Open Access

Global changes in the proteome of Cupriavidus necator H16 during poly-(3-hydroxybutyrate) synthesis from various biodiesel by-product substrates

AMB Express20166:36

https://doi.org/10.1186/s13568-016-0206-z

Received: 28 April 2016

Accepted: 7 May 2016

Published: 17 May 2016

Abstract

Synthesis of poly-[3-hydroxybutyrate] (PHB) by Cupriavidus necator H16 in batch cultures was evaluated using three biodiesel-derived by-products as the sole carbon sources: waste glycerol (REG-80, refined to 80 % purity with negligible free fatty acids); glycerol bottom (REG-GB, with up to 65 % glycerol and 35 % free fatty acids), and free fatty acids (REG-FFA, with up to 75 % FFA and no glycerol). All the three substrates supported growth and PHB production by C. necator, with polymer accumulation ranging from 9 to 84 % cell dry weight (cdw), depending on the carbon source. To help understand these differences, proteomic analysis indicated that although C. necator H16 was able to accumulate PHB during growth on all three biodiesel by-products, no changes in the levels of PHB synthesis enzymes were observed. However, significant changes in the levels of expression were observed for two Phasin proteins involved with PHB accumulation, and for a number of gene products in the fatty acid β-oxidation pathway, the Glyoxylate Shunt, and the hydrogen (H2) synthesis pathways in C. necator cells cultured with different substrates. The glycerol transport protein (GlpF) was induced in REG-GB and REG-80 glycerol cultures only. Cupriavidus necator cells cultured with REG-GB and REG-FFA showed up-regulation of β-oxidation and Glyoxylate Shunt pathways proteins at 24 h pi, but H2 synthesis pathways enzymes were significantly down-regulated, compared with cells cultured with waste glycerol. Our data confirmed earlier observations of constitutive expression of PHB synthesis proteins, but further suggested that C. necator H16 cells growing on biodiesel-derived glycerol were under oxidative stress.

Keywords

Short chain length polyhydroxyalkanoates scl-PHAs Poly(3-hydroxybutyrate) Biodiesel-derived glycerol Biodiesel-derived glycerol bottom Biodiesel-derived free fatty acids Proteomes Metabolism β-oxidation

Introduction

An increasing demand for renewable, bio-based products is driving the global market for bioplastics. Polyhydroxyalkanoates (PHAs) are natural polyesters synthesized by bacteria as carbon and energy storage molecules, and accumulate intra-cellularly (Anderson and Dawes 1990; Sudesh et al. 2000; Rehm 2010; Gao et al. 2011) under nutrient-limiting conditions in the presence of excess carbon. Limiting concentrations of oxygen, nitrogen, phosphate, and sulphur, or trace element ions like magnesium, calcium, and iron, have been shown to induce synthesis of PHAs (Lee 1996; Kessler and Witholt 2001).

PHAs are biodegradable, insoluble in water, nontoxic, biocompatible, piezoelectric, thermoplastic and/or elastomeric. These features make PHAs suitable for several applications in the packaging industry, medicine, pharmacy, agriculture, and food industry, as raw materials for the production of enantiomerically pure chemicals, and for the production of paints. PHAs are grouped into two main categories: short chain length PHAs (scl-PHAs), having carbon chains of C4-C5, and medium chain length PHAs (mcl-PHAs), which have carbon chains of C6–C14 (Khanna and Srivastava 2005).

Polyhydroxybutyrate (PHB), a polymer of 3-hydroxybutyrate, is a highly biodegradable natural polyester polymer produced for various applications. PHBs have been produced commercially, but their high production costs make them economically unattractive compared to petroleum-based plastics. Carbon substrates used for PHB synthesis account for 40–48 % of the production costs (Choi and Lee 1997) and there is now a global effort to decrease production costs by using low cost carbon substrates (Cavalheiro et al. 2009; Obruca et al. 2010; Povolo et al. 2010; Budde et al. 2011; Ashby et al. 2005; Solaiman et al. 2006).

Biodiesel is made through a chemical process called trans-esterification from animal fat and vegetable oils. The process generates two products, fatty acid alkyl esters, the chemical name for biodiesel, and glycerol (a valuable byproduct usually sold for soaps and other products). Ten (10) tons of glycerol is produced for every 100 tons of biodiesel produced. Some biodiesel-derived glycerol is refined to high purity for commercial applications, but this is a costly process (da Silva et al. 2009). Disposal of the waste crude glycerol in municipal sewage systems is prohibited in many jurisdictions, or is associated with a disposal fee. Moreover, it is not economically feasible for small biodiesel plants to purify glycerol. Thus, impurities such as free fatty acids, methanol, heavy metals and salts (NaCl) in crude glycerol render biodiesel-derived glycerol as a “waste” product (Chatzifragkou and Papanikolaou 2012; Vandamme and Coenye 2004; Fu et al. 2015). Impurities like free fatty acids in biodiesel-derived glycerol, however, may provide an additional carbon source for microbial growth and PHA production.

Cupriavidus necator H16 (also known as Ralstonia eutropha H16), a widely used bacterium for PHB production, can accumulate up to 85 % of cell biomass as PHB under nutrient-limiting conditions (Vandamme and Coenye 2004). Cupriavidus necator can utilize a wide variety of carbon substrates like starch and lipids (Almeida et al. 2012; Mazur et al. 2009; Mifune et al. 2010). The most commonly used substrates for the fermentative production of scl-PHA are sugars. However, agricultural residues have also been tested in recent years (Morais et al. 2014; Escapa et al. 2013).

Biodiesel-derived waste materials represent complex mixtures of carbon sources, which may effect microbial metabolism in different ways. PHB synthesis by C. necator strains using biodiesel-derived glycerol as a sole carbon source has been reported (Koller et al. 2005; Zhu et al. 2013) and crude glycerol bottom (a mixture of glycerol and fatty acids) or free fatty acids purified from biodiesel-diesel derived glycerol bottom, have been investigated for mcl-PHA production (Fu et al. 2015). Although salts like NaCl or K2SO4 present in some biodiesel byproducts have been reported to effect PHB synthesis (Peplinski et al. 2010; Lee et al. 2009), growth and PHB synthesis by C. necator were not affected by impurities present in biodiesel-derived glycerol (Cavalheiro et al. 2009). Similar observations have been made for growth and PHA synthesis by Pseudomonas putida KT2440 (Escapa et al. 2013) and P. putida LS46 for mcl-PHA synthesis (Fu et al. 2014).

Previous studies have investigated changes in gene and gene product expression in C. necator H16 (=R. eutropha H16) cultured under different growth conditions (Peplinski et al. 2010; Lee et al. 2009; Raberg et al. 2011; Schwartz et al. 2009; Ibrahim and Steinbüchel 2009; Potter and Steinbuchel 2005; Jendrossek and Pfeiffer 2014). However, global changes in protein expression profiles in C. necator H16 cultured with biodiesel production by-products (biodiesel-derived glycerol bottoms, semi-purified glycerol, and free fatty acids) have not been previously reported. We conducted one dimensional (1D) liquid chromatography followed by mass spectroscopy (LC/MS/MS) analysis to evaluate changes in protein expression levels of key metabolic pathways related to growth and PHB synthesis using these biodiesel by-product streams.

Materials and methods

Organism, media, and cultivation

Cupriavidus necator strain H16 DSM428 (equivalent to C. necator ATCC 17699 and Ralstonia eutropha H16) was procured from DSMZ, Germany. Cultures were reconstituted in Luria broth as described by DSMZ and streaked on LB plates for a single colony isolates. Single colony isolates were grown in Luria broth and preserved as glycerol stocks at −80 °C. Experiments were carried out in 500 mL baffled flasks containing 100 mL Ramsay’s minimal medium (RMM) (Ramsay et al. 1992) consisting of 6.7 g/L Na2HPO4·2H2O, 1.5 g/L KH2PO4, 1 g/L (NH4)2SO4, 0.2 g/L MgSO4·7H2O, 0.01 g/L CaCl2·2H2O, 0.06 g/L Fe(NH4)2(citrate)2, and 1 mL/L of trace element solution (0.3 g/L H3BO3, 0.2 g/L CoCl2, 0.1 g/L ZnSO4·7H2O, 0.03 g/L MnCl2·4H2O, 0.02 g/L NaMoO4·2H2O, 0.02 g/L NiCl2·6H2O, and 0.01 g/L CuSO4·5H2O).

PHB synthesis by C. necator H16 was investigated using biodiesel-derived waste products procured from Renewable Energy Group (REG), Seneca, IL USA. The substrates used were 2 % v/v biodiesel-derived glycerol (REG-80, a commercial glycerol from biodiesel industry containing on an average 85 % glycerol), 2.0 % w/v REG-glycerol bottoms (REG-GB), and 1 % v/v REG-free fatty acids (REG-FFA). The compositions of the three biodiesel-derived substrates are presented in Table 1. The pH of the medium was adjusted to 7.0. The flasks were incubated at 30 °C on rotary shaker up to 120 h.
Table 1

Composition of REG-glycerol, REG-fatty acids and REG-glycerin bottom

Component (%)

REG-80 (glycerol)

REG-FFA (free fatty acids)

REG-GB (glycerol bottom)

Glycerin

78 (82)a

ND

45–65 (54)

Potassium acetate

<15 (10)

ND

ND

Moisture/volatiles

<13 (11)

<6

2 [0 (0.5)]

MONGa/fatty acids

0.02 (1.0)

>60 (75)

20–35 (25)

Methyl esters

0.05 (0.3)

10–15

Methanol

<1

0.25

Ash

20 (13)

Density (Kg/m3)

1.25 (1.23)

0.74–0.77 (7.5)

Insoluble impurities

<2

Color

Brown

Brown

Black

Energy content (Btu/lb)

5000–10,000 (7000)

12,000–14,000 (13,000)

ND

ND not determined (Provided by Renewable Energy Group, Saneca, IL)

aFigures in parenthesis are average values

Inocula for batch experiments in flasks were prepared by picking a single colony of C. necator H16 from a streaked plate and inoculating Luria Bertini (LB) broth in glass tubes. Pre-experiment inocula cultures were incubated for 18 h at 30 °C. Experimental flasks were inoculated with 5 % of the experimental culture volume, incubated at 30 °C for 72 h, and then analyzed for cell biomass and PHB production. All experiments were conducted with three independent biological replicates.

Cell growth measurement

Samples (40 mL) of each culture, taken at 24, 48, 72, 96 and 120 h post inoculation (h pi) and centrifuged at 4500×g for 20 min in a Sorvall RC6-Plus centrifuge. The pellets were washed twice in 0.9 % NaCl and dried at 60 °C for 48 h to estimate cell dry mass as described earlier (Sharma et al. 2012).

Analysis of residual glycerol, free fatty acids, and ammonium nitrogen

Residual glycerol was estimated in REG-80, REG-GB, and REG-FFA containing culture media by ion chromatography using a Dionex ICS-3000 system fitted with a CarboPac PAI 4 × 250 mm column, with 0.05 mmol/L H2SO4 as the eluent, and a refractive index detector. Residual fatty acids in REG-80, REG-GB, REG-FFA containing cultures were estimated by as gas chromatography (GC) analysis. Five (5) g of REG-80, REG-GB, and REG-FFA were suspended in 5 mL water, mixed with 5 mL chloroform, and then mixed by vortexing. The mixture was allowed to stand overnight, after which 1 mL of the chloroform layer (bottom layer) was removed by pipette and mixed with 2 mL methanol containing 15 % sulphuric acid and 1 mL chloroform containing 0.5 mg/mL heptadecanoic acid. The mixture was heated at 100 °C in screw-capped tube. After 5 h, 1 mL water was added and the mixture was allowed to stand overnight. The lower layer containing fatty acid methyl esters was analyzed by gas chromatography (GC, Agilent 7890A) fitted with a flame ionization detector (FID) and DB-23 capillary column (30 mCell growth measurement × 320 × 0.25 µm; Agilent, California, USA). Samples (1 µl) were injected (using a CTC auto sampler) into the FID with a split ratio at 1:10. Helium was used as carrier gas at a flow rate of 1.78 mL/min. The injector and oven temperatures were 260 °C. The ramping program for the oven was as follows: initial temperature of 60 °C for 4 min; followed by an increase from 60 to 250 °C, with incremental increases of 15 °C/min; followed by a final hold at 260 °C for 4 min. A Supelco 37 component FAME mix (Sigma-Aldrich) was used as standard for peak identification and calculation of response factors for different fatty acids. Fatty acid peaks were quantified by comparing the peak area of the internal standard (1 mg/mL benzoic acid), and corrected with its response factor. Total fatty acid content was achieved by adding the individual fatty acid concentration together. Ammonia nitrogen was measured using the Quikchem method 10-107-06-1-I for determination of ammonia in wastewater by flow injection analysis (Lachat Instrument, Colorado, USA), and the measured concentrations were rounded-off and reported to 2 significant digits.

PHB quantification and monomer composition

Cultures were harvested by centrifugation at 4500×g for 30 min in a Sorvall RC6 Plus centrifuge. Cell pellets were dried for overnight at 60 °C and cell dry weights were determined. Dried samples were methylated as previously described (Braunegg et al. 1978) and was analyzed by gas chromatography (GC, Agilent 7890A) fitted with a flame ionization detector (FID) and DB-23 capillary column (30 m × 320 × 0.25 µm; Agilent, California, USA) as described earlier (Sharma et al. 2012). Molecular standards included 3-hydroxybutyrate and 3-hydroxyvalerate.

Proteomic analyses

Cupriavidus necator H16 was cultured in RMM medium containing REG-GB (2 % w/v glycerol bottom), REG-80 (2 % v/v glycerol), and REG-FFA (1 % v/v REG free fatty acids). Samples (40 mL) were collected REG-GB at 24 h pi and these samples were designated as GB-24 (REG-glycerol bottom at 24 h pi). Samples were also taken from REG-80 cultures at 24 h pi and were designated as R80-24 (REG-glycerol at 24 h pi). Finally, samples were taken from REG-FFA cultures at 24 h pi and was designated as FFA-24 (REG-free fatty acids at 24 h pi). Samples were also taken form these cultures at 48 h pi and designated as GB-48, R80-48, and FFA-48, respectively.

All samples taken from the cultures were centrifuged at 4500×g for 10 min, and the resulting pellets were washed three times with phosphate-buffered saline (NaCl, 8 g/L; KCl, 0.2 g/L; Na2HPO4, 1.44 g/L; KH2PO4, 0.24 g/L, pH 7.4). Each treatment was carried-out with two biological replicates, and the 12 samples (6 conditions × 2 replicates) were frozen at −80 °C until protein extractions could be performed. Protein extracts from the 24 h pi samples for the REG-GB, REG-80, and REG-FFA grown cultures were prepared, processed, and subjected to 1D-LC–MS/MS analyses, as described below.

Protein isolation, digestion, and peptide purification

Protein isolation

Frozen pellets were re-dissolved in 4 mL distilled-deionized (DI) water, of which 1 mL was used for total protein isolation. One mL of lysis Buffer (8 % of sodium dodecyl sulphate, 200 mM dithiothreitol, 200 mM Tris–HCl, pH 7.6) was added to each 1 mL sam ple of resuspended cell pellet and the solutions were mixed thoroughly by pipetting. The solutions were transferred to 10 mL Falcon tubes, which were then place in a boiling water bath for 5 min. After boiling, the samples were sonicated for 30 s, and then centrifuged at 16,000×g at room temperature. One mL of each supernatant was then transferred into a 1.5 mL Eppendorf tube.

Protein purification, digestion and peptide purification

One mL of total protein sample was transferred to an Amicon Ultra-15 10 K filter device (Millipore, Billerica, MA), followed by the purification, peptide digestion, and subsequent peptide purification steps described earlier (Verbeke et al. 2014), except that the trypsin: protein ratio used was 1:100 instead of 1:50. The purified peptides were lyophilized and re-dissolved in 0.1 % formic acid in water for subsequent 1D-LC–MS analysis.

Protein identification and quantification

Proteins were identified and quantified by 1D-LC–MS/MS as described by Gungormusler-Yilmaz et al. (2014), except that a specific peptide database derived from the C. necator H16 genome was used for alignment. Total ion count (TIC), which is the sum of all collision induced dissociation (CID) fragment intensities of member peptides were recorded.

Proteomic data analysis

Protein expression trends were analyzed using our in-house lobe/UNITY analysis platform (Verbeke et al. 2014), which organizes proteins into annotation membership across four “higher-order variables” (HOVs) extracted from the IMG-ER “Export Gene Information” function (Markowitz et al. 2009). These HOVs were selected to both overlap somewhat and provide useful degrees of biological granularity, and include METACYC pathways, enzyme class (EC) numbers, clusters of orthologous group (COG) letters, and KEGG pathway modules.

The expression data obtained from C. necator H16 cells cultured with REG-GB, REG-80, and REG-FFA were analyzed as previously described (Riffat et al. 2015) for cross-state differences (from two independent biological replicates) on a protein-by-protein level: (Z0 = log2[TIC REG-FFA24-1]−log2[TIC REG80 24-1]; Z1 = log2[TIC REG-FFA24-2]−log2[TIC REG80 24-2]), and for intra-replicate variation (from two independent biological replicates) on a protein-by-protein level (R0 = log2[TIC REG-FFA 24-1]−log2[TIC REG-FFA 24-2]; R1 = log2[TIC REG80 24-1]−log2[TIC REG80 24-2]). Cross-state and intra-replicate comparisons were also made for proteomes derived from cultures with each of the other substrates: i.e. REG-GB vs REG-FFA and REG-GB vs REG-80 glycerol. The same cross-state and intra-replicate comparisons were preformed with samples collected at 48 h pi.

Cross-state populations exhibited standard deviations >5-times greater than their intra-replicate counterparts. This was a good initial indication of the quality of the experimental design and runs. These three different populations were each normalized (mean 0; standard-deviation 1) and subjected to a simple algorithm for merging them into a single normalized expression value called “Znet scores” (Markowitz et al. 2009), which were used in subsequent protein differential expression analyses. This final differential expression distribution was with the outermost 33 % of the population, with Znet-scores of N ± 1.0, the outermost 10 % of the population having absolute-value Znet-scores of N ± 1.65, and the outermost 5 % of the population having absolute-value Znet-scores of N ± 1.96. In our analyses, a positive (+) Znet score for any protein represents higher expression level in one treatment in comparison to other treatment, while a negative (−) Znet score represents the opposite.

A simple function was developed to evaluate the statistical significance of a two-state × two-replicate dataset, on a protein-by-protein basis, which computes an overall measurement of the system quality as the ratio of the mean of the standard deviations of the cross-state and intra-replicate populations. This is known as the ‘system to noise’ (S/N) ratio. On an individual protein level, the S/N is the ratio of the vector magnitudes (protein expression levels) across the experimental states and intra-replicate normalized values, scaled by the overall system S/N values. A simple Monte-Carlo Model was used to derived functions relating ‘false discovery rates’ (FDR) to a defined S/N cut off: all proteins with a S/N >2.8 were found to have a “false discovery rate” (FDR) of 10 % or less. This function allowed us to reliably access smaller differential expression values across experimental states, provided the variation of their corresponding intra-replicate measurements were sufficiently small.

The analysis operations, in order, were as follows: (1) The overall number of identified proteins were calculated, with the condition that each protein had to be observed in both biological replicates; (2) The overall correlation between the proteomic log2 expression values was determined; (3) Differential expression values were calculated: (a) among cross-state samples: Z = (REG-GB/REG80 glycerol), (REG-GB/REG-FFA); (REG-FFA/REG-80 glycerol); (b): among intra-replicates: R = (intra-biological replicate differences); (4) the cross-state variation was compared with the intra-replicate, variation as a “signal to noise” (S/N) quality control; (5) data sets from difference populations were transformed into final Znet scores for relative expression analyses cross-states as described by Verbeke et al. (2014). A cut-off score of ±1.65 was used to represent the outer most 10 % of the data set population defined as an asymmetry (up-regulated or down-regulated) of expression of protein relative to the expression profiles of the overall population.

Results

Composition of REG-80, REG-GB, and REG-FFA

The compositions of REG-80, REG-GB, and REG-FFA were provided by Renewable Energy Group (Table 1). REG-80 contained (in mass %) 80.2 % glycerol (93.7 g/L) with 0.08 % free fatty acids, 0.02 % water and 100–200 ppm methanol. REG-GB contained 45–65 % glycerol (45.2 g/L), 20–35 % free fatty acids, and up to 2500 ppm methanol. REG-FFA (free fatty acids purified from crude, biodiesel-derived glycerol) contained up to 75 % free fatty acids (9 g/L), which were predominantly C18 (76.60 %) and C16 (22.97 %) fatty acids. The concentration of free fatty acids in REG-GB was lower compared to REG-FFA (Table 1).

Our analyses indicated that medium containing 2 % (v/v) REG-80 contained 23.4 mM glycerol, while medium containing 2 % (w/v) REG-GB contained 10.4 mM glycerol, which was approximately half the glycerol concentration in REG-80 medium. No glycerol was detected in REG-FFA. REG-FFA contained 10 times more free fatty acids than REG-80 medium, and 1.5 times more free fatty acids than REG-GB medium. In addition to this, REG-80 glycerol contained 10 % potassium acetate, which was not reported in REG-FFA. Thus, the substrates used in our experiments were complex mixtures of different carbon sources, in different concentrations, with or without additional chemicals such as methanol and/or salts.

Growth of C. necator H16 in REG-80, REG-GB, and REG-FFA media

All the three by-products of biodiesel production were able to support growth and PHB synthesis by C. necator H16 in RMM medium. However, based on the measurement of cell dry weight (cdw) at different times (Fig. 1), it is apparent that C. necator H16 cells were in different physiological states in media containing the different substrates at 24 and 48 h pi. In REG-80 (glycerol) cultures, total C. necator cdw (dried cell mass plus PHA mass), did not increase between 24 and 48 h pi (Fig. 1a). Very little total cdw (0.38 g/L cdw) and PHB accumulation (9.03 % of cdw) was observed at 24 h pi. The total cdw increased to 0.61 g/L with no further increase in PHB accumulation at 48 h pi. However, total cdw and PHB accumulation in REG-80 increased rapidly between 72 and 120 h pi, and with a maximum total cdw of 2.82 g/L and PHB accumulation of 32.85 % cdw at 120 h pi (Fig. 1a).
Fig. 1

Cell dry weight (dcw) and PHB synthesis by C. necator H16 in RMM with a REG-80, b REG-GB, and c REG-FFA as substrates

In contrast, the total cdw of C. necator H16 grown in REG-GB (glycerol bottom) and REG-FFA (free fatty acids) increased rapidly between 24 and 48 h pi. Cells grown in REG-GB produced 1.34 g/L total cdw with 18.7 % PHB accumulation at 24 h pi (Fig. 1b). The total cdw and PHB accumulation increased to up to 72 h pi with little further increase thereafter. Cells grown in REG-FFA produced 1.87 g/L total cdw and accumulated PHB to 57.9 % by 24 h pi (Fig. 1c). At 48 h pi the total cdw increased to 5.71 g/L. No further increase in cdw was observed after 48 h pi, but PHB had accumulated to 84.66 % cdw by 120 h pi.

Consumption of glycerol, free fatty acids and nitrogen in REG-80, REG-GB, and REG-FFA cultures

In the REG-80 cultures, only 10 mM of the glycerol substrate was consumed by C. necator H16 by 120 h pi, resulting in a total cell mass accumulation of 2.82 g/L (Fig. 2a). Similarly, only 7 mM of the glycerol substrate was consumed in the REG-GB cultures by 120 h pi, but the total cell mass accumulation was 3.99 g/L (Fig. 2a). Thus, the consumption of glycerol cannot account completely for the resulting cell mass production in the REG-GB culture, since the consumption of glycerol was similar for both the REG-80 and REG-GB. REG-FFA medium, which does not contain glycerol, supported the greatest cell mass accumulation (6.05 g/L).
Fig. 2

Consumption of glycerol, free fatty acids, and nitrogen by C. necator H16. a Consumption of glycerol by C. necator H16 in cultures containing REG-GB (glycerol bottom) and REG-80 (glycerol); b Consumption of fatty acids by C. necator H16 in cultures containing REG-GB (glycerol bottom) and REG-FFA (free fatty acids); c Consumption of ammonium nitrogen (NH4-N) by C. necator H16 in cultures containing REG-GB (glycerol bottom), REG-80 (glycerol), and REG-FFA (free fatty acids)

Free fatty acids were consumed at a slightly higher rate in REG-GB media compared with REG-FFA media (Fig. 2b). At 24 h pi, 5 g/L of the initial free fatty acids in the REG-GB medium were consumed, indicating a simultaneous consumption of glycerol and free fatty acids, while 2.5 g/L of free fatty acids in REG-FFA medium were consumed in the same period (Fig. 2b). At 120 h pi, 0.90 g/L of the free fatty acids remained unutilized in the REG-GB media, while 0.98 g/L of the free fatty acids remained unutilized in the REG-FFA medium.

Consistent with the different growth rates observed (Fig. 1), the rate of ammonium nitrogen (NH4 +-N) consumption and the time points at which nitrogen was depleted by C. necator H16 varied with the carbon source in the culture medium (Fig. 2c). NH4 +-N consumption was very rapid in REG-FFA cultures and significantly slower in REG-80 cultures. Nitrogen concentrations were below detectable levels in REG-FFA cultures by 24 h pi, by 48 h pi in REG-GB cultures, and by 96 h pi in REG-80 cultures. At 24 h pi, the nitrogen concentrations remaining in REG-GB and REG-80 media were 6.9 and 112 mg/L, respectively.

Proteomic analysis and global expression trends

Differential protein expression profiles in C. necator H16 cells grown in media containing REG-GB, which contains significant concentrations of both glycerol and free fatty acids were compared with the protein expression profiles from cells grown in media containing REG-FFA and REG-80 at 24 and 48 h pi. No significant differences in protein expression levels were observed in between 24 and 48 h pi for each substrate. Therefore, only the results from the 24 h pi samples are reported here (although data for the 48 h pi samples are shown in Additional file 1: Table S1). The culture conditions and time points discussed below were defined as: GB-24 (glycerol bottom) cultures at 24; FFA-24 cultures at 24; and R80-24 (glycerol) cultures at 24. Differential protein expression profiles were also compared among substrates (REG-GB, REG-FFA, and REG-80) at 24 h pi.

MS/MS analyses of peptides identified between 1042 and 1233 proteins from REG-GB, REG-80, and REG-FFA grown C. necator H16 cells (Additional file 1: Table S1). Good correlations were observed between biological replicates and variations in within treatments (Additional file 2: Figure S1, Additional file 3: Figure S2). Protein data was further corrected for the “false discovery rate” (FDR) using the signal to noise (S/N) ratio in the cross-state versus intra-replicates, with an S/N cut-off >2.8. In this way 785, 624, and 404 differentially expressed proteins were identified in REG-GB vs REG80, REG-GB vs REG-FFA, and REG-FFA versus REG-80 at 24 h pi, respectively (Fig. 3). Relative changes in protein expression levels between substrates and sampling times observed in the outer-most 10 % (Znet ≥1.65) of the populations are given in Additional file 4: Table S2, Additional file 5: Table S3, Additional file 6: Table S4, and Additional file 7: Table S5.
Fig. 3

Venn diagram showing the number and relationship of proteins differentially expressed for the Substrate comparisons. Circles represent the total of proteins identified in proteome of C. necator H16 grown in REG glycerol bottom (REG-GB), REG free fatty acid (REG-FFA) and REG glycerol (REG-80) for 24 h. The number of proteins differentially expressed is indicated in each subset. Green color indicates up-regulation and red color indicates down-regulation. Only the 10 % of proteins showing the largest variation (Znet >± 1.65) are listed

Proteins were grouped according to their COG designation to identify protein level responses to changing growth phases and substrates. Cross-comparison within substrates at 24 h pi revealed 39–49 up-regulated and 35–67 down-regulated (outer 10 % proteins with Znet > ± 1.65) proteins in cells grown on the three substrates after different incubation periods (Table 2). The highest number of proteins up-regulated in GB-24 vs R-80 at 24 h pi were observed in COG C (Energy production), COG E (Amino acid and metabolism), and COG I and COG P (Lipid transport and metabolism and Inorganic ion transport and metabolism), while highly down-regulated proteins belonged to COG C (Energy production), COG O (Post translational modification, protein turn over, chaperones), and COG X (Not in COG) (Table 2). In comparison up-regulated proteins in GB-24 vs FFA-24 at 24 h pi were present in COG E, COG M (Amino acid transport and metabolism, Cell wall/membrane envelope biogenesis), and COG I (Lipid transport and metabolism). The down-regulated proteins also belonged to COG C and COG R, (Energy production and General function prediction only) and COG I (Lipid transport and metabolism), in addition to proteins of COG E (Amino acid transport and metabolism). This was expected as both the REG-GB and REG-FFA substrates were rich in fatty acids.
Table 2

Cross comparison of up- and down-regulated proteins in specific COG classes in C. necator H16 cultured with different substrates at 24 h pi

COG

Description#

GB-24 vs R80-24

GB-24 vs FFA-24

FFA-24 vs R80-24

Up

Down

Up

Down

Up

Down

C

Energy production

9

11

4

10

6

10

D

Cell cycle control

0

0

0

0

0

0

E

Amino acid transport and metabolism

8

6

9

8

0

0

F

Nucleotide transport and metabolism

0

0

0

0

0

0

G

Carbohydrate transport and metabolism

0

3

1

2

2

1

H

Coenzyme transport and metabolism

4

1

0

0

0

0

I

Lipid transport and metabolism

6

2

5

9

8

1

J

Translation, ribosomal structure and biogenesis

2

1

2

1

0

0

K

Transcription

1

2

2

1

0

2

L

Replication, recombination and repair

0

0

2

0

0

0

M

Cell wall/membrane envelope biogenesis

0

0

7

1

4

0

N

Cell motility

1

2

1

2

0

0

O

Post translational modification, protein turn over, chaperone

0

10

1

3

1

8

P

Inorganic ion transport and metabolism

6

1

0

0

3

0

Q

Secondary metabolites biosynthesis, transport and catabolism

1

0

2

4

0

0

R

General function prediction only

2

3

2

10

5

3

S

Function unknown

4

5

0

0

2

1

T

Signal transduction mechanism

0

0

1

3

0

0

U

Intracellular trafficking, secretion and vesicular transport

1

2

0

5

0

3

V

Defense mechanism

0

0

2

1

0

2

X

Not in COG

4

7

5

7

8

4

 

Total

49

56

46

67

39

35

PHB synthesis proteins

Three enzymes are involved in PHB synthesis in C. necator H16: PhbA, PhbB, and PhbC. PhbA, (encoded by phbA) is a ketothiolase, which combines 2 acetyl–CoA to form acetoacetyl-CoA. PhbB, encoded by phbB, is an Acetoacetyl-CoA reductase, which reduces acetoacetyl-CoA to 3-hydroxybutynyl-CoA. Finally, PhbC, encoded by two genes, is a heterodimeric polyhydroxybutyrate synthase, which polymerizes the 3-hydroxybutynyl-CoA monomers to PHB (Table 4). These genes are encoded by the PHB operon, phbCAB (H16_A1437; H16_A1438; H16_A1439, and H16_A1440) and their role in PHB synthesis was confirmed earlier by cloning and expression in E. coli (Peoples and Sinskey 1989; Schubert et al. 1988). All three PHB-biosynthetic enzymes (PhbA, PhbB, and PhbC) are synthesized constitutively and the expression of the PHB-biosynthetic genes is regulated at both the transcriptional or translational levels (Haywood et al. 1988, 1989; Schubert et al. 1991). PHB accumulation is regulated by the kinetic parameters of the three enzymes, which are influenced by intracellular concentrations of substrates and cofactors (Peoples and Sinskey 1989).

Each of the gene products encoded by the four genes of the PHB operon, plus two PHB depolymerase gene products and three Phasin proteins were expressed by C. necator H16 in REG-GB, REG-80, and REG-FFA cultures (Table 4). Although none of the PHB synthesis enzymes showed significant changes in their expression levels in REG-GB or REG-FFA cultures when compared to REG-80 cultures, one Phasin protein, PhbP1 (H16_A1381), was highly up-regulated in REG-GB and REG-FFA cultures (compared with REG-80 cultures). Another Phasin Protein PhbP4 (H16_B2021) was down-regulated in REG-GB cultures when compared with REG-80 cultures (Table 3).
Table 3

Differential protein expression levels (Pnet) associated with poly-(3-hydroxybutyrate) synthesis and other accessory proteins of PHB granules in C. necator H16 cultured with different substrates

Locus tag

Protein

GB-24/R80-24

GB-24/FFA-24

FFA-24/R80-24

H16_A1437

Poly(3-hydroxybutyrate) polymerase PhaC

0.35

−0.41

−0.14

H16_A1438

Acetyl-CoA acetyltransferase PhaA

−0.07

0.08

−0.15

H16_A1439

Acetoacetyl-CoA reductase PhaB

−0.21

−0.01

−0.04

H16_A1440

transcriptional regulator of phasin expression PhaR

0.26

−0.29

−0.35

H16_A1381

Phasin (PHA-granule associated protein) PhaP1

2.96*

0.27

2.68

PHG202

Phasin (PHA-granule associated protein) PhaP2

ND

ND

ND

H16_A2172

Phasin (PHA-granule associated protein) PhaP3

ND

ND

ND

H16_B2021

Phasin (PHA-granule associated protein) PhaP4

−1.84

−0.58

0.88

H16_B1934

Phasin (PHA-granule associated protein) PhaP5

−0.06

ND

−0.51

H16_A2862

Intracellular poly(3-hydroxybutyrate) depolymerase

ND

0.34

ND

H16_A1150

Poly(3-hydroxybutyrate) depolymerase

0.27

−0.02

0.41

H16_B0339

Poly(3-hydroxybutyrate) depolymerase

ND

ND

ND

H16_B1014

Poly(3-hydroxybutyrate) depolymerase

ND

ND

ND

H16_B2073

Poly(3-hydroxybutyrate) depolymerase

ND

ND

ND

H16_B2401

Poly(3-hydroxybutyrate) depolymerase

ND

ND

ND

H16_A0141

Membrane associated protein PhaM

−1.39

−0.62

−0.49

Up- or down-regulation is limited to outer 10 % proteins with a relative Z-score expression ratio (Znet) outside (> ±1.65) from the population mean; italic numbers indicate significant up- or down-regulation of protein expression

Glycerol metabolism proteins

Glycerol transport across the cytoplasmic membrane occurs through facilitated diffusion mediated by the glycerol uptake facilitator protein GlpF (Sweet et al. 1990; Darbon et al. 1999). This protein (H16_A3690) was not detected in proteomes of C. necator H16 grown on any of the three substrates used in this work. Another ABC-type transporter gene, identified as glpV (H16_A2498), encodes another glycerol transport system substrate-binding protein that was down-regulated in REG-GB and REG-FFA cultures compared to the REG-80, but up-regulated compared to REG-FFA cultures (Fig. 4). It appears that this protein is inducible by glycerol, which was present in REG-GB and REG-80, but not in REG-FFA.
Fig. 4

Differential expression level of proteins of glycerol metabolism pathway, the TCA cycle, and glyoxylate shunt pathway in C. necator H16 grown in REG-GB, REG-FFA and REG-80 at 24 h pi. Expression levels are presented as normalized relative Z-score expression ratios (Znet >1.65). The green colored numbers indicate up-regulation of protein expression; red colored numbers indicate down-regulation of protein expression. White color no significant change. GB-24, REG-GB (glycerol bottom) at 24 h pi; R80-24, REG-80 (glycerol) at 24 h pi; FFA-24, REG-FFA (free fatty acids) at 24 h pi

Two sets of genes (H16_A2507-H16_A2508 and H16_B1199-H16_B11980) that are involved in glycerol uptake and metabolism were identified in C. necator H16 (Darbon et al. 1999). These genes encode a putative glycerol kinase (GlpK) and a putative FAD-dependent glycerol 3-phosphate dehydrogenase (GlpD). Both the genes were required for glycerol metabolism and in the absence of GlpD glycerol-3-phosphate accumulated in the cell. The glycerol kinase (H16_A2507 and H16_B1199) and glycerol-3-phosphate dehydrogenase (H16_A2508 and H16_A0336) are involved in the phosphorylation of intracellular glycerol to glycerol 3-phosphate, and the subsequent conversion to dihydroxyacetone phosphate. In REG-GB, REG-FFA, and REG-80 at 24 h pi grown cells, glycerol kinase (H16_A2507) was not changed significantly and the other glycerol kinase (H16_AB1199) was not detected in the proteomes. Glycerol-3-phosphate dehydrogenase (H16_A2508) was also down-regulated in REG-GB cultures compared with REG-80 cultures. Glycerol-3-phosphate is converted into dihydroxyacetone phosphate, which is introduced into gluconeogenesis or catabolized through the ED pathway via pyruvate to acetyl-CoA, the precursor for the tricarboxylic acid cycle and for PHB biosynthesis. Our proteomic data, however, showed that glycerol kinase (H16_A2507) and glycerol-3-phosphate dehydrogenase (H16_A2508) were expressed in C. necator H16 proteomes. However, higher expression of glycerol transporter was not associated with higher expression of glycerol utilizing proteins.

β-oxidation proteins in C. necator H16

Cupriavidus necator H16 can grow and produce PHB from vegetable oils (triacylglycerides). Use of vegetable oil triacylglycerides as a carbon source, however, requires cleavage of the fatty acid chains from glycerol before they may be metabolized via the β-oxidation pathway. Two lipase genes (H16_A1322 and H16_A3742) were identified previously in C. necator H16 transcriptomes from cells grown on palm oil (Brigham et al. 2010). However, neither of these proteins was present in the C. necator proteomes derived from cells grown on the three substrates tested, most likely because these substrates contained free fatty acids instead of triglycerides.

The β-oxidation pathway genes are present in two operons (H16_A0459-H16_A0464 and H16_A1526-H16_A1531) encoded by the C. necator H16 genome, and have been detected in previous C. necator proteomes (Pohlmann et al. 2006). In the current study, all the β-oxidation pathway gene products—the acyl-CoA dehydrogenases (FadE, H16_A0460 and H16_A1530), the 2-enoyl-CoA hydratases (FadB, H16_A0464 and H16_A1526), the 3-hydroxyacyl-CoA dehydrogenases (FadB, H16_A0461 and H16_A1531), and the 3-ketoacyl-CoA thiolases (FadB, H16_A0462 and H16_A1528) were detected in C. necator H16 cells cultured with REG-GB, REG-80, and REG-FFA (Fig. 5).
Fig. 5

Differential protein expression levels in the fatty acid oxidation pathway in C. necator H16 grown with different substrates at 24 h pi. Expression levels are presented as normalized relative Z-score expression ratios (Znet >1.65). The green colored numbers indicate up-regulation of protein expression; red colored numbers indicate down-regulation and white color no significant change of protein expression. GB-24, REG-GB (glycerol bottom) at 24 h pi; R80-24, REG-80 (glycerol) at 24 h pi; FFA, REG-FFA (free fatty acids) at 24 h pi

Between the two β-oxidation operons, proteins of one operon (H16_A0459-H16_A0464) were highly up-regulated in REG-GB and REG-FFA cultures in comparison to REG-80 cultures at 24 h pi. From the other operon (H16_A1526-H16_A1531), only acyl-CoA dehydrogenase (H16_A1530) was up-regulated in REG-FFA cultures compared with REG-80 cultures (Fig. 4). Recently, Insomphun et al. (2014) identified another operon associated with β-oxidation (H16_B0721-H16_B0724). In this operon, FadB (enoyl-CoA hydratase H16_B0724) was present with two acyl-CoA dehydrogenases (H16_B0721-H16_B0722). No protein from this operon was detected in the current C. necator H16 proteomes. The expression of β-oxidation pathway proteins (H16_A0459-H16_A0464) in REG-GB was up-regulated at 48 h pi in comparison to REG-80 cultures, but no changes were observed in REG-FFA cultures in comparison to REG-GB. The proteins of the other β-oxidation pathway (H16_A1526-H16_A1531) were not changed significantly in REG-GB and were down-regulated in REG-FFA cultures (Fig. 5).

Expression of hydrogenase proteins

The facultative chemolithoautotrophic proteobacterium C. necator H16 is able to use hydrogen as the sole energy source in aerobic environments. Cupriavidus necator H16 is known to have three distinct [NiFe]-hydrogenases which serve unique physiological roles: a membrane-bound hydrogenase (MBH) coupled to the respiratory chain; a cytoplasmic, soluble hydrogenase (SH) able to generate reducing equivalents by reducing NAD+ at the expense of H2; and a regulatory hydrogenase (RH), which acts in a signal transduction cascade to control hydrogenase gene transcription. The genes for these hydrogenases are present on plasmid pHG1 (Schwartz et al. 2003).

Both autotrophic growth and heterotrophic growth are known to induce hydrogenases. Ten (10) hydrogenase-related proteins were identified in the current C. necator H16 proteomes, including HoxG, HypD, HoxA, HoxC, HoxF, HypB1, HypB2, HoxI, and HoxH (Table 4). The hox regulon contains the structural and auxiliary genes for hydrogenase assembly. Expression levels of all 10 hydrogenase-related proteins were very low in REG-GB and REG-FFA cultures, possibly because the preferential carbon source fatty acids repressed the expression of these genes. In contrast, more than 80 % of the usable substrate in REG-80 was glycerol, with only trace amounts of fatty acids. Judger et al. (2015) observed the derepression of hydrogenase proteins during diauxic growth after fructose depletion. The low concentrations of free fatty acids in REG-80 culture could have contributed to derepression of the hydrogenase genes.
Table 4

Differential expression of proteins associated with hydrogenase activity in C.necator H16 grown in REG glycerol bottom in comparison to REG-FFA and REG80 glycerol cultures after 24 h pi

Accession #

Protein

Pnet scores

GB-24/R80-24

FFA-24/R80-24

GB-24/FFA-24

PHG002

Membrane bound (NiFe) hydrogenase large unit

−4.04

−4.96

ND

PHG019

Hydrogenase transcriptional regulatory protein, HoxA

−1.53

−1.79

ND

PHG016

[NiFe] hydrogenase metallocenter assembly protein HypD1

−1.79

−2.13

ND

PHG021

Regulatory [NiFe] hydrogenase large subunit

−1.98

−3.13

−0.84

PHG088

NAD-reducing hydrogenase diaphorase moiety large

−4.55

−5.59

ND

PHG095

[NiFe] hydrogenase nickel incorporation-associated protein HypB2

−2.06

−2.53

ND

PHG013

[NiFe] hydrogenase nickel incorporation-associated protein HypB1

−3.23

−4.99

−0.36

PHG091

NAD-reducing hydrogenase hydrogenase moiety

−4.43

−5.56

ND

PHG_00093

HoxI

−6.79

−5.39

−1.07

Up- or down-regulation is limited to outer 10 % proteins with a relative Z-score expression ratio (Pnet) outside (> ±1.65) from the population mean; italic numbers indicate significant up or down regulation of protein expression

Discussion

The objective of this study was to correlate differences in the physiological states of C. necator H16 grown with different biodiesel waste by-products with changes in protein expression level. The three substrates used represent complex mixtures of carbon sources, which effected microbial metabolism in different ways. REG-80 consisted of 80 % glycerol, with negligible amounts of free fatty acids (0.08 %). REG-GB contained up to 65 % glycerol and 35 % free fatty acids, while REG-FFA contained up to 75 % FFA and no glycerol. All the three substrates supported growth and PHB production by C. necator, but substrate-specific effects were observed for cell mass production, PHB accumulation, and protein expression levels of several key metabolic pathways, including glycerol metabolism, the fatty acid β-oxidation pathway, the Glyoxylate Shunt, and hydrogen (H2) synthesis pathways.

Expression of proteins related to PHB synthesis and accumulation

Raberg et al. (2011) compared the proteomes of R. eutropha H16 and a PHB mutant and concluded that PHB synthesis proteins were not expressed at higher levels in PHB synthesizing strains compared with the non-PHB-synthesizing strain. While our results were consistent with these findings with respect to PhbA, PhbB, and PhbC, we observed significant up-regulation of the Phasin P1 protein, and down-regulation of the Phasin P4 protein in REG-GB and REG-FFA cultures. Phasins are small proteins associated with inclusion bodies in bacterial cells. Most are granule-associated proteins involved in accumulation of polyhydroxyalkanoate polymers, like PHB. Of the seven Phasin proteins (PhaP1-PhaP7) encoded in the C. necator genome, only two, PhaP1 and PhaP4 (H16_A1381 and H16_B2021), were detected in our C. necator H16 proteomes. PhaP1 was up-regulated in REG-GB and REG-FFA in comparison to REG-80 cultures. This may be expected, as both of these cultures were actively accumulating PHB at the sampling time (t = 24 h pi), which correlated to exponential growth in the REG-GB and REG-FFA cultures. In contrast, REG-80 cultures were still in lag-phase at 24 h pi. Potter and Steinbuchel (2005) described the PhaP3 as a homologue of PhaP1, which is normally present in low amount in cell producing PHB and have different role in the cell (Pfeiffer and Jendrossek 2012). In a mutant where phaP1 was deleted, expression of phaP3 increased significantly. In our proteomes, PhaP3 was not detected, possibly due to very low levels of expression or higher levels of expression of PhaP1.

Expression of proteins related to regulation of nitrogen metabolism

PHB accumulation starts when nitrogen concentrations are depleted from the medium (Ramsay et al. 1991; Sharma et al. 2012). However, as noted above, the rate of nitrogen consumption and the time point at which nitrogen was depleted by C. necator H16 varied with the carbon source in the culture medium. Ammonium nitrogen (NH4 +-N) concentrations were below detectable levels in REG-FFA cultures by 96 h pi in REG-80 cultures, by 48 h pi in REG-GB cultures, and before 24 h pi in REG-FFA cultures. Available nitrogen in the media affects both cell growth and PHB accumulation, and high(er) nitrogen concentrations in cultures have been shown to increase the growth and PHB production of C. necator grown on oil (Wang and Yu 2001; Wang et al. 2007). Cupriavidus necator H16 produced PHB from vegetable oils in Tryptone Soya Broth containing 4.4 g/L nitrogen (Verlinden et al. 2011; Obruca et al. 2010). Likewise, Kahar et al. (2004) used nitrogen excess medium (4 g/L) to produce PHB in fed batch cultures using soybean oil.

A nitrogen regulatory protein P-II (glnK) that is sensitive to nitrogen concentrations, encoded at locus tag H16_A0320 of the C. necator H16 genome was highly up-regulated. PII proteins act as sensors of cellular nitrogen status and are regulated by covalent modifications, like uridylation. This protein was identified as an energy and nitrogen sensor in bacteria (Commichau et al. 2006; Javelle et al. 2004). The expression of the PII protein was previously reported to represses transcription of the PHB synthesis operon (Schwartz et al. 2009). At 24 h pi, this regulatory protein was expressed at very high levels in REG-GB (Znet = 3.31) and REG-FFA (Znet = 3.71) cultures relative to REG-80 cultures. However, expression levels of the PHB synthesis proteins in C. necator remained unchanged (Additional file 5: Table S3, Additional file 6: Table S4, and Additional file 7: Table S5).

Expression of proteins related to glycerol metabolism

Unlike C. necator JMP134 and C. necator DSM545, C. necator H16 is not an efficient glycerol user (Lopar et al. 2013; Mothes et al. 2007). Glycerol is not a preferred carbon source for growth of C. necator H16, and when cultured in glycerol containing biodiesel by-products, C. necator H16 cells are always under stress. Reactive Oxygen Species (ROS) may be produced when C. necator H16 is grown in glycerol and in response, proteins like catalases, peroxidases, and hydrogenases, are induced (Additional file 7: Table S5).

Glycerol-3-phosphate dehydrogenase was expressed a high levels in REG-80 cultures compared with REG-GB and REG-FFA cultures, and as expected, expression of glycerol metabolizing proteins was lowest in REG-FFA cultures, due to the absence of glycerol in this substrate. Low expression of glycerol kinase has been correlated with limited growth of C. necator H16 on glycerol. In contrast, recombinants that expressed glpF and glpK from E. coli grow at a faster rate (Fukui et al. 2014). Cupriavidus necator H16 has also been shown to use an alternative glycerol uptake system like PEP-PTS for glycerol transport (Kaddor and Steinbuchel 2011). In REG-GB cultures, a serine kinase of the Hpr protein (H16_A0383) and phosphotransferase system (H16_A0384), related to alternate PEP-PTS, were identified, but not expressed at significantly greater levels at 48 h pi with any of the substrates.

Expression of proteins related to the β-oxidation pathway

As expected, accumulation of PHB was higher in REG-FFA cultures than in REG-GB and REG-80 cultures at 24 pi. Most of the triglycerides were hydrolyzed in REG-GB and REG-FFA, therefore no induction of lipases (H16_A1322, H16_A1323) was observed. The β-oxidation pathway provided the precursor for PHB synthesis when cultivated on REG-GB and REG-FFA. It appears that activities of β-oxidation enzymes were maximum and no further up-regulation was required.

Of the two operons involved in β-oxidation, the enzymes of operon H16_A0459- H16_A0464 were highly up-regulated in REG-GB cultures, while the other operon (H16_A1526-H16_A1531) proteins were not expressed at same level in late exponential phase (t = 48 h pi). Mutants with deletions in H16_A0459-H16_A464 or H16_A1526-H16_A1531 did not show any decrease in growth or PHB accumulation from oils and both the operon complement each other (Brigham et al. 2010). Up-regulation of proteins of the H16_0459-H16_464 operon were observed, with no change in protein expression levels of the H16_A1526- H16_A1531operon. The reason for this selective up-regulation of β-oxidation proteins is not known. The presence of fatty acids in the REG-GB and REG-FFA media could act as an inducer of the β-oxidation pathway, resulting in very high levels of expression of these proteins.

The β-oxidation pathway in C. necator H16 is similar to the well-studied pathway of Escherichia coli with two fad operons that are induced by fatty acids containing 12 or more carbon atoms (Schubert et al. 1988). The genome sequence of C. necator H16 encodes a number of genes responsible for the degradation of fatty acids in this organism, and different sets of genes are expressed under different growth conditions. For example, expression of β-oxidation proteins encoded by operon H16_A0459-H16_A0464 was higher in REG-GB as well as REG-FFA grown cultures but not of other operon H16_A1526-H16_A1531 proteins. Proteins of fatty acid β-oxidation pathway were highly expressed in REG-FFA and REG-GB cultures compared to REG-80 cultures.

Both the substrates were rich in fatty acids and presence of fatty acids is known to induce β-oxidation proteins. Acetyl CoA produced by β-oxidation is converted to oxaloacetate by Glyoxylate Shunt. Wang et al. (2003) and Brigham et al. (2010) observed decreased growth of C. necator on oil when aceB gene (malate synthase) was deleted. We did not find any change in AceB protein expression in any substarates. However, in our proteomes two proteins IclA (H16_A2211), IclB (H16_A2227) designated as isocitate lyase were highly expressed in RG-GB and RG-FFA media. In addition to this, another protein, AcnB (aconitate hydratase) was also highly upregulated in GB-RG and RG-FFA. These results were consistent with Brigham et al. (2010), who showed (using microarray studies) that transcription of genes in the Glyoxylate Shunt pathway was up-regulated when culture was grown on trioleate. In our proteomes, however, different proteins of Glyoxylate cycle, which were not reported earlier, were highly up-regulated. The Glyoxylate Shunt pathway is known to play a major role in utilization of fatty acids, which are metabolized to acetyl-CoA, a key intermediate for synthesis of many cellular components (Brigham et al. 2010). REG-FFA and REG-GB medium had higher concentrations of free fatty acids than REG-80 medium and therefore β-oxidation was more prevalent in REG-FFAand RG-GB grown cultures.

In conclusion, one-dimensional liquid chromatography followed by mass spectroscopy (1D/LC/MS/MS) analyses of C. necator H16 cultured with biodiesel production by-products identified proteins of key metabolic pathways. Cell growth, PHB accumulation, and gene product expression patterns were similar in C. necator H16 cultures grown on REG-GB and REG-FFA, but these were very different in cultured grown on REG-80. Low levels of biomass production, PHB accumulation, and up-regulated expression of hydrogenase in REG-80 cultures confirmed that glycerol is a sub-optimal carbon source for C. necator H16. In contrast, C. necator H16 cells grew faster, achieved greater cell densities, and accumulated PHB to a much greater percentage of the cell dry weight in REG-FFA media, which contained biodiesel-derived free fatty acids. Thus, REG-FFA appears to be an effective, low cost substrate for PHB production.

PHB synthesis enzymes (PhbA, PhbB, and PhbC) and two PHB depolymerase gene products were expressed in C. necator H16 cultures containing glycerol (REG-80), glycerol and free fatty acids (REG-GB), and free fatty acids (REG-FFA), but no significant changes in expression levels were observed. However, expression levels of the Phasin P1 protein were significantly increased during active PHB accumulation in REG-GB and REG-FFA cultures cultured compared with REG-80 cultures. In contrast, expression levels of the Phasin P4 protein were significantly decreased in REG-GB cultured compared with REG-80 cultures.

Declarations

Authors’ contributions

Proteins were extracted and purified from C. necator by PKS and JF. MS/MS analyses were conducted by OVK. VS conducted data analyses. PKS drafted the manuscript. Revision and editing of the manuscript, tables, and figures was overseen by DBL with secondary reviews and corrections of the manuscript by NC and RS. DBL submitted the manuscript, tables, and figures. All authors read and approved the final manuscript.

Acknowledgements

The authors acknowledge the funding by Natural Sciences and Engineering Research Council of Canada (NSERC) through a Strategic Programs Grant (STPGP 306944-04), by Genome Canada, through the Applied Genomics Research in Bioproducts or Crops (ABC) program for the Grant titled, “Microbial Genomics for Biofuels and Co-Products from Biorefining Processes”, by the government of the Province of Manitoba through the Manitoba Research Innovation Fund (MRIF) and the Manitoba Rural Adaptation Council (MRAC), and by BioFuelNet, funded by the federal government of Canada through the Network Centre of Excellence program.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Biosystems Engineering, University of Manitoba
(2)
Department of Physics and Astronomy, University of Manitoba
(3)
Manitoba Centre for Proteomics and Systems Biology, University of Manitoba
(4)
Department of Microbiology, University of Manitoba

References

  1. Almeida JR, Fávaro LC, Quirino BF. Biodiesel biorefinery: opportunities and challenges for microbial production of fuels and chemicals from glycerol waste. Biotechnol Biofuels. 2012;5:48–64.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Anderson AJ, Dawes EA. Occurrence, metabolism, metabolic role, and industrial uses of bacterial polyhydroxyalkanoates. Microbiol Rev. 1990;54:450–72.PubMedPubMed CentralGoogle Scholar
  3. Ashby RD, Solaiman DKY, Foglia TA. Synthesis of short-/medium-chain-length poly (hydroxyalkanoate) blends by mixed culture fermentation of glycerol. Biomacromolecules. 2005;6:2106–12.View ArticlePubMedGoogle Scholar
  4. Brigham CJ, Budde CF, Holder JW, Zeng Q, Mahan AE, Rha CK, Sinskey AJ. Elucidation of β-oxidation pathways in Ralstonia eutropha H16 by examination of global gene expression. J Bacteriol. 2010;192:5454–64.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Budde CF, Riedel SL, Willis LB, Rha C, Sinskey AJ. Production of poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) from plant oil by engineered Ralstonia eutropha strains. Appl Environ Microbiol. 2011;77:2847–54.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Braunegg G, Sonnleitner B, Lafferty RM. A rapid gas chromatographic method for the determination of poly-b-hydroxy- butyric acid in microbial biomass. Eur J Appl Microbiol Biotechnol. 1978;6(1):29–37.View ArticleGoogle Scholar
  7. Cavalheiro JMBT, Almeida MCMD, Grandfils C, da Fonseca MMR. Poly (3-hydroxybutyrate) production by Cupriavidus necator using waste glycerol. Process Biochem. 2009;44:509–15.View ArticleGoogle Scholar
  8. Chatzifragkou A, Papanikolaou S. Effect of impurities in biodiesel-derived waste glycerol on the performance and feasibility of biotechnological processes. Appl Microbiol Biotechnol. 2012;95:13–27.View ArticlePubMedGoogle Scholar
  9. Choi J, Lee SY. Process analysis and economic evaluation for poly (3-hydroxybutyrate) production by fermentation. Bioprocess Eng. 1997;17:335–42.View ArticleGoogle Scholar
  10. Commichau FM, Forchhammer K, Stulke J. Regulatory links between carbon and nitrogen metabolism. Curr Opin Microbiol. 2006;9:167–72.View ArticlePubMedGoogle Scholar
  11. da Silva GP, Mack M, Contiero J. Glycerol: a promising and abundant carbon source for industrial microbiology. Biotechnol Adv. 2009;27:30–9.View ArticlePubMedGoogle Scholar
  12. Darbon E, Ito K, Huang HS, Yoshimoto T, Poncet S, Deutscher J. Glycerol transport and phophoenolpyruvate-dependent enzyme I- and HPr-catalysed phosphorylation of glycerol kinase in Thermus flavus. Microbiology. 1999;145:3205–12.View ArticlePubMedGoogle Scholar
  13. Escapa IF, Del Cerro C, García JL, Preito MA. The role of GlpR repressor in Pseudomonas putida KT2440 growth and PHA production from glycerol. Environ Microbiol. 2013;15:93–110.View ArticlePubMedGoogle Scholar
  14. Fu J, Sharma P, Spicer V, Krokhin OV, Zhang X, Fristensky B, Wilkin JA, Cicek N, Sparling R, Levin DB. Effects of impurities in biodiesel-derived glycerol on growth and expression of heavy metal ion homeostasis genes and gene products in Pseudomonas putida LS46. Appl Microbiol Biotechnol. 2015;99:5583–92.View ArticlePubMedGoogle Scholar
  15. Fu J, Sharma U, Sparling R, Cicek N, Levin DB. Evaluation of medium-chain-length polyhydroxyalkanoates production by Pseudomonas putida LS46 using biodiesel by-product streams. Can J Microbiol. 2014;60:461–8.View ArticlePubMedGoogle Scholar
  16. Fukui T, Chou K, Harada K, Orita I, Nakayama Y, Bamba T, Nakamura S, Fukusaki E. Metabolite profiles of polyhydroxyalkanoate-producing Ralstonia eutropha H16. Metabolomica. 2014;10:190–202.Google Scholar
  17. Gao X, Chen JC, Wu Q, Chen GQ. Polyhydroxyalkanoates as a source of chemicals, polymers, and biofuels. Curr Opin Biotechnol. 2011;22:1–7.View ArticleGoogle Scholar
  18. Gungormusler-Yilmaz M, Shamshurin D, Grigoryan M, Taillefer M, Spicer V, Krokhin OV, Sparling R, Levin DB. Reduced catabolic protein expression in Clostridium butyricum DSM 10702 correlate with reduced 1, 3-propanediol synthesis at high glycerol loading. AMB Express. 2014;4:63–77.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Haywood GW, Anderson AJ, Chu L, Dawes EA. Characterization of two 3-ketothiolases possessing differing substrate specificities in the polyhydroxyalkanoate synthesizing organism Alcaligenes eutrophus. FEMS Microbiol Lett. 1988;52:91–6.View ArticleGoogle Scholar
  20. Haywood GW, Anderson AJ, Dawes EA. The importance of PHB-synthase substrate specificity in polyhydroxyalkanoate synthesis by Alcaligenes eutrophus. FEMS Microbiol Lett. 1989;57:1–6.View ArticleGoogle Scholar
  21. Ibrahim MHA, Steinbüchel A. Poly (3-hydroxybutyrate) production from glycerol by Zobellella denitrificans MW1 via high-cell-density fed-batch fermentation and simplified solvent extraction. Appl Environ Microbiol. 2009;75:6222–31.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Insomphun C, Mifune J, Orita I, Numata K, Nakamura S, Fukui T. Modification of β-oxidation pathway in Ralstonia eutropha H16 for production of poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) from soybean oil. J Biosci Bioeng. 2014;117:184–90.View ArticlePubMedGoogle Scholar
  23. Javelle A, Severi E, Thornton J, Merrick M. Ammonium sensing in Escherichia coli: role of the ammonium transporter amtB and amtB-glnK complex formation. J Biol Chem. 2004;279:8530–8.View ArticlePubMedGoogle Scholar
  24. Jendrossek D, Pfeiffer D. New insights in the formation of polyhydroxyalkanoate granules (carbonosomes) and novel functions of poly(3-hydroxybutyrate). Environ Microbiol. 2014;16:2357–73.View ArticlePubMedGoogle Scholar
  25. Jugder BE, Chen Z, Ping DTT, Lebhar H, Welch J, Marquis CP. An analysis of the changes in soluble hydrogenase and global gene expression in Cupriavidus necator (Ralstonia eutropha) H16 grown in heterotrophic diauxic batch culture. Microb Cell Factor. 2015;14:42.View ArticleGoogle Scholar
  26. Kaddor C, Steinbuchel A. Implications of various phosphoenolpyruvate-carbohydrate phosphotransferase system mutations on glycerol utilization and poly(3-hydroxybutyrate) accumulation in Ralstonia eutropha H16. AMB Express. 2011;1:16.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Kahar P, Tsuge T, Taguchi K, Doi Y. High yield production of polyhydroxyalkanoates from soybean oil by Ralstonia eutropha and its recombinant strain. Polym Degrad Stab. 2004;83:79–86.View ArticleGoogle Scholar
  28. Kessler B, Witholt B. Factors involved in the regulatory network of polyhydroxyalkanoate metabolism. J Biotechnol. 2001;86:97–104.View ArticlePubMedGoogle Scholar
  29. Khanna S, Srivastava AK. Recent advances in microbial polyhydroxyalkanoates. Process Biochem. 2005;40:607–9.View ArticleGoogle Scholar
  30. Koller M, Bona R, Braunegg G, Hermann C, Horvat P, Kroutil M, Martinz J, Neto J, Pereira L, Varila P. Production of polyhydroxyalkanoates from agricultural waste and surplus materials. Biomacromolecules. 2005;6:561–5.View ArticlePubMedGoogle Scholar
  31. Lee SE, Qing X, Yu J. Diverse protein regulation on PHA formation in Ralstonia eutropha on short chain organic acids. Int J Biol Sci. 2009;5:215–25.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Lee SY. Bacterial polyhydroxyalkanoates. Biotechnol Bioeng. 1996;49:1–14.View ArticlePubMedGoogle Scholar
  33. Lopar M, Špoljarić IV, Atlić A, Koller M, Braunegg G, Horvat P. Five-step continuous production of PHB analyzed by elementary flux modes, yield space analysis and high structured metabolic model. Biochem Eng J. 2013;79:57–70.View ArticleGoogle Scholar
  34. Markowitz VM, Mavromatis K, Ivanova NN, Chen IMA, Chu K, Kyrpides NC. IMG ER: a system for microbial genome annotation expert review and curation. Bioinformatics. 2009;25:2271–8.View ArticlePubMedGoogle Scholar
  35. Mazur LP, da Silva DD, Grigull VH, Garcia MCF, Magalhães TO, Wagner TM, Einloft S, Dullius J, Schneider AL, Pezzin APT. Strategies of biosynthesis of poly (3-hydroxybutyrate) supplemented with biodiesel obtained from rice bran oil. Mat Sci Eng C. 2009;29:583–7.View ArticleGoogle Scholar
  36. Mifune J, Nakamura S, Fukui T. Engineering of pha operon on Cupriavidus necator chromosome for efficient biosynthesis of poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) from vegetable oil. Polym Degrad Stabil. 2010;95:1305–12.View ArticleGoogle Scholar
  37. Morais C, Freitas F, Cruz MV, Paiva A, Dionísio M, Reis MAM. Conversion of fat-containing waste from the margarine manufacturing process into bacterial polyhydroxyalkanoates. Int J Biol Macromole. 2014;71:68–73.View ArticleGoogle Scholar
  38. Mothes G, Schnorpfeil C, Ackermann JU. Production of PHB from crude glycerol. Eng Life Sci. 2007;7:475–9.View ArticleGoogle Scholar
  39. Obruca S, Marova I, Snajdar O, Mravcova L, Svoboda Z. Production of poly (3-hydroxybutyrate-co-3-hydroxyvalerate) by Cupriavidus necator from waste rapeseed oil using propanol as a precursor of 3-hydroxyvalerate. Biotechnol Lett. 2010;32:1925–32.View ArticlePubMedGoogle Scholar
  40. Peplinski K, Ehrenreich A, Doring C, Bomeke M, Reinecke F, Hutmacher C, Steinbuchel A. Genome-wide transcriptome analyses of the ‘Knallgas’ bacterium Ralstonia eutropha H16 with regard to polyhydroxyalkanoate metabolism. Microbiol. 2010;156:2136–52.View ArticleGoogle Scholar
  41. Peoples OP, Sinskey AJ. Poly-β-hydroxybutyrate (PHB) biosynthesis in Alcaligenes eutrophus H16: identification and characterization of the PHB polymerase gene (phbC). J Biol Chem. 1989;264:15298–303.PubMedGoogle Scholar
  42. Pfeiffer D, Jendrossek D. Localization of poly (3-Hydroxybutyrate) (PHB) granule-associated proteins during PHB granule formation and identification of two new phasins, PhaP6 and PhaP7, in Ralstonia eutropha H16. J Bacteriol. 2012;194:5909–21.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Pohlmann A, Fricke WF, Reinecke F, Kusian B, Liesegang H, Cramm R, Eitinger T, Ewering C, Pötter M, Schwartz E, Strittmatter A, Voß I, Gottschalk G, Steinbüchel A, Friedrich B, Bowien B. Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16. Nat Biotechnol. 2006;24:1257–62.View ArticlePubMedGoogle Scholar
  44. Potter M, Steinbuchel A. Poly (3-hydroxybutyrate) granule-associated proteins: impacts on poly (3-hydroxybutyrate) synthesis and degradation. Biomacromolecules. 2005;6:552–60.View ArticlePubMedGoogle Scholar
  45. Povolo S, Toffano P, Basaglia M, Casella S. Polyhydroxyalkanoates production by engineered Cupriavidus necator from waste material containing lactose. Bioresour Technol. 2010;101:7902–7.View ArticlePubMedGoogle Scholar
  46. Raberg M, Peplinski K, Heiss S, Ehrenreich A, Voigt B, Doring C, Bomeke M, Hecker M, Steinbuchel A. Proteomic and transcriptomic elucidation of the mutant Ralstonia eutropha g_1 with regard to glucose utilization. Appl Environ Microbiol. 2011;77:2058–70.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Ramsay BA, Saracovan I, Ramsay JA, Marchessault RH. Continuous production of long-side-chain poly-beta-hydroxyalkanoates by Pseudomonas oleovorans. Appl Environ Microbiol. 1991;57:625–9.PubMedPubMed CentralGoogle Scholar
  48. Ramsay BA, Saracovan I, Ramsay JA, Marchessault RH. Effect of nitrogen limitation on long-side-chain poly-ß-hydroxyalkanoate synthesis by Pseudomonas resinovorans. Appl Environ Microbiol. 1992;58:744–6.PubMedPubMed CentralGoogle Scholar
  49. Rehm BH. Bacterial polymers: biosynthesis, modifications and applications. Nat Rev Microbiol. 2010;8:578–92.View ArticlePubMedGoogle Scholar
  50. Riffat IM, Spicer V, Shamshurin D, Krokhin OV, Wilkins J, Ramachandran U, Sparling R, Levin DB. Quantitative proteomic analysis of the cellulolytic system of Clostridium termitidis CT1112 reveals distinct protein expression profiles upon growth on α-cellulose and cellobiose. J Proteomics. 2015;125:41–53.View ArticleGoogle Scholar
  51. Schubert P, Steinbuchel A, Schlegel HG. Cloning of the Alcaligenes eutrophus genes for synthesis of poly-phydroxybutyric acid (PHB) and synthesis of PHB in Escherichia coli. J Bacteriol. 1988;170:5837–47.PubMedPubMed CentralGoogle Scholar
  52. Schubert P, Krüger N, Steinbüchel A. Molecular analysis of the Alcaligenes eutrophus poly(3-hydroxybutyrate) synthase, PHB, biosynthetic operon: identification of the N-terminus of poly(3-hydroxybutyrate)-synthase and identification of the promotor. J Bacteriol. 1991;173:168–75.PubMedPubMed CentralGoogle Scholar
  53. Schwartz E, Henne E, Cramm R, Eitinger T, FriedrichB Gottschalk G. Complete nucleotide sequence of pHG1: a Ralstonia eutropha H16 megaplasmid encoding key enzymes of H2-based lithoautotrophy and anaerobiosis. J Mol Biol. 2003;332:369–83.View ArticlePubMedGoogle Scholar
  54. Schwartz E, Voigt B, Zuhlke D, Pohlmann A, Lenz O, Albrecht D, Schwarze A, Kohlmann Y, Krause C, Hecker M, Friedrich B. A proteomic view of the facultatively chemolithoautotrophic lifestyle of Ralstonia eutropha H16. Proteomics. 2009;9:5132–42.View ArticlePubMedGoogle Scholar
  55. Sharma PK, Fu J, Cicek N, Sparling R, Levin DB. Kinetics of medium-chain-length polyhydroxyalkanoate production by a novel isolate of Pseudomonas putida LS46. Can J Microbiol. 2012;58:982–9.View ArticlePubMedGoogle Scholar
  56. Solaiman DKY, Ashby RD, Foglia TA, Marmer WN. Conversion of agricultural feedstock and coproducts into poly(hydroxyalkanoates). Appl Microbiol Biotechnol. 2006;71:783–9.View ArticlePubMedGoogle Scholar
  57. Sudesh K, Abe H, Doi Y. Synthesis, structure and properties of PHA: biological polyesters. Prog Polym Sci. 2000;25:1503–55.View ArticleGoogle Scholar
  58. Sweet G, Gandor C, Voegele R, Wittekindt N, Beuerle J, Truniger V, Lin ECC, Boos W. Glycerol facilitator of Escherichia coli: cloning of glpF and identification of the glpF product. J Bacteriol. 1990;172:424–30.PubMedPubMed CentralGoogle Scholar
  59. Vandamme P, Coenye T. Taxonomy of the genus Cupriavidus: a tale of lost and found. Int J Syst Evol Microbiol. 2004;54:2285–9.View ArticlePubMedGoogle Scholar
  60. Verbeke TJ, Spicer V, Krokhin OV, Zhang X, Schellenberg JJ, Fristensky B, Wilkins JA, Levin DB, Sparling R. Thermoanaerobacter thermohydrosulfuricus WC1 shows protein complement stability during fermentation of key lignocellulose-derived substrates. Appl Environ Microbiol. 2014;80:1602–15.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Verlinden RAJ, Hill DJ, Kenward MA, Williams CD, Piotrowska-Seget Z, Radecka IZ. Production of polyhydroxyalkanoates from waste frying oil by Cupriavidus necator. AMB Express. 2011;1:11.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Wang J, Yu J. Kinetic analysis on formation of poly(3-hydroxybutyrate) from acetic acid by Ralstonia eutropha under chemically defined conditions. J Ind Microbiol Biotechnol. 2001;26:121–6.View ArticlePubMedGoogle Scholar
  63. Wang YJ, Hua FL, Tsang YF, Chan SY, Sin SN, Chua H, Yu PHF, Ren NQ. Synthesis of PHAs from waster under various C:N ratios. Bioresour Technol. 2007;98:1690–3.View ArticlePubMedGoogle Scholar
  64. Wang ZX, Brämer CO, Steinbüchel A. The glyoxylate bypass of Ralstonia eutropha. FEMS Microbiol Lett. 2003;228:63–71.View ArticlePubMedGoogle Scholar
  65. Zhu C, Chiu S, Nakas JP, Nomura CT. Bioplastics from waste glycerol derived from biodiesel industry. J Appl Polym Sci. 2013;130:1–13.View ArticleGoogle Scholar

Copyright

© The Author(s). 2016