Integrated Metabolomic and Proteomic Analysis Reveals Systemic

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Integrated metabolomic and proteomic analysis reveals systemic responses of Rubrivivax benzoatilyticus JA2 to aniline stress Md Mujahid, M Lakshmi Prasuna, Ch Sasikala, and Ch Venkata Ramana J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr500725b • Publication Date (Web): 12 Nov 2014 Downloaded from http://pubs.acs.org on November 16, 2014

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Journal of Proteome Research

Integrated metabolomic and proteomic analysis reveals systemic responses of Rubrivivax benzoatilyticus JA2 to aniline stress Md Mujahid§ , M Lakshmi Prasuna§, Ch Sasikala±, Ch Venkata Ramana§*

§

Department of Plant Sciences, School of Life Sciences, University of Hyderabad, P.O.

Central University, Hyderabad 500 046, India. ±

Bacterial Discovery Laboratory, Center for Environment, IST, JNT University Hyderabad,

Kukatpally, Hyderabad 500 085, India. *

Corresponding author:

Prof. Ch. V. Ramana, Department of Plant Sciences, School of Life Sciences, University of Hyderabad. Hyderabad-500 046, Telangana, India. E-mail: [email protected]; [email protected] Tel phone : +91 040 23134502 Fax: +91 040 23010120 & 23010145,

Running title: Systemic responses of Rubrivivax benzoatilyticus JA2 to aniline

1

ACS Paragon Plus Environment


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Abstract Aromatic amines are widely distributed in the environment and are major environmental pollutants. Although degradation of aromatic amines is well studied in bacteria, physiological adaptations and stress response to these toxic compounds is not yet fully understood. In the present study, systemic responses of Rubrivivax benzoatilyticus JA2 to aniline stress were deciphered using metabolite and iTRAQ-labelled protein profiling. Strain JA2 tolerated high concentrations of aniline (30 mM) with trace amounts of aniline being transformed to acetanilide. GC-MS metabolite profiling revealed aniline stress phenotype wherein amino acid, carbohydrate, fatty acid, , nitrogen metabolisms and TCA (tricarboxylic acid cycle) were modulated. Strain JA2 responded to aniline by remodelling the proteome and

cellular functions such as signalling, transcription, translation, stress

tolerance, transport and carbohydrate metabolism were highly modulated. Key adaptive responses such as transcription/translational changes, molecular chaperones to control protein folding and efflux pumps implicated in solvent extrusion were induced in response to aniline stress. Proteo-metabolomics indicated extensive re-wiring of metabolism to aniline. TCA cycle and amino acid catabolism were down-regulated while gluconeogenesis and pentose phosphate pathways were up-regulated leading to the synthesis of extracellular polymeric substances. Furthermore, increased saturated fatty acid ratios in membrane due to aniline stress suggest membrane adaptation. The present study thus indicates that

strain JA2

employs multi-layered responses; stress response, toxic compound tolerance, energy conservation and metabolic rearrangements to aniline. Key words. GC-MS, iTRAQ, Rubrivivax benzoatilyticus JA2, aniline stress, systemtic responses, Extracellular polymeric substances.

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Introduction Aromatic amines represent one of the most important classes of anthropogenic compounds and are common by-products of pesticides, dyestuffs, rubbers or pharmaceutical manufacturing in addition to coal and fossil fuel combustion.1, 2 These are widely distributed in the environment due to their extensive usage in various industrial and domestic applications and highly persistent due to their unique chemical composition.1,3

Many

aromatic amines are deleterious to life forms due to their genotoxic and/or cytotoxic properties1, 4 and accounts for 12% of chemicals that are carcinogens.2, 4 One such aromatic amine is aniline, which is extensively used in manufacturing of dyestuffs, plastics, rubber, herbicides, pesticides, paints and pharmaceuticals2, 5 and it is continuously released into the environment through industrial effluents as well as during use. Owing to its extensive usage aniline is widely distributed and often found in both terrestrial and aquatic environments.6, 7 Aniline is a recalcitrant xenobiotic compound; is listed as priority pollutant and its fate in the environment has been a serious concern as it is toxic to life forms.5,8 Many aniline degrading5, 8

or transforming bacteria were isolated 9 and their biodegradation pathways under both oxic5,

7

and anoxic7, 10 conditions are well reported. Though aniline acts as a potential carbon source

to bacteria, it acts as a stressor to both degraders and non-degraders and bacterial stress responses to aniline are not yet elucidated. Cellular adaptations/tolerance mechanisms to xenobiotics enable bacteria to thrive in the presence of toxic compounds and degrade them.11, 12

Thus gaining insights into toxic compound tolerance may eventually lead to the

development of effective bioremediation processes.13 Several studies on model organisms have attempted to explain the mechanism of toxic compound tolerance using genomic,11, 14, 15 transcriptomic16,

17

and proteomic18-20 approaches. Though informative, these approaches

identified only a small subset of predicted proteins in model organisms and these studies are largely confined to xenobiotics like toluene,18,

19

ethylbenzene,18 xylene,16 benzoate,16

benzene,20 polychlorinated biphenyl,21 cyclohexane.22 3



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However, how bacteria cope with aniline, and their possible stress response or tolerance mechanisms are not elucidated so far. Previous studies reported that an anoxygenic photosynthetic bacterium, Rubrivivax benzoatilyticus JA2 grows in the presence of high concentrations of aniline and other aromatic compounds.23 Although R. benzoatilyticus JA2 could not utilize aniline as a sole source of carbon or nitrogen, it tolerated high concentrations of aniline (20-30 mM) and its mono, di-substituted derivatives.24 Tolerance to

high

concentrations of anilines without metabolizing it is an intriguing aspect and in the present study, we used R. benzoatilyticus JA2 as a model organism to elucidate the tolerance/stress response mechanisms to aniline. GC-MS based global metabolite profiling followed by multivariate analysis revealed metabolic responses of strain JA2 in the form of key metabolites (presence and quantities) altered in the presence of aniline. Further, iTRAQ based quantitative proteomics provided the insights of molecular adaptations to aniline. Finally, integrated metabolomic and proteomic studies revealed metabolic shift or metabolic reprogramming of cell to aniline stress towards cell survival. Our data also suggest that general stress response and multiple tolerance strategies for cell survival were evoked under aniline stress. Materials and methods Organism and growth conditions Rubrivivax benzoatilyticus JA2 (ATCC

BBA-35)

was grown photoheterotrophically

(anaerobic, 30 ºC; light 2,400 lux) in a mineral medium25 supplemented with malate (22 mM) as carbon source and ammonium chloride (7 mM) as nitrogen source in fully filled screw cap test tubes (10x100 mm)/reagent bottles (250 ml) at pH 6.8 and a temperature of 30 ±1oC. Photoheterotrophically grown late log phase, 48 h culture (OD660nm 0.4) of strain JA2 was exposed to sterilized aniline (25 mM) and culture incubated under photoheterotrophic conditions. Cells were harvested at 4 oC and immediately frozen in liquid nitrogen and stored

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at -80 oC until further use. To evaluate the effect of aniline on growth, R. benzoatilyticus JA2 was grown on malate mineral medium under photoheterotrophic conditions with different concentrations of aniline (0-35 mM). Growth was monitored by measuring OD at 660 nm. Stable isotope feeding and extraction of metabolites To identify the transformation products of aniline, photoheterotrophically grown culture (0.4 OD at 660 nm) was exposed to 25 mM of unlabelled or deuterium labelled (aniline-2,3,4,5,6-d5, 98%-D, Sigma-Aldrich) aniline. After 48 h of incubation under photoheterotrophic conditions, cells were harvested by centrifugation (10,000 x g, 4 oC, 10 min), culture supernatant collected and acidified to pH 2.5 with 5N HCl. Acidified supernatant was extracted twice with equal volumes of ethyl acetate and ethyl acetate layers were pooled, evaporated to dryness under vacuum at 45 oC using flash evaporator (Heidolph, Germany). Finally, the dried residue was dissolved in HPLC grade methanol, filtered (0.2 µm membrane, Supro PALL) and stored at -20 oC for HPLC and mass analysis. HPLC and liquid chromatography mass spectrometry (LC-MS-ESI) HPLC and LC-MS analysis was done according to Mujahid et al.24 In brief, HPLC was performed on Shimadzu’s Prominence HPLC (LC-20AT) equipped with photodiode array detector. Metabolites separated by using Phenomenex reverse phase column (Luna C18, 5 µm, 250 x 4.6 mm) with a mobile phase which consisted of 1% acetic acid (solvent A) and acetonitrile (solvent B). Metabolites were eluted using linear gradient (0-100%) of acetonitrile for 30 min, at a flow rate 1.5 ml/min and metabolites detected at 260 nm. Mass spectral analysis was done on microTOF-Q (Brukers Deltonics) mass spectrometer linked to Agilent HPLC (1200 series). Metabolites were separated on C-18 column (Waters C-18, 5 µm, 150 x 4.6 mm) and separation conditions were same as described in HPLC analysis except that the flow rate was 0.8 ml/min. Electro spray ionization (ESI) positive (+) ion mode

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was used to detect the molecular ion mass (M+H) and fragmentation obtained by using collision energy of 10 eV and mass spectra recorded at 50-500 Da. Extraction of intracellular metabolites Aniline exposed and control cells (three independent experiments, each with two biological replicates, a total of 6 samples used per condition) were harvested by centrifugation (10,000 x g, 10 min, 4 ○C) and the cell pellet was washed with cold Milli-Q water, rapidly quenched in liquid nitrogen, and the cell pellet was lyophilized (Freeze Dryer, Labcanco USA) for 10 h. Metabolites were extracted from the lyophilized sample by methanol/chloroform method.26 Nine millilitres of methanol:chloroform:water (3:1:1 v/v/v) mixture was added to lyophilized cell pellet vortexed and sonicated for 15 sec, 7 times (8 cycle, 50% power, 4°C) with time interval of 1 min. Chloroform (1.5 ml) and 3 ml of water were added to sonicated sample, vortexed and centrifuged (12,000 x g, 4°C, 10 mins). A final polar phase which contains hydrophilic metabolites were collected, methanol was evaporated from the sample and lyophilized and stored at -20°C until further analysis by GC-MS. Sample derivatization and GC-MS analysis Metabolites were derivatized according to Jozefczuk et a.,27 Samples were first derivatized by adding 20 µl of 40 mg.ml-1 methoxyamine hydrochloride (Sigma-Aldrich) in pyridine (Sigma-Aldrich) and incubated at 30°C for 90 min, followed by adding 40 µl of BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) and TMCS (99:1) and incubated at 37°C for 30 min. GC-MS analysis was performed on Pegasus HT TOF-MS (Leco, USA) system equipped with Agilent series (7890) gas chromatography. 1 µl of derivatized sample was injected into HP-5 column (30 m, internal diameter 0.32 mm, thickness 0.25 µm), with helium as carrier gas at a constant flow of 1.2 ml.min-1 in split less mode. Initial oven temperature was held at 80°C for 2 min, ramped to 180°C by 3°C min-1 held for 1 min finally ramped to 310○C by 4○C min-1 and isocratic hold for 3 min (310○C). Inlet temperature was 6


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250○C, transfer line temperature 225°C, source temperature 230°C and ionization energy -70 eV. Mass spectra were recorded at 40-1000 m/z with 3 spectra /sec. Chromatograms were processed using Leco ChromaTOF software (version 4.21). A reference chromatogram was defined as that which had a maximum of detected peaks over a signal/noise ratio of 10 and this was used for automated peak identification. Metabolites were identified based on mass spectral comparison to the standard NIST 98 library, mass spectral matching was manually supervised and matches accepted with thresholds of match >800 (with a maximum match equal to 1000). Metabolite identity was further confirmed by comparing with mass spectral libraries of Golm Metabolome database (www.gmd.mpimpgolm.mpg.de), mass bank (www.massbank.jp) and some authentic (amino acids, carbohydrates, organic acid) standards run under identical conditions. Authentic standards mix (quality control sample) was run routinely before and along with the batch of test samples4. Technical consistency was assessed using coefficient of variations (CV) between quality control samples which was < 15%. GC-MS data processing and statistical analysis GC-MS data was further processed and subjected to statistical analysis. Relative metabolite abundances were calculated from peak areas (unique mass) of identified metabolites obtained from GC-MS analysis. Peak areas were normalized to dry weight of the sample and more than 50% of missing values excluded from the data. Data was log transformed and subjected to quantile normalization using MetaboAnalyst28 and normalized data was used for multivariate statistical analysis. Principal component analysis (PCA), partial least-squares discrimination analysis (PLS-DA) and hierarchical clustering analysis (HCA) were performed using MetaboAnalyst (www.metaboanalyst.ca/MetaboAnalyst). HCA was done using Pearson correlation as distance matrix. Data was subjected to t test analysis to identify metabolites significantly regulated between control and aniline exposed cells. Metabolites 7



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having fold change >2 and P- value ≤0.05 were considered as statistically significant and metabolites were annotated to metabolic pathways according to KEGG (Kyoto Encyclopedia of Genes and Genomes, www.genome.jp/kegg) database to identify metabolic pathways influenced by aniline. Scanning electron microscopy (SEM) Scanning electron microscopy was done according to Priester et al29 with slight modifications. Cells were harvested by centrifugation (4°C, 10,000 x g, 6 min) and pellet was suspended in 0.1 M phosphate buffer saline (PBS, pH 7.2), suspended cells were centrifuged (4°C, 10,000 x g, 6 min), PBS was discarded and the cells were pre-fixed in mixture of glutaraldehyde (2.4% final concentration), ruthenium red (0.01%) and incubated for 30 min. After pre-fixation, cells were removed by centrifugation (4°C, 10,000 x g, 6 min) and suspended in 2.4% glutaraldehyde and 0.01% ruthenium red for overnight at 4°C. Later, cells were removed by centrifugation (4°C, 10,000 x g, 6 min) from fixation solution, washed in PBS thrice and finally post-fixed in 1% osmium tetroxide solution for 2 h at 4 °C. After postfixation, sample was washed with PBS and dehydrated by a series of ethanolic washes starting from 20%, 30%, 50%, 70%, 90% and 100% (v/v) ethanol. After dehydration cells were mounted on glass pieces (0.5x0.5 cm) and dried in a critical point dryer using standard protocol, dried samples were fixed to SEM stubs and coated with gold. The specimens were examined by using SEM (Philips XL30 series) at different magnification ranges. Isolation of R. benzoatilyticus JA2 proteome Experimental design contained three independent experiments, each with three biological replicates and to minimize the biological variations all three biological replicates of an individual experiment were pooled together. In case of control, we pooled proteome from three independent experiments into one. Aniline exposed and control cells were harvested by centrifugation (4°C, 10,000 x g, 10 min) and the cell pellet was suspended in 50 8


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mM HEPES-KOH buffer (pH 7.5) and washed twice with the same buffer. Finally, cells were re-suspended in 3 ml of the HEPES-KOH buffer containing 0.1% SDS (w/v) and 0.1% tritonX-100(v/v) and sonicated (MS 70 probe, 45% power, 7 cycles, 4°C) to lyse the cells. Lysate was incubated for 30 min at 4 °C, centrifuged (4°C, 20,000 x g for 30 min) and the clear supernatant was taken as soluble proteome. Total proteins were precipitated by six volumes of pre-chilled acetone at -20°C (1: 6) overnight and precipitated proteins were centrifuged (4°C, 10,000 x g for 15 min), washed with 100% acetone twice. Then acetone was decanted and protein was lyophilized and stored at -20°C till analysis. Isobaric tag relative and absolute quantitation (iTRAQ) labelling of proteome Isobaric tag relative and absolute quantitation analysis was outsourced from California University, USA and the following protocol was adopted. One hundred fifty micrograms of each sample was re-suspended in TNE buffer [50 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA]. RapiGest SF reagent (Waters) was added to the mix to a final concentration of 0.1%. Samples were then heated for 5 min at 95°C. Proteins were reduced with 1 mM Tris-(2-carboxyethyl) phosphine (TCEP) (Pierce Chemical) for 30 min at 37°C and carboxymethylated with 0.5 mg/ml of iodoacetamide for 30 min at 37°C. Iodoacetamide was then neutralized with an additional 1 mM TCEP, proteins were digested with trypsin [trypsin ratio 1:100 (trypsin: protein)] overnight at 37°C.. Samples were then treated with 50 mM HCl at 37°C for 1 hour, followed by centrifugation at maximum speed for 30 min at 4°C to degrade and remove the RapiGest. The soluble fraction was then removed to a new tube and the pH of the solution was adjusted to 3.0 using NH4OH. The peptides were then extracted and desalted using Aspire RP30 Desalting Tips (Thermo Scientific). Peptides were re-quantified using bicinchonic acid assay and 100 µg of each sample was labelled with a unique iTRAQ tag (114, 115, 116, and 117) as described in the manufacturer’s protocol (ABSCIEX). Control sample was labelled with the number 114 and three aniline exposed 9



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samples with 115,116 and 117, respectively. The 4 samples were then combined and dried down to remove ethanol using a speed-vac. The samples were re-suspended in 100 µl of Buffer A (98% H2O, 2% ACN, 0.2% formic acid, and 0.005% TFA) and 5 µl sample was used for the MudPIT (multidimensional protein identification technique) analysis. Two dimensional Nano LC-ESI-MS/MS analysis iTRAQ labelled peptide mixture was analysed on a

QSTAR-Elite hybrid mass

spectrometer (Applied Biosytems/MDS Sciex) interfaced to the nano-flow HPLC. Peptides were separated using nano-flow high pressure liquid chromatography (HPLC) coupled with tandem mass spectroscopy (LC-MS/MS) using nano-spray ionization source. Strong cation exchange (SCX) fractionation was carried on BioX-SCX (5 µm particle size, 0.5 mm inner diameter x 15 mm. LC Packings P/N 161395) trap column. The sample was loaded onto the SCX column and eluted with 7.5 µl/min flow rate for 10 min using the gradient of buffer A and buffer C (5% ACN, 0.2% formic acid, and 0.5 M ammonium acetate) The SCX salt steps (first dimension) used for separation were 5%, 7.5%, 10%, 12.5%, 15%, 20%, 25%, 30%, 40%, 50%, 75% and 100% (w/v). In the second dimension (reverse phase) peptides were eluted from the ZorbaxTM C18 column (100 x 0.18 mm, 5-µm, Agilent Technologies, Santa Clara, CA) into the mass spectrometer using a linear gradient of 5–80% Buffer B (100% ACN, 0.2% formic acid, and 0.005% TFA) and Buffer A (98% H2O, 2% ACN, 0.2% formic acid, and 0.005% TFA) over 60 min at 400 nl/min flow rate. LC-MS/MS data was acquired in a data-dependent fashion by selecting the 6 most intense peaks with charge state of plus 2 to 4 that exceeds 35 counts, with exclusion of former target ions set to "60 seconds" and the mass tolerance for exclusion set to 100 ppm. Time-of-flight MS were acquired at m/z 400 to 2000 Da for 0.75 sec with 12 time bins to sum. MS/MS data were acquired from m/z 50 to 2,000 Da by using "enhance all" and 24 time bins to sum, dynamic background subtract, automatic collision energy, and 10


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automatic MS/MS accumulation with the fragment intensity multiplier set to 6 and maximum accumulation set to 2 s before returning to the survey scan. Mass spectral data analysis, protein identification and quantification The data was acquired using Analyst QS 2.0 software (Applied Biosystems/MDS Sciex). The peak list generation, protein identification, and peptide quantification were performed using ProteinPilot v3.0 (Applied Biosystems MDS-Sciex, USA) with default parameters. Data was combined into a single search for identification and quantification. The database search was performed against the genome project R. benzoatilyticus JA2 database (version AEWG00000000.1,) including 3,947 predicted genes. The Paragon algorithm in the ProteinPilot software was used for the peptide identification and further processed by the Pro Group algorithm where isoform-specific quantification was adopted to trace the differences between expressions of various isoforms. The defined parameters were as follows: (i) Sample type, iTRAQ 4-plex (Peptide Labelled), lysine and N-terminus modified iTRAQ tag; (ii) Cysteine alkylation, Iodoacetamide; (iii) Digestion, Trypsin with a maximum of one missed cleavage allowed; (iv) Instrument, QSTAR Elite ESI; (v) Special factors, none; (vi) Species, none; (vii) Specify Processing, Quantitate, bias correction; (viii) ID Focus, Biological modifications, amino acid modifications; (ix) Search effort, thorough. The default precursors and fragment mass tolerances for QSTAR ESI MS instrument were adopted by the software. The peak areas and the S/N ratios were extracted from the database by Protein Pilot 3.0 in order to process the raw data to yield quantification data. The peptide for quantification was automatically selected by Pro Group algorithm criteria (identified with higher confidence 99%, the peptide was not shared with another protein, iTRAQ reporter area is not zero) to calculate the reporter peak area, error factor (EF) and p-value. The resulting data set was auto bias-corrected to get rid of any variations imparted because of the unequal mixing during combining different labelled samples. To minimize false positive results, a strict cut off for 11



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protein identification was applied. Proteins that met the criteria ; unused protscore >2 (confidence level 99%) with two unique peptides (with 95% confidence) were considered in protein identification. The false discovery rate (FDR) of the identified protein (false discovery rate = [decoy hits/total hits] 100%) was estimated based on the decoy search strategy and plotted in the protein summary report (Supplementary data set). Approximately 756 proteins were identified with unused protscore > 2 with corresponding FDR 2, p ≤0.05, EF ≤2, FDR 1.35 were up-regulated.30, 31 iTRAQ reporter ratios were log-transformed (log10), mean and standard deviations were calculated from three independent experiments and consisted only of proteins with p ≤0.05 discovered in at least two out of three experiments. P-values of differential regulated proteins were subjected to multiple testing corrections by Benjamini and Hochberg method using an open-source software32 with significance level P ≤0.05.

Proteins were functionally

categorised according to KEGG (Kyoto Encyclopedia of Genes and Genomes, www.genome.jp/kegg/) database and in silico analysis of proteins was carried out by using ExPASy tools (www.expasy.org). Extraction and quantification of PHAs Aniline exposed (after 48 h of aniline exposure) or control cells were harvested by centrifugation (10,000 x g for 10 min, 4○C) and the cell pellet was washed twice with distilled water. Finally, cell pellet was re-suspended in distilled water and freeze dried (Labcanco USA). Freeze dried biomass (300 mg) was suspended in 30 ml of chloroform and incubated overnight in rotary shaker (120 rpm) at 30○C. Obtained suspension was filtered through Whatman No1 paper to remove cell debris and the filtrate was subjected to precipitation overnight at -20○C in 10 volumes of ethanol. Finally, obtained precipitate was concentrated by centrifugation (15,000 x g for 20 min) and dissolved in 5 ml of acetone. 12


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PHAs were purified by adding 20 ml of 70% ethanol (v/v), methanol in 1:1 ratio to PHAs suspended in acetone. Finally, pure PHAs were obtained by centrifugation (15,000 x g for 20 min), purified PHAs were air dried, sealed stored at -20○C. Indoles were estimated by Salper’s method using indole as standard33 and total sugars by the phenol-sulphuric acid method using glucose as standard34. Proteins were estimated by dye binding assay using BSA as standard.35 FAME analysis Fatty acids of the samples were identified by FAME analysis done at Royal Life Science Pvt Ltd. Fatty acids were extracted from lyophilized cells and fatty acid isolation, identification was done MIDI-MIS method36. First fatty acids were saponified, methylated, and extracted by using the protocol of the Sherlock microbial identification system (MIDI Inc.) procedure. The fatty acid methyl esters thus obtained were analyzed by gas chromatography equipped with Sherlock MIS software [Microbial ID; MIDI 6.0 version; Agilent: 6850; peak identification was done based on RTSBA6 database]. Results Growth and aniline tolerance Strain JA2 could not grow at the expense of aniline as sole carbon or nitrogen source. . However, strain JA2 was able to grow in the presence of high concentrations (5-25 mM) of aniline (Figure 1A) with minimum inhibition concentration (MIC) of 30 mM and IC50 of 23 mM. When mid log phase cultures were exposed to high concentrations aniline (25 mM), ~80% cells were viable (data not shown) which is in agreement with previous reports.24 To elucidate the responses of strain JA2 to aniline, 25 mM of aniline was added to the mid log phase cultures for all further experiments. SEM analysis of the aniline exposed cells revealed rough and altered cell surface with extracellular depositions as compared to the control (unexposed culture) (Figure 1B, C) indicating altered cell surface due to aniline stress. 13



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Aniline transformation studies Although strain JA2 did not grow at the expense of aniline, loss of aniline (1-1.5%) in the presence of culture indicated a possible biotransformation of aniline. To identify the aniline transformation products, deuterium labelled aniline (aniline-d5, Sigma-Aldrich) feeding experiments were performed.

LC-MS analysis of

labelled aniline fed culture

supernatants revealed two aniline bio-transformed products, compound I (Rt11.58 min) and compound II (Rt15.58 min) which were absent in control (unexposed) cultures (Figure 2A). Compound I had a molecular ion mass of 137 [M+H] from unlabelled fraction and 142[M+H] in labelled fraction (Supplementary figure 1A,B). Compound II had a mass of 136 [M+H] from unlabelled fraction (Figure 2B) and 141[M+H] (Figure 2C) from labelled fraction. Five mass units (5 amu) increase in molecular ion masses of both the compounds (137 to 142; 136 to 141) suggest that these compounds were derived from aniline. Mass spectrum of compound II was (136 [M+H], 94 m/z) was identical to that of authentic acetanilide mass spectrum (mass bank; www.massbank.jp) and it was co-eluted with acetanilide standard, confirming that compound II is indeed acetanilide. Only ~0.1 mM of aniline was transformed to acetanilide and compound I could not be identified due to lack of spectral similarity in the database. Metabolic responses (adaptations) to aniline stress GC-MS analysis of intracellular metabolites of control and aniline exposed cells of R. benzoatilyticus JA2 revealed a total of 161 metabolic features of which 61 metabolic features were identified based on data base (NIST similarity >800, Glom data base), by comparing to the mass spectra of authentic standards (sugars, amino acids and organic acid, aromatic acids) and 100 metabolic features remained unidentified. Identified metabolites (61) include aliphatic organic acids, amino acids and their derivatives, sugars, nucleotides, fatty acids, amines and aromatic acids. Identified metabolites were used for multivariate statistical analysis to identify significant metabolic variations and metabolic patterns. Hierarchical 14


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clustering analysis (HCA) of metabolites from control and aniline exposed cells resulted in separation of the two groups. All aniline exposed samples clustered as a single distinct group; likewise all control samples also clustered as a single group (Supplementary figure 2A). HCA analysis indicated metabolic dissimilarity between control and aniline exposed cells. To identify significant changes in metabolism and metabolic signatures associated to aniline stress, data was analysed by robust statistical methods. Five component PCA analyses of the data set explained 90% of the variance, 74.1% of the variation was explained by principal component1 (PC1) and 10.4% by principle component 2 (PC2). Control and aniline exposed samples were clearly separated from each other (Figure 3A) indicating metabolic differences in control and aniline exposed cells. However, sub-clustering of control groups in the PCA plot was largely due to variations in relative levels of metabolites (leucine, serine, alanyl-glycine, phosphoglyceric acid, gluconic acid, trehalose, erythrose, thymidine and ethanolamine) within the control group. Though the experimental conditions were identical, variation in metabolite levels within control groups is plausible due to intrinsic biological variations within samples. Nevertheless, PCA analysis of control and aniline exposed samples (Figure. 3) suggested that aniline exposed cells are metabolically different from the control. Further, partial least square discrimination (PLS-DA) analysis also separated control and aniline exposed groups (Figure 3B) strongly suggesting metabolic variations between groups. R2 value for this model was 0.9 which indicated goodness of fit and Q2 was 0.85 which indicates the goodness of predictability. High values of R2 and Q2 indicate that this model is representative of true differences in metabolism. Key metabolic pathways modulated by aniline stress Variable importance on projection (VIP) scores obtained from PLS-DA model was used to identify key metabolic features significant for group separation and VIP scores. Metabolites with VIP score >1 were considered to have a statistically significant contribution 15



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to the model. Thirty one metabolites were identified as statistically significant (VIP >1) from VIP score plot (Supplementary figure 2B) which were largely responsible for group separation in the model. Further, metabolites with VIP score >1 were selected and subjected to student’s t test to identify significantly regulated metabolites to aniline stress. Thirty one metabolites were identified whose response was altered significantly (fold change >2 and Pvalue ≤0.05) (Figure 4A). The relative concentration of 21 metabolites was high in aniline exposed cells while concentration of 10 metabolites were low compared to that of control (Figure 4A). Further, differentially regulated metabolites (identified in student’s t test) were annotated to their respective metabolic pathways according to the KEGG database (www.genome.jp/kegg) to identify metabolic pathways affected by aniline stress. Amino acid metabolism was highly (29%) affected by aniline stress followed by carbohydrate metabolism (14%), fatty acid metabolism (12%).

Butanoate (6%), nucleic acid (6%),

nitrogen (6%) and vitamins-cofactors metabolism and tricarboxylic acid cycle (6%) were also affected by aniline stress (Figure 4B). Proteomic inventory of Rubrivivax benzoatilyticus JA2 to aniline stress Isobaric Tag Relative and Absolute Quantification (iTRAQ) based comparative proteomic analysis was performed to decipher the proteomic responses of R. benzoatilyticus JA2 to aniline stress (Supplementary figure 3A). Seven hundred and fifty six proteins were identified (Supplementary table 1) after applying the cut off of the unused protein score > 2, EF ≤ 2 and a false discovery rate of < 1.0% (Supplementary data set.), which correspond to 16% of the total theoretical proteome (3,898 protein coding genes of R. benzoatilyticus JA2). All the 756 proteins were detected in all three independent experiments (Supplementary table 1) and linear regression analysis (fold changes) of the iTRAQ identified proteins of two biological replicates which had a correlation coefficient value (R, R2) of R=0.84, R2= 0.721 16


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which indicate that the data was highly reproducible (Supplementary figure 3B). Identified proteins were subjected to in silico analysis for theoretical molecular weight, isoelectric point (pI) and hydropathy analysis (www.expasy.org). Molecular weight versus isoelectric point map of identified proteins indicated two clusters of proteins with pI of 4.5- 7.0 and 9.0-11, respectively (Supplementary figure 4A). Of the total proteins identified, grand average hydropathy (GRAVY) analysis indicated 31.5% to be hydrophilic and 68.5% as hydrophobic proteins (Supplementary figure 4B). Proteins identified (756) by iTRAQ analysis were subjected to volcano plot analysis to identify differentially regulated proteins. A protein was considered differentially regulated if the fold change >1.35 (aniline exposed/control) in at least two biological replicates with p ≤0.05. A total of 114 proteins were identified as differentially regulated (Figure 5A) of which 58 proteins were up-regulated (aniline exposed/control ratio >1.35) and 56 proteins were down regulated (ratio 2, fold change of >1.35, Pvalue ≤0.05 (raw and multiple testing corrected) were presented in table and proteins with Pvalue > 0.05 are in italics. Proteins shaded in grey indicate up-regulated and un-shaded are down regulated.

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Figure for graphic abstract

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Figure 2


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Figure 3 242x138mm (300 x 300 DPI)



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Figure 4


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Figure 6


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Figure 8 158x114mm (300 x 300 DPI)


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Integrated Metabolomic and Proteomic Analysis Reveals Systemic

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