Antimicrobial activity of Alcaligenes sp. HPC 1271

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Sep 21, 2015 - One such potential niche is the common effluent treatment plant (CETP). .... and Alcaligenes faecalis subsp. phenolicus, DSM 16503, iso-.
Funct Integr Genomics DOI 10.1007/s10142-015-0466-8

ORIGINAL ARTICLE

Antimicrobial activity of Alcaligenes sp. HPC 1271 against multidrug resistant bacteria Atya Kapley 1,3 & Himgouri Tanksale 1 & Sneha Sagarkar 1 & A. R. Prasad 2 & Rathod Aravind Kumar 2 & Nandita Sharma 1 & Asifa Qureshi 1 & Hemant J. Purohit 1

Received: 9 March 2015 / Revised: 21 September 2015 / Accepted: 24 September 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract Alcaligenes sp. HPC 1271 demonstrated antibacterial activity against multidrug resistant bacteria, Enterobacter sp., resistant to sulfamethoxazole, ampicillin, azithromycin, and tetracycline, as well as against Serratia sp. GMX1, resistant to the same antibiotics with the addition of netilmicin. The cell-free culture supernatant was analyzed for possible antibacterials by HPLC, and the active fraction was further identified by LC-MS. Results suggest the production of tunicamycin, a nucleoside antibiotic. The draft genome of this bacterial isolate was analyzed, and the 4.2 Mb sequence data revealed six secondary metabolite-producing clusters, identified using antiSMASH platform as ectoine, butyrolactone, phosphonate, terpene, polyketides, and nonribosomal peptide synthase (NRPS). Additionally, the draft genome demonstrated homology to the tunicamycin-producing gene cluster and also defined 30 ORFs linked to protein secretion that could also play a role in the antibacterial activity observed. Gene expression analysis demonstrated that both NRPS and dTDP-glucose 4,6-dehydratase gene clusters are functional and could be involved in antibacterial biosynthesis.

Electronic supplementary material The online version of this article (doi:10.1007/s10142-015-0466-8) contains supplementary material, which is available to authorized users. * Atya Kapley [email protected]; [email protected] 1

CSIR-NEERI, Nehru Marg, Nagpur 440020, Maharashtra, India

2

CSIR-IICT, Uppal Road, Tarnaka, Hyderabad 500007, Andhra Pradesh, India

3

Environmental Genomics Division, National Environmental Engineering Research Institute, CSIR, Nehru Marg, Nagpur 440 020, India

Keywords Alcaligenes . Antimicrobial . Draft genome . Multidrug resistant . Tunicamycin

Introduction Microbial secondary metabolites offer great potential for the development of new medicines. They belong to a wide variety of chemical classes, and many of them have antitumor, antiviral, or antibiotic activities (Vaishnav and Demain 2011). Microorganisms have proven to be an excellent source of a wide variety of secondary metabolites that comprise most of the pharmaceuticals to date (Bérdy 2005; Harvey 2008; Demain and Sanchez 2009; Osbourn 2010). However, with the upsurge in resistance of the existing antibiotics and the emergence of new drug-resistant pathogens, there is a need for newer compounds that can overcome this problem. Although chemical modifications of the already existing antibiotics provide a solution to the problem of resistance, the need for finding novel natural compounds becomes unavoidable (Pelaez 2006; Davies and Davies 2010). Tools like nextgeneration sequencing are increasingly being used in the discovery of novel compounds, and comparative genomics gives information on the universality of targets in important pathogens (Vicente et al. 2006). The enormous microbial flora provides a treasure of natural products; hence, hunting for new sources of antibiotics in diverse ecological niches holds great promise. One such potential niche is the common effluent treatment plant (CETP). A CETP is a wastewater treatment system based on the activated sludge process that treats wastewater generated by a cluster of small-scale industries (Kapley et al. 2007a). The microbial diversity analyses of activated biomass from different CETPs (Kapley et al. 2007a; Rani et al. 2008; Kwon et al. 2010) prove that such environmental niches are a

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reservoir of diverse microflora. Such an environment promotes the microorganisms to produce secondary metabolites that act as their defense mechanisms from either co-existing life forms or environmental predators (Singh and Barrett 2006). Efforts to expand the range of bacteria that can be tapped for antibiotic production are being made in both directions—the conventional isolation and screening procedures, as well as the newer strategies involving next-generation sequencing (Clardy et al. 2006). Genome sequencing data makes the metabolic capabilities of the bacteria so transparent that hidden traits can be mined and exploited for synthesizing new molecules. This study reports an Alcaligenes sp. that demonstrates antibacterial property against two multidrug-resistant bacteria as well as against strains of E. coli, Bacillus, and Shigella sp. that are reported to be pathogens. The possible antibacterial being produced was characterized using analytical as well as genomic tools. The cell-free growth medium was extracted and analyzed by HPLC and liquid chromatography-mass spectrometry (LC-MS). The draft genome of the bacteria was sequenced and analyzed by Rapid Annotation using Subsystems Technology (RAST). The secondary metaboliteproducing clusters were identified using the antiSMASH platform. Gene expression at different stages of growth was studied to understand the genes involved in the biosynthesis of the antimicrobial agent.

Materials and methods Isolation, selection, and identification of the antibacterial strain The activated biomass collected from a CETP was serially diluted and plated on a Luria broth (LB) agar. Some colonies demonstrated a small zone of clearance around them. Such colonies were isolated, purified by subculturing and tested for antibacterial property as follows. The isolates were grown overnight at 30 °C in LB at 150 rpm and 50 μl, corresponding to 105 colony-forming units (cfu), and were spotted in wells made on an LB-agar plate seeded with 107 cfu of E. coli (ATCC 25922).The plates were incubated at 30 °C, and formation of clear zones around the wells was observed (data not shown). Isolates demonstrating the desired zones were further screened by the same protocol against two multidrug-resistant bacteria (MDR), Enterobacter sp., resistant to sulfamethoxazole, ampicillin, azithromycin, and tetracycline, and Serratia sp. GMX1, resistant to sulfamethoxazole, ampicillin, azithromycin, tetracycline, and netilmicin. (Both MDR strains were a gift from Dr. Ranadhir Chakraborty, North Bengal University). The isolate HPC1271 demonstrated activity against both multidrug resistant bacteria and was hence selected for further study (Kapley et al. 2013). The isolate was

identified by BLAST analysis of its 16S rRNA gene sequence as well as by sequence analysis of the draft genome. The isolate has been deposited at the Microbial Culture Collection (MCC), Pune, India, as a patent deposit under the International Depository Authority (IDA) and is catalogued with accession no. MCC 0025. Growth conditions and spectrum of antibacterial activity Various nutrient-rich and mineral media were tested to optimize detection of antibacterial activity (data not shown). Best results were observed under the following growth conditions: A single colony was inoculated in LB media and grown to an OD unit of 1 ml−1 (measured at A600) in a shaker incubator at 30 °C, at 150 rpm. Five milliliters of culture was harvested by centrifugation and inoculated in a 5-ml M9 media (5.5 g−l disodium hydrogen phosphate, 2.4 g−l potassium dihydrogen phosphate, 0.4 g−l sodium chloride, 0.8 g−l ammonium chloride, 0.5 g−l magnesium sulfate, 1.5 g−l calcium chloride, and 20 g−l glycerol). The isolate was grown in this media up to a stationary phase of growth (120 h), at 30 °C with 150 rpm, and antibacterial activity was tested at different time points. Antibacterial activity of the isolate was assayed using the agar well diffusion method (Kapley et al. 2007b), every 24 h up to120 h of growth in M9 glycerol medium. The cell-free supernatant of the bacterial culture was prepared by centrifugation at 5000×g for 10 min to remove the bacterial cells and debris, and the supernatant was filtered through a 0.22-μm pore membrane. The cell-free supernatant was tested against 107 cfu of the indicator strains using the pour plate technique. One hundred microliters of the cell-free extract was placed in each well and incubated at 30 °C for 16–18 h. The spectrum of antibacterial activity of HPC 1271 was tested against the following indicator strains: E. coli, Bacillus subtilis, Shigella flexneri, Salmonella paratyphi, Staphylococcus aureus, Enterobacter sp., and Serratia sp. GMX1. All indicator strains used in this study were grown on LB. Details of the bacterial strains used in this study can be seen in Table 1. Characterization of antibacterial agent using analytical tools Partial purification The isolate HPC 1271 was grown in 200 ml of M9 minimal medium, as described above. The extraction of the antibacterial agent from the cell-free supernatant was carried out using ethyl acetate in a 1:1 ratio (supernatant: solvent). Equal volume of the solvent was added to the cell-free supernatant and stirred on a magnetic stirrer for 30 min. The mixture was then transferred to a separating funnel and allowed to stand until the layers separated, and the organic layer was collected. The extract was dried under vacuum in a rotary evaporator and re-

Funct Integr Genomics Table 1

Antibacterial activity of Alcaligenes sp. HPC1271 against model test bacteria

The zone of inhibition was measured in millimeters of the diameter of the zone

suspended in methanol. Activity of the extracts was tested using the agar well diffusion method with methanol as control. HPLC analysis of the antibacterial extract Metabolite profiling was analyzed by HPLC. Twenty-five microliters of the extract, re-suspended in methanol, was injected into a reverse phase (Merck Chromolith RP-18 end-capped, 100×4.6 mm in diameter, particle size 2 μm) HPLC column. The system used was the Perkin Elmer NCI 900 system connected to a UV/Vis LC 295 detector. The chromatographic parameters were as follows: detection 210 nm, mobile phase A (water) and B (acetonitrile)—0–5 min 95 % A, 5–20 min 95 % B, 21–25 min 95 % A, 26–30 min 95 % B (Miao et al., 2006). During the 30-min run, fractions eluting out were collected every minute. Antibacterial activity of these collected fractions was analyzed by the agar well diffusion method. LC-MS analysis of the active fraction Fractions demonstrating antibacterial property were further analyzed by LC-MS. First, HPLC analysis was performed on a Waters LC system no. 695 equipped with a quaternary pump, a degasser, a diode array detector, an autosampler, and a column compartment. Chromatographic separation was achieved using an Alltech Econosphere C-18 column (4.60 mm×250 mm) with a 5-micron packaging. A gradient from 10 to 80 % of acetonitrile containing a constant concentration of buffer of 20 mM ammonium acetate and 10 mM

formic acid was run up to 30 min with a flow rate of 0.8 ml/ min. LC-MS analysis was performed on a Waters Quattro Micro triple quadrupole mass spectrometer (Micromass, Ltd, UK, Serial No. QAB 1792) API, equipped with an electrospray ionization (ESI) source. The data acquisition was under the control of the MassLynx software. The typical operating source conditions for MS scan in positive ESI mode were optimized as follows: capillary potential 3, 5 kV; the cone voltage (v) and extractor energy were 3.5 and 5, respectively; desolvation temperature 300 °C; source temperature 120 °C; nitrogen gas flow for desolvation 900 l/h; and cone gas (nitrogen) flow 90 l h−1. All the spectra were recorded under identical experimental conditions.

Analysis using genomic tools Genome sequencing Total DNA was sequenced on the Ion Torrent sequencing platform (Applied Biosystems) and assembled with MIRA (v3.4) into contigs.

Genome sequencing annotation and analysis The genome was annotated using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAP) (http:// www.ncbi.nlm.nih.gov/genomes/static/Pipeline.html) and was independently analyzed on the RAST v4.0 server. The

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annotation by both RAST and NCBI PGAP tool were used to describe the genome of HPC1271 in this study. For the prediction of probable antibacterial producing gene clusters, antiSMASH version 2 (antibiotics and Secondary Metabolite Analysis Shell) (http://antismash. secondarymetabolites.org/) was used. Mauve software was used (http://gel.ahabs.wisc.edu/mauve) for draft genome comparison of the A. faecalis subsp. faecalis strain NCIB 8687 with Alcaligenes sp. HPC1271. Homology of target genes with genes from tunicamycin biosynthesis gene cluster (GenBank accession no. HQ172897.1) was analyzed using MegAlign software of DNASTAR (Lasergene version 9, USA). This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. NZ_AMXV00000000. RNA isolation and real time PCR The culture was grown in LB as well as on glycerol media as described in the BGrowth conditions and spectrum of antibacterial activity^ section and was harvested for RNA preparation at six time points, starting at 24 h and ending at 120 h. Total RNA was extracted using RNeasy Kit (Qiagen, Germany) and quantified on a NanoDrop 1000 (Thermo Scientific). Realtime PCR was performed on an Applied Biosystems 7900HT Fast Real-Time PCR System (USA) using Power SYBR ® Green RNA-to-CT™1-Step Kit (Applied Biosystems, USA). Primer template optimizations were performed for maximum reaction efficiency. The reaction conditions were 30 min at 48 °C and 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 1 min at 60 °C. Reaction volume was 20 ul. The products were analyzed on 1.2 % of agarose gel to check for single product amplification. The gene expression was normalized to that of 16S rRNA gene. The genes analyzed by real-time analysis are listed in Table 2 with their primer sequence details. The primers used in this study were designed using DNASTAR’s Lasergene software.

Results Isolation and identification of the antibacterial strain Strain HPC1271 was identified based on its 16S rDNA sequence (GenBank accession no. JN187962) and whole genome sequence data analysis (BioProject: PRJNA182095, GenBank accession no. NZ_AMXV00000000) as Alcaligenes sp. A phylogenetic tree was constructed by using MEGA6 (Tamura et al. 2013), which demonstrates the closest neighbor in taxonomy. The tree was constructed using sequences of 16S rRNA genes retrieved from other Alcaligenes, draft or whole genomes, and some representatives of the other major taxonomic classes. Figure 1 demonstrates that A. faecalis subsp. faecalis strain NCIB 8687, isolated from arsenical cattle-dipping fluids from Queensland, and Alcaligenes faecalis subsp. phenolicus, DSM 16503, isolated from a wastewater bioprocessor, were the closest neighbors. The lab isolate is indicated in bold typeface. Spectrum of antibacterial activity Antibacterials are classified as broad, intermediate, or narrow spectrum, depending on the range of microbes susceptible to these compounds. The spectrum of activity of HPC 1271 was tested against seven different pathogenic strains. Positive results were observed in gram-negative (E. coli, Shigella flexneri), gram-positive (Bacillus subtilis), and multidrug-resistant strains, Enterobacter sp. and Serratia sp. GMX1. No zone of clearance was observed with Salmonella sp. and Staphylococcus sp. Activity is demonstrated as the zone of clearance, where the diameter of the zone was recorded and can be seen in Table 1. Results demonstrated that the antimicrobial agent produced by lab isolate HPC1271 is active against both gram-negative and gram-positive bacteria, suggesting a broad spectrum of activity.

Table 2

List of genes used for expression analysis (real-time PCR)

Sr. no.

Name

Primer sequence

Annealing temperature

Product size

Detail

1

PKS

60

285

Type 1 polyketide synthase domain

2

NRPS

60

288

Nonribosomal peptide synthase domain

3

GD

60

223

dTDP-glucose 4,6-dehydratase

4

IS

60

150

Isochorismate synthase

5

PD

F-5′-GAGCATCAGTACCGCGCCACCTTC-3′ R-5’ATCGCCGCCAACCGTCTGTCCTAT 3 F-5′-TACCCGCGACCGCTTCCATTCTCT-3′ R-5′-TAAGGGTCGGGCACAAAGCGTTCT-3′ F-5′-CGGTGGCCATAACGAGCAACAAA-3′ R-5′-GCGTAGGCCCGTCTCAAAGGTCTC-3′ F-5′-TGGCCACGATGCGCAACAGAAC-3′ R-5′-CCGGCAAGGGCAGGGCGTAGG-3′ F-5′-CGCAGCGTCGAAGTGGGTCAGG-3′ R-5′-CAAGGGCGAGTTCAGCAGCAAATC-3′

59

105

Prephante dehydratase

The primer sequences, annealing temperatures and expected product size are also listed. The primers were designed using the software from DNASTAR (Lasergene, USA)

Funct Integr Genomics 94 CP002287 Achromobacter xylosoxidans A8 92 KF557586 Alcaligenes sp. DH1f 98 NC_023061 Achromobacter xylosoxidans NBRC 15126 ATCC 27061 46 99

NC_010170 Bordetella petrii DSM 12804 CP002663 Pusillimonas sp. T7-7

Betaproteobacteria

AKMR01000044 Alcaligenes faecalis subsp. faecalis NCIB 8687 100 100

AUBT01000026 Alcaligenes faecalis subsp. phenolicus DSM 16503 100 89 JN187962 Alcaligenes sp. HPC1271 AF312022 Ralstonia basilensis strain DSM 11853 DQ108392 Burkholderia pseudomallei strain ATCC 23343 99 X96787 Pseudomonas multiresinivorans type strain ATCC 700690T

41 98

Gammaproteobacteria

Z93447 Acinetobacter sp. strain ATCC 17905

79

M88159 Wolinella succinogenes strain ATCC 29543

45

Epsilonproteobacteria

EF187256 Desulfoluna spongiiphila strain AA1

Deltaproteobacteria

AF503283 Sphingomonas sp. ATCC 53159

Alphaproteobacteria

NR_074504 Agrobacterium radiobacter K84 strain K84 99 NC_004722 Bacillus cereus ATCC 14579 AY196659 Methanobacterium formicicum

Firmicutes Archaebacteria

0.05

Fig. 1 Molecular phylogenetic analysis by maximum likelihood. The evolutionary history was inferred by using the maximum likelihood method based on the Tamura model. The tree with the highest log likelihood (−8297.0721) is shown. Bootstrap method was used for the test of phylogeny with 1,000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a

matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 18 nucleotide sequences. Codon positions included were 1st+ 2nd+3rd+Noncoding. All positions containing gaps and missing data were eliminated. There were a total of 1,198 positions in the final dataset. Evolutionary analyses were conducted in MEGA6

Identification of the antimicrobial agent

Genome coverage was 51.94-fold. The information contained in the draft genome comprises 4,270,933 bp sequence data, with a GC content of 56.6 %. Seventy-eight contigs cover a total of 3,914 genes, 57 tRNAs, 7 rRNAs, and 3,834 proteins. The sequence of the 16S rRNA gene (1,504 bp) of Alcaligenes sp. HPC1271 is 99 % identical to those of A. faecalis strain IAM12369 (ATCC 8750; NCBI accession no. NR_043445) and A. faecalis subsp. faecalis strain NCIB 8687 (NCBI accession no. AKMR00000000). A 16S RNA gene is present within a Bribosomal operon^ with the genes for 23S rRNA, tRNA-Ala (TGC), and tRNA-Ile(GAT). Mauve alignment analysis of the genome suggested that the HPC1271 genome differs from the genomes A. faecalis subsp. faecalis strain NCIB 8687 (data not shown).

HPLC was carried out using the ethyl acetate extract on an RP-18 column and fractions were collected every 1 min. Figure 2 demonstrates the HPLC profile of the ethyl acetate extract with major peaks with retention time of 1–2 min and smaller peaks after 3 min. Collected fractions were analyzed for antibacterial activity. The active fraction from peak with retention time of 18 min was analyzed by LC-MS. LC-MS analysis Fractions collected by HPLC were analyzed by LC-MS. The mass spectra generated for fraction 18 gave multiple peaks, m/z 846.4388 [M + H] that match to Tunicamycin (homologues-C) in the antibiotic library with critical mass 845 [M+ ] (Fig. 2). In this spectrum, m/z approximate around 415 (unlabeled) and 460.2663 (labeled) are the typical base peaks for Tunicamycin-C. Genome annotation and assembly A total of 1,303,717 reads were generated, of which 1,233,999 reads were assembled using MIRA (v3.4) into 78 contigs.

Secondary metabolite gene clusters The draft genome of the lab isolate, HPC1271, was analyzed for secondary metabolite-producing clusters using the antiSMASH software. Six gene clusters linked to the biosynthesis of potentially interesting metabolites were found. They are cluster 1: Ectoine, cluster 2: Butyrolactone, cluster 3: Phosphonate, cluster 4: Terpene, cluster 5: Type 1 polyketide synthase (T1PKS), and cluster 6: NPRS. All secondary

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Fig. 2 Identification of antibacterial agent from a cell-free extract using analytical tools. HPLC profile of cell-free ethyl acetate extract of isolate HPC1271was performed on Perkin Elmer NCI 900 system connected to

an UV/ Vis LC-295 detector. The active fractions were further characterized by LC-MS using Waters LC system no. 695. Inset demonstrates the structure of tunicamycin, the antibacterial agent identified in this study

metabolite clusters detected in the draft genome are either used for protection of the bacteria or reported in production of antibiotics. PKS and NRPS are multidomain components, wherein type-I modular PKSs consist of relatively large multifunctional polypeptides commonly associated with the production of highly reduced metabolites such as macrolide antibiotics (Zheng and Keatinge-Clay 2013). Figure 3 demonstrates all six clusters with the principle as well as related genes required for function. HPC1271 contains around

34 kb T1PKS cluster present on contig 54, and around a region of 50 kb of contig 22 and contig 51 encodes an NPRS cluster. Cluster characterization revealed 20 genes involved in the synthesis and 11 genes involved in transport. Domain analysis of NRPS indicated the presence of the synthase, responsible for the synthesis of the enzyme (Supplementary Fig. S1). The details of the principle genes and their closest match on BLAST analysis are listed in Supplementary Table 1. As can be seen in the table, the genes present in the

Fig. 3 Gene arrangement of the predicted secondary metabolite producing clusters from HPC1271 draft genome, analyzed using antiSMASH platform. The genes are represented as follows; red, principle genes; green, regulator; blue, transporter; gray, accessory genes

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lab isolate closely match the genes present in Alcaligenes faecalis subsp. faecalis strain NCIB 8687. However, sequence analysis of strain NCIB 8687 demonstrates only four potential secondary metabolite production gene clusters; Ectoine, Terpene, T1PKS, and NPRS.

stabilize in both nutrient-rich media as well as in mineral media. NRPS gene expression is significant after the cells have reached the stationary phase, in the case of nutrient media, but are already high in 6 h of glycerol media. Genes from the shikimate pathway, isochorisimate synthase and perphanate dehydratase are expressed only in the stationary phase.

Multiple secretion systems The antimicrobial agent being produced by isolate HPC1271 was demonstrated to be sensitive to heat and proteinase K treatment (data not shown). This prompted us to look for secretory proteins in the genome that are responsible for the synthesis of antimicrobial peptides. Thirty open reading frames (ORFs) were identified that are potentially linked to protein secretion. The ORFs belong to three categories: Type 1, which is responsible for transport of various molecules like ions, drugs, proteins etc.; Type 2, which is responsible for translocation of exo-proteins from the bacterial periplasm into the surrounding media; and the Type 4 secretion systems that mediate intracellular transfer of macromolecules via a mechanism ancestrally related to that of bacterial conjugation machineries. Details are listed in Supplementary Table 2. Gene expression analysis The expression of five target genes coding for enzymes involved in secondary metabolite production was analyzed under different growth conditions and time points. Quantification results are demonstrated in Fig. 4. As can be seen in the figure, the expression of NRPS and dTDP-glucose 4,6-dehydratase are very high as compared to PKS, isochorismate synthase, and prephenate dehydratase. dTDPglucose 4,6-dehydratase is seen to increase until 72 h and then Fig. 4 Gene expression analysis of the predicted genes involved in the synthesis of antibacterial compound

Sequence homology to genes from the tunicamycicn biosynthesis gene cluster Since the antimicrobial agent, identified by LC-MS was reported to be tunicamycin, we analyzed the draft genome for genes responsible for tunicamycin biosynthesis. This nucleoside antibiotic has been reported in several Actinomycetes, and its biosynthesis is encoded by tunA /B /C /D /E /F /G /H /I /J / K /L cluster (Chen et al. 2010; Wyszynski et al. 2012). The first gene in this cluster is tunA that codes for a putative UDP-GlcNAc epimerase/dehydratase (GenBank accession no. HQ172897.1). The draft genome presented in this study contains UDP-glucose 4-epimerase (EC 5.1.3.2) in contigs 23 and 36. The function designated in the KEGG database indicates its role in biosynthesis of secondary metabolites. Table 3 describes the similarity index to genes from the tunicamycin cluster reported in Streptomyces. The draft genome had genes with a similarity index ranging from 32 to 42 for the first four genes of this cluster, tunA, B, C, and D. The other genes in the reported cluster are carrier proteins or transporters, and the draft genome has a number of similar genes. However, they could not specifically be identified with the genes reported in Streptomyces with GenBank accession no. HQ172897.1. Gene expression analysis demonstrated the involvement of dTDP-glucose 4,6-dehydratase.

Funct Integr Genomics Table 3

Similarity index of genes present in the draft genome to the reported tunicamycin biosynthesis cluster from Streptomyces

S. no. Target gene from draft genome present in this study

Function related to antibiotic biosynthesis

Similarity Reference gene from Present in contig/ index annotated in NCBI tunicamycin biosynthesis gene cluster : GenBank submission accession no. HQ172897.1

1

dTDP-glucose 4,6-dehydratase

Reported in biosynthesis of antibiotics

2 3

EKU29954.1 (this study) C1 C12

Radical SAM family enzyme Generates de novo carbon-carbon bonds GCN5-related N-acetyltransferase Transfer of an acetyl group from one molecule to another is a fundamental biochemical process Probably functions as glycosyltransferase C5 N-acetylglucosamine transferase (EC 2.4.1.227)

4

tunA (NAD-dependent epimerase/dehydratase) tunB tunC

34.6 32.8 42.4

tunD

32.1

The functions related to antibiotic synthesis are reported from Chen et al. (2010) and tunicamycin gene sequences were downloaded from NCBI accession no. HQ172897.1. Similarity index was generated using MegAlign software (DNASTAR, Lasergene, USA)

Discussion The potential of microbial communities, in underexplored environmental niches, to yield new antibiotics is tremendous (Taylor 2013), and with the emergence of new multidrug resistant pathogens, the search for antimicrobials that can be converted to antibiotics is a priority. Unlike Actinomycetes, the Alcaligenes species are not well reported in the production of antimicrobials / antibiotics. Supplementary Table 3 describes four antibiotics that have been reported from this genus. Since most antibiotics are produced as secondary metabolites, we analyzed the genome sequence data for secondary metabolite producing clusters. Six clusters were found in the draft genome that could have a role to play. However, gene expression analysis suggests the dominant role of NRPS cluster. Quantifying the transcriptional levels of target genes was carried out to analyze the physiological state of the bacteria with reference to production of the antimicrobial agent. Five genes were selected for expression analysis. Two genes were selected from the secondary metabolite-producing clusters seen in the draft genome that are reported in the production of antibiotics, Type I-PKS and NRPS domains; two were selected from the pathways reported to be involved in the synthesis of precursors for secondary metabolites via the shikimate pathway, isochorismate synthase and prephenate dehydratase (Kloosterman et al. 2003; Du et al. 2004; O’Brien and Wright 2011), and dTDP-glucose 4,6dehydratase was selected since it is reported to play a role in antibiotic production (Du et al. 2004). 16S rRNA gene was used as an internal standard to accurately analyze the gene expression of target genes with a stable internal standard. Secondary metabolites are produced in the stationary phase, and, as can be seen in Fig. 4, NRPS gene expression is seen to increase over PKS and genes from the shikimate pathway after 48 h, reaching the maximum at 120 h in nutrient rich media. In mineral media, the expression is more or less uniform until

96 h, after which we see a negative expression, indicating a death phase of cells in mineral media. dTDP-glucose 4,6dehydratase has demonstrated a high expression throughout the period of study in both growth conditions indicating an active role in the production of the antimicrobial agent. A growth curve of the isolate grown in both nutrient-rich media and mineral media is demonstrated in Supplementary Fig. S2. Results of chemical analysis indicate the production of a nucleoside antibiotic and tunicamycin by the lab isolate. This antibiotic has been reported so far in Streptomyces and is a mixture of antibiotic homologues that differ in their fatty acid chain length. Homologous genes from the biosynthesis cluster have been found in the draft genome of Alcaligenes sp. HPC1271 (Table 3). Since tunicamycin has not been reported in Alcaligenes, we do not expect a very strong homology to the reported cluster from Actinobacteria. Additionally, since the expression of the dTDP-glucose 4,6-dehydratase gene is very high, we suspect that there may be more than one kind of antimicrobial agent being produced by the lab isolate. The dehydratase has been reported in the production of macrolide antibiotics that are made in bacteria from simple fatty acids and glucose (Vara and Hutchinson 1988). The presence of glycolysis and gluconeogenesis genes (conversion of glycerol in mineral media to glucose) in contigs 2, 5, 11, 12, 23, 36, and 54 of the draft genome corroborates this assumption. However, a detailed analytical study will be required to validate this assumption. This study reports a bacterial isolate from the genus Alcaligenes that demonstrates antibacterial activity against certain bacteria including multidrug resistant bacteria. Analytical analysis suggests the presence of tunicamycin, but further studies including construction of deletion mutants will have to be carried out in the future to prove this. However, genomic studies suggest that the bacterial isolate may be also producing other secondary metabolites that could be responsible for the broad spectrum of activity. Results suggest that a variety of genes may be expressed to bring about the

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antimicrobial activity. Alcaligenes is not a bacterial genus that is commonly associated with antibiotics, and this study highlights the importance of exploring the microbial diversity for new antimicrobial agents. Acknowledgments The work presented in this study was partly funded by the Department of Biotechnology (DBT), New Delhi, and partly by CSIR-NEERI. Authors are thankful to Dr. S.R. Wate, Director CSIRNEERI, for his support.

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