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Journal of Industrial Microbiology & Biotechnology logoLink to Journal of Industrial Microbiology & Biotechnology
. 2024 Feb 16;51:kuae008. doi: 10.1093/jimb/kuae008

Expanding the synthetic biology toolbox of Cupriavidus necator for establishing fatty acid production

Shivangi Mishra 1, Paul M Perkovich 2, Wayne P Mitchell 3, Maya Venkataraman 4, Brian F Pfleger 5,✉
PMCID: PMC10926325  PMID: 38366943

Abstract

The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator, and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein (gfp). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.

Keywords: Cupriavidus necator, Codon optimization, Medium chain length fatty acids, Marionette promoters, β-Oxidation

Graphical Abstract

Graphical Abstract.

Graphical Abstract

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Introduction

Cupriavidus necator, previously known as Ralstonia eutropha and Alcaligenes eutrophus, is a Gram-negative betaproteobacterium. The C. necator genome is distributed into two chromosomes (4 049 965 and 2 912 457 bp) and a mega-plasmid (452 139 bp) with an overall guanine or cytosine (GC) content of 66.36% (Little et al., 2019). Cupriavidus necator is a facultative chemolithotroph with an ability to grow on a wide range of substrates, including sugars, lipids, and organic acids via the Entner–Doudoroff (ED) pathway and the tricarboxylic acid (TCA) cycle (Budde et al., 2011; Cram, 2008; Lu et al., 2013; Yan et al., 2003). It can also grow autotrophically using hydrogen under aerobic conditions to power assimilation of carbon dioxide via the Calvin–Benson–Bassham (CBB) cycle (Jefffke et al., 1999). This metabolic versatility makes C. necator an attractive metabolic engineering chassis, especially for biochemical production from C1 substrates. Cupriavidus necator has been developed industrially to leverage its native ability to store, during nutrient limited conditions, a large amount of organic carbon as polyhydroxybutyrate (PHB), a biodegradable polyester, to about 70%–80% of its dry cell weight (Lu et al., 2016). PHB is the archetypal polyhydroxyalkanoate (PHA), a class of biopolymers that are biodegradable, can have properties similar to traditional plastics derived from fossil fuels, and can be made from renewable feedstocks. Metabolic engineers now desire to shift this synthetic ability from PHA to other higher value products such as alcohols, methyl ketones, terpenoids, and alkenes (Brigham et al., 2013; Crepin et al., 2016; Krieg et al., 2018; Lu et al., 2012; Muller et al., 2013).

To achieve these metabolic engineering goals, a synthetic biology toolbox, consisting of vectors, genome engineering protocols, and gene expression control strategies, must be developed. Early PHA work helped domesticate C. necator as a laboratory host by establishing stable plasmid transformation techniques and rudimentary recombineering protocols (Reinecke & Steinbüchel, 2009; Sato et al., 2013). However, progress in metabolic engineering has been slow because of the limited availability of well-established synthetic biology tools, including methods to control gene expression via (i) functional genetic elements such as promoters, both inducible and constitutive, and ribosome binding sites (RBSs), (ii) media composition, and (iii) codon optimization (Schmidt et al., 2023). Constitutive promoters characterized to date in C. necator mostly include the native promoters related to PHB synthesis (PphaC1), pyruvate metabolism (PpdhE), acetyl-CoA synthesis (PacoE), and translation (PrrsC), or a promoter library created by altering these native promoters (Priefert & Steinbüchel, 1992; Delamarre & Batt, 2006; Li & Liao, 2015). PphaC1 is the most widely used constitutive promoter. These native promoters are relatively weak compared to the heterologous promoters like Plac and its derivatives. Plac and its derivative promoters can work as constitutive promoters in C. necator because of the absence of native lacI and lacY homologs in the genome (Fukui et al., 2011). To have more effective control over the gene expression, inducible promoters are preferred. The arabinose inducible promoter system (AraC/ParaBAD) is the most widely used promoter in engineering studies of C. necator (Fukui et al., 2002). It can generate strong transcriptional activity but also has been reported to cause growth defects due to leaky expression (Fukui et al., 2009). The rhamnose inducible promoter system (RhaRS/PRhaBAD) also works well in C. necator with strong induction and lower leaky expression (Alagesan et al., 2018). Induction systems using cumate (CymR/Pj5-cmt) and anhydrotetracycline (TetR/PrrsC-tetO) have been found to have lower leaky expression and cause only slight growth defects (Gruber et al., 2016; Li & Liao, 2015). Other inducible promoter systems such as acrylate (AcuR/PacuRI), m-toluic acid (PM/Pxyls), and itaconate (YpItcR/Pccl) are found to work in C. necator but show high background or leaky expression levels (Alagesan et al., 2018; Bi et al., 2013; Hanko et al., 2018). A few native inducible promoters such as Pcbbl, induced in lithoautotrophic growth conditions, PphaP, induced in phosphate limiting conditions, and hydrogenase promoters, PSH and PMBH, induced by glycerol and fructose, respectively, have been identified (Dangel & Tabita, 2015; Lutte et al., 2012; York et al., 2001). Along with the promoter, an RBS also has a significant impact on protein synthesis (Mutalik et al., 2013) and can govern product biosynthesis in an engineered microorganism. The level of enzyme production in metabolic pathways can be balanced using RBSs with varied strengths that help to achieve different levels of protein synthesis for individual genes of an operon driven by a single promoter. This helps in maintaining efficient carbon flux, removing bottlenecks, and increasing titers.

Another important strategy for improving gene expression is codon optimization, the importance of which is often underappreciated. Codon usage bias is an essential feature of both prokaryotic and eukaryotic genomes (Ikemura, 1985; Plotkin & Kudla, 2011). Although most of the amino acids can be specified by more than one codon, only a subset of the codons is preferred and frequently used in highly expressed genes (Zhou et al., 2016). Codon usage can affect gene expression both at the transcription level and at the translation level. Different organisms have a distinct codon usage pattern that can be summarized using a codon usage table, which is defined by using a set of highly expressed genes as a reference (Sharp & Li, 1987). The codon adaptation index (CAI) is then calculated as the geometric mean of the frequencies of all the codons in a gene relative to the most often used synonymous codon, which is calculated from a set of highly expressed genes (Sharp & Li, 1987; Jansen et al., 2003). CAI is an effective way of predicting the expression level of a gene based on its codon sequence (Sharp & Li, 1987). The GC content of the genome is one of the determinants of the codon usage bias (Li et al., 2015). Codon optimization of a gene for heterologous expression can be done by selectively replacing codons in the original sequence with preferred codons of the new host as well as maintaining the GC content of the target host without changing the amino acid sequence of the protein. This can be done via two different ways: (i) by replacing all the native codons in the target gene with the most preferred codons in the target host organism or (ii) by replacing the native codons in the gene with the synonymous codon whose frequency in the target host most closely matches the frequency of the original codon in the native host, also known as codon harmonization (Angov et al., 2008). As varied CAI values are representative of the varied frequency of the most preferred codons in a target gene, it can be used to study the correlation between codon optimization and gene expression.

In this study, we report our characterization of some commonly used promoters in C. necator along with some novel constitutive and inducible promoters. We have also investigated the expression of green and red fluorescent proteins with different codon variants/CAIs in C. necator. Based on the correlation derived from the fluorescent gene expression versus CAI tests, we also investigated the activity of C12-specific thioesterase (BTE) from bay laurel (Voelker & Davies, 1994) with different codon variants in C. necator and demonstrated dodecanoic acid (C12FA) production. We also outlined key hurdles to large-scale production of fatty acids in C. necator referring to the fact that it harbors several paralogs of β-oxidation genes that remain uncharacterized.

Materials and Methods

Plasmids, Strains, and Growth Condition

Strains and plasmids used in this study are shown in Table 1. Escherichia coli and C. necator strains were grown in Luria–Bertani (LB) medium at 37°C and 30°C, respectively. Plasmids were propagated and maintained in E. coli DH5α using LB medium supplemented with the required antibiotics. Cupriavidus necator strains were grown at 30°C in LB or LB supplemented with 0.4% gluconate. Broth cultures of C. necator were supplemented with appropriate antibiotics, kanamycin (200 µg/mL), gentamicin (15 µg/mL), and chloramphenicol (50 µg/mL), and those of E. coli were supplemented with kanamycin (50 µg/mL) and chloramphenicol (50 µg/mL). Growth media were from Difco (Becton, Dickinson and Company, France), while high-performance liquid chromatography (HPLC)-grade organic reagents (chloroform, methanol, ethanol, and hexane) were from Sigma–Aldrich. Primers for cloning the plasmids were obtained from integrated DNA technologies (IDT), and the nucleotide sequences of these primers are listed in Table 2. Escherichia coli DH5α was used for cloning all the plasmids. Cupriavidus necator H16 and H16-6895 were obtained from JBEI culture collection. To construct plasmids pSM044, pSM045, and pSM046, EcBTE, BTE1, and BTE2 were amplified using respective primers (Table 2) and ligated to linearized plasmid pBTRck (with primers rSM118 and rSM119) via Gibson assembly (Gibson et al., 2009).

Table 1.

Strains and Plasmids Used in This Study

Strains or plasmids Description References or sources
Escherichia coli DH5α ΔlacU169 hsdR17 recA1endA1 gyrA96 thiLrelA1 Taylor et al., 1993
Cupriavidus necator strains
 H16 Wild-type strain, Gentamycin resistance (GmR) Walde, 1962
 H16-6895 H16 Δ(H16_A0459–0464, H16_A1526–1531)ΔphaCAB; GmR Muller et al., 2013
 6895-EcBTE H16-6895 with pSM044 This work
 6895-BTE1 H16-6895 with pSM045 This work
 6895-BTE2 H16-6895 with pSM046 This work
Plasmids
 pSM044 KmR, pBTRcK broad host range vector with EcBTE under Ptrc This work
 pSM045 KmR, pBTRcK broad host range vector with BTE1 under Ptrc This work
 pSM046 KmR, pBTRcK broad host range vector with BTE2 under Ptrc This work
 pMVV052 BBR1, KmR RhaS + RhaR + pRhaB-sfGFP Venkataraman et al., 2023
 pMVV099 BBR1, KmR CymRAM + PCymRC-sfGFP (cuminic acid) Venkataraman et al., 2023
 pMVV100 BBR1, KmR LuxR + PLuxB-sfGFP (3OC6-HSL) Venkataraman et al., 2023
 pMVV101 BBR1, KmR, VanRAM + PVanCC-sfGFP (vanillic acid) Venkataraman et al., 2023
 pMVV102 BBR1, KmR, LacIAM + PTac-sfGFP (IPTG) Venkataraman et al., 2023
 pMVV103 BBR1, KmR,TetR + PTet-sfGFP (aTc) Venkataraman et al., 2023
 pMVV104 BBR1, KmR AraCAM + araE + PBAD-sfGFP (arabinose) Venkataraman et al., 2023
 pMVV108 BBR1, KmR NahRAM + PSalTTC-sfGFP (salicylic acid) Venkataraman et al., 2023
 pMVV110 BBR1, KmR acuRAM + PAcu-sfGFP (acrylic acid) Venkataraman et al., 2023
 pSM006 BBR1, KmR, LacI + PTrc-sfGFP This work
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Table 2.

List of Primers Used in This Study

Primers Sequence (5′ to 3′)
rSM257 cagaccatTaggaggtaaaataATGACGCTGGAATGGAAGCC
rSM123 catccgccaaaacagcttaTTATACGCGCGGCTCGGCG
rSM120 cagaccatTaggaggtaaaataATGACGCTCGAATGGAAGCC
rSM256 tcatccgccaaaacagcttaCTAGATCACTGAAATGCCGCGG
rSM261 cagaccatTaggaggtaaaataatgactctagagtggaaacc
rSM262 tcatccgccaaaacagcttattaaacacgaggttccgccgg
rSM118 tattttacctcctaatggtctg
rSM119 taagctgttttggcggatg
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RBS Library Construction and Analysis

For RBS library construction, degenerate oligos with ANNANN as the SD sequence were used to amplify a codon-optimized version of sfgfp. The product was ligated with a linearized pBBR1-origin vector with sfgfp under a P10 constitutive promoter. The final library was transformed into DH5α for propagation. The diversity of the library was measured by sending 10 candidate plasmids from the transformants for sequencing. The library was transformed into C. necator via electroporation, and a total of 192 individual colonies were picked into 96-well plates for growth. After 24 hr, at 30°C cells were passaged into fresh plates and grown overnight, after which fluorescence and OD600 were measured.

Induction Curves for Different Promoters in C. necator

Cupriavidus necator H16 strains containing fluorescent gene (gfp/mCherry) expression plasmids were grown overnight in LB supplemented with respective antibiotics (Kan200/Chlor50). These overnight strains were inoculated in 200 μL of LB (with antibiotics) supplemented with varying amounts of inducer: vanillic acid, salicylic acid, and acrylic acid (0–0.3 mM), IPTG (0–3 mM), 3-oxohexanoyl-homoserine lactone (3OC6-HSL) (0–0.1 mM), anhydrotetracycline (0–0.0002 mM), cuminic acid (0–100 μM), and l-arabinose and rhamnose (0%–3% wt/vol). The optical density at 600 nm and fluorescence (GFP—excitation: 485 nm, emission: 510 nm; mCherry—excitation: 587 nm, emission: 610 nm) were measured using a Tecan Infinite M1000 plate reader 24 hr post-induction. RFU was calculated by subtracting a media blank, normalized with OD600 and plotted against inducer concentrations.

Designing Codon-Optimized Variants of Reporter Genes

The sequences of gfp and mCherry were generated by FSM and sequences with a varied range of CAIs were selected. Secondary structure around the translation start site of each design was inferred by measuring ΔGfold of a 60-base window (Cambray et al., 2018), centered on the initial ATG, calculated using RNA-fold in the Vienna Package (Lorenz et al., 2011). This window was constructed by concatenating 30 bases of upstream sequence (promoter or construct) to the first 30 bases in the gene coding sequence. Approximately equally stable 5′ secondary structures (<2.0 kcal/mol difference between the extreme ΔGfold values in the final gene set). RFU/OD versus CAI graph was plotted in R [version 4.3.1 (2023-06-16)] and RStudio [version 2023.06.1+524 (2023.06.1+524), using the ggplot2 package (version 3.4.2)].

Fatty Acid Production and Quantification

Plasmids pSM044, pSM045, and pSM046 were electroporated in C. necator strain H16-6895 to generate strains 6895-EcBTE, 6895-BTE1, and 6895-BTE2. The transformants were selected on LB agar plates supplemented with 15 μg/mL gentamycin and 200 μg/mL kanamycin. The strains were then grown overnight in LB media (with Gent15 and Kan200) and diluted in 40 mL of LB Gen15–Kan200 media supplemented with 0.4% gluconate and 2.5 mM calcium pantothenate in baffled flasks with initial OD600 ∼ 0.05. The cells were grown until ∼0.5 OD and induced with 1 mM final concentration of IPTG. Samples were collected every 4 hr post-induction to measure OD and product. Fatty acids were extracted from 2.5 mL of bulk cell culture, derivatized into fatty acid methyl esters, and separated using an Agilent Rtx-5 column (Santa Clara, CA, USA) and were quantified by comparing gas chromatography-flame ionization detection (GC-FID) peak areas against standard curves prepared with commercial standards as previously described (Banerjee et al., 2022).

Results

Promoters for Constitutive Gene Expression in C. necator

The Anderson library promoters are constitutive promoters isolated from a small combinatorial library that span 100-fold expression strengths (Anderson Promoter Collection, iGEM Registry). The relative strengths of the promoters are characterized in Escherichia coli, J23119 being the “consensus” promoter sequence and strongest member. These promoters have been characterized in E. coli and few other microbes but have not been tested in C. necator. Out of the 20 promoters in the Anderson library, we tested 13 promoters, including the consensus promoter sequence, the strongest and weakest promoters, and a few others spanning the range of relative promoter strengths (Table S1). We collected the data for these promoters after 24 hr of growth using the wild-type BBR1 origin and the green fluorescent protein (gfp) as a fluorescent reporter (Fig. 1). We show that these promoters similarly span a 100-fold range in C. necator, offering a diverse set of constitutive promoter strengths suitable for synthetic biology applications in C. necator. For comparison, please consider the PPhaC1 promoter, which has been widely used in C. necator (Claassens et al., 2020; Fukui et al., 2011; Li et al., 2012) and found to express ∼20-folds lower than the PTrc promoter (Arikawa & Matsumoto, 2016). Through this study, we found that highest expression generated by Anderson promoters J23110 and J23111 was almost 10-fold lower than the strong PTrc and hence almost twofold higher than PPhaC1.

Fig. 1.

Fig. 1.

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Characterization of Anderson promoters in Cupriavidus necator. (A) Schematic representation of the promoter–gfp construct on BBR1 origin vector with kanamycin resistance. (B) Quantitative evaluation of promoter strength by measuring relative fluorescence of constitutive promoters driving gfp expression. Relative fluorescence unit (blanked by subtracting the value from LB media) is normalized by OD600. (C) Correlation of relative strength of Anderson promoters between Escherichia coli and C. necator, normalized against J23101. Error bars represent standard deviation obtained from three biological replicates.

Relative promoter strengths in C. necator were calculated by normalizing the transcriptional strength of promoters against J23101 and compared with their published strengths in E. coli from the Anderson library. In E. coli, the relative promoter strengths of J23102, J23110, and J23112 have been found to be higher when compared to J23101, whereas in C. necator relative promoter strengths of J23108, J23110, J23111, and J23118 were found to be high when normalized against J23101. The plot between relative promoter strength in E. coli and that in C. necator showed almost no correlation (R2 = 0.0084) (Fig. 1C and Table S2). This suggests that the transcriptional machinery of C. necator and that of E. coli are inherently different and the promoter strengths of various promoters in E. coli cannot be used as estimates for the same in C. necator.

Through quantitative evaluation of promoter strength and relative promoter activity, we were able to distinguish among weak (J23102, J23107, J23112, J23113, and J23119), moderate (J23101, J23104, J23105, and J23115), and strong (J23108, J23110, J23111, and J23118) constitutive promoters for C. necator.

Promoters for Inducible Gene Expression in C. necator

PTrc, ParaBAD, and PRha have been used previously for inducible gene expression in C. necator (Alagesan et al., 2018; Arikawa & Matsumoto, 2016; Fukui et al., 2009). Other inducible promoters such as PCym, PAcu, and PTet have been shown to work but are not extensively used. We investigated gene expression from the various promoters from the Marionette Sensor Collection (Meyer et al., 2019). Out of 12 inducer systems, 8 were tested in C. necator for inducible gene expression, including cuminic acid (Cuma), vanillic acid (Van), isopropyl  β-d-1-thiogalactopyranoside (IPTG), anhydrotetracycline (aTc), l-arabinose (Ara), salicylic acid (Sal), acrylic acid (Acu), and 3OC6-HSL. These were selected based on their wider availability. Other inducible systems tested alongside the Marionette promoters include the rhamnose-inducible PRhaBAD, arabinose-inducible ParaBAD, and IPTG-inducible PTrc. We identified which promoters had high induction levels at different inducer concentrations and characterized them by titrating induction levels. A gfp reporter gene under the various inducible promoters was cloned into a BBR1 origin plasmid and used to measure the induction levels of the promoters. Of the Marionette promoters, PAcr and PVan had the highest expression levels at maximum induction levels (Fig. 2A). PSal had poor induction and had insignificant gfp expression (Fig. 2A). We hypothesized that the poor activity of PSal promoters might be due to lack of salicylic acid importer in the C. necator genome. Also, a growth defect was observed even with low concentration of salicylic acid. The cuminic acid inducible promoter PCym worked very well in C. necator with a very tight regulation (no fluorescence over background without induction) and around 30-fold range of GFP fluorescence when induced at different cuminic acid concentrations. Gene expression increased with increasing cuminic acid concentration but maintained a steady plateau after maximum induction at 25 µM (Fig. 2C).

Fig. 2.

Fig. 2.

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Characterization of inducible promoters in Cupriavidus necator. (A) Induction curves of promoters from the Marionette Sensor Collection, vanillic acid-inducible promoter (Pvan), isopropyl  β-d-1-thiogalactopyranoside (IPTG)-inducible promoter (PTac), salicylic acid-inducible promoter (PSal), and acrylic acid-inducible promoter (PAcr). (B) Induction of 3-oxohexanoyl-homoserine lactone (3OC6-HSL)- and anhydrotetracycline (aTc)-inducible promoters PLux and PaTc was tested under maximum induction based on Escherichia coli values. (C) Induction curve of cuminic acid-inducible promoter PCym. (D) Induction curve of IPTG-inducble promoter PTrc. (E) Induction curves for rhamnose- and arabinose-inducible promoters PRham and PAraBAD. Error bars represent standard deviation obtained from three biological replicates. Error bars overlap with some data point markers where the standard deviation is very low.

Other promoters from the Marionette collection, PLux and PaTc, were tested in C. nector by inducing expression using the published maximum inducer concentration in E. coli (Meyer et al., 2019). PLux and Patc had weak expression in C. necator (Fig. 2B). Along with the IPTG-inducible PTac from the Marionette Sensor Collection, we tested another IPTG-inducible promoter PTrc. We found a fivefold increase in gfp fluorescence with PTac (Fig. 2A) whereas 160-fold increase in gfp fluorescence with PTrc (Fig. 2D) as compared to the un-induced condition. We also tested arabinose and rhamnose induction in C. necator, which have been previously reported to work. Fluorescence data show an ∼200-fold increase in gfp expression under arabinose- and rhamnose-inducible promoters (Fig. 2E). We found higher leaky expression with arabinose-controlled promoters in the un-induced condition as compared to rhamnose-controlled promoters (Fig. 2E). Gene expression remained constant at higher inducer conditions for rhamnose and arabinose induction (Fig. 2E).

RBS Libraries for Tuning Gene Expression in C. necator

In order to assess the dynamic range of simple RBS tuning in C. necator, we measured the fluorescence of a GFP reporter from a library of plasmids with the same promoter but various Shine–Dalgarno (SD) sequences. The library of reporter constructs was generated using degenerate oligos with ANNANN as the SD sequence and cloned into a replicating vector with BBR1 origin under P10 constitutive promoter control (Miller, 1989). We chose a codon-optimized GFP sequence with a CAI of 0.736 to ensure relatively good expression at a detectable level across the RBS library. A subset of 8 of the C. necator colonies were sent for Sanger sequencing to characterize the diversity of the transformed library. Sequencing shows that six of the eight sequenced colonies possessed unique SD sequences, indicating that there was acceptable diversity in the transformed library.

The fluorescence data show a 50-fold range of expression for GFP (Fig. 3), demonstrating successful modulation of GFP expression via SD variation in the RBS only, which is higher than the 10-fold range previously obtained by altering whole RBS (Alagesan et al., 2018). This is a simple and efficient method to generate and select constructs with desired relative expression values. Further modification of the RBS, for example, in the 5′ untranslated region upstream of the SD or in the spacer region between SD and the start codon offers additional avenues for expression tuning.

Fig. 3.

Fig. 3.

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Evaluation of the ribosome binding site (RBS) library versus GFP fluorescence. Schematic representation of the RBS library–GFP plasmid construct used for the analysis. The plot represents evaluation of the RBS library as measured by relative fluorescence of GFP normalized by OD600, N = 192. The error bars represent standard deviation obtained from three technical replicates.

Effect of Codon Optimization on Gene Expression in C. necator

Over the years, a lot of work has been done to study the effect of codon optimization in different microorganisms (Schmidt et al., 2023; Zhou et al., 2016), but relatively little work has been done in C. necator to determine optimal codon usage for heterologous proteins. In this study, we investigated the expression of different codon-optimized variants of green and red fluorescent proteins (gfp and mCherry) in C. necator. A panel of gfp and mCherry genes with a range of codon biases was designed using a finite state machine (FSM). The FSM generates codons to produce a gene encoding the desired final protein product. By tuning the transition probabilities of the FSM, the codon bias of the final gene product can be varied. Using this procedure, a large set of DNA designs were created. The codon bias of each design was measured using the CAI (Sharp & Li, 1987). Based on the metadata (CAI and ΔGfold), a subset of gene designs were selected with a range of CAI values and approximately equally stable 5′ secondary structures denoted by their ΔGfold.

We cloned these gfp/mCherry variants under a constitutive P10 promoter on a BBR1 origin vector and tested their expression in C. necator H16, a “wild-type” strain. CAI was graphed against the fluorescence [relative fluorescence unit (RFU)] normalized by optical density measured at 600 nm (RFU/OD600), separately for GFP and mCherry. In general, we observed that increasing CAI increases expression (r2 = 0.81), but notably the trend was not completely linear, as shown by the spline fit to the data (Fig. 4A and B). Genes with CAI ∼ 0.7–0.8 produced maximum fluorescence, beyond which the expression decreases (Fig. 4A and B), indicating a range of CAIs that had maximum gene expression in C. necator. Gene expression is also a function of GC content. Gene expression is also found to positively correlate with the GC content of the third position of the codon (3GC) in the organisms that prefer G or C at the wobble position. (Zhou et al., 2016). We found that GC content in the third position of the codon (3GC%) in codon-optimized reporter genes, gfp and mCherry, shows a positive correlation with their expression in C. necator (Fig. 4C and D).

Fig. 4.

Fig. 4.

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Gene expression positively correlates with both codon adaptation index (CAI) and GC content in the third position of the codon (3GC%). (A) Spline fitted curve of GFP relative fluorescence unit/optical density (RFU/OD) versus CAI. (B) Spline-fitted curve of mCherry RFU/OD versus CAI. The shaded area represents 95% confidence interval. (C) Scatter plot of GFP RFU/OD versus 3GC%. (D) Scatter plot of mCherry RFU/OD versus 3GC% showing a linear correlation between gene expression and 3GC%. Data were obtained from three biological replicates and the error bar represents standard deviation among biological replicates.

Effect of Codon Optimization on Dodecanoic Acid Production

Based on the correlation derived from the expression of different codon-optimized variants of gfp and mCherry, we codon optimized a C12-specific fatty acid thioesterase (UcFatB2, a.k.a. BTE) from Umbellularia californica (California bay laurel) and obtained two variants with slightly varied CAIs and GC content within C. necator’s optimal range (Table 3). To demonstrate the effect of codon optimization on gene expression and furthermore on production of C12 free fatty acid, we obtained an existing strain of C. necator, H16-6895 with two putative β-oxidation operons and PHA biosynthesis genes removed (∆A0460–A0464, ∆A1526–A1531 ∆phaCAB) (Muller et al., 2013). This strain of C. necator is reported to have a completely blocked β-oxidation pathway, which typically consumes free fatty acids. The two variants of BTE, namely BTE1 and BTE2, were expressed in H16-6895 on a BBR1 origin plasmid under a PTrc promoter. As a control, an E. coli codon-optimized version of BTE, namely EcBTE, was also expressed and analyzed for dodecanoic acid production (Lennen et al., 2010). The engineered strains were grown in rich media and induced with IPTG at ∼0.5 OD600. It was found that, though the strains expressing EcBTE were producing dodecanoic acid, they were not accumulating it, and dodecanoic acid specific peak was completely absent in samples collected after 48 hr of growth (Fig. S1). This observation suggested that the fatty acid catabolic pathway was still active in C. necator H16-6895. As this strain (H16-6895) containing deletions of two β-oxidation operons could not accumulate fatty acids, we tested this strain for fatty acid consumption by feeding it ∼150 mg/L octanoic acid (C8), decanoic (C10), and dodecanoic acid (C12). We found that after 24 hr of growth all the C8, most of C10, and some of the C12 fatty acids were consumed. By 48 hr, all the fed free fatty acids were consumed by the strains (Fig. S2), indicating that the H16-6895 strain of C. necator could still catabolize fatty acids despite a lack of annotated β-oxidation genes.

Table 3.

Codon-Optimized Variants of Thioesterases BTE, BTE1, and BTE2

Name Length CAI %G + C %G + C(1) %G + C(2) %G + C(3)
BTE1 906 0.781 64.2 60.9 43.4 88.4
BTE2 906 0.727 63 60.9 43.4 84.8
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Note. Two variants of C12 fatty acid-specific thioesterase BTE were designed with different codon adaptation indexes (CAIs): BTE1 and BTE2. %G + C denotes the total GC percentage of the genes. %G + C(1), %G + C(2), and %G + C(3) denote the GC percentages of the first, second, and third nucleotides of the codon in the gene.

We supplemented the production media with gluconate, a more preferred carbon source for C. necator (González-Villanueva et al., 2019), to investigate whether the presence of a preferred carbon source lowers the fatty acid consumption via catabolite repression. The addition of gluconate did increase the dodecanoic acid titers compared to fructose supplementation (Fig. S3), but the fatty acids were still consumed at later time points in H16-6895 strain harboring BTE genes (Fig. 5). The dodecanoic acid titers increased over time and reached a peak at around 16 hr post-induction in strain 6895-BTE1, after which the titers begin to decline. 6895-EcBTE and 6895-BTE2 produced less free fatty acids, which may not have been at high enough level to see a significant consumption effect in 24 hr of growth. With the expression of EcBTE, we obtained ∼35 mg/L of C12 fatty acid maximum titer before the product was catabolized. The expression of optimized versions helped increase dodecanoic acid titers; BTE1 (CAI = 0.78) produced maximum titer of ∼100 mg/L of dodecanoic acid between 12 and 16 hr post-induction, whereas BTE2 (CAI = 0.72) produced a slightly higher titer than the unoptimized BTE at ∼70 mg/L before the product is catabolized (Fig. 5).

Fig. 5.

Fig. 5.

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C12 fatty acid production in H16-6895 strain with the expression of BTE (codon optimized for E. coli) and BTE1 and BTE2 (codon optimized for Cupriavidus necator). x-axis shows various time points taken for respective strains and y-axis shows mg/L of dodecanoic acid produced. OD600 measurements are represented as dots. Error bars represent standard deviation obtained from three biological replicates.

Discussion

Chemolithotrophs, such as C. necator, have the potential to be developed as a platform strain to produce bioproducts derived from a variety of carbon sources as well as carbon dioxide; however, continued genetic tool development is necessary to make full use of its potential as a biosynthetic platform. In this work, we tested and validated a comprehensive set of genetic elements that show meaningful differences to their performance in E. coli, and they can now be used to control the expression of heterologous pathways in C. necator. The Anderson library promoters that we tested had completely different expression strengths in C. necator from what was defined for E. coli. This indicates that the strength of promoters and other functional genetic elements cannot be predicted for C. necator based on their characterization in E. coli, suggesting that both have inherently different transcription machinery given their large evolutionary distance. Marionette promoters have been developed in E. coli for lower background activity, higher sensitivity, and limited crosstalk among the regulators; therefore, they presented a strong initial array of promoters to be tested in a new organism. The different promoters from the Marionette Sensor Collection show smaller fold changes in expression as compared to E. coli, indicating difference in regulation. In this work, eight promoters from the Marionette Sensor Collection were validated and added to the repertoire of characterized promoters in C. necator. In E. coli, PTrc confers roughly 90% stronger expression than PTac owing to a single base pair addition in the spacer between –35 and –10 elements (Brosius et al., 1985). In C. necator, PTac gives a fivefold change in expression during induction, while PTrc gives a 160-fold change in expression, mirroring the trend from E. coli but to a much larger extent, possibly suggesting that –35 to –10 spacer distance is especially important for C. necator. Additionally, RBS sequence was observed to be important for gene expression in C. necator. We obtained GFP fluorescence that varied over a 50-fold range using varied sequences of SD in the RBS.

In this study, we also validated codon optimization and elucidated the relationship between gene expression and CAI for C. necator. Different CAI values indicate the presence of varied frequencies of preferred codons for a specific organism, that is, high CAI denotes higher frequency of more preferred codons and vice versa. CAI tells a general level of frequency calculated on average for a whole open reading frame but does not distinguish between codon optimization (using the most frequent synonymous codon) and harmonization (using the synonymous codon that best matches the frequency of the original codon in the native host). Also, it is worth mentioning that CAI is only an approximate indication of the suitability of the codon usage within a gene and does not consider the distribution or order of codons (Sharp & Li, 1987). Nevertheless, we find a strong relationship between the calculated CAI and gene expression for C. necator. The CAI would suggest whether it is of any benefit to chemically synthesize a new gene and to include more preferred codons. We found by fluorescence assay of codon-optimized variants of gfp and mCherry that 0.7–0.8 CAI (calculated using a codon-usage table of C. necator) is a good range for gene expression in C. necator. This CAI can be used when synthesizing new genes or to infer whether existing sequences will be well expressed in this host.

In a previous study, it was found that gene expression had a strong correlation with codon usage bias via translation and could also be a function of GC content, particularly in organisms that prefer G or C over A or T (Zhou et al., 2016). However, in Neurospora spp., it was found that at the genome-wide level GC contents (of whole transcripts) showed no or a weak negative correlation with gene expression (Zhou et al., 2016), but GC content at the third position of codons (%3GC) was found to show a strong positive correlation with gene expression. To test this phenomenon for C. necator, we calculated the %3GC for the codon-optimized variants of gfp and mCherry and found a strong positive correlation between %3GC content and gene expression (Fig. 4C and D). However, in contrast to the previous study, we also found a similar correlation between gene expression and total GC content of the reporter genes (Fig. S4). It might be due to the reason that C. necator is an organism with a high GC content (∼66%), whereas Neurospora sp. has ∼50% GC content in its genome and hence preference of either G or C at the third position of the codon can be easily observed in C. necator when compared to the gene expression in Neurospora. As overall GC content is high for C. necator, the possibility of G or C at the first and second positions of the codon is also very high.

With the results obtained from our earlier characterization of C. necator tools, we demonstrated C12-fatty acid production in C. necator using two codon-optimized variants of the thioesterase, BTE, and were able to show that the level of codon optimization affects product biosynthesis in an engineered organism. Although we were able to produce ∼100 mg/L of dodecanoic acid, we still observed fatty acid consumption, which led to a decrease in the titers after 24 or 48 hr. As the oleochemical products were being catabolized at later time points even in the strain H16-6895, with β-oxidation operons A0459–A0464 and A1526–A1531 deleted, identification of other β-oxidation genes and deleting them remains critical. In a recent study, it was observed that growing the H16-6895 strain in media containing medium-chain-length fatty acids leads to the expression of many other β-oxidation genes, which was validated through RNAseq (Strittmatter et al., 2023). Although the possibilities of developing C. necator as a chassis for the production of fatty acids and their derivatives are currently limited by the large number of possible β-oxidation genes, the comprehensive toolbox of genetic elements that we characterized will be helpful in any metabolic engineering strategy applied in C. necator. Especially, in the current scenario of depleting fossil fuels, the power of C. necator to produce complex chemicals from carbon dioxide cannot be undermined.

Supplementary Material

kuae008_Supplemental_File
kuae008_supplemental_file.zip (573.1KB, zip)

Acknowledgments

The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Contributor Information

Shivangi Mishra, Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706,  USA.

Paul M Perkovich, Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706,  USA.

Wayne P Mitchell, LanzaTech, Skokie, IL 60077, USA.

Maya Venkataraman, Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706,  USA.

Brian F Pfleger, Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706,  USA.

Author Contributions

B.F.P. and S.M. conceptualized and planned the experiments. S.M., P.M.P., and M.V. performed the experiments. W.M. designed the reporter genes with varied CAIs. B.F.P., S.M., and P.M.P. wrote the manuscript.

Funding

The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency—Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001503. M.V. is the recipient of a Graduate Research Fellowship from the National Science Foundation and a traineeship through the NIH Biotechnology Training Program at UW-Madison (NIGMS T32 GM135066).

Conflict of Interest

W.M. is an employee of LanzaTech, a for-profit company pursuing commercialization of gas fermentation. S.M., P.M.P., M.V., and B.F.P. declare no conflicts of interest.

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