Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

Andrea M Sass; Tom Coenye

doi:10.1371/journal.pone.0236405

. 2020 Jul 23;15(7):e0236405. doi: 10.1371/journal.pone.0236405

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

Andrea M Sass ¹, Tom Coenye ^1,^*

Editor: Roy Martin Roop II²

PMCID: PMC7377471 PMID: 32702060

Abstract

Regulatory small RNAs play an essential role in maintaining cell homeostasis in bacteria in response to environmental stresses such as iron starvation. Prokaryotes generally encode a large number of RNA regulators, yet their identification and characterisation is still in its infancy for most bacterial species. Burkholderia cenocepacia is an opportunistic pathogen with high innate antimicrobial resistance, which can cause the often fatal cepacia syndrome in individuals with cystic fibrosis. In this study we characterise a small RNA which is involved in the response to iron starvation, a condition that pathogenic bacteria are likely to encounter in the host. BrrF is a small RNA highly upregulated in Burkholderia cenocepacia under conditions of iron depletion and with a genome context consistent with Fur regulation. Its computationally predicted targets include iron-containing enzymes of the tricarboxylic acid (TCA) cycle such as aconitase and succinate dehydrogenase, as well as iron-containing enzymes responsible for the oxidative stress response, such as superoxide dismutase and catalase. Phenotypic and gene expression analysis of BrrF deletion and overexpression mutants show that the regulation of these genes is BrrF-dependent. Expression of acnA, fumA, sdhA and sdhC was downregulated during iron depletion in the wild type strain, but not in a BrrF deletion mutant. TCA cycle genes not predicted as target for BrrF were not affected in the same manner by iron depletion. Likewise, expression of sodB and katB was dowregulated during iron depletion in the wild type strain, but not in a BrrF deletion mutant. BrrF overexpression reduced aconitase and superoxide dismutase activities and increased sensitivity to hydrogen peroxide. All phenotypes and gene expression changes of the BrrF deletion mutant could be complemented by overexpressing BrrF in trans. Overall, BrrF acts as a regulator of central metabolism and oxidative stress response, possibly as an iron-sparing measure to maintain iron homeostasis under conditions of iron starvation.

Introduction

Burkholderia cenocepacia J2315 is a member of the Burkholderia cepacia complex (BCC), a group of aerobic Gram-negative beta-proteobacteria which mainly live in the rhizosphere, but can also act as opportunistic pathogens, particularly in individuals with cystic fibrosis [1].

Iron is essential for living organisms, as part of the catalytic/active site of many enzymes, yet it is mostly inaccessible for bacteria due to the low solubility under oxic conditions at neutral pH [2, 3]. Iron acquisition mechanisms are therefore important for survival of bacteria [4], as well as mechanisms to conserve iron under conditions of iron depletion. On the other hand, higher intracellular concentrations of iron are toxic to cells [3]. Free iron can react with the superoxide anion and with H₂O₂, natural by-products of respiration and oxidase reactions, to form the very reactive hydroxyl radical via the Fenton reaction. All reactive oxygen species (ROS) can damage DNA, proteins and lipids, and an excess of ROS leads to cell death. For protection against ROS, cells produce detoxifying enzymes such as catalases and superoxide dismutases (SOD) [3].

The ferric uptake regulator (Fur) is important for iron homeostasis in many bacteria. Its best known mechanism of action is the repression of genes involved in iron uptake under iron replete conditions by iron-dependent binding to a specific sequence motif, the Fur box [2]. In Escherichia coli and Pseudomonas aeruginosa Fur also positively regulates SOD, the iron scavenger protein bacterioferritin and several enzymes of the tricarboxylic acid cycle (TCA) [5, 6]. This positive regulation was attributed to indirect effects mediated by Fur-regulated small RNAs (sRNAs), RyhB in E. coli [6] and PrrF in P. aeruginosa [5]. Under iron-depletion the Fur repression of these sRNAs is lifted, they bind to the mRNA of their targets and the sRNA-mRNA hybrid is then rapidly degraded, or the translation of targets is inhibited [7]. This reduces the demand for iron in the cell, since many targets have iron or iron-sulfur clusters as cofactor.

A small RNA highly upregulated under iron depletion was identified in B. cenocepacia J2315 by screening dRNA-Seq data for short transcripts [8, 9] and designated ncS63. Its computationally predicted targets included confirmed targets of E. coli RyhB and P. aeruginosa PrrF, such as sdhC and sodB [5, 6, 10, 11], yet ncS63 has no sequence similarity to any known Fur-regulated sRNAs. Here we report on the full characterisation of this low iron-induced sRNA. As our results suggest that B. cenocepacia ncS63 shows analogy to RyhB and PrrF, we suggest the name BrrF (Burkholderia regulatory RNA involving iron, Fe), analogous to P. aeruginosa PrrF [5] and Neisseria meningitidis NrrF [12].

Materials and methods

Media

Strains were routinely cultured in LB broth or agar (low-salt Lennox formulation: 10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl, 1.5% agar), supplemented with 600 μg/ml trimethoprim (TP) as selective antibiotic where appropriate. Gene expression from the plasmid was induced by adding rhamnose (Sigma) to a final concentration of 0.2% (w/v). Iron depletion was induced by adding the iron chelator 2,2’-dipyridyl (Sigma) at 200 μM final concentration to LB broth or agar.

For growth curves on single carbon compounds, a phosphate buffered mineral medium (basal salt medium, BSM) was used; which contained 18.6 mM K₂HPO₄, 7.2 mM NaH₂PO₄, 37.4 mM NH₄Cl, 0.1 g/L nitrilotriacetic acid, 0.2 g/L MgSO₄ x 7 H₂O, 0.012 g/L FeSO₄ x 7 H₂O, 0.003 g/L MnSO₄ x H₂O, 0.003 g/L ZnSO₄ x 7 H₂O, 0.001 g/L CoSO₄ x 7 H₂O, [13]. As B. cenocepacia J2315 does not grow in BSM when iron is depleted by addition of dipyridyl or by treating the medium with Chelex, low iron conditions were obtained by omitting iron sulfate.

All incubations were performed at 37°C.

Strains and plasmids

B. cenocepacia strain J2315 (S1 Table) was used as background for all experiments. A brrF deletion mutant was constructed by allelic recombination as described by Aubert et al. [14]. Deletion of brrF was confirmed by Sanger sequencing of a 449 nt long region spanning brrF. Amplification of all inserts was performed with the HotStar HiFidelity Polymerase Kit (Qiagen).

For overexpressing BrrF, a region ranging from 80 nt upstream of the putative processing site to 49 nt downstream of the computationally predicted terminator sequence was amplified and cloned into an overexpression vector with a rhamnose-inducible promoter [15], yielding pBrrF-d1. The overexpression vector had previously been modified to remove the ribosome binding site and start codons [16].

The transcript expressed from pBrrF-d1 needs to be processed/cleaved to produce the native BrrF. Most computationally predicted interactions with targets were located at the very 5’end of the cleaved transcript, making it the most interesting region to introduce point mutations for complementation experiments. Introducing mutations so close to the cleavage site could potentially interfere with the processing of the overexpression transcript. For this reason, a second overexpression vector was constructed by inverse PCR with pBrrF-d1 as template, removing the sequences upstream of the putative processing site, and yielding pBrrF-d2. In the same manner, a vector carrying a brrF derivative with point mutations at position 8 and 9 was constructed, also by inverse PCR with pBrrF-d1 as template, yielding pBrrF-d3.

Overexpression plasmid derivatives were created by inverse PCR with the LongRange PCR kit (Qiagen). PCR products were digested with DpnI (Promega) to remove template plasmid, blunted with T4 DNA polymerase (Promega) to remove A-overhangs and gel-cleaned. Amplified plasmid was then phosphorylated using T4 polynucleotide kinase (Promega) and self-ligated (T4 DNA ligase, Promega). All plasmid inserts were verified by Sanger sequencing. Plasmids were transformed into B. cenocepacia J2315 by triparental mating and expression of BrrF from the plasmid in the presence of rhamnose was confirmed by qPCR.

All primer sequences are listed in S2 Table.

Growth curves

Growth on single carbon sources was measured in round-bottom 96-well microtiter plates (SPL Life Sciences) in a temperature-controlled microplate reader (Envision, Perkin Elmer), with intermittent shaking. Cells were inoculated at 10⁶ CFU/ml and optical density (O.D.) was measured at 30 min intervals over >60 hours. The mineral medium was supplemented with 0.05% yeast extract and 0.05% casamino acids, without which strain J2315 would grow extremely slowly on only one carbon source. Controls with only the organic supplements were run on every plate. Compounds were purchased from Sigma-Aldrich, Fluka, Janssen, and Acros Organics. Every compound was tested in at least two independent biological replicates.

Growth in LB broth was investigated using glass flasks in a shaking incubator. Growth was monitored via backscattered light every 2 min using a Cell Growth Quantifier (CGQ, Aquila Biolabs).

Growth rates were determined using the equation μ * h^-1 = ln (x_t2/x_t1)/(t₂-t₁), where x denotes optical density or backscatter arbitrary units, and t₁ and t₂ refer to a point at the beginning or end of the analysed time interval, respectively [17]. The obtained values for μ of the biological replicates were then analysed either by One-way ANOVA or a two-tailed Student’s t-test using SPSS (v. 25).

Sensitivity to H₂O₂

Cell suspensions with an O.D. (595 nm) of 0.1 (approx. 10⁸ colony forming units (CFU) per ml) were spread on low-nutrient LB agar plates (1 g/L tryptone, 0.5 g/L yeast extract, 5 g/L NaCl, 1.5% agar) with a sterile cotton swab. Inoculated plates were pre-incubated for 1 hour to allow gene expression to start. Then, 10 μl of a 1% H₂O₂ (Sigma) solution were applied to 6 mm blank susceptibility testing discs (Oxoid) which were placed into the middle of the agar plate. Tests were performed in six replicates, on three different days, and growth inhibition zones were measured after 24 h incubation.

Superoxide dismutase activity measurements

SOD activity was determined with a SOD assay kit (Sigma 19160-1KT-F), which uses a water soluble tetrazolium salt that reacts with superoxide produced by a xanthine oxidase. SOD inhibits this reaction by removing the superoxide. Cells were grown in 25 ml LB broth in flasks on a shaker as described above, without addition of inducers, to an O.D. of 0.5 (5 x 10⁸ CFU/ml). Then the inducer was added and incubation continued for another hour (pulse expression). Cultures were then cooled in ice water and centrifuged at 4°C. The cells were washed twice in 25 ml 50 mM ice-cold sodium phosphate buffer, pH 7, re-suspended in 1 ml of the phosphate buffer supplemented with 1 mg/ml dithiothreitol, and centrifuged again. The supernatant was removed and the cell pellet stored at -80°C for a maximum of 24 hours. Cells were lysed in 1 ml ice cold phosphate buffer by bead beating with zirconia-silica beads (0.1 mm diameter, BioSpec). The lysate was centrifuged at max speed for 5 min at 4°C, the supernatant transferred into a fresh tube and kept on ice. The SOD assay was performed immediately according to the manufacturer’s instructions. The protein concentration in the extract was determined with a Bradford assay (Sigma) and used for normalisation of the SOD activity.

Aconitase activity measurements

Aconitase activity was determined by measuring the production of cis-aconitate. To this end isocitrate was added to a crude cell extract in the presence of high concentrations of citrate and the increase of absorbance at 240 nm was measured [18–20].

Cells were grown in flasks as mentioned for SOD activity measurements. After harvest (as above), cell were washed twice in a TRIS/citrate buffer (20 mM citric acid, 20 g/L TRIS, pH 8), resuspended in 1 ml ice cold TRIS/citrate buffer and immediately lysed by bead beating. The lysate was centrifuged and the supernatant kept on ice. For aconitase activation, freshly prepared cysteine and ammonium iron (II) sulfate solutions were subsequently added to a final concentration of 5 and 0.5 mM, respectively. Supplemented lysates were then incubated on ice for one hour before measurements.

DL-isocitrate (Na₃-salt, Sigma) was added to the extract to a final concentration of 40 mM, at room temperature in a Biodrop μLite spectrophotometer, with a microvolume sample port of 0.5 mm path length. The absorbance at 240 nm was measured in intervals of 5 seconds for a total of 5 min and corrected for absorbance of the same sample without isocitrate addition. The protein concentration in the extract was determined with a Bradford assay, and a standard curve was generated with cis-aconitate (Sigma) added to heat-inactivated lysates (supplemented lysates heated to 50°C for 15 min and centrifuged) containing 40 mM isocitrate.

qPCR

Cells were grown in 25 ml LB broth in flasks on a shaker as described above. Rhamnose and/or dipyridyl were added 30 min before flasks were cooled in ice water and 4 ml culture harvested by centrifugation in microcentrifuge tubes at 4°C and maximum speed. This “pulse-expression” method was chosen to minimise secondary effects of iron depletion or BrrF-overexpression on target gene expression. Pellets were stored at -80°C for a maximum of one week. RNA was extracted with the RiboPure bacteria kit (ThermoFisher) with the following changes to the manufacturer’s instructions: the crude extract was mixed with 1.25 x volumes ethanol before column cleaning to better retain sRNAs, and DNase I treatment time was increased to 60 min. RNA was transcribed to cDNA with the High Capacity cDNA RT kit (Applied Biosystems). qPCR was performed as described before [9], using the GoTaq qPCR Master Mix (Promega). Each reaction was run with two technical replicates, each condition with 3 biological replicates and a no-RT control, and no-template controls were performed for each gene. Cq values were normalised to control gene rpoD (BCAM0918). One-Way ANOVA with a Tukey Post-hoc test using SPSS (v. 25) was performed to determine statistical significance. All primer sequences are listed in S2 Table.

Computational methods

To identify loci with Fur boxes in the B. cenocepacia J2315 genome, and to predict the Fur box consensus sequence, a two-step procedure was employed. Fur box-containing upstream sequences of B. multivorans [21] were analysed using the MEME tool [22] and the resulting motif was submitted to FIMO [22] to identify Fur box-containing upstream sequences of genes induced under iron-depleted conditions according to a microarray reference dataset [23]. Of these, sequences comprising the entire 5’UTR and 150 nt upstream of the transcription start site (TSS) were extracted and returned to MEME for motif prediction. The resulting motif was then re-submitted to FIMO to screen for Fur boxes across the entire B. cenocepacia J2315 genome.

brrF homologs were screened for using BLASTn [24], with search parameters adjusted to short sequences with low similarity: word size 7, match/mismatch scores -1/1, gap costs 0/2 and expect threshold 0.001.

Global alignments of brrF homologs were generated with LocARNA [25], using default parameters (see S1 Fig for strains and sequences used). Secondary structures of BrrF and selected mRNAs were predicted with mfold [26] and visualised using StructureEditor [27].

BrrF target prediction was performed with CopraRNA [11], using default parameters and the Burkholderia strains from S1 Fig as input strains. This algorithm takes accessibility of interaction sites and conservation of putative targets into account. Sequences adjacent to start codon of annotated genes, from 200 nt upstream to 100 nt downstream of the first nucleotide, were considered for target prediction; most confirmed sRNA-mRNA interactions are located in those regions. Predicted interactions located upstream of a known TSS [8] were removed from the results list. Functional enrichment analysis of predicted targets was performed with the DAVID tool [28], which is incorporated in CopraRNA.

Results and discussion

Sequence, conservation, genome context and regulation of BrrF

BrrF is located within the 3’ end of BCAL2297, a small protein designated HemP for its involvement in heme iron uptake in Burkholderia multivorans (Fig 1A, [29]). BrrF appeared to be cleaved from the hemP mRNA, as indicated by the strong depletion of the BrrF transcript by treatment with a 5’-monophosphate-dependent exonuclease (TEX, [8]). Its size was determined as 126 nt by dRNA-Seq (position 2548559 to 2548684 on replicon 1), confirmed by Northern blotting [9]. BrrF does not extend into the computationally predicted rho-independent terminator (position 2548709 to 2548732) downstream of hemP, instead it terminates a at succession of U-residues without a preceding stem loop. The same genome context was identified for the BrrF homolog BTH_s39 in Burkholderia thailandensis, which was identified using tiling microarrays [30]. BTH_s39 is also located downstream of hemP and does not extend into its computationally predicted downstream rho-independent terminator. The size of BTH_s39 was determined by rapid amplification of cDNA ends and confirmed by Northern blotting, and is, with 130 nt, a close match to BrrF.

Fig 1 — A) Coverage from differential RNA-Sequencing of a B. *cenocepacia* J2315 biofilm [8]. RNA samples were divided and one sub-sample treated with a 5’-monophosphate-dependent exonuclease (TEX), which selectively degrades processed transcripts. Reads starting at position 2548277 are enriched in the TEX-treated subsample, representing a transcription start site (TSS). Reads starting at position 2548559 are depleted by TEX-treatment, suggesting cleaving of the mRNA at this site. The *hemP* TSS (black arrow) is preceded by a 19 nt long Fur binding site (green box) and putative OxyR binding sites (blue boxes). Underlined: -10 and -35 box. Grey boxes: predicted rho-independent terminators. Pink box: *hemP* start codon. The diamond-ended lines below *brrF* depict the sizes of plasmid inserts for complementation (orange) and of the qPCR amplicon (black). B) Conserved genome context of *brrF*. On the upstream side, *brrF* is always flanked by *hemP* (light blue), downstream either by the *ftr* cluster for iron transport (dark blue) or a heme binding protein (light green). Identical colour denotes homologous proteins.

Although the 5’end of BrrF (GUAUU) bears similarities with typical RNase E consensus recognition sites in E. coli (RN↓WUU [31]), it is not cut at the central AUU as is typical for RNase E in that bacterium, but upstream of the first guanosine residue (CGA↓GUAUU, Fig 2A). The RNase recognition sequences in B. cenocepacia have not been systematically investigated, but the putative cutting sites of two other processed B. cenocepacia sRNAs (ncS35 CGA↓UUC [16], ncS27 CGA↓AUG [32]) are somewhat similar to the putative BrrF cutting site. This indicates that RNase recognition sites in B. cenocepacia differ from those in E. coli, and that one possible consensus recognition site might be CGA↓NUN.

Fig 2 — A) Computationally predicted secondary structure of BrrF. Red bases are conserved across >95% of screened strains of genera *Burkholderia*, *Paraburkholderia*, *Pandoraea* and *Cupriavidus*. Yellow bases indicate the successive guanosine residues that were replaced by cytosine for overexpression experiments. Bases involved in predicted interactions with selected mRNA targets are shaded in colour. The black arrow indicates the putative cutting site. B) Computationally predicted interactions with mRNAs of *acnA*, *fumA*, *sdhC*, *katB*, *sodB* and *sdhA*. The numbers indicate nucleotide positions relative to the start of the coding sequence for the respective mRNA (upper sequence 5’ to 3’ from left to right) or the nucleotide position in BrrF (lower sequence, 5’ to 3’ from right to left). Nucleotide positions altered in overexpression experiments are in red.

brrF is conserved over the full length in the genera Burkholderia (100% coverage, 90–100% identity), Paraburkholderia (100% coverage, 81–89% identity) and in Pandoraea (100% coverage, 70–74% identity, S1 Fig). In particular the 5’ end (nucleotides 4–45) shows a high degree of conservation. In the genus Cupriavidus, only nucleotides 4 to 76 are present (57% coverage, 75–80% identity). No similar sequences were found outside the order Burkholderiales. The genome context is also conserved, brrF is always located downstream of a hemP homolog. Located downstream of brrF is either the ferrous iron uptake operon FtrABCD [33] or a heme-binding protein (Fig 1B). The computationally predicted secondary structure of BrrF shows extensive internal base pairing, with two conserved hairpins at the 5’ end (Fig 2A).

BrrF was previously found to be more than 50-fold up-regulated under iron-depleted conditions, whereas other tested growth conditions such as biofilm growth, oxidative stress, stationary phase and starvation did not induce BrrF expression [9]. hemP was upregulated by iron depletion to approximately the same fold change as brrF. HemP in B. multivorans is Fur regulated [21, 29]; therefore the Fur box consensus sequence for B. cenocepacia was determined (S2 Fig) and the whole genome screened for motif occurrences. Fur boxes were identified directly upstream of the TSS for hemP (Fig 1A), and upstream of known iron transport-related genes such as orbS and fecI. The sequence upstream of the hemP gene start in B. cenocepacia is identical to that upstream of hemP in B. multivorans for the first 77 nt, which includes the Fur box. This B. multivorans sequence was bound by B. multivorans Fur protein in an electrophoresis mobility shift assay [21], the respective sequence in B. cenocepacia is therefore likely also bound by Fur.

brrF itself did not have a Fur box directly upstream of its 5’ end. Expression of BrrF is therefore probably under control of the Fur regulator via co-expression with hemP. In contrast to that, the Fur-regulated sRNAs RyhB in E. coli and PrrFs in P. aeruginosa are directly preceded by a Fur binding site.

hemP is possibly also under the regulation of OxyR. A putative OxyR box was found upstream of hemP (Fig 1A), and hemP was upregulated 10-fold in H₂O₂-treated biofilms of B. cenocepacia J2315 [34] while in exponentially growing planktonic cells exposed to H₂O₂, neither hemP nor brrF change expression [9, 23]. However, in these studies biofilms were treated with 3% H₂O₂ for 30 min, whereas planktonic cultures were treated with 0.05 or 0.15% H₂O₂ for a shorter time period, which might account for the observed differences in hemP regulation under oxidative stress.

Computationally predicted targets of BrrF

Target prediction resulted in 206 genes with sequence complementarity to BrrF (p-value ≤ 0.01, S3 Table). The categories with the largest number of genes with known function were amino acid transport and metabolism (12.6%) and energy production and conversion (10.0%). Most predicted interactions involved the 5’ end of BrrF (S3 Table, S3 Fig), notable exceptions were targets sdhC and sdhA (Fig 2B). The predicted interaction regions in putative target mRNAs are located across the entire input region from 200 nt upstream to 100 nt downstream of the gene start (S3 Fig). Only a small proportion (17%) of the predicted mRNA interactions include the first 10 nt upstream of the gene start and the Shine-Dalgarno sequence, whereas most interactions were predicted for further upstream in the 5’UTR (38%) or within the coding sequence (45%, S3 Table). Secondary structure analysis of putative target mRNAs before and after virtual cleavage at the predicted interaction site did not indicate an occlusion of Shine-Dalgarno sequences by mRNA intramolecular structures. This indicates that binding of BrrF does not alter the rate of translation initiation, but that BrrF mainly acts by interfering with mRNA target degradation rate.

Functional enrichment analysis of predicted targets pointed to a relative over-representation of genes involved in aerobic respiration, iron- or heme-binding, and in the TCA cycle [9]. Furthermore, several genes involved in iron-sulfur cluster formation and in ROS detoxification, such as those encoding catalases and SOD, were predicted as targets (Table 1). These genes or functional categories are also targeted by RyhB and/or PrrF (see Table 1 for references).

Table 1. Regulation of computationally predicted BrrF target genes in response to iron depletion.

Predicted target	Gene	Annotation	Log2 fold change WT low Fe*	Log2 fold change ΔbrrF low Fe*	Ref.**
TCA and methylcitrate cycle
BCAM0967	sdhC	Heme-binding succinate dehydrogenase cytochrome b556 subunit	-1.28	n.s.	[5, 10]
BCAM0969	sdhA	Succinate dehydrogenase flavoprotein subunit	-2.25	n.s.	[5, 10]
BCAM0961	acnA	[Fe-S]-dependent aconitate hydratase A	-4.22	n.s.	[10, 35]
BCAL2287	fumA	[Fe-S]-dependent class I fumarate hydratase	-5.38	1.10	[10, 35]
BCAL2908	fumC	Class II fumarate hydratase, iron-free	1.76	0.42	[36]
BCAM2701	acnM	[Fe-S]-dependent 2-methylisocitrate dehydratase	-2.00	n.s.
Defense against ROS
BCAL2757	sodB	Fe²⁺-containing SOD	-2.33	0.75	[5, 10]
BCAL3299	katB	Bifunctional heme-containing catalase/peroxidase, homologous to katG in E. coli	-1.72	0.98	[36, 37]
BCAM0931		Monofunctional heme-containing catalase clade 1, homologous to katA in P. aeruginosa	-0.83	-0.18	[5]
Aerobic respiration
BCAL2336	nuoI	[Fe-S]-dependent NADH dehydrogenase subunit I	n.d.	n.d.	[10, 37]
BCAL2343	nuoB	[Fe-S]-dependent NADH dehydrogenase subunit B	-1.20	n.s.	[10]
BCAL0785	cydA	Heme-binding cytochrome bd-I ubiquinol oxidase subunit 1	n.d.	n.d.	[10]
BCAL2143	cyoB	Heme-binding cytochrome bo ubiquinol oxidase subunit 1	n.s.	n.s.	[38]
Iron-sulfur cluster formation
BCAL2196	iscA	[Fe-S] assembly accessory protein	n.d.	n.d.	[10, 37]
BCAL2198	iscS	Cysteine desulfurase	n.d.	n.d.	[10, 35, 36]
Other
BCAL3367	edd	[Fe-S]-dependent phosphogluconate dehydratase	-3.60	-1.54

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*Log2-fold change after addition of 200 μM dipyridyl to a culture at mid-log phase, harvest after 30 min of further incubation, compared to untreated culture. The threshold for reporting statistically significant fold changes was p ≤ 0.05, n. s. denotes “not significant”. The actual p-values from SPSS ANOVA analysis are presented in S4 Table. [Fe-S]: Iron-sulfur cluster, n.d. not determined. Ref.**: references for regulation by RyhB and/or PrrF.

The target prediction results and the similarity of BrrF to other Fur-regulated sRNAs suggests that BrrF is a regulator of the TCA cycle and of the oxidative stress response under conditions of iron depletion, most likely by down-regulating the putative target genes. Down-regulating genes encoding SdhC, SodB and other iron-containing proteins saves iron and facilitates iron homeostasis. Iron limitation and a decreased activity of TCA cycle enzymes leads to a decrease in ROS production, reducing the need for ROS detoxification. BrrF-dependent regulation could therefore also save energy and contribute to overall homeostasis in the bacterial cell.

A link between iron availability and expression of ROS detoxifying genes has previously been demonstrated in B. multivorans. A B. multivorans Fur mutant showed increased sensitivity to oxidative stress, reduced sodB expression and reduced SOD and catalase activities [21]; and also growth attenuation on many carbon compounds. These phenotypes could be complemented by deleting hemP together with the BrrF sequence from the B. multivorans genome [29].

BrrF affects growth in B. cenocepacia J2315

The deletion mutant ΔbrrF had no growth defect compared to wild type (WT) in iron-replete media, neither in a rich medium nor during growth on single carbon sources in a mineral medium (Fig 3A and S4 Fig). ΔbrrF was also not impaired in heme utilisation, indicating that expression of HemP is not affected by brrF deletion (S4 Fig). Under iron limitation, ΔbrrF grew marginally faster than WT. This is in contrast to observations with a PrrF double deletion mutant in P. aeruginosa, which showed a growth defect compared to wild type under iron depletion [39]. B. cenocepacia J2315 is a slow growing small colony variant and possibly less affected by iron starvation. That ΔbrrF is less growth attenuated than WT under iron limitation suggests that key metabolic genes are down-regulated by BrrF.

Overexpressing BrrF from a vector (WT-pBrrF-d1 and WT-pBrrF-d2) attenuated growth (Fig 3B). Overexpressing a derivative of BrrF with point mutations near its 5’end abolished this effect, showing that the growth attenuation depended on the BrrF region which produced the most computationally predicted interactions (Fig 3A).

To test whether growth attenuation by BrrF overexpression was substrate dependent, growth curves were obtained in iron-replete mineral medium supplemented with single carbon compounds. Overexpressing BrrF attenuated growth on nearly all carbon sources tested, including on TCA cycle intermediates such as succinate, citrate, malate and fumarate, on carbohydrates such as glycerol, glucose and gluconate, and on amino acids. Growth of ΔbrrF under iron depletion was tested on five compounds, and it grew faster than WT on four of them (Fig 3C and S4 Fig). This is consistent with a BrrF-dependent downregulation of the TCA cycle, since nearly all growth substrates are either directly or indirectly metabolised through the TCA cycle. The notable exception was propionate, growth on which was not affected by BrrF overexpression under iron-replete conditions, and on which ΔbrrF grew marginally slower than WT under iron depletion. Propionate is metabolised via the methylcitrate cycle in B. cenocepacia [40], and the lack of growth attenuation on this substrate suggests that this pathway is less affected by BrrF-dependent down-regulation.

BrrF regulates the TCA cycle

To test the hypothesis that BrrF induction causes down-regulation of the expression of genes encoding TCA cycle enzymes, all predicted target genes involved in the TCA cycle, and some additional TCA cycle genes which were not predicted targets, were assessed for BrrF-dependent gene expression changes. All samples were collected 30 min after adding rhamnose and/or dipyridyl to cultures initially grown without inducer.

In the WT strain, all TCA genes with complementarity to BrrF were differentially regulated in response to iron depletion. Most of the genes were downregulated, with acnA and fumA most affected (Table 1, Fig 4). fumC, encoding an iron-free fumarate hydratase with an analogous function to [4Fe-4S]-dependent FumA, was upregulated under iron depletion. In ΔbrrF, this regulation was attenuated or abolished (Table 1, Figs 4 and 5). Of the TCA cycle enzymes not predicted as targets, only gltA was downregulated significantly in WT under iron depletion. gltA is located downstream of and in the same operon as sdhABCD [41] and the reduced expression of gltA under iron depletion is therefore likely a downstream effect of sdhC and sdhA downregulation. Of the putative target genes tested, only fumC is upregulated under iron depletion, and in a BrrF-dependent manner. In E. coli fumC is upregulated under iron depletion via iron-dependent activation by the SoxR protein, probably as an iron-saving measure [42]. In P. aeruginosa, expression of fumC is Fur-regulated [43], whereas no Fur box is associated with fumC in B. cenocepacia J2315. The upregulation of fumC expression observed in the present study can therefore be a result of BrrF interacting with fumC mRNA.

Fig 4 — Iron depletion was invoked by adding dipyridyl to a final concentration of 200 μM to a culture in mid-log phase (LB broth, incubated on a shaker). Cells were then harvested for RNA extraction after 30 min further incubation. This pulse-expression set-up was chosen to minimize secondary effects of iron depletion on gene expression. Predicted targets are underlined. In colored boxes: log2-fold gene expression changes upon iron depletion in WT (left) and Δ*brrF* (right). Grey boxes denote no significant changes in expression (i.e. p > 0.05). Bottom right: Color scale for log2-fold changes.

sRNAs capable of both repressing and activating gene expression have been described [44]. The most common mechanisms for activation are reduction of secondary structure in the region of the Shine-Dalgarno sequence, and occluding RNase sensitive sites [45]. BrrF is predicted to interact with fumC mRNA in its 5’UTR, 65 to 75 nt upstream of the gene start. Secondary structure analysis of the fumC 5’UTR revealed a rho-independent terminator-like hairpin structure at position 32 to 51 from gene start, suggesting transcription termination as a possible mechanism of FumC expression regulation. However, the putative interaction site does not overlap the hairpin structure, and cleaving the 5’UTR at the interaction site does not change the structure of the predicted hairpin. The mechanism of transcription activation by BrrF is therefore not apparent from secondary structure analysis.

Overexpressing BrrF in ΔbrrF under iron-depletion complemented the downregulation of the tested genes (S4 Table, Fig 5), whereas overexpressing the BrrF derivative with mutations at its 5’ end (pBrrF-d3) abolished or reduced that effect, with the notable exception of sdhC and sdhA. The BrrF region predicted to be important for the interaction with these two genes is further away from the 5’ end and is not altered in pBrrF-d3 (Fig 2A and 2B). The abundance of the three BrrF derivatives over-expressed in trans was similar (S5 Table).

Under iron-replete conditions, overexpressing BrrF in the wild type strain significantly repressed acnA and fumA, and induced fumC (S4 Table). In line with this, aconitase activity was also reduced by BrrF overexpression (Fig 6). This suggests that growth attenuation during BrrF overexpression under iron-replete conditions (Fig 3B) is mainly due to repression of acnA. Expression of sdhA and sdhC is not affected in this condition, and metabolism of propionate via the methylcitrate cycle completely bypasses AcnA (Fig 4). Fumarate dehydratase activity might overall not be affected by BrrF overexpression, because FumC can replace FumA.

Fig 6 — Cultures were grown under iron-replete conditions in LB broth to mid-log phase and rhamnose was added one hour before harvest and protein extraction. Cell-free protein extracts in a 20 mM citrate buffer were supplemented with 40 mM DL-isocitrate and increase in absorbance was measured at 240 nm in a spectrophotometer. Asterisks: Significant difference to the respective control, with p ≤ 0.05. VC: vector control.

BrrF regulates the response to oxidative stress

Predicted targets genes sodB and katB were downregulated significantly in WT under low iron condition (Table 1, Fig 5). SOD activity was significantly reduced in WT cultures under iron limitation, while SOD activity in ΔbrrF remained unchanged compared to iron-replete condition (Fig 7A). Reduced SOD activity could be complemented by overexpressing the native BrrF in ΔbrrF in trans, while overexpressing the mutated BrrF derivative did not complement the phenotype. In a H₂O₂ sensitivity assay, overexpression of BrrF in ΔbrrF lead to a significant increase of size of inhibition zone (Fig 7B).

Fig 7 — A) SOD activity was determined in a cell-free protein extract from planktonic mid-log phase cells, indirectly via superoxide utilization of a xanthine oxidase. Results are presented as percent compared to control condition. B) Sensitivity to H₂O₂ was determined on low-nutrient agar plates, via formation of a growth inhibition zone around a filter disk containing 1% H₂O₂. Plates were pre-incubated for one hour to induce gene expression changes before applying the filter disks. Light grey bars: Response to iron depletion in wild type (WT) and Δ*brrF*. Dark grey bars: Overexpression of native BrrF with or without processing (pBrrF-d1 and pBrrF-d2) or of a derivative with point mutations near the BrrF 5’end (pBrrF-d3). Asterisks: Significant difference to the respective control, with p ≤ 0.05. VC: vector control.

B. cenocepacia J2315 has two SODs, the cytoplasmic iron-containing SodB (BCAL2757) and the periplasmic Cu-Zn-containing SodC (BCAL2643) [41, 46]. However, only sodB is predicted as target for BrrF, and only sodB changed expression significantly (S4 Table). The observed SOD activity changes are therefore probably caused by sodB expression changes.

The B. cenocepacia J2315 genome encodes four catalases [41], the bifunctional, heme containing catalase/peroxidases KatA (BCAM2107, [20]) and KatB (BCAL3299, [20, 47]), an additional monofunctional heme-containing catalase (BCAM0931) and a manganese-containing catalase (BCAS0635). katB and BCAM0931 are predicted as targets for BrrF. katA did not change expression upon iron depletion (S4 Table) and BCAS0635 is not expressed under iron-replete conditions [16]. Taken together, this suggest the increased sensitivity to H₂O₂ is linked to downregulation of katB, which is the major determinant for catalase activity in B. cenocepacia [20]. Concomitantly, baseline expression of katB is relatively high compared to katA, with a 2-fold reduction in the media containing rhamnose and trimethoprim which were used for overexpression experiments (S5 Table). This lower expression of katB could be the reason for the larger effect of BrrF overexpression on H₂O₂ sensitivity compared to induction of BrrF by low iron (Fig 7), as this would increase the excess of BrrF relative to its target katB.

BrrF-dependent regulation of other predicted targets

While computational target prediction often results in a large number of false positive predicted targets [11], BrrF likely regulates additional genes besides the ones mentioned above.

Other predicted targets of BrrF are also involved in metabolism. The nuo respiratory complex is known to be repressed by RyhB in E. coli under iron depletion [48], while cytochrome bo oxidase genes cyoABC are regulated by RyhB in Salmonella typhimurium in response to nitrosative stress [38]. cyoB and nuoB were tested for BrrF-dependent gene expression changes; nuoB was downregulated in WT under iron depletion (Table 1), while cyoB did not change expression.

The edd gene (BCAL3367; encoding a [Fe-S]-cluster containing phosphogluconate dehydratase) was downregulated in response to iron depletion in a BrrF-dependent manner (Table 1, S4 Table). edd is the first gene of the Entner-Doudoroff pathway for carbohydrate degradation, particularly important in B. cenocepacia because Burkholderia spp. lack a complete glycolysis pathway. Moreover, edd is upregulated by H₂O₂ stress [23], and has been implicated in the oxidative stress response of Pseudomonas putida [49]. In P. putida, an organism also lacking a complete glycolysis pathway, phosphogluconate dehydratase is necessary for generating NADPH in order to combat oxidative stress. Therefore, downregulating phosphogluconate dehydratase is possibly not only saving iron, but also contributing to overall NADPH balance in the cell.

Conclusion

Our results demonstrate that sRNA BrrF is involved in downregulating TCA and oxidative stress response in B. cenocepacia J2315. This sRNA can be added to the growing list of sRNA involved in regulating metabolism, growth and stress responses in the versatile opportunistic pathogen B. cenocepacia [16, 32].

The exact molecular mechanisms of BrrF regulation remain to be resolved. RyhB regulates target genes mainly by binding to its cognate sequence and subsequently increasing the rate of mRNA degradation. This process is Hfq- and RNase E-dependent [50]. It seems likely that BrrF also acts this way, as its sequence is complementary to homologs of confirmed RyhB targets, and most interactions do not include the Shine-Dalgarno sequence of target mRNAs. However, indirect regulation by BrrF via a different yet unknown mechanism cannot be ruled out.

Supporting information

S1 Fig. Conservation and secondary structure of BrrF homologues.

The full length of BrrF is very conserved throughout Burkholderia (first 8 lines), Paraburkholderia (lines 9–16) and Pandoraea sp. (lines 17–21). In Cupriavidus sp. (lines 22–26) only the first 45 nt of BrrF are present. Underlined are bases conserved in all sequences. Consensus secondary structures were computed using the sequences of the alignment. Red: fully conserved compatible base pairs. Alignment and consensus structures were computed using LocARNA [25].

(TIF)

Click here for additional data file.^{(1,009.7KB, tif)}

S2 Fig. Fur box consensus sequence for B. cenocepacia J2315.

The canonical 19 bp palindromic Fur binding site is indicated by a bracket.

(TIF)

Click here for additional data file.^{(108.3KB, tif)}

S3 Fig. Density plots of predicted interaction regions.

mRNA regions are depicted in the upper panel and sRNAs regions in the lower panel. The graphs represent all predictions with P< 0.01. x-axis depicts the nucleotide position, position 1 in mRNA is the first nucleotide of the coding sequence. The y-axis depicts the relative frequency of a nucleotide position being part of the predicted sRNA–target interactions.

(TIF)

Click here for additional data file.^{(74.4KB, tif)}

S4 Fig. Growth of B. cenocepacia J2313 strains during iron depletion and BrrF overexpression.

Growth was monitored in microtiter plates. y-axis: Optical density (590 nm). x-axis: Time (hours).

(TIF)

Click here for additional data file.^{(2.2MB, tif)}

S1 Table. Strains and plasmids used in this study.

(PDF)

Click here for additional data file.^{(79.6KB, pdf)}

S2 Table. Primers used in this study.

(PDF)

Click here for additional data file.^{(102.1KB, pdf)}

S3 Table. Computationally predicted targets of BrrF.

(XLSX)

Click here for additional data file.^{(33.1KB, xlsx)}

S4 Table. All log2-fold changes in gene expression determined by qPCR.

(PDF)

Click here for additional data file.^{(92.1KB, pdf)}

S5 Table. All qPCR raw Cq values.

(XLSX)

Click here for additional data file.^{(22.5KB, xlsx)}

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

TC received funding from the Belgian Science Policy Office (Belspo) through the Interuniversity Attraction Pole Program (Phase VII/2012–2017, Project P7/28). http://www.belspo.be/belspo/iap/index_en.stm The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0236405.r001

Decision Letter 0

Roy Martin Roop II

13 Jan 2020

PONE-D-19-34895

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

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'The sRNA has previuosly been reported in a manuscript giving an overview of small RNAs identified in Burkholderia cenocepacia. In that publication, computationally prediction of secondary structure and targets had already been performed, but reported with less detail. These results are included in this manuscript (Fig. 2A and Table S3), with more detail, because they are needed to explain the rationale and the results of the present study. '

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Reviewer #1: Yes

Reviewer #2: Partly

**********

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Sass and Coenye describe the characterization of BrrF, an iron regulated sRNA that is conserved in Burkholderia spp. Similar to other iron-regulated sRNAs, BrrF negatively affects the levels of multiple mRNAs encoding iron containing proteins, including those involved the TCA cycle and oxidative stress protection. The authors also describe several very interesting distinctions of BrrF biology compared to previously described iron-regulated sRNAs. Most notably, BrrF is seemingly the result of processing of the 3’ end of the hemP mRNA, which encodes for a transcriptional regulator of heme uptake. The paper is well-written and the conclusions are supported by the results. I do have a couple of issues that I would like the authors to address to more appropriately place this study in the larger body of literature on bacterial iron-regulated sRNAs.

-Line 341-342: BfrB in P. aeruginosa is not regulated by PrrF (see Figure 5 in Wilderman, et al, PNAS 2004). In E. coli, RyhB was suggested to regulate FtnA, not BfrB, but latter studies showed that iron induction of FtnA is similarly independent of RyhB (Nandal, et al, Mol Micro 2010).

-The conclusion section should be expanded to discuss the following points:

1. Figure 3. It seems counter that loss of a gene that is so highly conserved would result in a growth enhancement, especially when the loss of ryhB, PrrF, and other low-iron induced sRNAs results in a growth defect in low iron condition due to loss of iron sparing. This should at the very least be discussed in the concluding section.

2. The authors state that many of the complementarities with BrrF targets do not overlap the SD or start site, which is contrast to other negatively regulated targets of trans-acting sRNAs. Have the authors looked at the structures of any of the target mRNAs to determine how binding by BrrF may affect access to the SD and/or start site?

3. Related, I’m curious if any complementarity was identified in the fumC mRNA - this distinction from how fumC is regulated by iron in other species (directly by Fur versus via the sRNA), is very interesting.

Reviewer #2: The manuscript by Sass and Coenye describes a small regulator RNA (sRNA) in Burkholderia cenocepacia, and the authors have named this sRNA BrrF for Burkholderia regulatory RNA involving iron, Fe. The group previously identified BrrF (called ncS63) as an sRNA that is significantly upregulated in response to low iron conditions, and in this work the authors hypothesized that BrrF is an functional analog of the RyhB sRNA of E. coli that is a well described sRNA involved in iron homeostasis.

In this work, the authors demonstrate that that the brrF gene is likely co-expressed with an upstream gene called hemP that encodes a small protein involved heme iron uptake in other Burkholderia species, and it is predicted that hemP and brrF are regulated by the ferric uptake regulator, Fur. Computational analyses were used to predict regulatory targets of BrrF, and these approaches predicted that BrrF controls the expression of iron-containing enzymes of the TCA cycle and other iron-containing proteins, such as superoxide dismutase and catalase. brrF deletion and over-expression strains were used to assess the regulation of the predicted genes by BrrF, and some of the predicted targets were determined to be authentic targets of BrrF. Subsequent experiments examined the role of BrrF in the response of B. cenocepacia to oxidative stress.

Overall, the work is highly speculative on several of the conclusions that are made, and the authors need to address several important issues, which are outlined below:

-In lines 27-28, the authors state that "BrrF is a Fur-regulated small RNA," however, in lines 296-297, the authors state that "BrrF is therefore probably under the control of the Fur regulator." There is not direct evidence that Fur regulates brrF (or hemP for that matter) in B. cenocepacia. The authors are relying solely on the presence of putative "Fur boxes" for this conclusion. While suggestion that brrF "might" be controlled by Fur is an acceptable proposition, there authors have not presented any direct evidence that brrF is actually Fur-regulated.

-Regarding the deletion and over-expression of brrF, there are several important data that are missing. The authors have relied on qPCR to demonstrate levels of BrrF in all of their strains (Fig. S5), but the authors should employ another method, such as northern blot analysis. For example, Fig. S5 shows that the Cq value for BrrF in the brrF deletion strain in unstressed conditions is approximately 31-32, and the Cq for BrrF in the same strain under low Fe conditions is approximately 25. How can there be such a substantial increase in the levels of BrrF in the brrF deletion strain in different conditions? This shows the unreliable nature of qPCR for analyzing sRNAs. Moreover, given this issue, much of the data in the manuscript are difficult to interpret.

-Also regard the deletion strain, are hemP levels altered by deletion of brrF? If so, it is very difficult to conclude that any phenotypes observed are related only to BrrF.

-Figure 3 should be statistically analyzed. The authors state that strains are "marginally" different, but are these differences statistically significant?

-Lines 453-460 and Figure 7. These data are extremely difficult to understand. To begin with, the authors use confusing terminology: "significant increase of inhibition." This appears to translate to increase sensitivity, but it is hard to know exactly. Regarding the data, WT vs. WT in low Fe shows no difference in sensitivity to oxidative stress, and there should be a >50-fold increase in BrrF in the WT-low Fe condition compared to WT based on the authors previous work (Ref. 9). Additionally, the brrF deletion strain in both conditions has similar sensitivity levels to those of the WT and WT-low Fe. However, when you over-express versions of brrF in the brrF deletion strain, these strains exhibit increased sensitivity to oxidative stress. Is there a greater than 50-fold increase in BrrF in these strains? Can over-expression of these brrF genes in the WT strain similarly increase sensitivity to oxidative stress. The authors need to carefully evaluate the results from these experiments.

**********

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PLoS One. 2020 Jul 23;15(7):e0236405. doi: 10.1371/journal.pone.0236405.r002

Author response to Decision Letter 0

9 Apr 2020

Manuscript “ Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia” by Sass and Coenye

Response to reviewers’ questions.

Reviewer 1:

Answer:

The reviewer is correct, this was an oversight on our side and we deleted the sentence from the discussion.

-The conclusion section should be expanded to discuss the following points:

Answer:

The B. cenocepacia strain used in this study, J2315, is a slow growing small colony variant. Strain J2315 permanently displays a growth deficiency compared to other B. cenocepacia laboratory strains such as K56-2 and H111. It is therefore probably less affected by iron starvation and can maintain growth for longer under those conditions compared to “normally” growing bacteria. Absence of downregulation of key metabolic genes, as in �brrF, can then increase growth, and not be detrimental to the survival of the bacteria.

We have added this rationale to the manuscript main text (lines 370-375: “This is in contrast to observations with a PrrF double deletion mutant in P. aeruginosa, which showed a growth defect compared to wild type under iron depletion [39]. B. cenocepacia J2315 is a slow growing small colony variant and possibly less affected by iron starvation. That �brrF is less growth attenuated than WT under iron limitation suggests that key metabolic genes are down-regulated by BrrF.”).

Answer:

We have performed secondary structure analysis for the genes listed in Table 1, including the 5’UTR to max. 200 nt upstream of the gene start and to 100 nt downstream of the gene start. The putative interaction region was not involved in occluding the SD or the gene start, and repeating the analysis after virtual cleavage of the mRNA at the predicted interaction site did not reveal occlusion of SD or gene start after cleavage. Computational secondary structure analysis was therefore not informative regarding mechanism of action of BrrF.

We have added the findings from secondary structure analysis to the manuscript text (lines 330-332: “Secondary structure analysis of putative target mRNAs before and after virtual cleavage at the predicted interaction site did not indicate an occlusion of Shine-Dalgarno sequences by mRNA intramolecular structures.”).

Answer:

Activation of expression by a small RNA is less common than downregulation. The most common mechanisms for activation are reduction of secondary structure in the region of the SD sequence, and occluding RNase sensitive sites.

Secondary structure analysis of the 5’UTR of fumC mRNA showed that it contains a hairpin formation at position 32 to 51 upstream of the gene start, which could act as a rho-independent terminator. The SD or gene start are not occluded by secondary structure formation according to this analysis. The binding site of BrrF is predicted for position 65 to 75, not overlapping the putative terminator structure. We repeated the secondary structure analysis after removing the RNA sequence upstream of and including the interaction site, and the terminator structure still formed unchanged. It is therefore not apparent from structure analysis what the exact mechanism of activation is.

We added the special characteristics of the fumC 5’UTR to the manuscript text (lines 431-441: “sRNAs capable of both repressing and activating gene expression have been described [44]. The most common mechanisms for activation are reduction of secondary structure in the region of the SD sequence, and occluding RNase sensitive sites [45]. BrrF is predicted to interact with fumC mRNA in its 5’UTR, 65 to 75 nt upstream of the gene start. Secondary structure analysis of the fumC 5’UTR revealed a rho-independent terminator-like hairpin structure at position 32 to 51 from gene start, suggesting transcription termination as a possible mechanism of FumC expression regulation. However, the putative interaction site does not overlap the hairpin structure, and cleaving the 5’UTR at the interaction site does not change the structure of the predicted hairpin. The mechanism of transcription activation by BrrF is therefore not apparent from secondary structure analysis.”).

Reviewer 2:

Answer:

In the particular B. cenocepacia strain used for this study, Fur is an essential gene (reference Wong et al., 2016, Candidate essential genes in Burkholderia cenocepacia J2315 identified by genome-wide TraDIS. Front. Microbiol. 7:1288. doi: 10.3389/fmicb.2016.01288). In other Burkholderia strains, Fur can be deleted, and it has been deleted in B. multivorans, also a member of the Burkholderia cepacia complex and relatively closely related to B. cenocepacia. The Fur-dependent expression of the HemP protein upstream of BrrF has been demonstrated for that bacterium (ref. 21). The sequences upstream of hemP are identical in B. cenocepacia J2315 and the B. multivorans strain used in that study, including the Fur box. The sequence upstream of hemP of B. multivorans has been used in a Fur titration assay in that study (ref. 21), showing that E. coli Fur protein can bind to this Fur box sequence. The same sequence was also bound by B. multivorans Fur in an EMSA experiment (ref. 21). It is therefore very likely that the hemP upstream sequence in B. cenocepacia is also bound by Fur.

We have added these observations to the manuscript text, lines 302-307 (“The sequence upstream of the hemP gene start in B. cenocepacia is identical to that upstream of hemP in B. multivorans for the first 77 nt, which includes the Fur box. This B. multivorans sequence was bound by B. multivorans Fur protein in an electrophoresis mobility shift assay [21], the respective sequence in B. cenocepacia is therefore most likely also bound by Fur.”)

Answer:

The reason to show the qPCR raw data was to point out that the levels of BrrF in overexpression experiments under low iron conditions are comparable to the levels of BrrF in wild type under low iron conditions. In the brrF deletion mutant, Cq values for BrrF are between 25 and 32, which is indeed relatively high, given that BrrF should not have been detected. However, our no-RT controls showed that primers for BrrF always give signals in this order of magnitude, as Cq values for BrrF in no-RT controls ranged from 32 to 27. In contrast, no-RT control Cq values for all other genes investigated were always larger than 34. It appears that, for reasons unknown we had measurable brrF signals that were larger than for all the other genes investigated. However, whatever the reason for this high background signals, we are convinced they have no influence on our conclusions. After all, the Cq values for BrrF in wild type under iron replete conditions are approx. 15, while those under conditions where BrrF is induced are approx. 10-11. This difference between Cq values of 10-15 on one hand, and Cq values of 25-32 on the other hand, is large enough. Generally, no-RT and no-template control Cq values should be >5 cycles larger than sample Cq values, to contribute <3% to the signal (Nolan et al., 2006, Quantification of mRNA using real-time RT-PCR. Nat. Meth. 1:1559, doi:10.1038/nprot.2006.236), and this is clearly the case for our data . Because of this we believe our qPCR data are reliable and we decided not to quantify BrrF expression in another way.

We have supplied all qPCR raw Cq values, including no-RT controls, as supplementary material, in Table S5, and have removed Figure S5, since it depicts data which is now available in Table S5.

-Also regard the deletion strain, are hemP levels altered by deletion of brrF? If so, it is very difficult to conclude that any phenotypes observed are related only to BrrF.

Answer:

hemP mRNA levels were shown in Figure S5 on the right hand side, they are not altered by brrF deletion. We have now supplied all qPCR raw data in Table S5.

We conducted additional growth curves to assess the functionality of HemP. HemP is necessary for heme uptake in B. multivorans (ref. 29); and our results showed that a brrF deletion mutant was not impaired in heme utilization (Figure S4). It is therefore reasonable to assume that HemP expression is not altered by brrF deletion, and that the observed phenotypes of �brrF are not related to hemP.

We have added the results of the additional growth tests to the main text (lines 368-369: “�brrF was also not impaired in heme utilisation, indicating that expression of HemP is not affected by brrF deletion (Fig. S4).”), and added representative growth curves, showing the effect of heme addition to the low iron medium, to Figure S4 in supplementary material.

-Figure 3 should be statistically analyzed. The authors state that strains are "marginally" different, but are these differences statistically significant?

Answer:

To perform the statistical analysis for all available data as consistently as possible, we analyzed the growth rates. Part of the growth curves in Figure 3 were obtained with a Cell Growth Quantifier, which runs with CGQuant software with automated growth rate calculation. The equation used by the CGQuant software to calculate time averaged growth rates was applied to the output of the microplate reader. The resulting growth rates were then statistical analyzed. The method section has been amended accordingly (lines 140-144: “Growth rates were determined using the equation µ * h-1 = ln (xt2/xt1)/(t2-t1), where x denotes optical density or backscatter arbitrary units, and t1 and t2 refer to a point at the beginning or end of the analysed time interval, respectively [17]. The obtained values for µ of the biological replicates were then analysed either by One-way ANOVA or a two-tailed Student’s t-test using SPSS (v. 25).”), as well as the legend to Figure 3 (lines 392-394: “Significant differences in growth rate of the wild type or wild type vector control culture and the respective test condition are indicated by asterisks (* = p < 0.05, ** = p < 0.01).“). Statistically significant different growth rates are indicated in Figure 3.

Answer:

We have changed the wording of this sentence from “significant increase of inhibition” to “significant increase in size of inhibition zone” (line 493) to clarify.

-Regarding the data, WT vs. WT in low Fe shows no difference in sensitivity to oxidative stress, and there should be a >50-fold increase in BrrF in the WT-low Fe condition compared to WT based on the authors previous work (Ref. 9). Additionally, the brrF deletion strain in both conditions has similar sensitivity levels to those of the WT and WT-low Fe. However, when you over-express versions of brrF in the brrF deletion strain, these strains exhibit increased sensitivity to oxidative stress. Is there a greater than 50-fold increase in BrrF in these strains? Can over-expression of these brrF genes in the WT strain similarly increase sensitivity to oxidative stress. The authors need to carefully evaluate the results from these experiments.

Answer:

The variations of the effect of BrrF induction on H2O2 sensitivity in WT under low iron (not significant) and in �brrF with BrrF expression complemented under low iron condition (significant) is probably not due to a difference in BrrF expression in these two strains as the reviewer suggested, but due to a reduction of katB expression under the condition of BrrF overexpression. katB is approx. 2-fold lower expressed in media containing rhamnose and trimethoprim, which are used for overexpression experiments. This increases the excess of BrrF in comparison to its target katB mRNA, and could cause an increase in katB down-regulation, which is also apparent in katB Cq values under this condition.

We have supplied all the qPCR raw data (Table S5), and added the above explanation to the manuscript text (lines 519-524: “Concomitantly, baseline expression of katB is relatively high compared to katA, with a 2-fold reduction in the media containing rhamnose and trimethoprim which were used for overexpression experiments (Table S5). This lower expression of katB could be the reason for the larger effect of BrrF overexpression on H2O2 sensitivity compared to induction of BrrF by low iron (Fig 7), as this would increase the excess of BrrF relative to its target katB.”).

Attachment

Submitted filename: Reviewer comments PLoS One BrrF rebuttal.docx

Click here for additional data file.^{(30.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0236405.r003

Decision Letter 1

Roy Martin Roop II

13 May 2020

PONE-D-19-34895R1

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

PLOS ONE

Dear Dr. Sass,

Both reviewers have pointed out that apparently it has not been experimentally established that Fur regulates the iron responsive expression of brrF in Burkholderia cenocepacia. If this is correct, then these reviewers are correct that this point needs to be clarified. Other than clarification of this important point, both reviewers agree that the manuscript presents important findings. Consequently, I am going to ask that you submit a revised version of the paper that directly addresses this issue and once this point is clarified, I will accept the manuscript.

We would appreciate receiving your revised manuscript by July 15, 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript!

Sincerely,

R. Martin Roop II, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Almost all comments have been addressed adequately. I have only one remaining issue. In the abstract the authors state that BrrF is a "Fur-regulated small RNA" (line 27), but as indicated by the other reviewer in the first review Fur-regulation of BrrF is not directly determined for B. ceenocepacia BrrF. This is a reasonable assumption as all data are consistent with this model, but the language in the abstract needs to be modified to remove the certainty.

Reviewer #2: The authors have adequately addressed the comments from the reviewers, and overall, the work is sound.

always give signals in this order of magnitude." Does this not concern you that the primers you are using are not efficient and/or not reliable? I really am trying to be helpful, as I just want to make sure that the data are as accurate as possible. I would suggest that the authors consider employing northern blot analyses as they continue to work in the area of sRNAs. This will only serve to complement your qRT-PCR results, and it will significantly enhance your ability to analyze smaller differences in sRNA levels.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 Jul 23;15(7):e0236405. doi: 10.1371/journal.pone.0236405.r004

Author response to Decision Letter 1

1 Jul 2020

Response:

We have changed the wording of the abstract to “BrrF is a small RNA highly upregulated in Burkholderia cenocepacia under conditions of iron depletion and with a genome context consistent with Fur regulation.” (lines 27-28). We also softened the related statement in lines 307-8 in the main manuscript by changing the wording.

Reviewer #2: The authors have adequately addressed the comments from the reviewers, and overall, the work is sound.

However, I would like to encourage the authors to seriously assess their use of qRT-PCR for sRNA levels. I am still not entirely convinced that the qRT-PCR data is accurately depicting the levels of BrrF under different conditions and in different strains. The rebuttal from the authors includes the comment that "our no-RT controls showed that primers for BrrF always give signals in this order of magnitude." Does this not concern you that the primers you are using are not efficient and/or not reliable? I really am trying to be helpful, as I just want to make sure that the data are as accurate as possible. I would suggest that the authors consider employing northern blot analyses as they continue to work in the area of sRNAs. This will only serve to complement your qRT-PCR results, and it will significantly enhance your ability to analyze smaller differences in sRNA levels.

Response:

We performed Northern blotting for a previous publication (DOI:10.1038/s41598-017-15818-3), using DIG labelled probes and eventually exposing our hybridised membranes to X-ray film. Our lab does not have the setup nor permission for working with radioactively labelled probes. Accurate quantification of the expression levels of BrrF is to our knowledge not possible with that method, because black lines on exposed X-ray film would have to be analysed (please refer to the supplementary material of the publication https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-15818-3/MediaObjects/41598_2017_15818_MOESM1_ESM.pdf). Northern blotting with this method will probaby be no improvement on qPCR. On the other hand, qPCR, although not perfect, is to our knowledge sufficient to show overexpression of sRNA.

The exact level of overexpression of BrrF from the vectors is dependent on the rhamnose concentration in the medium, and is by definition very similar for all constructs. Exactly replicating the BrrF expression level of wild type cells under iron depletion is not necessary for the purpose of showing that complementation does happen. The purpose of the qPCR experiments in this manuscript is monitoring the changes in levels of putative target mRNAs, and prove that those levels are declining upon BrrF overexpression. The exact fold changes were not subject of further interpretation.

We therefore feel that the additional work and cost involved in setting up Northern blotting experiments is not justified.

Attachment

Submitted filename: response to Reviewers second revision.docx

Click here for additional data file.^{(13.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0236405.r005

Decision Letter 2

Roy Martin Roop II

8 Jul 2020

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

PONE-D-19-34895R2

Dear Dr. Sass,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Sincerely,

R. Martin Roop II, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0236405.r006

Acceptance letter

Roy Martin Roop II

10 Jul 2020

PONE-D-19-34895R2

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

Dear Dr. Sass:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Conservation and secondary structure of BrrF homologues.

(TIF)

Click here for additional data file.^{(1,009.7KB, tif)}

S2 Fig. Fur box consensus sequence for B. cenocepacia J2315.

The canonical 19 bp palindromic Fur binding site is indicated by a bracket.

(TIF)

Click here for additional data file.^{(108.3KB, tif)}

S3 Fig. Density plots of predicted interaction regions.

(TIF)

Click here for additional data file.^{(74.4KB, tif)}

S4 Fig. Growth of B. cenocepacia J2313 strains during iron depletion and BrrF overexpression.

Growth was monitored in microtiter plates. y-axis: Optical density (590 nm). x-axis: Time (hours).

(TIF)

Click here for additional data file.^{(2.2MB, tif)}

S1 Table. Strains and plasmids used in this study.

(PDF)

Click here for additional data file.^{(79.6KB, pdf)}

S2 Table. Primers used in this study.

(PDF)

Click here for additional data file.^{(102.1KB, pdf)}

S3 Table. Computationally predicted targets of BrrF.

(XLSX)

Click here for additional data file.^{(33.1KB, xlsx)}

S4 Table. All log2-fold changes in gene expression determined by qPCR.

(PDF)

Click here for additional data file.^{(92.1KB, pdf)}

S5 Table. All qPCR raw Cq values.

(XLSX)

Click here for additional data file.^{(22.5KB, xlsx)}

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PERMALINK

Low iron-induced small RNA BrrF regulates central metabolism and oxidative stress responses in Burkholderia cenocepacia

Andrea M Sass

Tom Coenye

Roles

Abstract

Introduction

Materials and methods

Media

Strains and plasmids

Growth curves

Sensitivity to H2O2

Superoxide dismutase activity measurements

Aconitase activity measurements

qPCR

Computational methods

Results and discussion

Sequence, conservation, genome context and regulation of BrrF

Fig 1. Genome context and expression of brrF.

Fig 2. Interactions of BrrF with mRNA targets.

Computationally predicted targets of BrrF

Table 1. Regulation of computationally predicted BrrF target genes in response to iron depletion.

BrrF affects growth in B. cenocepacia J2315

Fig 3. Growth of B. cenocepacia J2313 strains during iron depletion and BrrF overexpression.

BrrF regulates the TCA cycle

Fig 4. BrrF-dependent regulation of genes of the tricarboxylic acid and methylcitrate cycle in response to iron depletion.

Fig 5. BrrF-dependent expression of computationally predicted targets under low iron condition.

Fig 6. BrrF-dependent reduction in aconitase activity.

BrrF regulates the response to oxidative stress

Fig 7. BrrF-dependent regulation of the oxidative stress response.

BrrF-dependent regulation of other predicted targets

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Roy Martin Roop II

Roles

Author response to Decision Letter 0

Decision Letter 1

Roy Martin Roop II

Roles

Author response to Decision Letter 1

Decision Letter 2

Roy Martin Roop II

Roles

Acceptance letter

Roy Martin Roop II

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Sensitivity to H₂O₂