Modulation of multiple gene clusters’ expression by the PAS-LuxR transcriptional regulator PteF

Citation: Vicente, C.M.; Payero, T.D.; Rodríguez-García, A.; Barreales, E.G.; Pedro, A.d.; Santos-Beneit, F.; Aparicio, J.F. Modulation of Multiple Gene Clusters’ Expression by the PAS-LuxR Transcriptional Regulator PteF. Antibiotics 2022,11, 994. https://doi.org/10.3390/ antibiotics11080994 Academic Editor: Manuel Simões Received: 28 June 2022 Accepted: 22 July 2022 Published: 24 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). antibiotics Article Modulation of Multiple Gene Clusters’ Expression by the PAS-LuxR Transcriptional Regulator PteF Cláudia M. Vicente 1,2,3, Tamara D. Payero 1,2, Antonio Rodríguez-García1,2 , Eva G. Barreales 1, Antonio de Pedro 1, Fernando Santos-Beneit 2,4 and Jesús F. Aparicio 1,* 1Department of Molecular Biology, Area of Microbiology, Faculty of Biology, Universidad de León, 24071 León, Spain; [email protected] (C.M.V.); [email protected] (T.D.P.); [email protected] (A.R.-G.); egar[email protected] (E.G.B.); [email protected] (A.d.P.) 2Institute of Biotechnology INBIOTEC, Parque Científico de León, Avda. Real, No 1, 24006 León, Spain; [email protected] 3 Toulouse Biotechnology Institute (TBI), CNRS, INRAE, INSA, Universitéde Toulouse, 31077 Toulouse, France 4Environmental Technology, Institute of Sustainable Processes, University of Valladolid, 47011 Valladolid, Spain *Correspondence: [email protected]; Tel.: +34-987-293420 Abstract: PAS-LuxR transcriptional regulators are conserved proteins governing polyene antifungal biosynthesis. PteF is the regulator of filipin biosynthesis from Streptomyces avermitilis. Its mutation drastically abates filipin, but also oligomycin production, a macrolide ATP-synthase inhibitor, and delays sporulation; thus, it has been considered a transcriptional activator. Transcriptomic analyses were performed in S. avermitilis ∆ pteF and its parental strain. Both strains were grown in a YEME medium without sucrose, and the samples were taken at exponential and stationary growth phases. A total of 257 genes showed an altered expression in the mutant, most of them at the exponential growth phase. Surprisingly, despite PteF being considered an activator, most of the genes affected showed overexpression, thereby suggesting a negative modulation. The affected genes were related to various metabolic processes, including genetic information processing; DNA, energy, carbohydrate, and lipid metabolism; morphological differentiation; and transcriptional regulation, among others, but were particularly related to secondary metabolite biosynthesis. Notably, 10 secondary metabolite gene clusters out of the 38 encoded by the genome showed altered expression profiles in the mutant, suggesting a regulatory role for PteF that is wider than expected. The transcriptomic results were validated by quantitative reverse-transcription polymerase chain reaction. These findings provide important clues to understanding the intertwined regulatory machinery that modulates antibiotic biosynthesis in Streptomyces. Keywords: antifungal agent; gene regulation; LuxR; PAS domain; polyene macrolide; Streptomyces 1. Introduction Polyene macrolide antifungals are natural products produced by Streptomycetes and related bacteria. These are filamentous soil-dwellers that undergo a complex life cycle involving differentiation and sporulation and they are well known for their ability to produce an impressive array of bioactive compounds. The control of these compounds’ production is a rather complex process involving multiple levels of intertwined regulation. Typically, the lowest level is governed by pathway-specific transcriptional regulators, which are encoded within the respective biosynthetic gene clusters. PAS-LuxR regulators are transcription factors that combine an N-terminal PAS sensory domain [ 1 ] with a C-terminal helix-turn-helix (HTH) motif of the LuxR type for DNAbinding [ 2 ]. The sensory domain is thought to detect a physical or chemical stimulus and regulate, in response, the activity of the effector domain [ 3 ]. The archetype of this class of regulators, PimM, was first identified in the antifungal pimaricin biosynthetic Antibiotics 2022,11, 994. https://doi.org/10.3390/antibiotics11080994 https://www.mdpi.com/journal/antibiotics Antibiotics 2022,11, 994 2 of 19 gene cluster from Streptomyces natalensis [ 4 ]. It was characterized as a transcriptional activator of pimaricin biosynthesis because antifungal production was abolished upon gene deletion, and later, its mode of action was characterized at the molecular level [ 5 ]. Since its discovery, homologous regulatory proteins have been found to be encoded in all the known biosynthetic gene clusters of antifungal polyketides (polyenes), and they have been shown to be functionally equivalent, to the extent that the production of pimaricin is restored in S. natalensis ∆ pimM upon the introduction of heterologous regulators of the PAS-LuxR class, such as nysRIV (nystatin), amphRIV (amphotericin), or pteF (filipin), into the strain [ 6 ]. Furthermore, the introduction of a single copy of pimM into the amphotericin-producing strain S. nodosus, into the filipin-producing strain S. avermitilis, or into the rimocidin producing strain S. rimosus, boosted the production of all polyenes, thus indicating that these regulators are fully exchangeable [ 6 ]. Interestingly, these regulatory proteins have only been found to be encoded in polyene gene clusters, in which they participate as the final transcriptional regulator of the regulatory cascade leading to antifungal biosynthesis. Although PAS-LuxR regulators were initially considered pathway-specific transcriptional regulators due to their location in the chromosome, recent results have shown that they should be considered regulators with a wider range of implications. The canonical operator of PimM was used to search for putative targets of the orthologous protein PteF in the genome of S. avermitilis, finding multiple binding sites located inside or upstream from genes involved in different aspects of both primary and secondary metabolism [ 7 ], thus suggesting that the regulator could govern those processes. These included genetic information processing, DNA replication and repair, energy metabolism, carbohydrate metabolism, lipid metabolism, morphological differentiation, transcriptional regulation, and secondary metabolite biosynthesis, among others. Several of these operators were selected, and their binding to PimM DNA-binding domain was demonstrated by electrophoretic mobility shift assays (EMSAs). As a proof of concept, the biosynthesis of the ATP-synthase inhibitor oligomycin, whose gene cluster included two operators, was studied [ 7 ]. The pteF-deleted mutants, who showed a severe loss of filipin production and delayed spore formation in comparison to that of the wild-type strain [ 8 ], also showed a severe loss of oligomycin production and reduced expression of olm genes. Gene complementation of the mutant restored the phenotype; thus, PteF was able to co-regulate the biosynthesis of two related secondary metabolites, the polyketide macrolides filipin and oligomycin [ 7 ]. Therefore, this cross-regulation could be extended to all the clusters where operators were found, which suggests that PAS-LuxR regulators may affect a plethora of processes previously unforeseen. In this sense, the introduction of PAS-LuxR regulatory genes into different Streptomyces hosts has already proven useful for the awakening of dormant secondary metabolite biosynthetic genes [9,10]. Herein, we have used microarrays to study the transcriptome of an S. avermitilis ∆ pteF mutant in comparison with that of its parental strain in order to deepen our knowledge about the processes in which PteF is involved, corroborating our previous results and providing the first evidence that PAS-LuxR regulators can behave as wide domain regulators and control the expression of multiple genes, either directly or indirectly, not only related to secondary metabolism but also to essential cellular functions. Their implication in the regulation of several secondary metabolite gene clusters is particularly noteworthy. 2. Results and Discussion 2.1. Identification of Genes with an Altered Expression Profile in S. avermitilis ∆pteF Mutant S. avermitilis ∆ pteF and its parental strain S. avermitilis NRRL 8165 were grown in a YEME medium without sucrose, and samples were taken at the end of the exponential and at the middle of the stationary growth phases (Figure 1). A transcriptomic analysis was performed by microarray hybridization to assess the genes with an altered expression in the mutant when compared with the parental strain at two different times during the growth curve. Given that PteF has been demonstrated to control filipin and oligomycin production as well as have an impact on sporulation [ 7 , 8 ], the sampling times were selected to coincide Antibiotics 2022,11, 994 3 of 19 with the onset of secondary metabolite production and with the metabolic changes linked with morphological differentiation, namely, at the end of exponential phase (t1) and early stationary phase of growth (t2). The genomic DNA was used as a universal reference for all the hybridizations. A result was considered statistically significant if the BH-corrected p-value was <0.05. It is worth noting that these conditions are quite stringent, given that the genes that constituted direct targets of PteF (e.g., the filipin polyketide synthases pteA1 and pteA2; [ 8 ]) were not statistically significant. With this criterion, a microarrays analysis showed significant differences (with a fold change above or below ± 2) in the expression of 208 genes of the pteF-negative mutant at the end of the exponential phase, and 99 at the stationary phase of growth (Table 1; Figure 2). Antibiotics 2022, 11, 994 3 of 19 in the mutant when compared with the parental strain at two different times during the growth curve. Given that PteF has been demonstrated to control filipin and oligomycin production as well as have an impact on sporulation [7,8], the sampling times were selected to coincide with the onset of secondary metabolite production and with the metabolic changes linked with morphological differentiation, namely, at the end of exponential phase (t1) and early stationary phase of growth (t2). The genomic DNA was used as a universal reference for all the hybridizations. A result was considered statistically significant if the BH-corrected p-value was <0.05. It is worth noting that these conditions are quite stringent, given that the genes that constituted direct targets of PteF (e.g., the filipin polyketide synthases pteA1 and pteA2; [8]) were not statistically significant. With this criterion, a microarrays analysis showed significant differences (with a fold change above or below ±2) in the expression of 208 genes of the pteF-negative mutant at the end of the exponential phase, and 99 at the stationary phase of growth (Table 1; Figure 2). Figure 1. Growth and antibiotic production in YEME medium without sucrose. Strains S. avermitilis wt (red), and ΔpteF mutant (ochre). (A) Growth curves; (B) Filipin production; (C) Oligomycin production. Arrows indicate RNA samples’ harvesting times. Table 1. Differential transcription and functional classification of genes affected by pteF deletion. The number of genes that are under- (↓) or over-expressed (↑) are indicated. Genes Underexpressed a Genes Overexpressed a Total Exponential phase (t1) 63 145 208 Stationary phase (t2) 35 64 99 Identified Genes b Function t1 t2 Genetic informationand protein-processing; amino acid metabolism 20 (7 ↓; 13 ↑) 11 (5 ↓; 6 ↑) Nucleotide and vitamin metabolism; DNA replication, recombination, and repair 16 (4 ↓; 12 ↑) 4 (1 ↓; 3 ↑) Carbohydrate metabolism 13 (3 ↓; 10 ↑) 1 (1 ↓) Lipid metabolism 8 (1 ↓; 7 ↑) 4 (4 ↑) Energy production 2 (2 ↓) 1 (1 ↓) Figure 1. Growth and antibiotic production in YEME medium without sucrose. Strains S. avermitilis wt (red), and ∆ pteF mutant (ochre). ( A ) Growth curves; ( B ) Filipin production; ( C ) Oligomycin production. Arrows indicate RNA samples’ harvesting times. Surprisingly, the lack of PteF resulted in the overexpression of a majority of the differentially transcribed genes, at both sampling times, thus indicating that this regulator acts as a negative modulator for the expression of those genes. This was unexpected given that PteF is an activator of both the antifungal filipin [ 8 ] and the ATP-synthase inhibitor oligomycin’s [7] biosynthesis. Antibiotics 2022,11, 994 4 of 19 These genes were related to different cellular processes, including genetic information processing; energy, carbohydrate, and lipid metabolism; DNA replication and repair; morphological differentiation; and transcriptional regulation, among others, but particularly to secondary metabolite biosynthesis (Table 1). Table 1. Differential transcription and functional classification of genes affected by pteF deletion. The number of genes that are under- (↓) or over-expressed (↑) are indicated. Genes Underexpressed a Genes Overexpressed aTotal Exponential phase (t1) 63 145 208 Stationary phase (t2) 35 64 99 Identified Genes b Function t1 t2 Genetic informationand protein-processing; amino acid metabolism 20 (7 ↓; 13 ↑) 11 (5 ↓; 6 ↑) Nucleotide and vitamin metabolism; DNA replication, recombination, and repair 16 (4 ↓; 12 ↑) 4 (1 ↓; 3 ↑) Carbohydrate metabolism 13 (3 ↓; 10 ↑) 1 (1 ↓) Lipid metabolism 8 (1 ↓; 7 ↑) 4 (4 ↑) Energy production 2 (2 ↓) 1 (1 ↓) Transport and external signals’ processing 20 (12 ↓; 8 ↑) 8 (4 ↓; 4 ↑) Cell envelope biosynthesis and morphological differentiation 9 (4 ↓; 5 ↑) 5 (4 ↓; 1 ↑) Regulation 27 (12 ↓; 15 ↑) 12 (3 ↓; 9 ↑) Secondary metabolism 60 (34 ↓; 26 ↑) 6 (2 ↓; 4 ↑) Miscellaneous 38 (11 ↓; 27 ↑) 19 (1 ↓; 18 ↑) a Only statistically significant genes with a fold-change value equal higher to ± 2 are included. b All identified genes were accounted for. 2.1.1. Genes Involved in Genetic Informationand Protein-Processing and Amino Acid Metabolism This group includes 24 genes that showed differential transcription in at least one of the sampling times (Table 1). These genes code for enzymes involved in amino acid metabolism (seven genes), proteins involved in transcription (eight genes, including five sigma factors), the ribosomal protein L28 (SAVERM2675), two putative acetyltransferases of ribosomal proteins (SAVERM703 and SAVERM758), and enzymes involved in protein processing (five genes) (Supplementary Materials Table S1). Interestingly, while sigma factors sig10 (SAVERM898), sig13 (SAVERM997), and sig60 (SAVERM213), and ribosomal proteins acetyltransferases SAVERM703 and SAVERM758 showed increased transcription levels in the mutant, sig32 (SAVERM3888), sig40 (SAVERM4561), the L28 ribosomal protein encoding gene rpmB1, and the whiB-like transcriptional factor wblE were clearly underexpressed in the mutant. The Wbl family of transcriptional factors is exclusive of actinobacteria, and their members have been correlated with diverse roles in morphological differentiation and secondary metabolism [ 11 , 12 ]. Notably, the genes rocA (SAVERM2723) and putA (SAVERM2724), which encode delta-1-pyrroline-5-carboxylate dehydrogenase and proline dehydrogenase, respectively, and that have been related to proline catabolism [ 13 ], and rocD2 (SAVERM7112) and SAVERM4551, which encode putative ornithine aminotransferases and are also involved in proline metabolism, were underexpressed in the mutant, while leuB (SAVERM2718), which is involved in valine, leucine, and isoleucine biosynthesis; paaI (SAVERM1986), which encodes the phenylacetic acid thioesterase; and putative cysteine desulfurase SAVERM1061 were overexpressed. Antibiotics 2022,11, 994 5 of 19 Antibiotics 2022, 11, 994 4 of 19 Transport and external signals’ processing 20 (12 ↓; 8 ↑) 8 (4 ↓; 4 ↑) Cell envelope biosynthesis and morphological differentiation 9 (4 ↓; 5 ↑) 5 (4 ↓; 1 ↑) Regulation 27 (12 ↓; 15 ↑) 12 (3 ↓; 9 ↑) Secondary metabolism 60 (34 ↓; 26 ↑) 6 (2 ↓; 4 ↑) Miscellaneous 38 (11 ↓; 27 ↑) 19 (1 ↓; 18 ↑) a Only statistically significant genes with a fold-change value equal higher to ±2 are included. b All identified genes were accounted for. Surprisingly, the lack of PteF resulted in the overexpression of a majority of the differentially transcribed genes, at both sampling times, thus indicating that this regulator acts as a negative modulator for the expression of those genes. This was unexpected given that PteF is an activator of both the antifungal filipin [8] and the ATP-synthase inhibitor oligomycin’s [7] biosynthesis. These genes were related to different cellular processes, including genetic information processing; energy, carbohydrate, and lipid metabolism; DNA replication and repair; morphological differentiation; and transcriptional regulation, among others, but particularly to secondary metabolite biosynthesis (Table 1). Figure 2. Differentially expressed genes in the mutant strain ΔpteF. Volcano plots show differential gene expression distribution during exponential phase (A) and stationary phase (B). Statistically significant genes are shown in green (log10 p-value ≥ 1.3). Figure 2. Differentially expressed genes in the mutant strain ∆ pteF. Volcano plots show differential gene expression distribution during exponential phase ( A ) and stationary phase ( B ). Statistically significant genes are shown in green (log10 p-value ≥1.3). 2.1.2. Genes Involved in Nucleotide and Vitamin Metabolism, and DNA Replication, Recombination, and Repair Eighteen genes falling into this category were found to be differentially transcribed in the mutant (Table 1). Ten of them are involved in DNA replication, recombination, and repair. Of these, seven putative transposases belonging to different families showed an enhanced transcription in the mutant. Additionally, two genes involved in DNA repair, ku2 (SAVERM879), which is probably involved in non-homologous DNA endjoining [ 14 ], and uvrD1 (SAVERM3463) that codes for a putative ATP-dependent helicase, were also upregulated. Conversely, int12 (SAVERM4626), which encodes a tyrosine-family recombinase/integrase, showed reduced transcription levels at the stationary phase. The remaining genes were differentially transcribed only in the exponential phase. Four genes are involved in vitamin metabolism, three of them with lower transcription in the mutant, including cobalamin methylase cobJ (SAVERM6407), adenosyltransferase cobA Antibiotics 2022,11, 994 6 of 19 (SAVERM6413), and alkaline phosphatase phoA (SAVERM5915), which besides being part of the PhoRP two-component system [ 15 ] is also involved in folate metabolism. The fourth gene, thiC (SAVERM4265), is a thiamine biosynthesis protein (Supplementary materials Table S1). The remaining genes are involved in purine metabolism, including pgmA,purA, and purN, all with an enhanced transcription, and cpdB, possessing a lower transcription. 2.1.3. Carbohydrate Metabolism Genes Thirteen genes fall into this category, including four most likely belonging to the same operon (SAVERM1009,galE5,mpg2, and SAVERM1014) and putatively involved in galactose metabolism, and showing an enhanced transcription in the mutant. Other genes involved in the metabolism of this sugar were the alpha-galactosidase agaB1 (SAVERM1082), which was underexpressed in the mutant, and the phosphoglucomutase pgmA (SAVERM803), which showed the opposite behavior. Interestingly, three genes of the tricarboxylic acid/glyoxylate cycle (citrate synthase citA2, citrate lyase citE2, and methylmalonyl-CoA mutase meaA1) were overexpressed in the mutant (Supplementary materials Table S1). 2.1.4. Lipid Metabolism Genes Nine genes related to lipid metabolism were differentially transcribed. These include the putative 3-oxoacyl-ACP synthase II fabB2 (SAVERM2944), the acyl carrier protein fabC4 (SAVERM217), the enoyl-CoA hydratase echA1 (SAVERM492), and the acetyl/propionyl CoA carboxylase alpha subunit accA2 (SAVERM3866), which are all presumably involved in fatty acid biosynthesis, and the 1-acylglycerol-3-phosphate O-acyltransferase plsC1 (SAVERM1485) putatively involved in glycerophospholipid biosynthesis, among others. Interestingly, all these genes showed increased transcription in the mutant during the exponential phase except fabB2, which was underexpressed (Supplementary materials Table S1). However, during the stationary phase, fabB2 also showed enhanced transcription. Notably, the direct binding of the PteF orthologue PimM to the promoters of two of these genes has been already demonstrated [ 7 ]; thus, they have been included in Supplementary materials Table S1, although they did not meet the statistical criteria. These were the acyltransferase plsC1 [ 16 ] whose transcription was increased in the mutant (Mc 0.88, uncorrected p-value 0.0471) and fabB2 whose transcription was reduced (Mc − 0.84, uncorrected p-value 0.0410 in t1) or increased (Mc 1.12, p-value 0.0048 in t2) depending on the growth phase. 2.1.5. Energy Production Genes Only three genes belonging to this group were found to be differentially transcribed in the mutant. All of them are involved in oxidative phosphorylation and have a reduced transcription in the mutant: two of them belong to the operon nuo (nuoJ1 and nuoK1), and the other one is the ATP synthase atpF (Supplementary Materials Table S1). Interestingly, all the genes belonging to the nuo operon (SAVERM4837-SAVERM4850), although in several cases not meeting the statistical criteria, showed the same decreased transcription profile in the mutant. 2.1.6. Transport and External Signals Processing This group includes 25 genes that showed differential transcription in at least one of the sampling times (Table 1). Interestingly, twelve of them code or participate in the formation of ATP-binding cassette transporters (Supplementary Materials Table S1). Of these, four are putatively involved in sugar transport (SAVERM1804,SAVERM2246,SAVERM2247, and SAVERM2609) and showed reduced transcription in the mutant. Four transporters belonging to the major facilitator superfamily showed differential transcription in the mutant: SAVERM2455 with a reduced transcription, SAVERM610, the sulfate transporter SAVERM4600, and SAVERM6941 with an enhanced transcription. Notably, in agreement with the enhanced transcription of SAVERM610, the genes fecC1 (SAVERM600) and fecB (SAVERM602), which constitute part of a putative ABC Antibiotics 2022,11, 994 7 of 19 transporter iron(III)/siderophore transport system, were also overexpressed. Based on protein similarity, SAVERM600-602 could constitute an ABC transport system homologous to the system FecBCD from E. coli involved in iron dicitrate transport [ 17 ]. The SAVERM600 and SAVERM611 genes flank a gene cluster involved in the biosynthesis of the siderophore nrp6 whose expression is also upregulated in the mutant (see below and Table 2and Supplementary Materials Table S1). Altogether, these results suggest that the ABC system SAVERM600-602 and the transporter SAVERM610 would be involved in iron transport using the siderophore nrp6. These transcriptomic results are further supported by the direct binding of PimM to the promoters of SAVERM602 and SAVERM610 [7]. Table 2. Transcriptional values of genes belonging to differentially expressed secondary metabolite gene clusters in S. avermitilis ∆ pteF when compared to its parental strain. (t1). The p-values are indicated in bold type when found statistically significant. Mc values higher than 1 and their corresponding fold-change above 2 are also in bold. Gene Description Fold-Change Mc Corrected p-Value p-Value Filipin cluster (pte) 407 pteH Thioesterase 1.93 0.95 0.1395 0.0076 408 pteG cholesterol oxidase 4.14 2.05 0.0025 0.0000 410 pteR SARP-family transcriptional regulator 4.53 2.18 0.0009 0.0000 411 pteE Ferredoxin 2.14 −1.10 0.2533 0.0233 412 pteD cytochrome P450 monooxygenase 6.68 −2.74 0.0005 0.0000 413 pteC cytochrome P450 monooxygenase 5.03 −2.33 0.0004 0.0000 414 pteB Dehydrogenase 5.06 −2.34 0.0000 0.0000 415 pteA5 modular polyketide synthase 2.01 −1.01 0.1136 0.0054 416 pteA4 modular polyketide synthase 2.43 −1.28 0.0095 0.0002 417 pteA3 modular polyketide synthase 1.56 −0.64 0.3514 0.0457 418 pteA2 modular polyketide synthase 1.83 −0.87 0.3639 0.0506 419 pteA1 modular polyketide synthase 1.74 −0.80 0.4388 0.0752 Non-ribosomal peptide-6 (nrp6) 600 fecC1 ABC transporter iron(III)/siderophore transport system ATP-binding protein 5.28 2.40 0.0003 0.0000 601 fecD1 ABC transporter iron(III)/siderophore permease 1.68 0.75 0.5625 0.1290 602 fecB ABC transporter iron(III)/siderophore-binding protein 7.73 2.95 0.0006 0.0000 603 nrps6 non-ribosomal peptide synthetase 1.60 0.68 0.3119 0.0342 604 hypothetical protein 2.27 1.18 0.0224 0.0005 605 fadD2 acyl-CoA synthetase 2.30 1.20 0.0049 0.0001 606 hypothetical protein 1.64 0.71 0.3525 0.0464 607 taurine catabolism dioxygenase 2.22 1.15 0.0017 0.0000 608 fabC2 acyl carrier protein 2.03 1.02 0.1136 0.0054 609 fabH4 3-oxoacyl-ACP synthase III 2.22 1.15 0.1182 0.0058 610 MFS transporter protein 2.28 1.19 0.0166 0.0004 611 beta-hydroxylase 3.12 1.64 0.0005 0.0000 Antibiotics 2022,11, 994 8 of 19 Table 2. Cont. Gene Description Fold-Change Mc Corrected p-Value p-Value Avermectin cluster (ave) 935 aveR LuxR-family transcriptional regulator 4.00 2.00 0.0049 0.0001 936 aveF C-5 ketoreductase 1.51 0.59 0.6016 0.1518 937 aveD C5-O-methyltransferase 1.35 0.43 0.6148 0.1603 938 aveA1 type I polyketide synthase 2.27 1.18 0.1820 0.0122 939 aveA2 type I polyketide synthase 1.31 0.39 0.7264 0.2537 940 aveC post-polyketide modification protein 1.00 0.00 0.9993 0.9950 941 aveE cytochrome P450 monooxygenase 2.06 1.04 0.1376 0.0074 942 aveA3 type I polyketide synthase 1.40 0.49 0.6328 0.1725 943 aveA4 type I polyketide synthase 1.66 0.73 0.4435 0.0764 944 aorf-1 Reductase 1.10 0.14 0.9330 0.6990 945 aveBI dTDP-L-oleandrose transferase (glycosyltransferase) 1.62 0.70 0.6052 0.1543 946 aveBII dTDP-glucose 4.6-dehydratase 1.17 0.23 0.8027 0.3477 947 aveBIII glucose-1-phosphate thymidyltransferase 2.11 1.08 0.2027 0.0154 948 aveBIV dTDP-4-keto-6-deoxy-L-hexose 4-reductase 1.21 −0.28 0.8828 0.5032 949 aveBV dTDP-4-keto-6-deoxyhexose 3.5-epimerase 1.60 0.68 0.5387 0.1160 950 aveBVI dTDP-4-keto-6-deoxy-L-hexose2.3-dehydratase 1.79 0.84 0.4308 0.0711 951 aveBVII dTDP-6-deoxy-L-hexose 3-O-methyltransferase 4.03 2.01 0.0020 0.0000 952 aveBVIII dTDP-4-keto-6-deoxy-L-hexose 2.3-reductase 3.56 1.83 0.0049 0.0001 953 aveG Thioesterase 4.59 2.20 0.0018 0.0000 Melanin cluster (melC-1)b 1136 melC1 tyrosinase co-factor protein 3.20 −1.68 0.0776 0.0015 1137 melC2 Tyrosinase 3.61 −1.85 0.0078 0.0000 γ-butyrolactone cluster (gbl) 2266 avaC Phosphatase 1.97 −0.98 0.0794 0.0030 2267 avaB Oxidoreductase 2.30 −1.20 0.2777 0.0279 2268 avaL2 TetR-family transcriptional regulator 8.88 −3.15 0.0078 0.0001 2269 avaA gamma-butyrolactone biosynthesis protein 3.01 −1.59 0.1456 0.0083 2270 avaL1 TetR-family transcriptional regulator 6.41 −2.68 0.0035 0.0000 Polyketide-3 cluster (pk3) 2272 hypothetical protein 5.46 −2.45 0.2000 0.0150 2273 Isomerase 151.17 −7.24 0.0208 0.0005 2274 secreted protein 32.45 −5.02 0.0473 0.0014 2275 transmembrane efflux protein 19.97 −4.32 0.1124 0.0053 2276 3-oxoacyl-ACP synthase III 18.90 −4.24 0.1242 0.0063 2277 Thioesterase 12.30 −3.62 0.2222 0.0183 2278 F420-dependent dehydrogenase 11.47 −3.52 0.3037 0.0327 2279 acyl-CoA synthetase 7.84 −2.97 0.3522 0.0462 2280 pks3-1 modular polyketide synthase 2.30 −1.20 0.6288 0.1702 2281 pks3-2 modular polyketide synthase 3.73 −1.90 0.5893 0.1436 2282 pks3-3 acyl carrier protein 4.08 −2.03 0.3974 0.0593 Antibiotics 2022,11, 994 9 of 19 Table 2. Cont. Gene Description Fold-Change Mc Corrected p-Value p-Value Oligomycin cluster (olm) 2890 ccrA1 crotonyl-CoA reductase 1.34 −0.42 0.4368 0.0743 2891 hypothetical protein 2.27 −1.18 0.2280 0.0193 2892 olmA4 modular polyketide synthase 1.26 −0.33 0.4760 0.0913 2893 olmA5 modular polyketide synthase 1.16 −0.22 0.6421 0.1789 2894 olmB cytochrome P450 monooxygenase 2.13 −1.09 0.1512 0.0087 2895 olmA7 modular polyketide synthase 1.56 −0.64 0.0746 0.0028 2896 olmA6 modular polyketide synthase 1.67 −0.74 0.1443 0.0080 2897 olmA3 modular polyketide synthase 1.25 −0.32 0.3610 0.0493 2898 olmA2 modular polyketide synthase 1.24 −0.31 0.5785 0.1377 2899 olmA1 modular polyketide synthase 1.53 −0.61 0.1817 0.0121 2900 P450-like protein 1.48 −0.57 0.2474 0.0224 2901 olmRII LuxR-family transcriptional regulator 2.77 −1.47 0.0712 0.0026 2902 olmRI LuxR-family transcriptional regulator 2.95 −1.56 0.0006 0.0000 2903 olmC Thioesterase 3.63 −1.86 0.1235 0.0062 Neopentalenolactone cluster (ptl) 2989 MarR-family transcriptional regulator 2.08 −1.06 0.0487 0.0015 2990 gap1 glyceraldehyde-3-phosphate dehydrogenase 2.57 −1.36 0.2469 0.0222 2991 ptlH 1-deoxypentalenic acid 11-beta hydroxylase 1.91 −0.93 0.1529 0.0089 2992 ptlG transmembrane efflux protein 1.29 −0.37 0.9068 0.5988 2993 ptlF 1-deoxy-11beta-hydroxypentalenic acid dehydrogenase 1.69 −0.76 0.2639 0.0251 2994 ptlE Baeyer-Villiger monooxygenase 2.46 −1.30 0.0087 0.0001 2995 ptlD Dioxygenase 2.31 −1.21 0.0962 0.0041 2996 ptlC hypothetical protein 2.10 −1.07 0.5367 0.1153 2997 ptlB farnesyl diphosphate synthase 1.95 −0.96 0.5690 0.1322 2998 ptlA pentalenene synthase 2.53 −1.34 0.5592 0.1269 2999 ptlI pentalenene C13 hydroxylase; cytochrome P450 2.36 −1.24 0.4943 0.0993 3000 ptlR AraC-family transcriptional regulator 1.49 −0.58 0.5379 0.1158 3001 ptlJ Lyase 1.24 −0.31 0.6109 0.1579 3002 ptlL hypothetical protein 1.02 −0.03 0.9754 0.8867 Albaflavenol/albaflavenone cluster (ezs) 3031 cyp14 epi-isozizaene hydroxylase (cytochrome P450 monooxygenase) 2.00 1.00 0.2918 0.0301 3032 ezs epi-isozizaene synthase (sesquiterpene cyclase) 2.14 1.10 0.1621 0.0097 Non-ribosomal peptide-3 cluster (nrp3) 3155 MbtH-like protein 2.27 −1.18 0.2696 0.0261 3156 nrps3-1 non-ribosomal peptide synthetase 2.95 −1.56 0.0867 0.0035 3157 export protein 1.58 −0.66 0.6407 0.1773 3158 nrps3-2 non-ribosomal peptide synthetase 1.79 −0.84 0.6149 0.1623 3159 nrps3-3 non-ribosomal peptide synthetase 1.91 −0.93 0.5290 0.1123 3160 Aminotransferase 2.19 −1.13 0.4384 0.0749 3161 dapF2 diaminopimelate epimerase 2.04 −1.03 0.5045 0.1029 3162 hypothetical protein 2.87 −1.52 0.1668 0.0101 3163 hypothetical protein 2.36 −1.24 0.3227 0.0384 3164 hypothetical protein 3.01 −1.59 0.0003 0.0000 Polyketide-4 cluster (pk4) 7184 pks4 modular polyketide synthase 9.32 −3.22 0.2980 0.0315 7185 UDP-glucose:sterol glucosyltransferase 8.28 −3.05 0.2753 0.0271 7186 cyp26 cytochrome P450 hydroxylase 7.36 −2.88 0.2419 0.0213 Antibiotics 2022,11, 994 16 of 19 The microarray data have been deposited in the National Center for Biotechnology Information-Gene Expression Omnibus under accession number GSE185887. 3.5. Assessment of Filipin and Oligomycin Production Filipin production was quantified as described elsewhere [ 39 ], whereas oligomycin was measured following the procedure described by Vicente et al. [7]. 3.6. Reverse Transcription-Quantitative PCR Reverse transcription of total RNA was performed on selected samples with 5 µ g of RNA and 12.5 ng/ µ L of random hexamer primer (Invitrogen, Waltham, MA, USA) using SuperScript ™ III reverse transcriptase (Invitrogen, Waltham, MA, USA) as described previously [ 60 ]. Reactions were carried out on two biological replicates with three technical replicates each and appropriate controls were included to verify the absence of gDNA contamination in RNA and primer-dimer formation. Primers (see Supplementary Materials Table S4) were designed to generate PCR products between 97 and 153 bp, near the 5 0 end of mRNA. The PCR reactions were initiated by incubating the sample at 95 ◦ C for 10 min followed by 40 cycles at 95 ◦ C for 15 s, 62–70 ◦ C (depending on the set of primers used) for 34 s, and 72 ◦ C for 30 s. To check the specificity of real-time PCR reactions, a DNA melting curve analysis was performed by holding the sample at 60 ◦ C for 60 s followed by slow ramping of the temperature to 95 ◦ C. Baseline and threshold values were determined by the StepOnePlus software. Ct values were normalized with respect to rrnA1 mRNA (encoding 16S rRNA). Relative changes in gene expression were quantified using the Pfaffl method [ 61 ] and the REST © software [ 62 ]. The corresponding real-time PCR efficiency (E) of one cycle in the exponential phase was calculated according to the equation E=10[−1/slope] [63] using 5-fold dilutions of genomic DNA ranging from 0.013 to 40 ng (n= 5 or 6 with three replicates for each dilution) with a coefficient of determination R2> 0.99 (Supplementary Materials Figure S2). Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/antibiotics11080994/s1, Figure S1: Validation of microarray results using RT-qPCR; Figure S2: Primer efficiency; Table S1: Differentially expressed genes in S. avermitilis ∆ pteF when compared to its parental strain; Table S2: Determination of the quality flag for array spots; Table S3: Assigned weights to each spot flags; Table S4: Sequence of primers used for qPCR. Author Contributions: Conceptualization, C.M.V. and J.F.A.; methodology, C.M.V., T.D.P., E.G.B. and A.R.-G.; validation, F.S.-B. and A.d.P.; formal analysis, C.M.V. and J.F.A.; investigation, C.M.V., T.D.P., E.G.B., A.d.P. and A.R.-G.; resources, F.S.-B.; writing—original draft preparation, J.F.A.; writing— review and editing, C.M.V. and J.F.A.; project administration, C.M.V. and J.F.A.; funding acquisition, J.F.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Spanish Ministerio de Economía, Industria y Competitividad (grants BIO2013-42983-P and PCIN-2016-190 to J.F.A.), FPU contracts of the Ministerio de Educación, Cultura y Deporte (AP2007-02055 to T.D.P., FPU13/01537 to A.d.P.), a contract from the Junta de Castilla y León co-financed by the European Social Fund (to E.G.B.), and a fellowship from the Portuguese Fundação para a Ciência e a Tecnologia (SFRH/BD/64006/2009 to C.M.V.). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The microarray data are deposited in the National Center for Biotechnology Information-Gene Expression Omnibus under accession number GSE185887. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Antibiotics 2022,11, 994 17 of 19 References 1. Hefti, M.H.; Francoijs, K.-J.; de Vries, S.C.; Dixon, R.; Vervoort, J. The PAS fold: A redefinition of the PAS domain based upon structural prediction. Eur. J. Biochem. 2004,271, 1198–1208. [CrossRef] 2. Santos, C.L.; Correia-Neves, M.; Moradas-Ferreira, P.; Mendes, M.V. A walk into the LuxR regulators of Actinobacteria: Phylogenomic distribution and functional diversity. PLoS ONE 2012,7, e46758. [CrossRef] 3. 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