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Transcriptomic Analyses Elucidate Adaptive Differences of Closely-Related 1

Strains of P. aeruginosa in Fuel 2

Thusitha S. Gunasekera1, Loryn L. Bowen1, Carol E. Zhou2, Susan C. Howard-Byerly1, William 3

S. Foley3, Richard C. Striebich1, Larry C. Dugan2 , Oscar N. Ruiz3# 4

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1. Environmental Microbiology Group, University of Dayton Research Institute, University 6

of Dayton, Dayton OH 45469, USA 7

2. Lawrence Livermore National Laboratory, Livermore, CA, USA 8

3. Fuels and Energy Branch, Aerospace Systems Directorate, Air Force Research 9

Laboratory, Wright-Patterson AFB, OH 45433, USA 10

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# Corresponding author: Oscar N. Ruiz, [email protected] 12

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Running Title: Adaptive differences of P. aeruginosa strains to fuel 14

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AEM Accepted Manuscript Posted Online 17 March 2017Appl. Environ. Microbiol. doi:10.1128/AEM.03249-16Copyright © 2017 American Society for Microbiology. All Rights Reserved.

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ABSTRACT 20

Pseudomonas aeruginosa can utilize hydrocarbons, but different strains have varying degrees of 21

adaptation despite their highly conserved genome. P. aeruginosa ATCC 33988 is highly adapted 22

to hydrocarbons while strain PAO1, a human pathogen, is less-adapted and degrades jet fuel at a 23

slower rate than does ATCC 33988. We investigated fuel specific transcriptomic differences 24

between these strains in order to ascertain the underling mechanisms utilized by the adapted 25

strain to proliferate in fuel. During growth in fuel, the genes related to alkane degradation, heat-26

shock response, membrane proteins, efflux pumps and several novel genes were upregulated in 27

ATCC 33988. Overexpression of alk genes in PAO1 provided some improvement in growth, but 28

not as robust as that of ATCC 33988, suggesting the role of other genes in adaptation. 29

Expression of the function unknown gene PA5359 from ATCC 33988 in PAO1 increased the 30

growth in fuel. Bioinformatic analysis revealed that PA5359 is a predicted lipoprotein with a 31

conserved ‘Yx(FWY)xxD’ motif, which is shared among bacterial adhesins. Overexpression of 32

the putative RND-efflux pump PA3521-PA3523 increased the growth of ATCC 33988 strain 33

suggesting a possible role in fuel tolerance. Interestingly the PAO1 strain cannot utilize nC8 34

and nC10. Expression of GFP under the control of alkB promoters confirmed that alk gene 35

promoter polymorphism affects the expression of alk genes. Promoter fusion assays further 36

confirmed that regulation of alk genes was different in the two strains. Protein sequence analysis 37

showed low amino acid differences for many of the upregulated genes, further supporting 38

transcriptional control as the main mechanism for enhanced adaptation. 39

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IMPORTANCE 42

These results support that specific signal transduction, gene regulation and coordination of 43

multiple biological responses are required to improve survival, growth and metabolism of fuel in 44

adapted strains. This study provides new insight into the mechanistic differences between strains 45

and helpful information that may be applied in the improvement of bacterial strains for resistance 46

to biotic and abiotic factors encountered during bioremediation and industrial biotechnological 47

processes. 48

INTRODUCTION 49

Pseudomonas aeruginosa has the unique ability to colonize a wide range of ecological niches, 50

including animal tissues, soil, and hydrocarbon-contaminated habitats (1). The ubiquity of P. 51

aeruginosa is primarily due to its metabolic versatility, which is linked to plastic but precisely 52

controlled genetic systems. The genomes of environmental and pathogenic strains of P. 53

aeruginosa are highly conserved (2). Thus, the phenotypic variability of strains cannot be solely 54

attributed to significant protein coding sequence variability (3). The highly conserved core 55

genome represents 88% of the average P. aeruginosa genome (4). Genes involved in aerobic and 56

anaerobic respiration, transcription and translation regulation, DNA and protein repair, 57

hydrocarbon degradation, and multi-drug efflux systems are all represented in the core-genome. 58

These genetic systems enable strains to adapt to different conditions rapidly. 59

P. aeruginosa ATCC 33988, isolated from a fuel tank in Oklahoma, is highly adapted to 60

hydrocarbon-containing environments and is considered to be an efficient alkane degrader (5, 6). 61

The 6.4 Mb genome of strain ATCC 33988 has 5,975 predicted coding sequences (7). In 62

contrast, the genome of P. aeruginosa PAO1, an important opportunistic human pathogen, is 63


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6.26 Mb in size (8) with 5,696 genes (http://www.pseudomonas.com). Although there is a high 64

degree of genome similarity between these two strains, including the presence of the long-chain 65

alkane monooxygenase genes alkB1 and alkB2 (5, 7, 9), the growth of the PAO1 strain in jet fuel 66

is markedly slower compared to that of ATCC 33988. In particular, we noticed the PAO1 strain 67

exhibits a reduced growth rate and a longer lag phase than ATCC 33988 when hydrocarbons are 68

its sole carbon source. The alkB1 and alkB2 gene coding regions of ATCC 33988 and PAO1 are 69

highly conserved, presenting only two synonymous single-nucleotide polymorphisms (SNPs) 70

between them (7). Other genes important for growth and survival in hydrocarbon-containing 71

environments were found to be at least 99% conserved between PAO1 and ATCC 33988. These 72

genes included those encoding the essential electron transfer proteins, the rubredoxins (RubA1, 73

RubA2), and FAD-dependent NAD(P)H2 rubredoxin reductases, the pel and alg genes for 74

biofilm formation, fur, pvc, pch and pfe for iron acquisition, and Mex efflux pumps and porins 75

(7). The high degree of similarity in the coding regions of these essential genes in PAO1 and 76

ATCC 33988 suggests that transcriptional regulation may play an essential role in the adaption 77

of these strains to fuel. 78

The mechanism that regulates the alk genes in P. aeruginosa is not well understood. 79

However, in P. putida GpO1, the alk genes are regulated by AlkS in the presence of alkanes (10, 80

11). In addition, the AlkS regulator is controlled by other global regulators (10, 11). For 81

instance, the expression of genes encoding components of the alkane degradation pathway is 82

negatively regulated by the catabolic repression control complex formed by the Crc and Hfq 83

protein, depending on the availability of other carbon sources. Recent research by Moreno et al 84

(12) shows it is the Hfq protein that binds to the catabolite activity (CA) motif- AAnAAnAA of 85

alkS (12). The binding of Hfq to the CA motifs of the alkS is stabilized by co-complexing with 86


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Crc. Therefore, the Crc/Hfq complex regulates AlkS at the translational level by binding to the 5’ 87

end of the alkS mRNA adjacent to the alkS start codon, repressing genes involved in 88

hydrocarbon assimilation when other preferred carbon compounds are present (11, 12). The 89

regulatory effect of Crc/Hfq complex is antagonized by the CrcZ, a non-coding RNA (13). 90

Although, AlkS or AlkS homologs have not been identified in P. aeruginosa ATCC 33988 and 91

PAO1, a CRC-dependent regulatory network exists in P. aeruginosa (13). Cell signaling and 92

protective mechanisms are vital for bacteria survival in hostile environments. The adaptation of 93

the ATCC 33988 strain to hydrocarbon-rich environments may be due to the presence of 94

multiple single nucleotide polymorphisms (SNPs) in the cis-acting regulatory region of 95

promoters in homologous genes. This type of genetic variation often explains organism evolution 96

and adaptation to a particular environment (14). These SNPs may affect the binding of regulatory 97

protein factors to the promoter influencing gene transcription and expression (15). 98

When the fuel adapted strain P. aeruginosa ATCC 33988 was grown in Jet A fuel and its 99

transcriptome compared with cells grown in glycerol, a large number of genes were differentially 100

expressed. (5). Upon exposure to fuel, strain ATCC 33988 transcriptionally regulated several 101

genes that included genes encoding efflux pumps (mexCD-OprJ, mexEF-OprN, mexAB-oprN) 102

and porins (oprF, oprG), extracellular polysaccharides (algD operon, pel operon) and genes 103

associated with biofilm formation, production of siderophores such as pyoverdine (pvc-A-F) and 104

pyocheline (pchC, pchD, pchF) for iron acquisition, and a range of transcriptional regulator 105

proteins (e.g., Fur, Hfq). Likely, these adaptive mechanisms were important in the survival and 106

proliferation of strain ATCC 33988 in fuel (5). 107

In this study, we characterized the genetic regulation, growth and metabolism of 108

Pseudomonas aeruginosa strains with similar genetic backgrounds but with different levels of 109


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adaption to hydrocarbon fuels in order to determine which genes, pathways, and regulatory 110

mechanisms contribute to the adaptive difference observed between these closely related strains. 111

P. aeruginosa ATCC 33988 and PAO1 cannot utilize aromatic hydrocarbons. Here, ATCC 112

33988 was shown to degrade n-alkanes ranging from n-C8 to n-C16 and isoparaffinic 113

hydrocarbons while PAO1 lacked the ability to degrade isoparaffins and n-C8 and n-C10 alkanes. 114

Genome-wide microarray analyses of the transcriptome of fuel-adapted (ATCC 33988) 115

and clinical (PAO1) strains in hydrocarbons were combined with reverse-transcriptase 116

quantitative PCR analyses (RT-qPCR) to characterize the regulation of genes and pathways in 117

the presence of fuel. Bioinformatic analyses were used to predict the function of unknown genes 118

that were highly upregulated in the ATCC 33988 strain during hydrocarbon consumption in 119

comparison to PAO1. Recombinant DNA approaches were used to study the regulation and 120

activity of strain-specific promoters in the presence of hydrocarbons. Additionally, genes highly 121

up-regulated in ATCC 33988 were overexpressed in PAO1 to determine whether each gene 122

product promotes fuel growth. The growth and hydrocarbon degradation rates of wild type and 123

genetically modified strains were closely monitored by performing growth assays and gas 124

chromatography-mass spectrometry (GC-MS) analyses. Understanding the contribution of 125

strain-specific transcriptional responses, novel genes, and regulatory sequence polymorphisms in 126

the adaptation of bacteria to adverse conditions and environments is crucial to developing 127

improved bacterial systems for biotechnological and environmental applications including 128

production of biopharmaceuticals, added value products, and bioremediation. 129

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MATERIALS AND METHODS 132

Bacteria strains and growth conditions 133

Pseudomonas aeruginosa ATCC 33988 and Pseudomonas aeruginosa PAO1 were grown in LB 134

(Luria-Bertani) media overnight at 28o C. Cells were washed twice using 1X M9 minimal media 135

or PBS and inoculated at an OD600 of 0.03 into 20 ml of 1X M9 minimal media overlaid with 5 136

ml Jet A fuel in 50 ml polystyrene tubes. Four sample replicates per strain and condition were 137

used. Samples were incubated at 28oC and shaken at 200 rpm. The initial inoculum level was 138

adjusted to an OD600 of 0.03. The Jet A fuel used was comprised of 21.8 wt% aromatics, (16.8 139

wt% alkylbenzenes, 0.5 wt% alkylnaphthalenes, 4.5 wt% indanes and tetralins), 32.8 wt% 140

isoparaffins, 23.9 wt% n-alkanes, and 21.5 wt% cycloparaffins. In tests were normal alkane 141

blend (20% n-C8, 20% n-C10, 20% n-C12, 20% n-C14, 20% n-C16) and pure n-alkane solvents were 142

used, 20 ml of M9 minimal media were amended with 500 µL of alkanes. For RNA extractions, 143

the cells were grown to mid log phase (0.4 -0.6 OD600) and then cooled rapidly on ice. Cells were 144

harvested by centrifugation (4oC) with 10% ethanol/phenol (19:1) solution and the pellet was 145

frozen immediately on dry ice and stored at -80oC. 146

RNA extraction, DNA microarray, and expression analyses. For statistical confidence in 147

differential gene expression, four biological replicates per test condition were employed. All the 148

samples including fuel and alkane amended were RNA extracted for qPCR and microarray using 149

the RNeasy Mini Kit as described by the manufacturer (Qiagen, Valencia, CA). The RNA was 150

treated with DNase to remove any residual DNA (TURBO DNA-free Kit, Ambion). The quality 151

of the RNA and DNA removal was confirmed by electrophoresis through a 1% agarose gel. 152

RNA integrity was analyzed using the BioAnalyzer chip based microcapillary electrophoresis 153


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systems (Agilent 2100 BioAnalyzer, Agilent Technology, USA) for RIN value >9 and 23S 154

rRNA to 16S rRNA ratio between 1.5-2.0. RNA concentrations and purity were measured using 155

a NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, USA). Only those samples 156

with a 260/280 nm ratio between 1.8-2.1 were used for cDNA synthesis. DNA-free total RNA 157

was used for cDNA synthesis with random primers using SuperScript II, reverse transcriptase. 158

Five µg of cDNA were fragmented and labeled with biotin. The labeled-cDNA was hybridized to 159

Affymetrix® microarray chips. Arrays were then washed and stained as described in the 160

Affymetrix® GeneChip Expression Analysis Technical Manual, using the instructions 161

specifically for P. aeruginosa PAO1. After washing and staining, microarray chips were scanned 162

using the Affymetrix® GeneChip Scanner 3000. The initial data analyses were performed using 163

Affymetrix® Microarray Analysis Suite (MAS), version 5.1. Each array was scaled to a target 164

intensity of 500 for all probe sets. The Cell files (.CEL) were uploaded into the Expression 165

Console (ver 1.2.0.20), and background corrections, and normalizations were performed using 166

MicroArray Suite 5.0 (MAS5.0) algorithms. MAS-5 calculates a robust average of all the 167

contained probes using Tukey’s bi-weight function. The MAS-5 normalized dataset was filtered 168

by excluding probe sets with 100% ‘absent’ calls. The MAS5.0 normalized CHP files were 169

imported into the Affymetrix® Transcriptome Analysis Console (TAC). Comparison analyses 170

were performed using TAC software. TAC uses Tukey’s bi-weight function to determine the 171

averages among the analysis groups. Additionally, TAC employs traditional unpaired One-Way 172

ANOVA to determine variance and significance (p-value). The complete dataset (CEL and CHP 173

files) has been deposited at the NCBI/GEO database (accession number GSE93305). 174

Gene annotation methodology. The P. aeruginosa PAO1 and ATCC 33988 genomes were 175

processed through automated annotation using RAST (16) and the PSAT system (17), which 176


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runs EFICAz 2.5 (18), blastp against the KEGG, MetaCyc, BRENDA, STRING databases (19, 177

20, 21, 22), InterProScan (23), and SignalP (24). Differentially regulated unknown hypothetical 178

genes from P. aeruginosa ATCC 33988 were further annotated using TMHMM version 2.0 (25). 179

Each unknown hypothetical gene was considered in the context of other genes in close proximity 180

in the genome (i.e., gene neighborhood, using graphical information provided by the 181

Pseudomonas Database; 26), and the Transport Database (27) was consulted to identify putative 182

membrane proteins among these genes. In the majority of cases, the automated annotations for 183

ATCC 33988 were identical to those of the corresponding genes in PAO1, but when there were 184

differences, the annotations for ATCC 33988 were used. Literature searches were performed for 185

all corresponding PAO1 genes in order to identify empirical functional information and 186

annotations previously assigned in the literature. Priority was given to function calls presented in 187

the literature or made by RAST, but all contributing information was considered in proposing 188

one or more putative functions. When appropriate, a gene assigned to a given protein family or 189

broad function category was aligned using Clustal Omega (28) to verify similarity to related, 190

annotated genes from other organisms. Functional annotations were assigned as “putative” in the 191

absence of experimental evidence of function. Additional sequence-based annotations were 192

performed for the ATCC 33988 homolog of PA5359. The sequence was analyzed using CD-193

search (29), SPRINT-CBH (30), and blastp against the P. aeruginosa PAO1 protein sequences in 194

the CAZy database (31). 195

Reverse transcription quantitative PCR (RT-qPCR) 196

RT-qPCR was used to estimate the expression of alkB1 and alkB2 by quantifying mRNA 197

abundance in the sample. Several differentially expressed genes (e.g., alk genes, outer 198

membrane proteins) were also validated using RT-qPCR. The expressions of oprF and oprG 199


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genes in alkanes and Jet A was measured by RT-qPCR and compared to the corresponding 200

expression of these genes in glycerol as a carbon source. Also the expressions of several 201

regulatory small RNA that are not represented in the microarray chips were assessed using RT-202

qPCR. Gene-specific primers were designed using PrimerQuest software 203

(https://www.idtdna.com/Primerquest/Home/Index). Reverse transcription was carried out using 204

1µg of total RNA, random primers and 75 U of iScript MMLV-RT (RNaseH+). The protocol for 205

reverse transcription reaction is as follows: priming for 5 min at 25o C, reverse transcription for 206

30 min at 42o C followed by RT inactivation for 5 min at 85o C. cDNA was diluted five-fold and 207

stored at -80o C. Negative controls lacking reverse transcriptase were performed using the same 208

program to ensure there was no gDNA carryover into RNA samples. Triplicate qPCR reactions 209

were performed with the cDNA for each condition using SYBR Green Master Mix (Bio-Rad). 210

Five µl of the cDNA were amplified using 0.2 µM of each gene-specific primers. After the 211

initial denaturation step at 95o C samples were subjected to 40 cycles of PCR amplification using 212

30s annealing/extension step at 60o C. A melt-curve analysis was performed at the end of the 213

cycle to determine that one specific product was produced. The threshold cycle values (Ct) were 214

obtained from the amplification curves and the gene expression fold changes was calculated 215

using 2-∆ΔCT method. The housekeeping gene proC (32) or 16s rRNA gene was used to normalize 216

the qPCR data to cDNA input levels. 217

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Overexpression of alkB1 and alkB2 genes with native promoters in P. aeruginosa PAO1 219

The alkB1 and alkB2 genes of ATCC 33988 and PAO1 with their respective proximal promoters 220

were PCR-amplified from PAO1 or ATCC 33988 genomic DNA with the primers Forward 5’- 221


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ATC GCC ATG GCG TGA CGT GAG GCT CCT TGT TTG GAA-3’ and Reverse 5’-ATC 222

GTC TAG AGA TTG CCC GAA CGA AGA GCT ATT-3’, and . Forward 5’-ATC GCC ATG 223

GCG TGA GGT GAT CCT TTT ATC CAG GGG-3’ and Reverse 5-ATC GTC TAG ATC AGG 224

AAG CTG CCG GCC GC-3’, respectively. The purified PCR products were cloned into the 225

pHERD20T plasmid using NcoI and XbaI restriction enzymes. All clones were confirmed by 226

PCR , restriction digestion, and DNA sequencing. The plasmid DNA manipulation was initially 227

carried out in JM109 cells (Promega). The pHERD20T plasmid with alk genes and their 228

respective promoters were electro-transformed into PAO1 and ATCC 33988 cells. The empty 229

plasmid pHERD20T (33) was transformed into PAO1 wild-type cells and used as a control for 230

the expression, growth and Jet A degradation studies described below. 231

Construction of alkB1 and alkB2 promoter-GFP constructs 232

In order to compare the strengths and regulation of the native alkB1 and alkB2 promoters from 233

PAO1 and ATCC 33988 strains, individual alkB1 and alkB2 promoters were fused to the green 234

fluorescent protein (GFP) gene. The alkB1 and alkB2 promoters were PCR-amplified from 235

PAO1 or ATCC 33988 genomic DNA with the primers AlkB1pro-FOR (5’-ATC GGG ATC 236

CCG TGA GGC TCC TTG TTT GGA AAT TGG-3’) and AlkB1pro-REV (5’-CAC CGG ATC 237

CGC GGC CGC CGA CAT ATG TGC GCT CCA GTT TTT GTC CGA CA-3’) or AlkB2pro-238

FOR (5’-ATC GGG ATC CGG TGA TCC TTT TAT CCA GGG GCG C-3’) and AlkB2pro-239

REV (5’-CAC CGG ATC CGC GGC CGC CGA CAT ATG AAG TCC TCG TAT TTA TCT 240

TGT TAG ATT GTC TG-3’) . Gel-purified alkB1 and alkB2 promoter PCR products were cut 241

with BamHI and ligated into BamHI-digested pHERD20T+KanR, replacing the plasmid pBAD 242

promoter. The DNA fragment 6His-GFP was cut from pCR2.1-prrn-6His-GFP using NdeI and 243

NotI and ligated into NdeI/NotI-digested pHERD20T-KanR-AlkB1pro and pHERD20T-KanR-244


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AlkB2pro plasmids to create alkB1 and alkB2 promoter-driven 6His-GFP constructs for 245

transformation of P. aeruginosa strains. P. aeruginosa PAO1 and ATCC 33988 were electro-246

transformed with the above plasmids. Plasmid presence was confirmed by PCR using genomic 247

DNA prepared from transformation isolates and primers specific for GFP and the KanR gene. 248

Plasmid-containing isolates were cultured in M9 minimal media with alkanes, or glycerol. Cells 249

were analyzed by fluorescence microscopy to test for GFP expression. 250

To compare relative GFP expression levels, cultures were inoculated at 0.03 OD600 in 15 251

ml of M9 with 200 µg/ml carbenicillin and 1 ml n-alkane blend (comprised of 20 wt% of the 252

followings: n-C8, n-C10, n-C12, n-C14, n-C16), or with 0.2% glycerol. Cultures were grown at 28oC 253

with shaking at 220 rpm and growth (OD600) and GFP expression were measured periodically. 254

To estimate GFP expression, cells were excited at 488nm and the GFP emission peak (509 nm) 255

was measured using a fluorometer (Cary Eclipse Fluorescence Spectrophotometer, Agilent 256

Technologies). 257

Fuel degradation profiles by GC-MS. Gas chromatography-mass spectrometry (GC–MS) 258

analyses were conducted to investigate which specific hydrocarbons in Jet A fuel were 259

preferentially degraded by different P. aeruginosa strains, including the strains with alk gene 260

overexpression plasmids. One-ml samples of P. aeruginosa in M9 media were spiked with 10 µL 261

of Jet A fuel in a 10 ml glass vial sealed with a Teflon-lined lid. The sealed samples were 262

incubated at 28o C. Multiple sample replicates were incubated simultaneously and tested in 263

triplicates for each condition and time point. Sample vials were removed from the incubator at 264

the time of testing so that no sample would be disturbed during the exposure. Small sample 265

volumes and a M9-to-fuel ratio of 100:1 were used to allow more of the fuel to interact with P. 266

aeruginosa, making analytical changes more detectable. In this instance, the purpose of the 267


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experiment was to observe which of the fuel components were more readily consumed by the 268

bacteria. 269

As each M9-fuel sample with or without bacteria was removed, it was placed in a 270

refrigerator (4o C) until analyses could be performed. In order to extract the hydrocarbons and 271

other non-polar degradation products from the fuel-M9 matrix, sample extractions were prepared 272

as previously described but substituting methylene chloride by hexane as the extraction solvent 273

(5, 6). Briefly, 2-ml of HPLC-grade hexanes (Fisher Scientific) was used for extracting the 274

solution of 1 ml media and 10µL of organic materials. The solution was hand-shaken for 275

approximately for 1 minute and 1 ml of the hexane extract was removed for analysis. The 276

recovered extractants were analyzed by GC-MS (Agilent 7890-5973). The analyzed samples 277

were compared to control samples using an extracted ion signal for each compound of interest. 278

Sample responses for each compound were adjusted for the farnesane (2, 6,10-279

trimethyldodecane) in the jet fuel, which is not consumed by the bacteria investigated. 280

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Efflux pump inhibition assay. The efflux pump inhibition assay was performed accordingly to 282

Gunasekera et al (5). Briefly, P. aeruginosa ATCC 33988 was inoculated at 0.03 OD600 into 5 ml 283

of M9 broth amended with 0, 20, 40, and 80 µg/ml Phe-Arg-naphthylamide dihydrochloride 284

(Sigma-Aldrich P4157-250MG), and overlaid with 2 ml of Jet A fuel n-hexadecane and n-285

dodecane. Hexadecane and dodecane were used because they are the primary normal alkane 286

constituents of Jet A fuel. Cultures were grown at 28 ºC aerobically in a shaker incubator (200 287

rpm) and the growth recorded by measuring OD600 for 11 days. Triplicates were used for each 288

fuel and inhibitor condition tested. 289


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RESULTS AND DISCUSSION 290

P. aeruginosa strains have evolved to thrive in different environments and these environment-291

specific strains are more robust and better adapted to their particular niche than other genetically 292

similar strains from a different environment. Understanding what makes these genetically similar 293

strains differ in terms of their resistance to adverse conditions and ability to exploit specific 294

resources is critical to elucidate novel cellular and metabolic mechanisms for biotic and abiotic 295

resistance that can be used in environmental and biotechnological applications. DNA 296

microarrays have been used to compare the global expression profiles of two P. aeruginosa 297

strains growing in hydrocarbon fuel; strain ATCC 33988, which was isolated from a fuel tank 298

and is well-adapted to hydrocarbon-rich environments, and PAO1, which was isolated from 299

human tissue and is therefore not well adapted to a hydrocarbon-containing environment. Both 300

strains have functional alkB1 and alkB2 genes, which are essential for alkane degradation. Poor 301

growth rates and a longer lag phase observed in the PAO1 strain in alkanes maybe due to weak 302

induction of alk genes in combination with less-adapted mechanisms for survival in a harsh fuel 303

environment. We have hypothesized that differential transcriptional regulation between these 304

two strains largely explains, the different fuel-adaptive phenotypes. In addition, this approach 305

could lead to the discovery of novel gene functions that are related to hydrocarbon degradation 306

and adaptation in the fuel resistant strain. We have used DNA microarrays to perform a whole 307

genome transcriptomic analysis of coding sequences shared by PAO1 and ATCC 33988 strains. 308

Although RNA sequencing is a rapidly advancing technology for measuring gene expression 309

levels, microarray remains a useful tool to study the transcriptome of well-defined model 310

organisms such as E. coli and P. aeruginosa. In contrast to RNAseq, microarrays do not require 311

depletion of rRNA and multiple probes per gene are used to capture the expression of even 312


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weakly expressed genes. However, it has the drawback of being unable to detect the genes that 313

are not present in the type strain. In contrast, RNAseq can detect other species of transcripts such 314

as non-coding RNAs, SNPs and regulatory sequences but requires intensive bioinformatics and 315

massive computational capabilities. Nevertheless, RNA sequencing and DNA microarray 316

methods have shown very strong correlation in bacterial whole-genome gene expression studies 317

(34). 318

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Genome-wide expression profiling 320

Whole genome sequencing of Pseudomonas aeruginosa ATCC 33988 revealed that this genome 321

is at least 99% similar to the PAO1 strain for the known genes contained in PAO1 chips (7; 322

Table S1). Several genes of ATCC 33988 were confirmed to have 100% sequence homology to 323

the PAO1 strain (e.g., rubA1, rub, oprF, oprG, mexE, oprN, pvdE, PA3235, PA2779, PA3819, 324

PA3451, PA0038, PA0276, PA3986, PA5424, PA0109, PA2024). DNA sequence analyses of P. 325

aeruginosa ATCC 33988 alkB1 and alkB2 genes revealed two synonymous SNPs. However, the 326

probes to detect alkB1 or alkB2 in the Affymetrix® Microarray chips (Pae_G1A) did not target 327

the two SNP regions. Therefore these mismatches were not present to confound hybridization 328

signal intensity. It was confirmed that many of the genes considered important in fuel adaptation 329

and metabolism that were characterized in this study presented higher than 98% homology 330

between the two strains (Table S1). For microarray data analyses, fold change cut-off of 2.0 and 331

significance p-value of <0.05 were used as filtering criteria to group up-regulated and down-332

regulated genes as described previously (5, 35). Of the 5,570 annotated open reading frames 333

corresponding to P. aeruginosa PAO1, 447 genes were up-regulated and 528 genes were down 334

regulated in P. aeruginosa ATCC 33988 compared to the PAO1 strain in jet fuel (Figure 1). Of 335


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the 447 induced genes, the majority of the genes are classified to have unknown functions. 336

Analyses showed that 32% of the induced and 36.5% of the repressed genes were hypothetical 337

genes of unknown functions in P. aeruginosa. Of the induced known genes, 20% were related to 338

translation, post-translational modification and degradation of cellular components (Figure 2). 339

The majority of genes encoding ribosomal proteins were significantly up-regulated in the ATCC 340

33988 strain compared to the PAO1 strain. These differences likely reflected the faster growth 341

rates observed in the ATCC 33988 strain in jet fuel compared to the PAO1 strain. Fast-growing 342

bacteria increase the ribosomal protein fraction in order to meet protein synthesis demand. The 343

relationship between ribosome concentration and growth rate is well-established (36). 344

Of the repressed genes, significant numbers of genes were related to putative enzymes, 345

membrane proteins and transport of small molecules (Figure 2). Of the known down-regulated 346

genes, 32% were related to either membrane proteins or transport of small molecules (Figure 2), 347

indicating that in the presence of jet fuel, membrane transport is down-regulated, possibly to 348

reduced intake of toxic compounds present in jet fuel. The ability to tightly regulate membrane 349

transport is likely a major factor in the ability of P. aeruginosa ATCC 33988 to thrive in toxic 350

environments. The down-regulation of genes encoding membrane proteins may suggest that this 351

strain shifts its transcriptional machinery and changes its outer-membrane composition to endure 352

stress caused by the fuel. In addition significant numbers of putative enzymes (14.5%) and 353

genes related to protein secretion (6.9%) were down regulated in the ATCC 33988 strain 354

compared to the PAO1. 355

As would be expected, genes related to energy and central intermediary metabolisms 356

were induced by 8.4% and 3.7%, respectively in the ATCC 33988 strain compared to the PAO1 357

strain (Figure 2). The genes that encode the NADH-quinone oxidoreductase subunits (NuoA, 358


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NuoD, NuoE), essential genes for the electron transport chain, were up-regulated in the ATCC 359

33988 strain. In addition genes encoding cytochrome C oxidase subunits (CoxAB), and the 360

periplasmic nitrate reductase (NapEFDABC) were up-regulated in the ATCC 33988 strain. Of 361

particular note, in P. aeruginosa ATCC 33988, alkB1 and alkB2 genes, encoding alkane 362

hydroxylases were up-regulated, 10.76 and 15.27- fold, respectively. The expression of alk genes 363

is dependent on the bacterial growth phase (37); expression of alkB2 peak during the early 364

exponential phase, while alkB1 was induced at the late exponential phase as described by Marín 365

et al., (37). Our experiments were performed using early to mid-exponentially growing cells in 366

jet fuel or alkanes as carbon sources. In addition to alk genes, several genes involved in fatty 367

acid oxidation have been significantly upregulated. (Table 1 provides a list of selected genes that 368

were up-regulated in ATCC 33988 in fuel environments; see Table S2 for a complete list of 369

genes that were up-regulated in ATCC 33988 related to energy metabolism and cell adaptation). 370

However, the PA2475 and PA3331 genes encoding two Cytochrome P450, involved in 371

degradation of medium chain alkanes (n-C5 to n-C16) were not differentially expressed between 372

these two strains. 373

Of the up-regulated genes 6% were related to adaptation and protection. Among these 374

genes were katA, which encodes catalase, and sodB, which encodes superoxide dismutase. In 375

addition, a gene encoding lon-protease, which is a central player in adaptive response to adverse 376

conditions (38), was induced 3.76-fold in the ATCC 33988 strain. Changes in transcript levels of 377

genes related to heat shock proteins and chaperones were observed (Figure 2). The well-known 378

genes encoding the heat shock proteins, dnaK and groES, were induced in the ATCC 33988 379

strain, 2.83 and 5.15-fold, respectively (Table 1). In addition, transcription of the heat shock 380

sigma factor gene rpoH, was induced in the ATCC 33988 strain 3.9-fold (Table 1). Significant 381


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up-regulation of genes encoding heat-shock/chaperone proteins suggests that ATCC 33988 cells 382

have developed efficient adaptive mechanisms to prevent protein mis-folding and to degrade the 383

irreversibly denatured proteins. Studies using cDNA microarrays have shown the induction of a 384

large number of heat shock-related proteins in P. putida KT2440 cells when cells were exposed 385

to different organic solvents such as toluene, o-xylene and 3-methylbenzoate (39). These results 386

indicate that organic solvents can trigger a heat shock response in the fuel-adapted strain P. 387

putida KT2440. In a recent review by Ramos et al.,(40) it was indicated that stress response is 388

one of the major mechanisms involved in solvent tolerance. Our study suggests that the fuel-389

adapted strain ATCC 33988 employs a rapid heat shock response as a mechanism to tolerate 390

stresses produce by the hydrocarbons and other toxic compounds in jet fuel. 391

It has been shown that aromatics such as toluene and naphthalene can be transported into 392

the cell through the porins OprF and OprG, respectively (41, 42). The RT-qPCR data showed 393

both oprF and oprG genes were significantly down regulated in ATCC 33988 and PAO1 strain 394

when grown in fuel as compared to cells grown in 0.2% glycerol (Table 2). However, oprF and 395

oprG were more repressed in the PAO1 strain than in the ATCC 33988 strain (Table 2). For 396

example, RT-qPCR data showed oprF and oprG were repressed -42.46 and -311.44-fold in the 397

PAO1 strain while the corresponding down regulation of oprF and oprG in ATCC 33988 was 398

only -26.27 and -54.60, respectively. This indicates that PAO1 may be more sensitive than 399

ATCC 33988 to the fuel environment, and that reducing the abundance of these porins may 400

control the uptake of toxic organic compounds from jet fuel. It is likely that down-regulation of 401

porins in PAO1 could contribute to the lower growth rate of this strain in fuels by resulting in 402

lesser ability to transport cell nutrients through these down-regulated outer membrane proteins. 403

Interestingly, the oprF and oprG genes were also highly down regulated in the presence of pure 404


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n-alkanes (Table 2). This finding is novel in being the first time it has been shown that the 405

expression of oprF and oprG are regulated by n-alkanes. These results suggest OprF and OprG 406

may be involved in the uptake of n-alkanes. The RT-qPCR analyses revealed that OprF and 407

OprG were controlled differentially by n-alkanes and aromatic compounds in fuel. The RT-408

qPCR showed that oprF expression in PAO1 was reduced 4.6-fold in comparison to ATCC 409

33988 when grown in pure n-alkanes (Table 2). However, the repression of oprF in PAO1 410

compared to ATCC 33988 was only 1.6-fold in jet fuel. On the contrary, oprG was 5.7-fold more 411

repressed in PAO1 than in ATCC 33988 grown in Jet A, which contains aromatics, but only 1.2-412

fold repressed in n-alkanes (Table 2). Based on these results, it appears that OprF may have a 413

more central role in alkane uptake than OprG. Both OprF and OprG have been shown to play an 414

important role in resistance to aromatic hydrocarbons in P. aeruginosa (41, 42). 415

In addition to being able to prevent the internalization of toxic hydrocarbons, cells have 416

also developed adaptive mechanisms to extrude these toxic compounds. The efflux-mediated 417

extrusion of toxic compounds has been shown to be a major determinant in bacterial resistance to 418

exogenous toxic compounds, including aromatics (43, 44). The two P. aeruginosa strains 419

compared in this study cannot degrade aromatic hydrocarbons; therefore accumulation of any 420

toxic compounds could lead to cell death. The efflux mediated extrusion of toxic compounds is 421

one of the major adaptive mechanisms in P. aeruginosa. P. aeruginosa has at least 20 different 422

efflux pumps and some of them are uncharacterized. The tripartite resistance-nodulation-cell 423

division (RND) efflux pumps are one of the most effective ways of detoxifying and removing 424

aromatic solvents (44). The genetic overexpression of RND efflux systems from P. putida in E. 425

coli conferred biofuel tolerance (45). Recent results have demonstrated that multiple RND efflux 426

pumps were upregulated in P. aeruginosa ATCC 33988 when grown in fuel (5). Furthermore, 427


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blocking the efflux pumps with an efflux blocker molecule during fuel growth led to growth 428

reduction in P. aeruginosa (5). These results indicated that the outer membrane porins and the 429

tripartite RND efflux systems in Gram negative bacteria could be working in tandem to provide 430

fuel tolerance. Elevated adaptation to fuel toxicity can be achieved via activating efflux pumps 431

or by reducing the uptake of toxic materials into the cell (46). In this study, microarray data 432

showed the mexG and mexH efflux pump genes of ATCC 33988 were highly induced, 7.58-and 433

4.07-fold, respectively, in jet fuel compared to PAO1 (Table 1). However, differential expression 434

of other efflux pumps such as MexCD, and MexEF in jet fuel was not observed. These results 435

suggested that while the MexGHI-OpmD efflux pump could be conferring additional resistance 436

to jet fuel in ATCC 33988, most Mex efflux pumps could be acting as a general mechanism of 437

resistance in P. aeruginosa by the extrusion of toxic hydrocarbons that enter the cell. 438

It has been shown that efflux pumps have an important role in resistance to aromatic 439

solvents such as toluene, benzene and ethylbenzene but their role in extrusion and resistance to 440

n-alkanes have not been characterized. To determine if efflux pumps also have a role in the 441

resistance of P. aeruginosa to alkanes, the known efflux pump inhibitor compound, Phe-Arg-442

naphthylamide (PAβN), a c-capped dipeptide, was utilized to block the RND efflux pumps and 443

determine the effects this blockage may have on cell viability in different fuels. The compound 444

PAβN, also known as MC-207 is a generalized RND efflux pump inhibitor acting directly on the 445

RND efflux pumps without affecting the proton gradient and the electrical potential across the 446

inner cell membrane (47, 48) 447

448

In this assay, PAβN was applied at concentrations of 0, 20, 60, and 80µg/ml to M9 449

minimal media containing the different fuels. As previously reported by Gunasekera et al., (5), 450


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our results showed that treating P. aeruginosa cells with PAβN while growing in the presence of 451

Jet A fuel led to growth inhibition for several days (Figure 3a). Furthermore, the efflux pump 452

inhibitor is effective against P. aeruginosa in n-dodecane and n-hexadecane solvents (Figure 3 b, 453

c). These results indicated that blocking efflux pumps in the presence of alkanes can also lead to 454

cell toxicity as was previously reported for aromatic hydrocarbons (5). As previously 455

demonstrated (5), the efflux pump inhibitor PAβN is non-toxic and does not inhibit the growth of 456

P. aeruginosa in the absence of hydrocarbons. Furthermore, the results indicated that P. 457

aeruginosa efflux pumps are involved in alkane extrusion and resistance to fuel of diverse 458

hydrocarbon compositions. Thus, efflux pump inhibitors may be used to prevent bio-459

contamination in multiple fuels independently of their hydrocarbon composition. 460

461

Regulation of gene expression in alkanes. alkB1 and alkB2 genes encoding alkane 462

monooxygenases were up regulated in the ATCC 33988 strain compared to the PAO1 strain 463

(Figure 4). By microarray alkB1 and alkB2 were up-regulated 10.26-and 15.27- fold 464

respectively. Microarray expression results were validated by RT-qPCR, which showed the 465

expression alkB1 and alkB2 genes were 2.93 and 3.63-fold higher in ATCC 33988 than in PAO1 466

(Figure 4). Both microarray and RT-qPCR showed alkB1 and alkB2 were highly induced in P. 467

aeruginosa ATCC 33988 compared to the PAO1 strain. The expression of alk genes was highly 468

induced in fuel compared to glycerol, suggesting that both strains have active alkane 469

monooxygenases. To address whether the weaker expression of alk genes in PAO1 was the 470

primary reason for the slower growth in jet fuel; we overexpressed the alkB1 and alkB2 genes in 471

the PAO1 strain. 472


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Furthermore, to explore the possibility that strain-specific polymorphisms in the promoter 473

region of alk genes (presented later) were affecting the transcriptional regulation of these genes, 474

we tested whether the ATCC 33988 alkB1 and alkB2 promoters would stimulate higher 475

expression of the alk genes in PAO1. To this end, we cloned the alk genes with their strain-476

specific promoters into the pHERD20T plasmid and overexpressed these genes in the wild type 477

PAO1 strain. When alkB1 was overexpressed with PAO1 and ATCC 33988 native promoters in 478

the PAO1 strain, the alkB1 mRNA level increased 17- and 15.9-fold, respectively. Similarly, 479

when alkB2 was overexpressed with PAO1 and ATCC 33988 native promoters in the PAO1 480

strain, the alkB2 mRNA level increased 9.87- and 4.37-fold, respectively (Figure 5b). 481

Overexpression of alk genes using native promoters stimulated the growth of PAO1 in fuel 482

(Figure 5). Significant growth induction and faster growth rate was noticed with the 483

overexpression of the alkB2 gene. These results support the transcriptomic data that indicated 484

alkB2 is poorly expressed in the PAO1 strain. Overall, the over-expression of either alk gene in 485

PAO1 did not shorten the lag phase or increase the growth rates to the levels of the ATCC 33988 486

strain, indicating that other cellular factors and adaptive mechanisms may have contributed to the 487

ability of P. aeruginosa ATCC 33988 to be an aggressive hydrocarbon degrader. 488

The rate of hydrocarbon degradation by P. aeruginosa strains and recombinant cell lines 489

carrying additional copies of the alkane monooxygenease genes was measured by GC-MS 490

(Figure 6). P. aeruginosa ATCC 33988 was shown to degrade n-C12, n-C14, n-C16, and 491

isoparaffins significantly (p<0.01) faster than the PAO1 strain (Figure 6). The genetically 492

modified PAO1 strain carrying additional alkane monooxygenease genes degraded n-C12, n-C14, 493

and n-C16 faster than did the wild type PAO1 strain and PAO1 cell line with the empty plasmid. 494

The alkane degradation rate of strains carrying additional alkB genes was consistent with the 495


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growth and mRNA expression results. However, overexpression of alkB1 or alkB2 in PAO1 did 496

not increase the degradation of isoparaffins suggesting that P. aeruginosa ATCC 33988 utilizes a 497

different mechanism to degrade isoparaffinic hydrocarbons. 498

The substrate specificity of the two P. aeruginosa strains was further characterized using 499

pure normal alkanes n-C8, n-C10, n-C12, n-C14, n-C16 as a sole carbon source. Both strain utilized 500

n-C12, n-C14, and n-C16 (Figure 7). However, due to the limited O2 availability in the culture 501

tubes, the observed growth rates and total biomass were somewhat lower than those presented by 502

Marín et al., (37) when P. aeruginosa RR1 was grown in well aerated flasks. Interestingly, the P. 503

aeruginosa PAO1 strain could not utilize n-C8 or n-C10 as a carbon source, indicating that these 504

two strains have different alkane specificities (Figure 7). 505

To determine if the lack of growth by PAO1 in n-C8 was due to an inability to metabolize 506

n-C8 or toxicity, both PAO1 and ATCC 33988 were grown in n-C8 and n-C10 in the presence of 507

0.2% glycerol as a source of simple carbon, and the growth compared against cells in 0.2% 508

glycerol. It was expected that if n-C8 was toxic to the cells, a growth reduction would be 509

observed in samples containing alkanes with glycerol. The results indicated that n-C8 was 510

somewhat toxic to both strains but it showed a higher toxic towards the PAO1 strain (Figure 8). 511

This indicates ATCC 33988 was better adapted to overcome n-C8 toxicity and utilize n-C8 as a 512

sole carbon source. The n-C10 did not pose toxicity to any of the two strains (Figure 8). 513

Since alkB1 and alkB2 are structurally the same at the amino acid level in both strains, it 514

is assumed either alk genes are not induced by n-C8 and n-C10 in PAO1 or that an additional 515

functional alkane monooxygenase may be present in P. aeruginosa ATCC 33988 strain. 516

517


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Promoter polymorphism and alk genes regulation in Pseudomonas aeruginosa strains 518

Transcriptomic data revealed that the expression of alkB1 and alkB2 genes was weaker in the 519

PAO1 strain than in the ATCC 33988 strain when fuel was used as a carbon source (Figure 4). In 520

comparison to the ATCC 33988 strain, alkB1 and alkB2 coding regions in the PAO1 strain have 521

two synonymous SNPs; therefore, the nucleotide differences do not translate into amino acid 522

differences (Table S1). Further analysis of coding sequences showed that most SNPs in genes 523

involved in hydrocarbon adaptation (5) were silent (Table S1). Thus, these results supported the 524

notion that differences in the hydrocarbon adaptation between strains was due to changes in 525

transcriptional response rather than changes in the amino acids sequences of the corresponding 526

proteins. 527

However, SNPs were also found in the promoter regions of genes alkB1 and alkB2 528

(Figure S1 & S2). Transcription start sites of the alkB1 and alkB2 promoters have been 529

discussed previously (30). Two nucleotide polymorphic sites were identified in the alkB1 530

promoter (Figure S1). One SNP was found at position -60 with the base guanine in PAO1 being 531

replaced by the base cytosine in the ATCC 33988 strain. At position -55 an additional guanine 532

was found in the promoter of PAO1 strain. In the alkB2 promoter, a single polymorphic site at 533

position -22 were cytosine in PAO1 was changed to thiamine in ATCC 33988 (Figure S2). 534

These observations raised the possibility that nucleotide differences in the promoters of each 535

strain could be responsible for the differential expression of the alk genes. 536

To address this possibility, alkB1 and alkB2 gene promoters from PAO1 and ATCC 537

33988 were individually fused to the green fluorescence protein (gfp) reporter gene, cloned into 538

the pHERD20T vector and transformed into PAO1 and ATCC 33988 genetic backgrounds. 539


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Transformed cells were grown in M9 minimal media with alkanes, and the promoter induction 540

determined by measuring the levels of GFP fluorescence using a fluorometer. In the transformed 541

P. aeruginosa ATCC 33988 strain, expression from the PAO1 alkB1 and alkB2 promoters was 542

weaker than expression from the ATCC 33988 alkB1 and alkB2 promoters (Figure 9). The 543

alkB1 promoters were less active than the alkB2 promoters in the PAO1 strain; where reliably 544

measurable levels of induction were present only after 17 days (Figure 9). However, both 545

versions of the alkB1 promoters were measurably active in ATCC 33988 as early as day nine in 546

fuel. These results suggested that regulatory genes that induced the expression of alkB1 in 547

ATCC 33988 may be missing or defective in the PAO1 strain. 548

In contrast to the results obtained with the alkB1 promoters, the alkB2 promoter from 549

ATCC 33988 was highly induced in both ATCC 33988 and PAO1 genetic backgrounds 550

producing high level of GFP expression. However, induction levels were significantly higher in 551

the ATCC 33988 strain than in PAO1 (Figure 9). The result indicated the alkB2 promoter of 552

PAO1 was not as active as the alkB2 promoter from ATCC 33988. These functional results 553

confirmed the transcriptomic results obtained from P. aeruginosa cDNA arrays in which alkB1 554

and alkB2 genes were significantly induced in the ATCC 33988 strains compared to the PAO1 555

strain. The poor induction of the PAO1 alkB2 promoter in the ATCC 33988 strain compared to 556

its own alkB2 promoter indicated the PAO1 alkB2 promoter may bind relatively weakly to RNA 557

polymerase or associated transcription factors. The SNPs in the ATCC 33988 alkB2 promoter 558

may have contributed to this difference. A single nucleotide polymorphism in the promoter 559

region of the orf2-folC-operon of Streptococcus suis was shown to influence the bacterial 560

pathogenicity dramatically (15). This study demonstrated polymorphism of a single nucleotide in 561

the promoter region is sufficient to significantly change the gene expression. A single-nucleotide 562


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mutation in the -10 promoter region was shown to inactivate the narK2X promoter in 563

Mycobacterium bovis and Mycobacterium bovis BCG (49). Similarly, here we have 564

demonstrated using GFP as a reporter gene, that alk gene promoter polymorphisms greatly affect 565

the expression of alk genes. 566

The regulation of the alk genes in P. aeruginosa has not been well-characterized. An 567

alkS holmolog has not been found in the P. aeruginosa genome. However, the crc and hfq genes 568

whose products repress AlkS expression were down-regulated 1.95 and 4.52–fold, respectively, 569

in P. aeruginosa ATCC 33988 grown in Jet A as compared to cells grown in 0.2% glycerol. RT-570

qPCR results further confirmed that crc and hfq genes were significantly down-regulated in both 571

strains when grown in Jet A as compared to glycerol. This indicates the Crc/Hfq translational 572

repressors may play a role in controlling expression of alk genes in P. aeruginosa strains. In P. 573

putida, CrcZ, a non-coding RNA which contains six CA motifs binds to Hfq and negatively 574

regulates Hfq levels in the cell (12). Both P.aeruginosa strains encode the 407 nucleotide non-575

coding RNA, CrcZ. The RT-qPCR results indicated that CrcZ was upregulated 1.52-fold in 576

ATCC 33988 strain. The protein GntR was proposed to play a role in alk gene expression in P. 577

aeruginosa, (50) and our transcriptional study showed marginal induction (1.85-fold) of gntR in 578

the ATCC 33988 strain grown in Jet A as compared to glycerol. However, differential 579

expression of gntR between ATCC 33988 and PAO1 when grown in Jet A was not observed. 580

Moreover, none of the alk promoters were induced in the presence of 0.2% glycerol, 581

suggesting alk genes are inducible and not constitutively expressed in P. aeruginosa. Therefore, 582

it is possible that an alk gene-specific transcriptional regulator present in P. aeruginosa but the 583

identity of such factor is currently unknown. 584


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Predicted functions of hydrocarbon induced novel genes. To gain a deeper understanding of 585

the genes induced by hydrocarbons, the microarray data from this work was compared to a 586

previous microarray study (5), which was conducted to find the differentially expressed genes of 587

the ATCC 33988 strain in jet-fuel compared to glycerol. The analyses revealed that 105 genes 588

were uniquely up-regulated in the ATCC 33988 strain in comparison to the PAO1 strain when 589

grown in jet fuel. Furthermore, these 105 genes were also induced when ATCC 33988 cells were 590

grown in Jet A fuel compared to ATCC 33988 cells grown in 0.2% glycerol. The up-regulation 591

of some of these genes including, PA2779 (11.90-fold increase), PA0588 (17.86-fold increase), 592

PA0830 (2.10-fold increase), PA0365 (2.66-fold increase), and PA5359 (3.97-fold increase) was 593

further confirmed by RT-qPCR. Of these 105 genes, those with unknown function and those 594

differentially expressed were the most likely candidates conferring enhanced adaptation to the 595

ATCC 33988 over PAO1 in fuel. Since induction of these highly up-regulated unknown genes is 596

unique to the fuel adapted ATCC 33988 strain, we have used bioinformatic tools to predict the 597

putative functions of the most up-regulated genes (Table 3 & Table S3). 598

One example of a uniquely induced gene in ATCC 33988 was PA5359, a homolog of 599

PAO1 PA5359. This gene presented a more than 5-fold increase in expression (Table 3) by 600

microarray and 3.97-fold increase in expression by RT-qPCR. Alignment of the RAST-annotated 601

gene sequence from ATCC 33988 with PA5359 taken from the Pseudomonas Genome Database 602

(PGDB) suggested a difference in the predicted open reading frames, whereby the predicted 603

ATCC 33988 gene began at a start codon 72 nucleotides downstream from that of PA5359 604

(Figure 10). To further explore this difference, we re-annotated the ATCC 33988 and PAO1 605

genomes using several gene callers (Prodigal, Glimmer 3, and GeneMarkS) (51, 52, 53), and 606

found that all 3 gene finding methods predicted the shorter gene call for the corresponding gene 607


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in both genomes. Whereas the PGDB predicts the length of PA5359 to be 456 base pairs, our 608

analyses predicted the PA5359 gene to be 384 base pairs in length. 609

Promoter prediction analyses using BPROM confirmed the presence of a strong 610

ribosomal binding site (RBS) of consensus sequence AGGAG upstream of the second ATG start 611

codon further supporting the notion that this gene may be mis-annotated in PGDB; a consensus 612

RBS sequence could not be found in the proximal region upstream to the start codon predicted 613

by PGDB (Figure 10). Furthermore, the ATG start codon predicted by PGDB was shown to 614

reside within the -10 box and this is a characteristic feature of the six base pair -10 consensus 615

sequence of Pseudomonas promoters (54). The promoter analyses identified the substitution of 616

adenine for a guanine at the -10 box in ATCC 33988 strain. 617

To characterize if the single nucleotide polymorphism at the -10 promoter region may 618

play a role in the increased expression of ATCC 33988 PA5359 in the presence of hydrocarbons, 619

the PA5359 gene from ATCC 33988 and PAO1 with their native promoters were cloned into the 620

pHERD20T expression vector and expressed individually in the PAO1 strain in the presence of 621

hydrocarbon. Overexpression of the PA5359 gene from ATCC 33988 in PAO1 improved the 622

growth of the PAO1 strain significantly but not so the expression of PA5359 gene from PAO1 in 623

the PAO1 (Figure 11a). These results suggested a possible role of the PA5359 protein in 624

hydrocarbon tolerance (Figure 11a), and the SNP in the promoter apparently is playing a role in 625

the expression and regulation of this gene. Interestingly, a published study using a pathogenic 626

strain of P. aeruginosa, showed PA5359 was up-regulated in the presence of proanthocyanidins, 627

and was implicated in biofilm formation (55). Bioinformatic analysis revealed that PA5359 is a 628

predicted lipoprotein with a conserved ‘Yx(FWY)xxD’ motif of unknown function (COG4315), 629

shared with the bacterial adhesins. Furthermore, by means of blastp it was found that PA5359 630


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aligned over a length of 49 residues, including the ‘Yx(FWY)xxD’ motif, with PA1784, a gene 631

of unknown function that is found in the CAZy database (31), a database of genes believed to be 632

involved in carbohydrate metabolism. Analysis of PA5359 using SPRINT-CBH (30) predicted 633

the initial ‘Y’ of this motif to be a carbohydrate-binding residue. Taken together, these results, 634

suggests a possible role for PA5359 in cellular adhesion related to biofilm formation. Therefore, 635

it stands to reason that PA5359 may be involved in cell surface chemistry, binding, or biofilm 636

formation, all of which are requisite mechanisms for survival under adverse conditions, such as 637

hydrocarbon-rich environments. 638

Also in ATCC 33988, the operon PA3521-PA3523, which is a probable Resistance-639

Nodulation-Cell Division (RND) efflux pump, was highly up-regulated in Jet A compared to 640

cells grown in glycerol (5). The genes PA3521, PA3522 and PA3523 were upregulated 10.80, 641

5.97 and 15.4 -fold respectively. To understand its potential physiological role, the entire operon 642

(6.2 kb) with its native promoter was cloned into pHERD20T and transformed into ATCC 33988 643

and PAO1 cell lines. Our experimental data showed (Figure 11b) that the ATCC 33988 cell line 644

expressing the PA3521-PA3523 operon grew better, particularly during the exponential phase, 645

than cell lines carrying the empty plasmid vector (Figure 11b). However, overexpression of 646

PA3521-PA3523 in the PAO1 strain did not produce a similar growth increase. No nucleotide 647

differences were found in the promoter region of the PA3521-PA3523 operon between ATCC 648

33933 and PAO1, but the coding region of PA3521 and PA3522 had three and two amino acid 649

polymorphisms, respectively. So far an efflux substrate has not been identify for PA3521-650

PA3523 but this operon was shown to be induced by the presence of heavy metals in the 651

environment (56). 652


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Here we have demonstrated that PA5359, the putative efflux pump PA3521-PA3523, and 653

likely other highly up-regulated known and unknown genes may have important roles in the 654

adaptation of P. aeruginosa ATCC 33988 to fuel, and that a transcriptomics approach together 655

with functional assays can be used to discover novel adaptive genes and mechanisms. 656

Competition between closely related hydrocarbon degraders is an important factor affecting the 657

bioremediation process (57). This study provides new insight into the mechanistic differences 658

between closely related strains in relation to adaptation and survival in hydrocarbon rich 659

environment. 660

ACKNOWLEDGMENT 661

This material is based on research sponsored by Air Force Research Laboratory under agreement number 662

FA8650-10-2-2934. The views and conclusions contained herein are those of the authors and should not 663

be interpreted as necessarily representing the official policies or endorsements, either expressed or 664

implied, of Air Force Research Laboratory or the U.S. Government. 665

666

FUNDING INFORMATION 667

Research reported in this article was supported by funds from the United States Air Force Research 668

Laboratory, Fuels and Energy Branch (FA8650-10-2-2934), and the U.S. Department of Energy in 669

collaboration with Lawrence Livermore National Laboratory (IAA DE-NA0002320/0002) to O.N.R. This 670

work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore 671

National Laboratory under Contract DE-AC52-07NA27344. 672

673

674

675

676


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What It Takes to Be a Pseudomonas aeruginosa? The Core Genome of the Opportunistic 688

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Figure 1 950

951

952

953

447 genes up-regulated 528 genes down-regulated

Fold change

Sign

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nce

(-10L

og10

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alkB2

alkB1


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954

955

956 957

958

Figure 2


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959

Figure 3

a

b

c


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960

961

962

963

cDNA

Cop

ies/

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ATCC3398

8 Gly

ATCC3398

8 Jet

A

PAO1 Gly

PAO1 Jet

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964

965

966

Gene/condition

Microarray Fold change qRT-PCR Fold Change

alkB1 – Jet A (ATCC 33988/PAO1)

+10.26 +2.93

alkB2 –Jet A (ATCC 33988/PAO1)

+15.27 +3.68

967

968

Figure 4

b

c


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Figure 5a 969

970

Strain alkB1 induction Fold change pHERD20T in PAO1 cells 1 pHERD20T-alkB1 PAO1 in PAO1 cells 17.0 pHERD20T-alkB1 Ps ATCC 33988 in PAO1 cells 15.9

Figure 5b 971

OD

(600

nm

)

972

Strain alkB2 induction Fold change pHERD20T in PAO1 cells 1 pHERD20T-alkB2 PAO1 in PAO1 cells 9.87 pHERD20T-alkB2 Ps ATCC 33988 in PAO1 cells 4.32


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973

974

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Figure 7


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C 8 + 0 .2 % g ly c e ro l

T im e (h rs )

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n C8 a n d 0 .2 % g ly c e ro l - P . a e ru g in o s a P A O 1

n C 8 a n d 0 .2 % g ly c e ro l - P . a e ru g in o s a A T C C 3 3 9 8 8

0 .2 % g ly c e ro l- P . a e ru g in o s a P A O 1

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C 1 0 + 0 .2 % g ly c e ro l

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Figure 8


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990

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992

993

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Figure 9

d

e

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995

Figure 10 996

997

ATCC33988_PA5359 GGTCTTGCCTCGGGTCGATCTGTCTTGCGCCGCATTAGATAACGAAATTGTGTCAGGTATG 998

PAO1_PA5359 GGTCTTGCCTCGGGTCGATCTGTCTTGCGCCGCATTAGATAACGAAATTGTGTCAGGTATG 999

1000

TGACACAACGGCGTAGTTTCCGCCGACTGCCCGCGGCGGGGCTATGCTCAACAGGTTCCGGTGCCGTCCCTGCGG 1001

TGACACAACGGCGTAGTTTCCGCCGACTGCCCGCGGCGGAGCTATGCTCAACAGGTTCCGGTGCCGTCCCTGCGG 1002

1003

GCGGCGAACGCTCCACCTGAAGACACAGGAGAGAGACGCGATG 1004

GCGGCGAACGCTCCACCTGAAGACACAGGAGAGAGACGCGATG 1005

1006

1007

-10 Box -35 Box

RBS


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Figure 11 1008

(a) 1009

1010

(b) 1011

1012

Days after inoculation0 5 10 15 20 25 30

0.0

0.5

1.0

1.5

2.0

2.5

3.0

P. aeruginosa ATCC 33988 pHERD20T::PA3521-PA3523P. aeruginosa ATCC 33988 pHERD20TP. aeruginosa PAO1 pHERD20T::PA3521-PA3523P. aeruginosa PAO1 pHERD20T

1013

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1015


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Table 1 Differentially expressed genes that are possibly related to energy metabolism and adaptation. 1016 Compete gene list are shown in Table S2 1017

1018

1019 Gene ID Gene

Name Fold

Change Gene function/description COG

Genes related to energy metabolism

PA2574 alkB1 10.76 alkane-1-monooxygenase Carbon compound catabolism

PA1525 alkB2 15.27 alkane-1-monooxygenase 2 Carbon compound catabolism

PA5427 adhA 3.55 alcohol dehydrogenases Carbon compound catabolism

Genes related to Membrane Protein/Transport of Small Molecules/Efflux Pumps

PA1777 oprF 21.38 Major porin and structural outer membrane porin OprF precursor

Membrane proteins; Transport of small molecules

PA2853 oprI 19.3 Outer membrane lipoprotein OprI precursor

Membrane proteins

PA4067 oprG 4.12 Outer membrane protein OprG precursor

Membrane proteins

PA4205 mexG 7.58 probable RND efflux membrane fusion protein precursor

Membrane proteins

PA4206 mexH 4.07 probable RND efflux membrane fusion protein precursor

Membrane proteins

PA0958 oprD 7.89 Basic amino acid, basic peptide and imipenem outer membrane porin OprD precursor

Transport of small molecules

PA1178 oprH 4.48 PhoP/Q and low Mg2+ inducible outer membrane protein H1 precursor

Membrane proteins; Adaptation, Protection; Transport of small molecules

PA1777 oprF 21.38 Major porin and structural outer membrane porin OprF precursor

Membrane proteins; Transport of small molecules

PA2853 oprI 19.3 Outer membrane lipoprotein OprI precursor

Membrane proteins

Heat Shock Proteins/Adaptation & Protection/Oxidative Stress/Stress Regulation

PA0376 rpoH 3.9 sigma factor RpoH Transcriptional regulator

PA4761 dnaK 2.83 DnaK protein Chaperones & heat shock proteins


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PA3622 rpoS 4.92 sigma factor RpoS Transcriptional regulator

PA1803 lon 3.76 Lon protease Adaptation, Protection

PA4386 groES 5.15 GroES protein Chaperones & heat shock proteins

PA4366 sodB 6.08 superoxide dismutase Adaptation, Protection

PA4236 katA 4.16 catalase Adaptation, Protection

PA1801 clpP 3.82 ATP-dependent Clp protease proteolytic subunit

Chaperones & heat shock proteins

PA1802 clpX 4.17 ClpX Chaperones & heat shock proteins; Cell wall / LPS / capsule

PA3227 ppiA 2.5 peptidyl-prolyl cis-trans isomerase A Chaperones & heat shock proteins

PA1444 fliN 2.29 flagellar motor switch protein FliN Chemotaxis; Adaptation, Protection; Motility & Attachment

Transcriptional Regulators

PA3622 rpoS 4.92 sigma factor RpoS Transcriptional regulators PA0376 rpoH 3.9 sigma factor RpoH Transcriptional regulators PA0762 algU 3.84 sigma factor AlgU Transcriptional regulators PA3351 flgM 10.09 FlgM Transcriptional regulators PA5253 algP 9.01 alginate regulatory protein AlgP Transcriptional regulators PA0763 mucA 8.67 anti-sigma factor MucA Transcriptional regulators;

Cell wall / LPS / capsule PA5499 zur 5.98 transcriptional regulator np20 Transcriptional regulators

1020


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1021 1022 Table 2. Down-regulation of genes encoding OprF and OprG outer membrane porins 1023

Gene/condition qRT-PCR fold change

oprF ATCC 33988 jetA/glycerol -26.27 oprF ATCC 33988 Alkanes /glycerol -23.83 oprF PAO1 jetA/glycerol -42.46 oprF PAO1 Alkanes /glycerol -110.07 oprG ATCC 33988 jetA/glycerol -54.60 oprG ATCC 33988 Alkanes /glycerol -228.47 oprG PAO1 jetA/glycerol -311.44 oprG PAO1 Alkanes /glycerol -265.02

1024

1025


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Table 3. Functional annotation of up-regulated unknown hypothetical genes from P. aeruginosa 1026 ATCC33988 1027 1028 PAO1 Corresponding Gene ID

Fold Change

Putative Function Category

Gene Function / Description Literature Citation

PA3235 37.04 Membrane-associated protein; putative membrane protein 2C, clustering with ActP (cation/acetate symporter)

58, 59, 60,61

PA2779 16.14 Secreted or membrane-anchored protein of unknown function 62

PA0588 14.03 Phosphorylation Putative serine/threonine protein kinase, PrkA; P-loop motif 63, 64, 65, 66

PA0830 12.84 Hydrolysis Putative metal-dependent hydrolase

PA0365 11.22 Protein translocation Putative membrane-anchored or secreted protein involved in protein translocation

PA3819 8.8 Membrane integrity Putative exported protein (YPO1624); SlyB-like outer membrane lipoprotein, aka peptidoglycan-associated lipoprotein cross-reactive protein (PALs)

67,68

PA0586 8.18 Sporulation or Biofilm Putative member of sporulation stage V protein family 66

PA5212 7.21 Membrane-associated protein of unknown function or ribonucelotide reductase 2C alpha subunit-containing protein

PA3451 6.6 Uncharacterized protein

PA1542 6.04 Hydrolysis Putative metal-dependent hydrolase

PA0038 5.85 Metabolism Putative dodecin-like protein 69

PA1728 5.78 Gene regulation Putative regulator of ribonuclease activity; DNA/RNA helicase 2C SNF2 family protein

PA3284 5.33 Secreted protein of unknown function

PA5359 5.32 Biofilm Putative membrane-associated lipoprotein of unknown function; possibly related to biofilm formation

55

PA4575 5.21 Protein of unknown function

PA5106 4.92 Metabolism hutF; Deaminase (EC 3.5.4.-) or deiminase, possibly metal dependent; N-formimino-L-glutamate deaminase (EC 3.5.3.13); lysine carboxylase (EC 4.1.1.18)

70

PA2562 4.75 Secreted or membrane-associated protein of unknown function (DUF3315)

PA0276 4.69 Membrane-anchored protein of unknown function

PA4290 4.43 Chemotaxis / sensory transduction

Putative secreted methyl-accepting chemotaxis protein 71

PA3568 4.42 Carbon fixation Putative acetyl-CoA synthetase, propionyl-CoA synthetase/ligase (EC 6.2.1.17)

72,73,74,75

PA1333 4.06 Lipoprotein Secreted lipoprotein of unknown function

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Figure Legends 1030

Figure 1. Visualization of microarray expression data as volcano plot. Up and down-regulated 1031

genes in Pseudomonas aeruginosa ATCC 33988 compared to the PAO1 strain. X axis is the 1032

Log2 of fold change and the experiment used 2-fold cut off criterion for the consideration of 1033

whether the individual gene was differentially expressed or not; Y axis is -10log10 p-value of the 1034

ANOVA p-values. The significant genes are identified by the ANOVA test (p value <0.05) and 1035

by the fold change cut-off (< -2 or > 2). These genes are located in the upper-left (down-1036

regulated genes) and upper-right (up-regulated genes) parts of the plot. 1037

Figure 2. Regulation of P. aeruginosa functional genetic classes in the ATCC 33988 strain 1038

compared to the PAO1. Functional classifications are according to the P. aeruginosa genome 1039

project (www.pseudomonas.com). Black and gray bars represent induced and repressed genes, 1040

respectively. Of 447 upregulated genes, 145 are function unknown hypothetical genes, which 1041

were not considered for the analyses. Similarly, 193 function unknown hypothetical genes from a 1042

total 528 down regulated genes were not considered for the analyses. 1043

Figure 3. Effect of efflux pump inhibitor (c-capped dipeptide) on P. aeruginosa ATCC 33988 1044

growth in fuels of different hydrocarbon composition. Bacteria were grown in M9 minimal 1045

media containing Jet A fuel (a), n-hexadecane (b) and n-dodecane (c) as sole carbon sources for 1046

growth in the presence of 0, 20, 40, and 80 µg/ml of Phe-Arg-naphthylamide (PAβN). 1047

Figure 4. RT-qPCR expression comparison of alkB1 and alkB2 genes in PAO1 and ATCC 33988 1048

cell lines when grown in Jet A fuel compared to glycerol as a carbon source. The table compares 1049

microarray and RT-qPCR expression results. 1050

Figure 5. Overexpression of alkB1 and alkB2 genes in P. aeruginosa PAO1. The alkB1 (a) and 1051

alkB2 (b) genes from ATCC 33988 and PAO1 strains with their respective native promoters 1052

were cloned and expressed in the PAO1 strain. The pHERD20T empty plasmid was 1053

transformed into wild-type PAO1 cells and used as the control strain. Ps: P. aeruginosa; Bacteria 1054

were grown in M9 minimal media containing Jet A fuel. 1055

Figure 6. Degradation of Jet A fuel hydrocarbons by P. aeruginosa cell lines overexpressing 1056

alkB1 and alkB2. Comparison of the degradation of 4 different jet fuel hydrocarbons: n-C12, n-1057


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C14, n-C16, and total isoparaffins. The samples were analyzed by GC-MS with dual flame 1058

ionization detection (FID) and mass spectrometry detection. 1059

Figure 7. Substrate specificity of P. aeruginosa ATCC 33988 and PAO1 strains for individual 1060

normal alkanes as sole carbon source. M9 minimal media was supplemented with n-C8, n-C10, n-1061

C12, n-C14, and n-C16 as carbon source and the growth measured using an spectrophotometer. 1062

Figure 8. Evaluating toxicity of n-C8 and n-C10 against P. aeruginosa strains. P. aeruginosa 1063

PAO1 and ATCC 33988 were grown in n-C8 and n-C10 in the presence of 0.2% glycerol as a 1064

source of simple carbon, and the growth compared against cells in 0.2% glycerol. 1065

Figure 9 Characterization of alkB1 and alkB2 promoter strength in different Pseudomonas 1066

aeruginosa strains. The alkB2 promoter from ATCC 33988 was fused to green fluorescent 1067

protein (gfp) gene and transformed into ATCC 33988 and PAO1 strains. (a) Fluorescence image 1068

of GFP expression from the alkB2 promoter after 9 days growing in the presence of alkanes (b) 1069

Bright-field image and (c) Fluorescence image of GFP expression from alkB2 promoter after 9 1070

days in 0.2% glycerol. No fluorescence was observed in 0.2% glycerol. (d) Green fluorescent 1071

protein (GFP) expression in the ATCC 33988 strain driven by the alkB1 and alkB2 promoters 1072

from ATCC 33988 and PAO1. (e) GFP expression in the PAO1 strain driven by the alkB1 and 1073

alkB2 promoters from ATCC 33988 and PAO1. To estimate GFP expression, cells were excited 1074

at 488nm and the GFP emission peak (509 nm) was measured using a fluorometer. The GFP 1075

expression of the cells was normalized to the cells density (OD600). Background expression of 1076

the empty vector (control) was subtracted from Relative Fluorescence Units (RFUs). Cells were 1077

observed using a 100X oil immersion objective under the bright-field and green fluorescence 1078

emission at 505-530 nm. 1079

Figure 10. Promoter analyses of PA5359 genes from the PAO1 and ATCC 33988 strain. Note 1080

the substitution of a Guanine for an Alanine at -10 box. The promoter analyses were performed 1081

using bacterial sigma 70 promoter prediction program (BPROM: 1082

http://linux1.softberry.com/berry.phtml?topic=bprom&group=programs&subgroup=gfindb). The 1083

analyses revealed that the ATG site 72 bases downstream of the -10 sequence is the likely gene 1084

start codon not the ATG within the -10 box predicted by the Pseudomonas Genome Database 1085

http://www.pseudomonas.com. 1086


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Figure 11 (a). Effect of overexpression of unknown genes on growth and adaptation. 1087

Overexpression of PA5359 in PAO1 cells. ● Wild-type P. aeruginosa PAO1; ▼Wild-type P. 1088

aeruginosa ATCC 33988; ▲ P. aeruginosa PAO1 with pDERD20T+PA5359 from ATCC 1089

33988; ▄ P. aeruginosa PAO1 with pDERD20T+PA5359 from PAO1. 1090

(b) Overexpression of PA3521-3523 in P. aeruginosa ATCC 33988 and PAO1 cells. ▼ Wild-1091

type P. aeruginosa PAO1; ▄ Wild-type P. aeruginosa ATCC 33988; ▲ P. aeruginosa PAO1+ 1092

pDERD20T with PA3521-3523 from ATCC 33988; ● P. aeruginosa ATCC 33988 + 1093

pDERD20T::PA3521-3523 from ATCC 33988. 1094


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