minimum PWM match score of 22.875; below this threshold, no mean anti-correlated expression was identified. Only a single gene with a significant PWM score, the GntR-type regulator SCO6159, was positively co-expressed with dmdR1 (PCC = 0.69), and the transcription pattern of three putative target genes (SCO2267, SCO2350, SCO5999) did not correlate significantly with that of dmdR1, suggesting a more complicated regulation by multiple TFs. DmdR1 target genes were placed into predicted operons using the gene co-expression matrix, as well as strand and intergenic distance, expanding the putative direct regulon of DmdR1 from 25 to 58 genes, which are found across 16 genomic loci (Fig 2b). As expected, DmdR1 binding sites were recovered in the coelichelin and DFO BGCs but not the ZuR-controlled coelibactin BGC, supporting the use of regulatory analysis for linking metallophore BGCs to their corresponding metal. Other logical gene annotations present in the regulon include siderophore-independent iron acquisition, mobilization of stored iron, and oxidative stress response.
3. Description of the DmdR1 regulon in S. coelicolor
DmdR1 uses Fe(II) as a co-repressor, and thus the intracellular concentrations of both iron and DmdR1 will affect the expression of the regulon. Although the majority of research on metal-sensitive regulatory proteins focuses on the metal concentration, the anti-co-expression between dmdR1 and its putative target genes (Fig 2) suggests a considerable role for DmdR1 concentration under the tested conditions [32]. Most of the target genes are more tightly co-expressed with each other (median PCC = 0.90) than anti-co-expressed with dmdR1 itself (median PCC = –0.66, p = 1.2e−8, one-sided Mann–Whitney U test). Intracellular Fe(II) concentrations or other post-transcriptional factors likely decouple the expression of dmdR1 and its regulon. At least two other repressors directly control the expression of dmdR1: DasR (in the absence of glucosamine-6-phosphate) [24], and PhoP (during phosphate starvation) [35]. In the processed RNA-Seq dataset, the expression of dmdR1 and dasR had no linear relationship (PCC = −0.02), while dmdR1 and phoP had anti-correlated expression (PCC = −0.71). However, aspects of post-transcriptional control or not considered, such as by nutrients or posttranslational modification. For example, the DNA binding activity of DasR is controlled by different phosphates, which connects the PhoP and DasR regulons [36].
3.1. Siderophore-mediated iron acquisition.
The majority of the DmdR1 regulon is putatively involved in siderophore pathways. In total, we identified four DmdR1 binding sites in the coelichelin locus (cch, SCO0489-0499) and two binding sites in the DFO locus (desABCD, SCO2780-2785). After biosynthesis, the ABC transporter complex CchGI (SCO0491 and SCO0493) is presumed to export coelichelin from the cell [30]. The DFO locus does not encode a putative export protein; one of the DmdR1-controlled major facilitator superfamily transporters encoded by SCO0680 or SCO6822 may be responsible. Iron-bound siderophores are transported back into the cell through ABC transporter complexes. The coelichelin locus encodes one complete uptake system, CchCDEF (SCO0494-0497), while the DFO binding protein DesE (SCO2780) is proposed to partner with the incomplete ABC complex encoded by SCO1785-1787. A third, standalone iron-siderophore ABC importer has been characterized, CdtABC (SCO7400-7398) [37,38]. SCO7397, putatively co-operonic with cdtABC, is part of the RNase T/ DNA polymerase III family of exonucleases (IPR013520); any relation to iron-starvation is unclear. SCO6426 shows homology to an ABC transporter ATP-binding component; however, ATP-binding residues found in homologs are absent [39]. SCO6426 may be the remnant of an ancestral siderophore ABC transporter system which can still be found complete in other Streptomyces species (ex: S. olivaceus, KVH11_RS17020–35). After entering the cell, siderophore-bound Fe(III) is reduced by a “siderophore interacting protein” (SIP) that allows for iron release [40]. The DFO locus encodes one SIP reductase, DesF (SCO2781) [30], while putative SIPs SCO0459 and SCO1623 may be involved in iron release from coelichelin and/or xenosiderophores.
3.2. Siderophore-independent iron acquisition.
SCO2267 to SCO2277 are 11 co-expressed genes that contain three predicted DmdR1 binding sites. None of these genes have been characterized, but they are homologous to siderophore-independent iron uptake systems in other organisms. SCO2275-2277 are homologous to efeO, efeB, and efeU, respectively, from an E. coli transport system which imports Fe(II) under aerobic, low-pH conditions [41]. A homolog of SCO2268 in Bordetella bronchiseptica (BB3589) is adjacent to an unrelated low-pH ferrous iron transport system, ftrABCD [42]. However, a BB3589 mutant showed no growth defect, and likewise the exact function of SCO2268 is unclear. SCO2267 and SCO2270-2274 are homologous to Corynebacterium diptheriae genes hmuO, htaA, and hmuTUV that are required for acquiring iron from heme and hemoglobin [43–46]. Distant gene SCO0799 encodes a HugZ-like heme oxygenase that may also be involved in Fe-heme utilization [47,48]. SCO2269 is a hypothetical protein that does not match any known protein families; however, its location suggests a role in a siderophore-independent iron acquisition pathway.
3.3. Mobilization of stored iron.
Under iron-replete conditions, S. coelicolor stores excess iron as a ferric oxide biomineral within a bacterioferritin nanocage, encoded by SCO2113 [49]. Neighboring SCO2114 has a putative DmdR1 site and encodes a bacterioferritin-associated ferredoxin (Bfd). This family of ferredoxins has been demonstrated to mobilize stored iron for metabolic use by transferring electrons to bacterioferritin for reductive demineralization [49–51]. In turn, SCO0681 encodes a ferredoxin reductase, which could serve as the terminal electron source for Bfd.
3.4. Links to other pathways.
Unchelated intracellular iron catalyzes the formation of reactive oxygen species, linking iron acquisition to oxidative stress responses [52]. SCO5830 encodes a putative thioredoxin-like ferredoxin that was previously found to be highly expressed under copper overload conditions [53], and thus may be involved in a shared response to metal-induced reactive oxygen species. SCO2350 is in the DoxX-like family of membrane proteins, members of which are upregulated during oxidative stress in Mycobacterium tuberculosis and Haemophilus influenzae [54,55]. SCO6159, GntR-type regulator of the FadR sub-family [56], was the only gene downstream of a putative DmdR1 binding site that was strongly co-expressed with DmdR1 itself (PCC = 0.76). Divergently-transcribed SCO6158 is not a match to any known protein families except for a short C-terminal domain with a [2Fe-2S] binding motif that is identical to that of the siderophore reductase FhuF [57]. This iron-sulfur cluster may be involved in electron transport, like in FhuF itself, or in sensing environmental signals for a regulatory pathway [58]. A DmdR1 binding site between SCO5998 and SCO5999 links DmdR1 to primary metabolism. SCO5998 (MurA2) encodes one of two uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) transferases in S. coelicolor, which catalyze the first committed step of peptidoglycan biosynthesis for cell walls [59]. A DasR-mediated link between GlcNAc and DmdR1 has previously been established [24]. SCO5999 encodes aconitase (AcnA), which isomerizes citrate to isocitrate in the citric acid cycle [60]. A secondary role for AcnA has been reported in E. coli and B. subtilis, and proposed in S. coelicolor: under iron starvation or oxidative stress, a [4Fe-4S] cluster disassembles, and AcnA becomes a post-transcriptional regulator of gene expression that binds to mRNA sequences called “iron responsive elements” [60,61]. While SCO5998 expression was anti-correlated with dmdR1, SCO5999 expression was uncorrelated.
4. Metabolic profiling of an unexplored DmdR1-controlled locus
This systematic mapping of the DmdR1 regulon then provided the opportunity to investigate whether new operons or gene clusters could be identified that would be predicted to specify compounds functioning in iron acquisition. Upon close examination of all individual genes across the regulon, the uncharacterized region from SCO4045 to SCO4052 stood out due to sequence similarity to biosynthetic genes (Fig 2b). The hypothetical protein encoded by SCO4046 is predicted to be intrinsically disordered by MobiDB [62]. SCO4047 is homologous to the HhH-GPD superfamily of base excision repair enzymes [63]. SCO4051 and SCO4052 encode a putative sugar epimerase and dehydrogenase, respectively. Interestingly, SCO4050 encodes a protein similar to the N-acyltransferase DesC (encoded by SCO2784), which catalyzes the conversion of N-hydroxycadaverine to N-hydroxy-N-succinylcadaverine (HSC) and N-hydroxy-N-acetylcadaverine (HAC), the direct precursors of DFO B, in vitro [64]. SCO4048 is a paralog of desF (SCO2781), which encodes ferrioxamine reductase. Furthermore, SCO4049 is homologous to genes designated as desG in other streptomycetes, and is predicted to encode a penicillin amidase family protein; phylogenetic analysis in Actinomycetota revealed that desG, if present, either colocalized with the DFO cluster, or with a separate DmdR1-controlled locus [65]. DesG was originally hypothesized to increase DFO structural diversity by producing phenylacetic acid-capped derivatives in some strains; however, no arylated DFOs have been identified in S. coelicolor. Together, SCO4048, SCO4049, and SCO4050 (further referred to as desJ, desG, and desH, respectively) appear to comprise a previously undetected locus, not identified by antiSMASH, putatively related to DFO biosynthesis [65].
To analyze the role of the DmdR1-controlled locus in the production of DFOs, we applied the CRISPR-based editing system (CRISPR-BEST) [66] to construct three knock-out mutants in which either SCO4048 (desJ), SCO4049 (desG), or SCO4050 (desH) had been inactivated. The system allows the introduction of a premature stop codon in the target ORF, thus preventing the production of a functional protein. Using this method, we created null mutants of SCO4048 (desJ) with mutations W55* or Q68*, resulting in 186 aa or 173 aa truncation of the gene product, respectively. The introduction of a stop codon at W61 in SCO4049 (desG) led to a substantial 721 aa shortening, while mutations W43* or Q91* in SCO4050 (desH) resulted in truncations of 163 aa or 115 aa, respectively. PCR followed by DNA sequencing was used to verify the correctness of the knock-out mutants.
To obtain extracts for metabolomics, S. coelicolor M145 and its mutant derivatives were grown in a liquid iron-limited medium (ISP-2) for five days. The metabolites produced were adsorbed on Diaion HP20 resin, which was subsequently extracted with methanol and analyzed using liquid chromatography–mass spectrometry (LC–MS), which revealed changes in the production of DFO-related metabolites in each of the mutants compared to the wild-type strain (Fig 3a). The metabolites were annotated by matching the high-resolution mass spectrometry and tandem mass spectrometry (MS/MS) spectra to previously published ones (S2 Fig) [67–69]. Statistical analyses showed that only the levels of DFO B (DFOB) were significantly increased in extracts of the desJ mutant as compared to the parental strain (S3 Fig). Metabolomic analysis of ∆desG and ∆desH revealed an approximate 1000-fold and 16-fold decrease in DFOB production, respectively (Figs 3 and S3). Conversely, the mutants exhibited a significant increase in DFO E (DFOE) and its metal complexes, most likely as a result of the nearly abolished DFOB production.
a, Extracted ion chromatograms for m/z values corresponding to DFO-related metabolites in culture extracts of the knock-out mutants of SCO4048 (desJ), SCO4049 (desG), and SCO40450 (desH) compared to the parent Streptomyces coelicolor M145 strain. The desG mutant fails to produce DFOB, while a 16-fold decrease in DFOB biosynthesis was seen in desH mutants (cf. S3 Fig). Details on the mass spectrometry data can be found at https://zenodo.org/records/15106944. b, Proposed biosynthetic pathway for assembly of desferrioxamines E and B. Main biosynthetic enzymes presented in bold face. DesG and DesH balance intracellular N-hydroxy-N-succinylcadaverine (HSC) and N-hydroxy-N-acetylcadaverine (HAC) concentrations by converting HSC to HAC. In the absence of DesG and/or DesH, the cells likely fail to produce sufficient levels of HAC, thereby strongly attenuating the production of DFOB. Although DesC has been shown to be able to catalyze the acetylation of N-hydroxycadaverine in vitro, the enzyme can only modestly compensate for the loss of DesH in vivo, underlining the important role played by DesG and DesH in DFOB production (S4 Fig).
We genetically complemented the mutants to determine if the effects were due solely to the gene inactivation and not to second-site mutations. For this, constructs were introduced that expressed the respective wild-type genes desJ, desG or desH from the constitutive gap promoter. The complementation constructs were based on vector pSET152 [70], which integrates at the bacteriophage ΦC31 attachment site on the S. coelicolor genome. The complemented mutants showed recovery of DFOB production in the complemented strains (S5 Fig). Taken together, our mutational analysis shows that the attenuation of DFOB production in the mutants can be fully explained by the inactivation of desG and desH.
5. Comparative analysis of desferrioxamine biosynthesis across Actinomycetota
DFOB and other capped DFOs have been isolated from many Streptomyces strains, as well as several other Actinomycetota. To see if the proposed biosynthetic role for DesGH applies more generally to DFO biosynthesis in other Actinomycetota, we performed a meta-analysis of published DFO producers. In total, we identified reports of DFO production in 46 sequenced strains, comprising mostly Streptomyces species (n = 34), as well as other Actinomycetota (n = 7), Pseudomonadota (n = 4), and one member of Bacteroidota (S3 Table). Homologs of desG and desH were found in 36 of the genomes, all Actinomycetota. One sequenced DFO producer, Gordonia rubripertincta CWB2 was found to produce acetyl-capped DFOs B and A1, but the genome contains only a desG homolog without desH [71]. A manual inspection of this BGC reveals a distinct GCN5-related acetyltransferase GCWB2_02925 that may be responsible for the acetylated DFOs. The G. rubripertincta CWB2 siderophore locus also encodes for mycobactin-like salicylate synthase and non-ribosomal peptide synthetase (NRPS) genes (S7 Table), all of which were upregulated under low-iron conditions [71]. Only one NIS/NRPS hybrid siderophore family has been identified to date, nocardichelins A and B from the unsequenced Nocardia strain Acta 3026 [72]. The G. rubripertincta CWB2 gene cluster is consistent with the biosynthesis of nocardichelins, as proposed in S8 Fig. Furthermore, nocardichelins were recently detected in extracts from unsequenced Gordonia strain Berg02-22.2 [73]. Therefore, the G. rupripertincta DFO locus is part of a larger BGC that putatively encodes the biosynthesis of the cryptic nocardichelins, and one of the two other acyltransferase genes in the BGC has presumably replaced desH. In all other cases, desG and desH are putatively co-operonic, and the two genes are fused in Streptomyces atratus and Micrococcus spp. CH3 and CH7. Among collected reports of DFO production, DFOB (Fig 3b) and other acetyl, fatty-acyl, or aryl “capped” DFOs were common, isolated from 34 of 47 sequenced strains. However, in line with our discovery, the nine strains lacking desGH exclusively produced DFOE (Fig 3b) and other “uncapped” DFOs with succinylated monomers (S6 Fig).
6. Proposed revision of the desferrioxamine biosynthetic pathway
Based on the combination of the above data, we propose the following pathway for DFO biosynthesis in S. coelicolor, now including the newly identified desG and desH (Fig 3b). The biosynthesis of DFOE is encoded by the canonical biosynthetic locus desABCD (SCO2782-85): DesA and DesB convert l-lysine to N-hydroxycadaverine, DesC succinylates N-hydroxycadaverine to form HSC [64], and DesD cylcotrimerizes HSC to produce DFOE [74]. In contrast, DesG (SCO4049) and DesH (SCO4050) enable DFOB production (Fig 3). A recent study of DesD concluded that the relative intracellular concentrations of HSC and HAC must be controlled for DFOB formation [74]. Previous investigations of DesC in vitro have shown that it is able to catalyze the conversion of N-hydroxycadaverine to both HSC and HAC, using succinyl and acetyl-CoA, respectively [64]. However, the relative catalytic efficiency of these two processes has yet to be elucidated. Our experiments strongly suggest that the main function of DesC in vivo is to catalyze the production of HSC, while HAC results primarily from the action of DesH. We propose that DesG, which shows sequence similarity to amidases, de-succinylates HSC to regenerate N-hydroxycadaverine, which is then acetylated by the putative acetyltransferase DesH to boost the levels of HAC relative to HSC in high level DFOB producers. Gene fusions of desGH observed in some strains are equipped to exploit the high local effective concentration of N-hydroxycadaverine generated by the DesG domain, enabling the DesH domain to acetylate N-hydroxycadaverine before it can be re-succinylated. The production of DFOB in the ∆desH mutant is strongly attenuated but not abolished, consistent with the previously reported ability of DesC to catalyze acylation of N-hydroxycadaverine with acetyl-CoA in addition to succinyl-CoA (Fig 3a). Taken together, these data indicate that DesC strongly prefers succinyl-CoA as a substrate over acetyl-CoA, and that DesG and DesH are required to ensure sufficient quantities of HAC are produced to support high level DFOB production in vivo. This biosynthetic model is in line with the available phylogenomic, metabolomic, and genetic evidence, as well as the canonical catalytic chemistry of DesG and DesH homologs.
7. Future perspectives
Considering that each Actinobacterial genome may encode up to a thousand TFs, the 17 precalculated TFBS prediction matrices available in this study represent only a small glimpse into the full potential of regulatory genome mining [22,75]. We envision that upcoming high-throughput DNA binding assays, such as DNA Affinity Purification Sequencing (DAP-seq) [76], will significantly expand the available TFBS datasets, greatly increasing the applicability and precision of this approach. In particular, the recently developed multiplexed version, multiDAP [77], offers the possibility to study regulatory conservation across multiple species in parallel, enabling us to assess how well predictions in one species translate to others. Additionally, advancements in peak calling, motif discovery algorithms, and annotations based on gene architecture will further enhance our ability to interpret these datasets [78,79]. By integrating these data with functional annotation approach of this study, paired with gene expression profiles, we anticipate that regulatory genome mining will advance substantially in the near future.
Conclusions
In conclusion, we have developed a novel computational omics strategy for functional inference of BGCs in microbes, which uses regulatory information to provide clues regarding their functional roles. In this work, we identified novel BGCs involved in siderophore biosynthesis based on regulation by an iron master regulator, but in principle the concept can be extended to any functional class. For example, we anticipate that BGCs that respond to specific environmental signals will be regulated in a similar manner, and such regulatory signatures can thus serve to find BGCs with in a similar ecological role. Uniquely, this method leverages genome-wide gene regulation information derived from TFBS detection combined with gene co-expression network analysis to link biosynthetic genes to their potential functions. A key application of this method is showcased in our study of S. coelicolor M145, a well-studied model organism, where we predict the regulons of 17 well-known regulators. Along with the resulting regulatory network, which includes both novel and experimentally validated interactions, we identified nine high-confidence functional associations between known BGCs and regulatory elements. Of these, we selected the iron-dependent repressor DmdR1 and its strong connection to the regulation of siderophore biosynthesis for showcasing the effectiveness of our approach. This analysis, which involved TFBS prediction of the DmdR1 regulon, alongside the detection of co-expression patterns under iron starvation conditions, allowed us to detect an uncharacterized gene cluster with a functional link to iron metabolism. Furthermore, we present evidence that the putative amidase and acyltransferase encoded by desG and desH, respectively, in this cluster collaborate in the efficient biosynthesis of DFO B by SCO4049 and SCO4050 CRISPR-cBEST knockout mutants and subsequent metabolic profiling experiments. These findings not only validated our hypothesis, but also enabled identification of a novel pathway within the complex biosynthetic route to DFOs. Overall, our results demonstrate the effectiveness of our method in identifying and inferring the function of novel BGCs that escaped detection despite the availability of state-of-the-art genome mining tools. We anticipate that transcriptomics-guided regulatory genome mining, by combining function prediction with application of elicitors that may activate BGCs of interest, will provide pointers as to how to select and activate cryptic BGCs in the extant biosynthetic diversity. This will aid in the identification of their roles in microbiome interactions and guide the discovery of bioactive natural products that are of value for pharmaceutical, agricultural, and biotechnological applications.
Methods
Construction of the position weight matrix and sequence motif
Ten previously reported DmdR1 binding sites from S. coelicolor were collected from literature [29]. Thereafter, the occurrences of each nucleotide across all positions of the sequences were counted to construct a position frequency matrix (PFM). This PFM was converted to a PWM by applying Bioconductor’s seqLogo v5.29.8 algorithm [80], which calculates the log-likelihood of each nucleotide in the matrix, while taking into account the background nucleotide distributions. Additionally, the information content (IC) of the resulting PWM was calculated using Shannon’s entropy calculation methods. The IC was visualized as a sequence motif with the use of Logomaker v 0.8 [81].
Identification of DmdR1 binding sites
The genome assembly of S. coelicolor A3(2) was downloaded from NCBI using accession GCA_000203835.1. The coding and non-coding regions, as well as the regions spanning from −350 bp to +50 bp relative to the start codons of each gene were extracted with MiniMotif [22] (https://github.com/HAugustijn/MiniMotif). We employed MOODS v1.9.4.1 [82] to query these regions for DmdR1 PWM matches, using a p-value threshold of 0.01 and background distribution of 72% representing the GC percentage of S. coelicolor. The ratio of hits in non-coding versus coding regions was visualized using the R package ggplot2 [83].
RNA-Seq data processing and co-expression analyses
S. coelicolor A3(2) RNA-Seq data, collected by Lee and colleagues [32], was retrieved from the European Nucleotide Archive (PRJEB25075) [84]. Raw read quality was assessed with FastQC [85]. Reads were mapped to the reference genome NC_003888.3 using STAR v2.7.6a [86]: Index files were generated with the parameters “--genomeSAindexNbases 10 --sjdbGTFfeatureExon CDS”, and reads were aligned with the parameter “--alignIntronMax 1”. Mapped reads were indexed using SAMtools v1.3.1 [87] and visualized with the Integrative Genomics Viewer [88]. Per-gene read count tables were generated with featureCounts v2.0.1 [89] using the parameters “-O -M -t CDS -s 2 --fraction”.
The per-gene RNA-Seq count data was further analyzed in R. A minimum gene expression cutoff was applied (≥5 counts in 50% of samples), then counts were normalized by Trimmed Mean of M-values (TMM) and log2 transformed using a hyperbolic arcsine pseudocount [90]. A co-expression bias associated with lowly- and highly-expressed genes (of unknown origin, but present in several other RNA-Seq datasets [34]) was mitigated by regressing out the first principal component using the sva_network function from the sva package (S7 Fig) [33]. The resulting correlation matrix still had an expression-correlated broadening of correlation coefficients, which was corrected by spatial quantile normalization (S7 Fig) [34] and used for further analyses. An all-to-all PCC matrix with corrected two-sided Student p-values was calculated using the corAndPValue function from the package WGCNA [91]. A p-value of 0.05 corresponded to a minimum absolute PCC value of 0.43. The correlation matrix was corrected for remaining expression-level-dependent PCC distribution broadening using spatial quantile normalization (spqn::normalize_correlation) with the following parameters: ngrp = 20, size_grp = 337, ref_grp = 18 [34]. Subsets of the resulting correlation matrix were used for all downstream analyses.
Comparative genomics
DFO core loci (desABCD) and accessory loci (desGH) were found in Streptomyces genomes using a modified version of antiSMASH 7 [92] (https://github.com/zreitz/antismash/tree/desGH-7-1). The “desABCD” rule requires matches to all of the following Pfam models with a maximum intergenic distance of 5 kbp: PF00282.22 (desA), PF13434.9 (desB), PF13523.9 (desC), and PF04183.5 (desD). The “desGH” rule requires matches to PF01804.21 (desG) and PF13523.9 (desH) with a maximum intergenic distance of 1 kbp. Genome assemblies for previously reported DFO producers (S3 Table) were downloaded from NCBI Genbank on 21 Nov, 2023, in Genbank format using ncbi-genome-download [93]. The multiSMASH pipeline [94] was used to scan the genomes with antiSMASH and tabulate the results [92]. A gene phylogeny of the resulting desABCD loci was obtained from CORASON, run as part of BiG-SCAPE v1.1.5 using settings “--mix --no-classify --clans-off --cutoffs 1” [95]. The resulting phylogenetic tree was annotated using iTOL v5 [96].
Bacterial strains and media
E. coli strains DH5ɑ and ET12567/pUZ8002 [97] were used for routine cloning and for interspecific conjugation, respectively. E. coli transformants were selected on Luria Bertani agar media containing the relevant antibiotics and grown O/N at 37 °C. S. coelicolor A3(2) M145 was used as parental strain to construct mutants. All media and routine Streptomyces techniques are described in the Streptomyces manual [98]. Soy flour mannitol agar plates were used to grow Streptomyces strains for preparing spore suspensions.
Growth conditions and extraction
The cultures were grown in triplicate in 100 mL Erlenmeyer flasks with 1 g of Diaion HP-20 resin (Resindion, Mitsubishi) in 15 mL of International Streptomyces Project-2 medium (ISP-2; yeast extract 4 g/L, malt extract 10 g/L, and dextrose 4 g/L at pH 7.2). The medium was inoculated using 1 μL of spore stock and incubated in a rotary shaker at 30 °C. After five days of growth, the resin was vacuum filtered, washed three times with Milli-Q water, and extracted with 3 × 5 mL of methanol. The crude extracts were then dried, weighed, and dissolved in methanol at a final concentration of 1 mg/mL. Media blanks were extracted and prepared in a similar way as negative controls.
LC–MS-based metabolic profiling
Liquid chromatography-tandem mass spectrometry (LC–MS/MS) acquisition was performed using Shimadzu Nexera X2 ultra high-performance liquid chromatography (UPLC) system, with attached photodiode array detector (PDA), coupled to Shimadzu 9030 QTOF mass spectrometer, equipped with a standard electrospray ionization (ESI) source unit, in which a calibrant delivery system (CDS) is installed. A total of 2 µL of dissolved extracts were injected into a Waters Acquity HSS C18 column (1.8 μm, 100 Å, 2.1 × 100 mm). The column was maintained at 30 °C, and run at a flow rate of 0.5 mL/min, using 0.1% formic acid in H2O as solvent A, and 0.1% formic acid in acetonitrile as solvent B. A gradient was employed for chromatographic separation starting at 5% B for 1 min, then 5%–85% B for 9 min, 85%–100% B for 1 min, and finally held at 100% B for 3 min. The column was re-equilibrated to 5% B for 3 min before the next run was started. The LC flow was switched to the waste the first 0.5 min, then to the MS for 13.5 min, then back to the waste to the end of the run.
The MS system was tuned using standard NaI solution (Shimadzu). The same solution was used to calibrate the system before starting. Additionally, a calibrant solution made from ESI tuning mix (Sigma-Aldrich) was introduced through the CDS system, the first 0.5 min of each run, and the masses detected were used for post-run mass correction for the file, ensuring stable accurate mass measurements.
System suitability was checked by regularly measuring a standard sample made of the compounds displayed in Table 1.
All the samples were analyzed in positive polarity, using data dependent acquisition mode. In this regard, full scan MS spectra (m/z 100–1,700, scan rate 10 Hz, ID enabled) were followed by two data-dependent MS/MS spectra (m/z 100–1,700, scan rate 10 Hz, ID disabled) for the two most intense ions per scan. The ions were selected when they reach an intensity threshold of 1,500, isolated at the tuning file Q1 resolution, fragmented using collision-induced dissociation with fixed collision energy (CE 20 eV), and excluded for 1 s before being re-selected for fragmentation. For the ESI source, the parameters were set to interface voltage 4 kV, interface temperature 300 °C, nebulizing gas flow 3 L/min, and drying gas flow 10 L/min. The parameters used for the CDS probe include an interface voltage 4.5 kV, and nebulizing gas flow 1 L/min.
Comparative metabolomics
Raw LC–MS data were converted to open source mzXML format using LabSolutions software (Shimadzu), and the converted files were imported into MZmine 3.3.0 [99] for data processing. Unless specified otherwise, m/z tolerance was set to 0.002 m/z or 10.0 ppm, RT tolerance was set to 0.05 min, MS1 noise level was set to 1.0E3, MS2 noise level to 1.0E1, and the minimum absolute height was set to 5.0E2. The option to detect isotope signals below noise level was selected. For feature detection and chromatogram building, the ADAP chromatogram builder [100] was used with positive polarity, centroid mass detector, minimum group size of 5 in number of scans, and a 2.0E3 group intensity threshold. The obtained peaks were smoothed (width: 9), and the chromatograms were deconvoluted using the local minimum search with a 90% chromatographic threshold, 1% minimum relative height, minimum ratio of peak top/edge of 2 and peak duration of 0.03 to 3.00 min. The detected peaks were deisotoped (monotonic shape, maximum charge: 5; representative isotope: most intense). Peak lists from different extracts were aligned (weight for m/z: 20, weight for RT: 20, compare isotopic pattern with a minimum score of 50%). The gap filling algorithm was used to detect and fill missing peaks (intensity threshold 1%, RT tolerance: 0.1 min). Duplicate peaks were filtered, and artifacts caused by detector ringing were removed (m/z tolerance: 1.0 m/z or 1,000.0 ppm). The aligned peaks were exported to a MetaboAnalyst. From here, peaks were additionally filtered to keep only peaks present in all 3 replicates and not in the media blanks, using in-house scripts. The resulting MetaboAnalyst peak list was uploaded to MetaboAnalyst [101], log transformed, and normalized with Pareto scaling without prior filtering. Missing values were filled with half of the minimum positive value in the original data. Volcano plots were generated using default parameters. Additionally, extracted ion chromatograms have been obtained for the ions of the DFO-related metabolites (m/z tolerance 0.001 or 5 ppm, S4 Table). An in-house python script was used to visualize these chromatograms with matplotlib v3.7.2 pyplot [102].
Plasmids, constructs, and oligonucleotides
All plasmids and constructs described in this work are summarized in S5 Table. The oligonucleotides are listed in S6 Table.
Fragment containing gapdh promoter was digested from previously published plasmid pGWS1370 [103] and cloned into pCRISPR-cBEST [66] via the same restriction sites to generated pGWS1384, where the expression of Cas9n (D10A), cytidine deaminase, and uracil-DNA glycosylase inhibitor (UGI) were under the control of gapdh promoter instead of tipA promoter. Spacers of each targeted gene were selected on CRISPy-web [104] and cloned into NcoI-digested pGWS1384 via single-strand DNA (ssDNA) oligo bridging method. ssDNA oligos SCO4048_W55 and SCO4048_Q68b were used to generate SCO4048 knockout constructs pGWS1582 and pGWS1584, respectively. Similarly, SCO4049 knockout construct pGWS1585 was created using oligo SCO4049_W61. SCO4050 knockout constructs pGWS1598 and pGWS1590 were created employing oligos SCO4050_W43 and SCO4050_Q91, respectively. All the generated knockout constructs were validated by Sanger sequencing using primer sg_T7_R_SnaBI.
For the complementation of SCO4048 null mutant, pGWS1596 was used, an integrative vector based on pSET152 and harboring SCO4048 under the control of gap promoter. The gap promoter and the entire coding region (+1/+724) of SCO4048 were amplified from S. coelicolor M145 genomic DNA using primer pairs Pgap_F and Pgap_R, and SO4048_F and SCO4048_R, respectively. Fragments were cloned into EcoRI and XbaI digested pSET152 via Gibson assembly to generate pGWS1596. Similarly, pGWS1597 and pGWS1598 were created for the complementation of SCO4049 and SCO4050 null mutants, respectively. The coding region (+1/+2,347) of SCO4049 in pGWS1597 was amplified using primers SCO4049_F and SCO4049_R, while the coding region (+1/+619) of SCO4050 in pGWS1598 was amplified using primer pair SCO4050_F and SCO4050_R.
Supporting information
S1 Fig. DmdR1 PWM threshold setting and identification of novel iron boxes in Streptomyces coelicolor.
Gray violin plot of the distribution of the number of matches to the PWM. The blue S-curve indicates the ratio (in %) of hits in the non-coding versus coding regions of the genome. The orange dotted line is the threshold set by the median score of the hits. The data underlying this Figure can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s001
(PNG)
S2 Fig. Measured MS and MS/MS spectra of desferrioxamine (DFO) B and E compared to their respective GNPS library spectra with accessions CCMSLIB00001059066 and CCMSLIB00005467773 of GNPS Mirror Match.
Details on the mass spectrometry data can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s002
(PNG)
S3 Fig. Volcano plots showing the relative intensities of the mass features detected in the generated knock-out mutants of Streptomyces coelicolor in comparison with the wild-type.
Mass features were regarded as significantly upregulated (red colored) or downregulated (blue colored) in the knock-out strains, with at least a 2-fold change in intensity and p-value ≤0.05. The data underlying this figure can be found in at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s003
(PNG)
S4 Fig. Extracted ion chromatograms of DFOB and DFOE related metabolites in the knock-out (Δ) mutants of SCO4048 (desJ), SCO4049 (desG) and SCO40450 (desH) compared to the parent S. coelicolor M145 strain.
Details on the mass spectrometry data can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s004
(PNG)
S5 Fig. Extracted ion chromatograms of DFO-related metabolites in the knock-out (Δ) and complementation (C) mutants of SCO4048 (desJ), SCO4049 (desG) and SCO40450 (desH) compared to the parent S. coelicolor M145 strain.
Details on the mass spectrometry data can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s005
(PNG)
S6 Fig. Midpoint-rooted phylogenetic gene tree of the desABCD locus in previously reported DFO producers (S3 Table), produced by CORASON.
Green squares indicate that the genome of the strain also contains homologs of desGH in addition to desABCD, and dark blue squares indicate that the strain was reported to produce acetyl, fatty-acyl, or aryl “capped” DFOs. The genome of Gordonia rubripertincta CWB2 contains a homolog of desG in an expanded locus. The Newick file can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s006
(PNG)
S7 Fig. Mean-expression biases in correlation coefficient distributions after standard processing (top), after regression of the first principal component (middle), and after spatial quantile normalization (bottom).
Genes were sorted by mean expression and split into 10 bins of equal size. All-to-all Pearson correlation coefficients were calculated within each bin, and the density was calculated and plotted with the R package ggridges. Deviations from a zero-centered distribution (dashed line) suggest non-biological confounders. Solid black lines give the first, second, and third quartiles. The data underlying this figure can be found at https://zenodo.org/records/15106944.
https://doi.org/10.1371/journal.pbio.3003183.s007
(PNG)
S8 Fig. Proposed biosynthesis for nocardichelins and desferrioxamine B in Gordonia rubripertincta CWB2.
Gene names correspond to S7 Table, while underscores indicate the locus tag prefix “GCWB2_”.
https://doi.org/10.1371/journal.pbio.3003183.s008
(PNG)
S1 Table. Refinement of BGC boundaries through literature evidence and gene co-expression patterns.
The following genes, located more than 10 genes from another gene in the cluster, were removed: BGC 6, SCO1195; BGC 13, SCO5826; BGC 25a, SCO7218; BGC 25b, SCO7239.
https://doi.org/10.1371/journal.pbio.3003183.s009
(PDF)
S3 Table. Overview of DFO producing strains and detected homologs of the desG and desH genes.
https://doi.org/10.1371/journal.pbio.3003183.s011
(PDF)
S4 Table. DFO-related metabolites (m/z tolerance 0.001 or 5 ppm).
https://doi.org/10.1371/journal.pbio.3003183.s012
(PDF)
S5 Table. Overview of the plasmids and constructs.
https://doi.org/10.1371/journal.pbio.3003183.s013
(PDF)
S7 Table. Desferrioxamine gene cluster of Gordonia rubripertincta CWB2.
https://doi.org/10.1371/journal.pbio.3003183.s015
(PDF)
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