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Multi-omics analysis of niche specificity provides
new insights into
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ecological adaptation in bacteria
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Bo Zhu1*, Muhammad Ibrahim1*, Zhouqi Cui1, Guanlin Xie1, Gulei Jin2, Michael
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Kube3, Bin Li1$, Xueping Zhou1$
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University, Hangzhou 310029, China
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Hangzhou Guhe Info Co., Ltd, Hangzhou 310029, China
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Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences,
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Humboldt-Universität zu Berlin, 14195 Berlin, Germany
State Key Laboratory of Rice Biology, Institute of Biotechnology, Zhejiang
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Running title: Ecological adaptation in B. seminalis
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*Authors contribute equally to the work
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$
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Bin Li, Xueping Zhou
Corresponding author:
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Mailing address: State Key Laboratory of Rice Biology, Institute of Biotechnology,
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Zhejiang University, 310058, Hangzhou, China.
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Phone: 86-571-88982412. Fax: 86-571-88982412.
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libin0571@zju.edu.cn; zzhou@zju.edu.cn
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Conflict of Interest Statement
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The authors declare no conflict of interest.
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Materials and Methods
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Strains used in this study
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B. seminalis strains DSM 23518 (= LMG 24067 T), 0901, S9 and R456 originated
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from CF patient’s sputum (Vanlaere et al 2008), diseased apricot (Fang et al 2009),
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westlake water (Fang et al 2011), and rice rhizospheric soil (Li et al 2011),
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respectively. Unless otherwise specified, cultures of bacterial strains were maintained
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on nutrient agar (NA) or nutrient broth (NB) media at 30°C prior to use. Cultures
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were stored long term in 20% aqueous glycerol at -80°C.
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Characterization of ecological roles
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B. seminalis strains were tested for virulence in the alfalfa model (Bernier et al 2003),
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which was carried out as described by Ibrahim et al. (2012). Pathogenicity of B.
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seminalis to apricot was examined according to the method of Fang et al. (2009)
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except that premature fruits were inoculated with 10 μL of bacterial suspensions at the
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concentration of 1 × 105 CFU/mL using sterilized tips. Inhibition of B. seminalis on
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the mycelial growth of R. solani was determined according to the method of Li et al.
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(2011). The morphology of bacterial cells was observed using a JEOL JSM-6400
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scanning electron microscope (Hitachi, Tokyo, Japan).
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Growth in various niches
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Adaptation of B. seminalis strains to various niches were investigated by incubating
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the four strains under CF, water and soil extract media, respectively, while plant
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condition was excluded for only strain 0901 was pathogenic to apricot. Water medium
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that contains M9 minimal salts with 3% glycerol, was used to simulate the water
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environment (Schell et al 2011). CF medium was prepared to mimic the sputum of CF
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patients according to the method of (Dinesh 2010). Soil extract medium was prepared
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to mimic soil conditions based on recent paper (Yoder-Himes et al 2009) with the
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exception that soil was collected from the rice rhizosphere, which was the original
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niche for strain R456. In addition, three different niche conditions were tested for
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each of strains S9, DSM 23518, and R456. The bacterial numbers were counted based
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on the measurement of OD 600 value (Ibrahim et al 2012).
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Whole genome sequencing, assembly and annotation
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Bacterial genomic DNA, isolated using Wizard Genomic DNA Purification Kit
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(Promega, Madison, WI, USA), was used for whole-genome sequencing, which was
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performed by using Pacbio sequencing (Pacific Biosciences, Menlo Park, CA, USA),
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454 sequencing (Roche, Branford, CT, USA) and Illumina sequencing (Illumina, San
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Diego, CA, USA). Sequence runs for four single-molecule real-time (SMRT) cells
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were performed on the PacBio RS II sequencer with a 120-minute movie time/SMRT
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cell. SMRT Analysis portal version 2.1 was used for read filtering and adapter
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trimming, with default parameters, and postfiltered data of 350 - 580 Mb (around 40 -
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60X coverage) on each cell/per strain with an average read length of 7 kb were
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considered for further assembly. All the four genomes were first de novo assembled
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using HGAP assembly protocol, which is available with the SMRT Analysis packages
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and accessed through the SMRT Analysis Portal version 2.1. After this first round,
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PBJelly V14.1.14 was used to fill and reduce as many captured gaps as possible to
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produce upgraded draft genomes (English et al 2012). As B. seminlais genomes are
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much bigger than that of the normal bacteria, around 50 scaffolds were generated
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after this step. Then quality filtered Illumina and 454 sequencing reads were then used
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to correct the false SNPs and Indels due to the low coverage in some regions. Also,
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these reads were used enabling gap closure on the pre-assembled genomes by using
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WGS-assembler and SSPACE (Boetzer et al 2011, Myers et al 2000). Finally, the
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consensus was obtained based on the above procedure. If it was not complete
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sequence, scaffolding and gap closure were repeated again until we get the almost
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complete bacterial genome sequences.
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Coding DNA Sequences (CDSs) were predicted using Prodigal version 2.6 with
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default parameters (Hyatt et al 2010). To refine the accuracy, RNA-Seq results were
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also used for improvement of gene prediction. Gene functions were automatically
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assigned by RAST annotation engine (Aziz et al 2008) Predicted genes were
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compared via Blastn against the genomic sequences to verify the accuracy. rRNA
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operons and tRNA were predicted by RNAmmer and tRNAscan-SE (Lagesen et al
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2007, Lowe and Eddy 1997), while additional analysis was carried out by using
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NCBI’s uniprot database (http://www.ncbi.nlm.nih.gov/), COG (Tatusov et al 2001)
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KEGG (Ogata et al 1999) and GO terms (Ashburner et al 2000).
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Variant calling
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Paired-end reads generated from Illumina sequencing were mapped onto genome
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sequence by using Burrows–Wheeler Alignment (Li and Durbin 2009). Default
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settings were used except the maximum edit distance was set to 0.02 (-n 0.02).
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MarkDuplicates command in Picard (http://picard.sourceforge.net/) was used to
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remove the reads that mapped to the same positions in strain DSM 23518 genome
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(PCR duplications). After IndelRealigner and BaseRecalibrator, SNPs and Indels
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were called using GATK (Gac et al 2013, Tenaillon et al 2012). Default settings were
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used except the maximum read depth in GATK was set to 500 (-dcov 500). The
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generated SNPs and Indels were then filtered using custom Perl scripts to minimize
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the false positive mutation calls. First, mutations with a total read depth below 20X
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were discarded. Second, SNPs and Indels with a Phred quality score below 30 were
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removed. Third, the mutation calls were only kept when at least 80% of the reads was
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positive. The lists of SNPs/Indels were then annotated by in-house Perl scripts. For
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the mutations that happened in the coding regions, PROVEAN was used to predict
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whether a protein sequence variation is deleterious or neural (Chieng et al 2012).
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Phylogenetic and comparative genome analysis
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The sequences from four whole genome sequenced strains were aligned and
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visualized by using Murasaki software (Popendorf et al 2010). For genome-based
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phylogeny, in addition to the four B. seminalis genomes that sequenced in this study,
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28 complete Burkholderia genome sequences were obtained from Burkholderia
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Genome Database (Winsor et al 2008). Furthermore, a well-resolved phylogenetic
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tree were also generated based on the multi-locus sequence analysis (MLSA) of the
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atpD, gltB, gyrB, lepA, phaC, recA and trpB genes, which has been widely applied in
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identification and discrimination of the Burkholderia species (Spilker et al 2009). The
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identity of strains was confirmed by calculating whole-genome average nucleotide
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identity (ANI) based on Blast and MUMer algorithm by using JSpecies (Richter and
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Rosselló-Móra 2009). Multiple sequence alignment was done by using Muscle 3.8
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(Edgar 2004) and ML tree was generated by MEGA 6 (Tamura et al 2013). In addition,
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GIs were detected by applying IslandViewer which integrated with mostly used GI
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detection algorithem IslandPick, SIGI-HMM and IslandPath-DIMOB (Langille and
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Brinkman 2009).
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DNA methylation analysis pipeline
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SMRT generated data was analyzed with RS_Modification and Motif Analysis
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pipeline in SMRT analysis 2.2, which was provided by Pacific Biosciences SMRT
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portal with default parameters. In this default parameters, coverage and IPD
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(inter-pulse duration) ratio were calculated by dividing a methylated base in the DNA
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template to an incorporation opposite of a canonical base (Lluch-Senar et al 2013).
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All the data sets contain kinetic values for each reference position and DNA strand
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with the corresponding sequences generated from assembly procedure. For statistical
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analysis, methylation site positions were divided into three parts (up-stream 200 bp
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coding region, coding region and down-stream 200 bp coding region). For every gene,
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top methylated strain was then selected out for further analysis.
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Growth conditions for RNA-Seq analysis
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In order to simulate the original niche environments of four B. seminalis strains, 2 mL
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of overnight cultured bacteria were inoculated into 50 mL of the following four types
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of media. Water medium that contains M9 minimal salts (0.6% Na2PO4 + 0.3%
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KH2PO4 + 0.05% NaCl + 0.1% NH4Cl + 0.02% MgSO4 + 0.015% CaCl2) with 3%
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glycerol, was used for simulation of the water environment (Schell et al 2011). CF
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medium was prepared according to the method of (Dinesh 2010). Briefly, 5.0 g/L
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mucin from pig stomach mucosa (Sangon Biotech), 4.0 g/L low molecular-weight
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salmon sperm DNA (Fluka), 5.9 mg diethylenetriaminepentaacetic acid (DTPA)
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(Sigma), 5.0 g/L NaCl (Sigma), 2.2 g/L KCl (Sigma), 1.8 g/L Tris base (Sigma), were
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mixed together autoclaved and 5.0 mL/L egg yolk emulsion (Oxoid), 5.0 g/L
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casamino acids (Sangon Biotech) were added when temperature reached to 37°C after
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autoclaving. Soil extract medium was prepared to mimic soil conditions based on
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recent paper (Yoder-Himes et al 2009) with the exception that soil was collected from
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the rice rhizosphere, which was the original niche for strain R456. Plant condition to
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obtain in vivo bacteria was prepared according to the method of our recent paper (Li
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et al 2014).
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Total RNA harvesting
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Each bacterial strain was incubated under its condition to stationary phase. After
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centrifugation of 4500 g at 4°C, pellets were re-suspended in 3 mL of PBS. One
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milliliter of bacterial culture was subjected to RNA purification by RNeasy Mini Kit
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(Qiagen) and eluted in 50 µl of RNase-free water. Samples were treated with DNaseI
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to remove any residual DNA and purified by phenol-chloroform-isoamyl alcohol
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extraction and ethanol precipitation.
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mRNA purification and cDNA synthesis
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Ten micrograms from each total RNA sample was treated with the MICROBExpress
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Bacterial mRNA Enrichment kit (Ambion) and RiboMinus™ Transcriptome Isolation
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Kit (Bacteria) (Invitrogen) following the manufacturer’s instructions. Samples were
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resuspended in 15 μL of RNase-free water. Bacterial mRNAs were chemically
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fragmented to the size range of 200-250 bp using 1 × fragmentation solution (Ambion)
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for 2.5 min at 94°C. cDNA was generated according to instructions given in
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SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen). Briefly, each mRNA
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sample was mixed with 100 pmol of random hexamers, incubated at 65°C for 5 min,
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chilled on ice, mixed with 4 μL of First-Strand Reaction Buffer (Invitrogen), 2 μL of
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0.1 M DTT, 1 μL of 10 mM RNase-free dNTPmix, 1 μL of SuperScript III reverse
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transcriptase (Invitrogen), and incubated at 50°C for 1 h. To generate the second
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strand, the following Invitrogen reagents were added: 51.5 μL of RNase-free water,
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20 μL of second-strand reaction buffer, 2.5 μL of 10 mM RNase-free dNTP mix, 50 U
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E. coli DNA Polymerase, 5 U E. coli RNase H, and incubated at 16 °C for 2.5 h.
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RNA Sequencing
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The Illumina Paired End Sample Prep kit was used for RNA-Seq library creation
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according to the manufacturer’s instructions as follows: Fragmented cDNA was
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end-repaired, ligated to Illumina adaptors, and amplified by 18 cycles of PCR.
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Paired-end 100-bp reads were generated by high-throughput sequencing with the
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Illumina Hiseq2000 Genome Analyzer instrument.
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RNA-Seq data analysis
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After removing the low quality reads and adaptors, RNA-Seq reads were aligned to
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the corresponding B. seminalis genome using TopHat 2.0.7 (Trapnell et al 2009),
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allowing for a maximum of two mismatches. If reads mapped to more than one
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location, only the one showing the highest score was kept. Reads mapping to rRNA
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and tRNA regions were removed from further analysis. After getting the reads number
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from every sample, edgeR with TMM normalization method was used to determine
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the DEGs. Significantly differentially expressed genes (FDR value < 0.05 and at least
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two fold changes) were selected for further analysis. Cluster 3.0 and Treeview 1.1.6
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were used to generate the heatmap cluster based on the RPKM values (de Hoon et al
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2004, Saldanha 2004).
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COG enrichment analysis
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All the DEGs between different strains or conditions will be classified by COG
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category (Tatusov et al 2001). Based on the whole-genome COG classification, the
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significance of COG category about DEGs under the same COG category will be
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tested based on the Hypergeometric Distribution,



p
 
n
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N M
n i
M
i
i x
N
n
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In which, N means the number of genes in the genome, M means the number of genes
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assigned to one COG category in the whole genome, n means the number of DEGs
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and I means the number of genes fill into one COG category in DEGs. The results
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were shown on Table S4.
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Validation of mix sample method
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Each sample was derived from a pool of five biological replicates, which has been
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developed to increase the efficiency and cost-effectiveness with equivalent statistical
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power (Greenwald et al 2012, Peng et al 2003). To validate the accuracy of
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mix-sample method, single biological RNA sample from SE of strain DSM 23518
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were prepared. Correlation coefficient between samples was determined by statistical
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analysis.
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Quantitative real-time PCR
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Total RNAs were extracted from exponentially growing cells, using an RNeasy Mini
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spin columns Kit (Qiagen) and was treated with a unit of RNase-free DNase I
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(Qiagen), and cDNA synthesis was performed with a Moloney murine leukemia virus
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reverse transcriptase first-strand cDNA synthesis kit (QIAGEN). The cDNA was then
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used directly as the template for qRT-PCR using a SYBER Green master mix (Protech
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Technology Enterprise Co., Ltd.) on an ABI Prism 7000 sequence detection system
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(Applied Biosystems). Primers for quantitative real-time PCR (qRT-PCR) of the
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selected genes were designed by using Primer 3 based on the genome sequences
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(Untergasser et al 2012). All these primers are listed in Table S3 and an annealing
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temperature of 58ºC was used for all the primers. Short-chain dehydrogenase
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(BCAL2694), which has been proved to be stably expressed in Bcc, was used as
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internal control (Van Acker et al 2013). Fold changes were calculated according to the
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delta-delta CT method and the values were also shown on Table S3. The correlation
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between RNA-Seq results and qRT-PCR results were tested by Pearson's correlation
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method.
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Supplementary Figure and Table Legends
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Figure S1: Distribution of differentially expressed genes along the chromosome.
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Grey thick circles sorted by strain 0901 from inner to outer represent strains 0901,
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DSM 23518, R456 and S9 chromosomes, respectively. The red, green, blue and black
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peaks outside the chromosome represent the log2 RPKM values of genes under CF,
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apricot, soil and water conditions. Outside the black peak (water RPKM value) is the
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heatmap of genes density every 10 kb along the chromosome from blue to red.
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Figure S2: Full genome alignment among the four strains 0901, DSM 23518, R456
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and S9 of Burkholderia seminalis.
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Figure S3: Expression pattern cluster based on the normalized RPKM values. The
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cluster of RNA-Seq samples based on the log2 RPKM values.
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Figure S4: The histogram of the number of DNA methylation in Burkholderia
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seminalis strains 0901, S9, R456 and DSM 23518.
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Figure S5: Phylogenetic relationship of four Burkholderia seminalis strains to other
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species of Burkholderia. (a) Maximum-likelihood tree was constructed by using
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MLSA from four sequenced B. seminalis strains in this study and other 28
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Burkholderia strains. Among these strains, B. seminalis DSM 23518 (= LMG 24067),
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B. lata 383, B. thailandensis E264, B. mallei ATCC 23344, B. phymatum STM815, B.
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phytofirmans PsJN and B. xenovorans LB400 are type strains. (b) Maximum
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likelihood tree was constructed based on whole genome sequences. Among these
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strains, the type strains are the same as that of (a).
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Figure S6: Correlation coefficient between SE-single sample and SE-mix sample of
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strain DSM 23518 based on the log2 RPKM values.
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Figure S7: Correlation coefficient between SE-mix sample and W-mix sample of
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strain DSM 23518 based on the log2 RPKM values.
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Table S1: Physiological characteristics of Burkholderia seminalis strains 0901, S9,
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R456 and DSM 23518.
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Table S2: Comparison of general genomic features between Burkholderia seminalis
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strains 0901, DSM 23518, R456 and S9.
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Table S3: Summary of RNA-Seq results (Illumina HiSeq 2000).
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Table S4: Integrated information of Burkholderia seminalis strains 0901, S9, R456
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and DSM 23518.
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Table S5: Average Nucleotide Identity (ANI) among the Burkholderia seminalis
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genomes and the selected Burkholderia cenocepacia genomes.
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Table S6: COG enrichment results from DEGs. a), strain 0901; b), strain DSM 23518;
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c), strain S9; d), strain R456.
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Table S7: Gene clusters involved in niche adaptation.
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Table S8: (a): Primers of qRT-PCR used in this study. (b): Internal primer used in
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qRT-PCR and its RPKM values in different strains and conditions.
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