Draft - tspace.library.utoronto.ca109 0.1% rifampicin solution (50 mg/ml) to maintain plasmids. 110 Shake-flask growth conditions 111 An overnight culture of A. tumefaciens EHA105

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Quantitative proteomic profiling of shake-flask versus bioreactor growth reveals distinct responses of

Agrobacterium tumefaciens for preparation in molecular pharming

Journal: Canadian Journal of Microbiology

Manuscript ID cjm-2020-0238.R2

Manuscript Type: Article

Date Submitted by the Author: 22-Aug-2020

Complete List of Authors: Prudhomme, Nicholas; University of GuelphGianetto-Hill, Connor; University of GuelphPastora, Rebecca; PlantForm Corporation CanadaCheung, Wing-Fai; PlantForm Corporation CanadaAllen-Vercoe, Emma; University of GuelphMcLean, Michael D.; PlantForm Corporation CanadaCossar, Doug; PlantForm Corporation CanadaGeddes-McAlister, Jennifer; University of Guelph, Molecular and Cellular Biology

Keyword: Agrobacterium tumefaciens, bioreactor, shake-flask, quantitative proteomics, molecular pharming

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/cjm-pubs

Canadian Journal of Microbiology

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1 Title: Quantitative proteomic profiling of shake flask versus bioreactor growth reveals

2 distinct responses of Agrobacterium tumefaciens for preparation in molecular pharming.

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4 Authors: Prudhomme, N.1, Gianetto-Hill, C.1, Pastora, R.2, Cheung, W.-F.2, Allen-Vercoe, E.1,

5 McLean, M.D.2, Cossar, D.2, Geddes-McAlister, J.1*

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7 Affiliations: 1Department of Molecular and Cellular Biology, University of Guelph, Guelph,

8 Ontario, N1G 2W1

9 2PlantForm Corporation Canada, Toronto, Ontario, M4S 3E2

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11 Corresponding author: Jennifer Geddes-McAlister, Department of Molecular and Cellular

12 Biology, University of Guelph, Guelph, Ontario, N1G 2W1. Phone: 519-824-4120 ext. 52129.

13 Email: [email protected]

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23 Abstract.

24 The preparation of Agrobacterium tumefaciens cultures with strains encoding proteins

25 intended for therapeutic or industrial purposes is an important activity prior to treatment of plants

26 for transient expression of valuable protein products. The rising demand for biologic products such

27 as these, underscores the expansion of molecular pharming and warrants the need to produce

28 transformed plants at an industrial scale, requiring large quantities of A. tumefaciens culture, which

29 is challenging using traditional growth methods (e.g., shake flask). To overcome this limitation,

30 we investigate the use of bioreactors as an alternative to shake flasks to meet production demands.

31 Here, we observe differences in bacterial growth amongst the tested parameters and define

32 conditions for consistent bacterial culturing between shake flask and bioreactor. Quantitative

33 proteomic profiling of cultures from each growth condition defines unique growth-specific

34 responses in bacterial protein abundance and highlights the functional roles of these proteins,

35 which may influence bacterial processes important for effective agroinfiltration and

36 transformation. Overall, our study establishes and optimizes comparable growth conditions for

37 shake flask vs. bioreactors and provides novel insights into fundamental biological processes of A.

38 tumefaciens influenced by such growth conditions.

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40 Keywords: Agrobacterium tumefaciens, bioreactor, shake flask, quantitative proteomics,

41 molecular pharming

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42 Introduction.

43 Agrobacterium tumefaciens, a Gram-negative, motile bacterium found ubiquitously in the

44 soil, is the causative agent of Crown Gall disease (Hughes 1996). The pathogenic properties of A.

45 tumefaciens are determined by the tumour-inducing plasmid (Ti plasmid), which contains the vir

46 region, encoding for proteins responsible for the initiation of DNA transfer. This region also

47 encodes for proteins that aid in excision of the transfer DNA (T-DNA) from the Ti plasmid,

48 movement of transfer strands (T-strands) into the plant cell, and incorporation of the T-DNA into

49 the plant genome (Hughes 1996; Zhu et al. 2000; Aguilar et al. 2010). Within the T-DNA region,

50 right and left border sequences aid in T-DNA excision from the Ti plasmid and any proteins

51 encoded within these borders are incorporated into the plant genome, representing an opportunity

52 to produce desired products on a large scale. This novel ability of A. tumefaciens to insert a portion

53 of DNA into a plant genome has been exploited by the biotechnology industry as transformed

54 plants or plant cells producing desired antibodies or other therapeutic proteins represents a cost-

55 effective alternative to traditional mammalian cell systems (Arntzen 2008; Mclean 2017). In

56 addition, plant transformation for biologic drug production lends itself to rapid, large-scale

57 production of novel drugs or vaccines during emergency situations, including infectious disease

58 outbreaks or an act of bioterrorism (Davey et al. 2016).

59 The first step of stable-transgenic or transient plant transformation includes the preparation

60 of bacterial cultures, which traditionally relies on growth in shake flasks. Shake flask cultures are

61 often limited in volume for large-scale expression of biologic proteins and may have quality

62 control problems regarding growth condition parameters that result in batch inconsistencies. An

63 alternative bacterial growth strategy includes microbial bioreactors, which enable production of

64 large quantities of bacteria in batch culture while promoting the fine tuning of growth parameters,

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65 including pH, oxygen concentration, and temperature (Suzuki et al. 2006). Bioreactors can

66 increase bacterial biomass at the initial stage of plant transformation for improved efficiency and

67 target protein production. Notably, given the use of A. tumefaciens as a target DNA transmitter,

68 both culturing conditions are applicable for downstream applications. However, knowledge of

69 variable growth conditions, incubation parameters, and considerations for expansion for large-

70 scale culturing of A. tumefaciens is limited. Recently, examination of scale-up for A. tumefaciens

71 batch fermentation of 5 L and 100 L bioreactor working volumes revealed maximum biomass

72 concentrations, specific growth rates, and transient protein expression levels (Leth and McDonald

73 2017). These data provide valuable insight into the feasibility of bioreactors for bacterial growth

74 prior to agroinfiltration and provides a basis for our comparison of shake flask vs. bioreactor

75 proteome changes.

76 Mass spectrometry-based proteomics is a powerful tool to define and quantify changes in

77 protein abundance in a variety of biological systems under a plethora of conditions (Aebersold and

78 Mann 2016). Proteomic profiling informs cellular process remodelling, secretion or release of

79 specific proteins into extracellular environments, and the interplay between biological systems

80 during co-culture or infection. Specific to bacteria, proteomics explores growth stage-specific

81 changes, environmental influence, and growth condition parameters on cellular processes and

82 bacterial survivability (Folsom et al. 2014; Muthusamy et al. 2017; Muselius et al. 2020;

83 Prudhomme et al. 2020). Previous A. tumefaciens proteomics experiments explored the bacterial

84 response to different environmental conditions, including heat shock, pH downshift, oxidative

85 stress, and light (Rosen and Ron 2011). However, a comprehensive analysis of protein-level

86 changes associated with bioreactor vs. shake flask growth and the role of bacterial growth

87 conditions in preparing the bacteria for agroinfiltration has not been defined.

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88 In this study, we establish parameters for providing comparable bacterial growth between

89 traditional (e.g., shake flasks) and alternative (e.g., bioreactors) bacterial culturing techniques and

90 we assess new conditions (e.g., altered pH, elevated fructose, and light availability) that may

91 enhance bacterial growth. Next, we use state-of-the-art mass spectrometry to define changes in

92 proteome profiles of A. tumefaciens grown under the specific conditions. We observe growth-

93 specific alterations in protein abundance and significant reorganization of a diverse array of

94 biological processes. Functional characterization reveals an importance of metabolism and

95 biosynthesis for shake flask cultures, whereas bioreactor cultures alter proteins of transportation

96 and locomotion. From these data, we predict bacterial growth conditions to support adaptability of

97 the bacterium to downstream processes (e.g., agroinfiltration) during plant transformations for

98 molecular pharming.

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103 Materials and Methods.

104 Bacterial strains

105 A modified version of the A. tumefaciens strain EHA105 (transformed with the T-DNA

106 vector pPFC0058 for expression of the monoclonal antibody Trastuzumab) was used for the

107 experiments (Hood et al. 1993). The bacteria were maintained on Lysogeny Broth (LB) agar plates

108 (Fisher BioReagents) at 28°C supplemented with 0.1% kanamycin sulfate solution (50 mg/ml) and

109 0.1% rifampicin solution (50 mg/ml) to maintain plasmids.

110 Shake-flask growth conditions

111 An overnight culture of A. tumefaciens EHA105 (pPFC0058) was initiated from a single

112 colony on LB media into 5 ml of liquid LB at 28℃ and 220 rpm overnight with 0.1% kanamycin

113 and rifampicin. Sub-culturing (1/100) was performed in 1 to 4 L shake-flasks containing 500 ml

114 to 1 L LB and 0.1% kanamycin and rifampicin at 28℃, 170 rpm, and pH 7 (measured) for 18 h

115 (OD600nm = 1.4-1.5). Two biological and two technical replicates were performed.

116 Bioreactor growth conditions

117 An overnight culture of A. tumefaciens EHA105 (pPFC0058) was initiated from a single

118 colony on LB media into 5 ml of liquid LB with 0.1% kanamycin and rifampicin at 28℃ and 220

119 rpm overnight. For bioreactor optimization experiments, the BIOSTAT Bplus twin 5L double

120 jacket bioreactor (Sartorius) was used. The inoculated bioreactor vessels contained 4 L of LB

121 stirred at 600 rpm, temperature at 28℃, including addition of 200 µL of antifoam B silicone

122 emulsion (Avantor Performance Materials, Inc.) every 4 h with 0.1% kanamycin and rifampicin.

123 The parameters of pH control (controlled at 6, 7.6, 8, and 9 with 10% HCl and 10% NaOH),

124 concentration of dissolved oxygen (airflow directed into the medium for standard dissolved

125 oxygen concentrations vs. airflow directed into the vessel headspace for lower dissolved oxygen

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126 conditions), fructose concentration (for high fructose conditions 5 g/L of fructose solution was

127 added), and light/dark conditions (for dark conditions, the bioreactor was wrapped in aluminum

128 foil vs. light conditions with a 1650 lumens light bulb placed 0.5 meters away from the bioreactor)

129 were modified accordingly. The standard bioreactor run was performed with pH held at 7, stirrer

130 speed at 600 rpm, bubbling air flow, and a growth temperature of 28℃. Two biological and two

131 technical replicates were performed.

132 For mass spectrometry experiments, sub-culturing (1/100) was performed in bioreactor

133 vessels (Sartorius) containing 500 ml of LB with 0.1% kanamycin and 0.1% rifampicin at 28℃

134 and 600 rpm for 12 h (OD600nm = 1.4-1.5) in the dark.

135 Sample collection

136 For protein extraction, 1 ml of culture was collected by centrifugation at 1,500 x g for 10

137 min. Supernatants were collected for further processing. Cell pellets were washed twice with cold

138 phosphate buffered saline and collected for protein extraction. Samples were stored on ice before

139 processing.

140 Sample preparation for proteomic analysis.

141 Protein extractions were performed as previously described, with modifications (Ball and

142 Geddes-McAlister 2019; Prudhomme et al. 2020). Briefly, bacterial cell pellets were resuspended

143 in 100 mM Tris-HCl (pH 8.5) containing a protease inhibitor cocktail tablet (Roche). Samples

144 were lysed by probe sonication (ThermoFisher Scientific) on ice bath for 3 cycles (30% power, 30

145 s on/30 s off). Two percent sodium dodecyl sulphate (SDS) and 10 mM dithiothreitol (DTT) was

146 added, followed by incubation at 95°C for 10 min with shaking at 800 rpm. Samples were cooled

147 and 55 mM iodoacetamide (IAA) was added followed by room temperature incubation for 20 min

148 in the dark. Next, 100% ice cold acetone (final concentration of 80%) was added prior to storage

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149 at -20°C overnight. Samples were collected by centrifugation at 10,000 x g at 4°C for 10 min,

150 washed twice with 80% acetone, and air dried. Pellets were resolubilized in 8M urea/40 mM

151 HEPES and a bovine serum albumin (BSA) tryptophan assay determined protein

152 concentrations(Wis̈niewski and Gaugaz 2015). Samples were diluted in 50 mM ammonium

153 bicarbonate and digested overnight with a mixture of LysC and trypsin proteases (Promega,

154 protein:enzyme ratio, 50:1). Digestion was stopped with 10% v/v trifluoroacetic acid (TFA) and

155 50 µg of the acidified peptides were loaded onto STop And Go Extraction (STAGE) tips

156 (consisting of three layers of C18) to desalt and purify according to the standard protocol

157 (Rappsilber et al. 2007). Samples were stored as dried peptides at -20°C until measured on the

158 mass spectrometer. All mass spectrometry experiments were performed in biological

159 quadruplicate.

160 For secretome analysis, the culture supernatant was filtered through 0.22 µM syringe

161 filters. For each sample, 500 µl of filtered supernatant was treated with DTT, IAA, followed by

162 digestion using LysC and Trypsin. Digested peptides were desalted and purified as described

163 above.

164 Mass spectrometry.

165 Samples were eluted from STAGE-tips with 50 µl buffer B (80% acetonitrile (ACN) and

166 0.5% acetic acid), dried, and resuspended in 12 µl buffer A (0.1% TFA). Six µl of each sample

167 (approx. 3 to 5 µg) was analyzed by nanoflow liquid chromatography on an Ultimate 3000 LC

168 system (ThermoFisher Scientific) online coupled to a Fusion Lumos Tribrid mass spectrometer

169 (ThermoFisher Scientific) through a nanoelectrospray flex-ion source (ThermoFisher Scientific).

170 Samples were loaded onto a 5 mm µ-precolumn (ThermoFisher Scientific) with 300 µm inner

171 diameter filled with 5 µm C18 PepMap100 beads. Peptides were separated on a 15 cm column

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172 with 75 µm inner diameter with 2 µm reverse-phase silica beads and directly electrosprayed into

173 the mass spectrometer using a linear elution gradient from 4% to 30% ACN in 0.1% formic acid

174 over 45 min at a constant flow of 300 nl/min. The linear gradient was followed by a washout with

175 up to 95% ACN to clean the column followed by an equilibration stage to prepare the column for

176 the next run. The Fusion Lumos was operated in data-dependent mode, switching automatically

177 between one full scan and subsequent MS/MS scans of the most abundant peaks with a cycle time

178 of 3 s. Full scan MS1s were acquired in the Orbitrap analyzer with a resolution of 120,000, scan

179 range of 400-1600 m/z. The maximum injection time was set to 50 ms with an automatic gain

180 control target of 4e5. The fragment ion scan was done in the Orbitrap using a Quadrupole isolation

181 window of 1.6 m/z and HCD fragmentation energy of 30 eV. Orbitrap resolution was set to 30,000

182 with a maximum ion injection time of 50 ms and an automatic gain control target set to 5e4.

183 Data analysis.

184 For proteome data analysis .Raw files were analyzed using MaxQuant software (version

185 1.6.0.26.) (Cox and Mann 2008). The derived peak list was searched with the built-in Andromeda

186 search engine against the reference A. tumefaciens (Dec. 16, 2019; 5,344 sequences,

187 https://www.uniprot.org/) supplemented with vector-specific sequences (Cox et al. 2011;

188 Prudhomme et al. 2020). The parameters were as follows: strict trypsin specificity, allowing up to

189 two missed cleavages, minimum peptide length was seven amino acids, carbamidomethylation of

190 cysteine was a fixed modification, N-acetylation of proteins and oxidation of methionine were set

191 as variable modifications. A minimum of two peptides was required for protein identification and

192 peptide spectral matches and protein identifications were filtered using a target-decoy approach at

193 a false discovery rate (FDR) of 1%. ‘Match between runs’ was enabled with a match time window

194 of 0.7 min and an alignment time window of 20 min (Cox et al. 2014). Relative, label-free

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195 quantification (LFQ) of proteins used the MaxLFQ algorithm integrated into MaxQuant using a

196 minimum ratio count of one (Cox et al. 2014). The mass spectrometry proteomics were deposited

197 in the PRIDE partner repository for the ProteomeXchange Consortium with the data set identifier:

198 PXD018384.

199 Further analysis of the MaxQuant-processed data (‘proteingroups.txt’ file) was performed

200 using Perseus (version 1.6.2.2) (Tyanova et al. 2016). Hits to the reverse database, contaminants,

201 and proteins only identified with modified peptides were eliminated. LFQ intensities were

202 converted to a log scale (log2), and valid-value filter of three in four replicates in at least one group

203 was used. Missing values were imputed from a normal distribution (downshift of 1.8 standard

204 deviations and a width of 0.3 standard deviations). A Student’s t-test identified proteins with

205 significant changes in abundance (p-value ≤0.05) with multiple hypothesis testing correction using

206 the Benjamini-Hochberg FDR cutoff at 0.05. A principal component analysis (PCA) was

207 performed to assess separation components within the dataset. A Pearson correlation with

208 hierarchical clustering by Euclidean distance was performed on the LFQ intensity values of the

209 measured proteins to determine replicate reproducibility. For 1D annotation enrichment, Student’s

210 t-test (permutation-based FDR = 0.05; s0 = 1) was performed followed by 1D annotation

211 enrichment function in Perseus (Cox and Mann 2012). This analysis generates a numerical ‘score’

212 value, which represents the direction in which the protein LFQ intensities within a given category

213 tend to deviate from the overall distribution of all proteins. Visualization of enrichment by Gene

214 Ontology was performed within the RStudio platform (http://www.R-project.org/) (R Foundation

215 for Statistical Computing. 2018).

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218 Results.

219 To establish and optimize bacterial growth conditions in bioreactors for comparison to

220 current shake-flask parameters used by PlantForm for molecular pharming we assessed the impact

221 of pH, fructose, and light availability (Garabagi et al. 2012; Mclean 2017). The goals of this study

222 include: i) establish comparable growth conditions for shake flask vs. bioreactor; and ii) detect

223 protein-level differences between the conditions to provide new insight into growth-specific

224 responses, which may influence plant transformation and ultimately, target protein production.

225 pH influences bacterial growth and defines sub-optimal parameters

226 To assess an influence of pH on A. tumefaciens within a bioreactor, we measured optical

227 density (OD600nm) for bioreactor runs held at pH values of 6, 7.6, 8, and 9. We did not observe a

228 significant difference in OD600nm between standard bioreactor runs (held at pH 7) and experiments

229 performed at pH of 6, 7.6, and 8 (Fig. 1A). However, as anticipated, we observed a significant

230 reduction in growth at pH 9 (p-value = 0.000078), suggesting an optimal range of pH for adequate

231 bacterial growth.

232 High fructose influences bacterial growth

233 Preliminary experiments in shake flasks suggest a beneficial impact on biomass

234 accumulation by fructose addition to the medium on A. tumefaciens growth (data not shown).

235 Therefore, we compared a high fructose bioreactor run (5 g/L) to the standard run conditions (no

236 fructose). We observed a significant increase in OD600nm of A. tumefaciens in high fructose medium

237 at 14 h post inoculation (hpi) (Fig. 1B). We also measured bacterial biomass for the fructose

238 conditions and observed 9.7 x 108 ± 4.5 x 108 colony forming units (CFU)/ml at high fructose and

239 1.4 x 109 ± 8.1 x 108 CFU/ml at standard bioreactor run, demonstrating no significant differences

240 in biomass.

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241 Altering light availability conversely effects bacterial growth

242 Previous reports suggest that growing A. tumefaciens in the dark increases bacterial

243 motility and T-DNA transfer (Oberpichler et al. 2008). To evaluate the impact of light and dark

244 conditions on A. tumefaciens, we measured OD600nm and observed an increase in bacterial growth

245 in light (OD600nm = 2.07) compared to dark (OD600nm = 1.82) conditions, suggesting improved

246 growth under light conditions; however, these values were on par with average OD600nm

247 measurement for the standard bioreactor run (OD600nm = 1.67). We also measured bacterial

248 biomass at these collection points and observed 8.3 x 108 ± 3.6 x 108 CFU/ml in the light and 3.0

249 x 109 ± 1.8 x 109 CFU/ml in the dark compared to 1.2 x 109 ± 3.9 x 108 CFU/ml under standard

250 bioreactor run. Notably, although A. tumefaciens bioreactor cultures grown in light conditions

251 produced a higher OD600nm it corresponded with lower bacterial biomass, conversely to the dark

252 conditions. These conflicting results may be associated with biological or technical variation and

253 require further investigation to tease apart the precise impact of light availability but suggest an

254 advantage for dark culturing conditions for molecular pharming.

255 Bacterial growth conditions define distinct proteome profiles

256 Given the assessment of shake-flask growth conditions, optimised for bioreactor runs, we

257 used state-of-the-art mass spectrometry-based proteomics to uncover distinct cellular modeling

258 profiles associated with growth response (Fig. 2). In total, we identified 2,861 proteins (54% of

259 open reading frames) in the cellular proteome and secretome samples (before valid value filtering)

260 and pursued further analysis of 2,292 proteins. A comparison of proteins identified under shake-

261 flask vs. bioreactor conditions highlights >90% commonality of the proteomes and defines distinct

262 growth-specific responses with 137 proteins unique to bioreactor conditions and 81 proteins

263 produced solely during shake-flask growth (Fig. 3A). Notably, proteins identified in the

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264 supernatant did not meet the valid value filtering criteria and we, therefore, focused our analysis

265 on the cellular proteome results. Biological replicate reproducibility was > 96% for all samples,

266 with a protein imputation rate of 13.7% (Fig. 3B). A principal component analysis (PCA)

267 demonstrated distinct clustering by bacterial growth conditions (e.g., bioreactor vs. shake-flasks)

268 (component 1, 32.6%), and a second component by biological variability (component 2, 18.6%)

269 (Fig. 3C; Supp. Fig. 1).

270 Transporters, enzymes, and transcriptional regulators describe specific cellular responses of A.

271 tumefaciens to growth conditions

272 We set out to define proteins with significant changes in abundance and identified 37

273 significantly different proteins, including 25 with higher abundance during bioreactor growth and

274 12 with higher abundance during shake-flask growth (Fig. 4A; Table 1). A closer look at the impact

275 of bioreactor growth reveals eight proteins involved in metabolic and catalytic activities, including

276 four nitric/nitrite reductases as the most abundant proteins (NorQ, NirK, NorC, NorD). Notably,

277 transporter proteins (five ABC transporters and a ferrienterobactin transporter) and proteins

278 involved in biosynthetic and catabolic processes (e.g., siderophore biosynthesis protein, Atu3676),

279 as well as translation (e.g., PRC domain-containing protein, Atu8119) showed increased

280 abundance during bioreactor growth. A transcriptional regulator belonging to the AraC family

281 (Atu0167) and six uncharacterized proteins demonstrated significantly higher abundance during

282 bioreactor growth. Conversely, during shake-flask growth, six proteins with roles in metabolic and

283 catalytic activities displayed higher abundance, including a sarcosine oxidase subunit (SoxA) and

284 a peptidase (Atu0288). A transcriptional regulator belonging to the LysR family (Atu3889) also

285 showed higher abundance during shake-flask growth, along with five uncharacterized proteins.

286 Taken together, these data revealed the diverse impact of growth conditions on the cellular

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287 responses of A. tumefaciens and highlighted distinct roles of transporters, enzymes, and

288 transcriptional regulators specific to bacterial growth.

289 Functional characterization of global proteome changes revealed broad impacts of bacterial

290 growth conditions on biological processes and cellular compartments

291 Examining the impact of bacterial growth conditions from a global perspective provides

292 novel insights into the vast cellular remodelling processes. We explored the changes in categories

293 based on Gene Ontology Biological Processes (GOBP) for A. tumefaciens grown in shake-flask

294 vs. bioreactor (Fig. 4B) (Ashburner et al. 2000). We observed an enrichment of proteins associated

295 with metabolic, biosynthetic, and cellular processes, as well as translation for shake-flask

296 conditions. Conversely, we observed an enrichment of categories associated with flagellar

297 motility, movement, and cell motility during bioreactor growth, suggesting distinct impacts of the

298 growth conditions on bacterial cellular processes. Next, we profiled changes in Gene Ontology

299 Cellular Compartments (GOCC), and again observed an array of category enrichments for shake-

300 flask growth, including organelles, cytoplasm, and membranes, whereas enrichment of categories

301 associated with the periplasmic space and flagellum were seen under bioreactor growth conditions.

302 Overall, these functional analyses shed light on the diversity of cellular remodeling during shake-

303 flask growth for A. tumefaciens and highlighted the focused response of bacteria during bioreactor

304 growth to alter cellular responses associated with motility.

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306 Discussion.

307 The process of molecular pharming is initiated with culturing of A. tumefaciens. In this

308 study, we used previously established growth parameters for traditional (e.g., shake-flasks)

309 bacterial culturing methods to test and optimize alternative (e.g., bioreactor) conditions. For test

310 tested bioreactor parameters, including pH, fructose, and light availability, we observed anticipated

311 in bacterial growth at pH

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329 influences measurement of optical density but does not impact target protein production (Young

330 et al. 2015). For light/dark conditions, previous experiments demonstrated high expression of

331 flagella genes and increased bacterial virulence by aiding in attachment of the bacterium to plant

332 cells, permitting increased plant cell transformations with the T-DNA, leading to production of

333 more T-DNA encoded protein (Oberpichler et al. 2008). Here, we observed an increase in bacterial

334 biomass upon growth in the dark; however, light availability did not influence FlaA abundance,

335 but we did observe a positive enrichment of proteins associated with flagellar and cell motility

336 under bioreactor growth. Notably, in a complementary study, we observed an increase in

337 production of flagellar proteins (FlaA) in A. tumefaciens upon exposure to agroinfiltration medium

338 (regardless of shake-flask or bioreactor growth), supporting a role in nutrient acquisition and stress

339 response (e.g., dark growth conditions) (Prudhomme et al. 2020). These data suggest that growth

340 in the dark may promote increased production of motility-associated proteins, which may provide

341 a benefit for plant transformation and target protein production.

342 Using mass spectrometry-based proteomics, we defined changes in A. tumefaciens under

343 shake-flask vs. bioreactor growth conditions and we uncovered new modes of cellular remodeling

344 specific to the growth conditions, suggesting functional roles that promote bacterial adaptability

345 for optimized plant transformation. For example, we observed an increase in several ABC

346 transporter proteins and proteins involved in iron uptake (e.g., transport and siderophore) during

347 bioreactor growth, which play active roles in nutrient sensing in the presence of limiting

348 environments and support remodeling of the bacteria to promote survivability (Tanaka et al. 2018;

349 Prudhomme et al. 2020). It is worth noting that such changes in transport-associated proteins may

350 be connected to differences in growth rate and nutrient consumption under the specific growth

351 conditions. Further evaluation of the interconnectivity among these parameters could provide

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352 clarity to the distinct roles of transporters in bioreactor vs. shake-flask culturing conditions. We

353 also observed regulation of two transcription factors dependent on the growth conditions. For

354 example, Atu0167, a transcriptional regulator of the AraC family showed increased abundance in

355 bioreactor growth. The AraC family of transcriptional regulators is involved in carbon metabolism,

356 stress responses, and bacterial virulence by responding to environmental chemical signals (e.g.,

357 urea, biocarbonate ions), particularly at sites where the bacterial pathogen colonizes and damages

358 its host (Gallegos et al. 1997; Yang et al. 2011). Induction of an AraC transcriptional regulator

359 during bioreactor growth is likely attributed to chemical signals in the environment and may

360 influence virulence of A. tumefaciens for improved target protein production during infection.

361 Conversely, Atu3889, a transcriptional regulator of the LysR family showed increased abundance

362 with shake-flask growth. LysR-type transcriptional regulators have been linked to bacterial

363 virulence, and in Agrobacterium, OccR (octopine catabolic regulator) and NocR (nopaline

364 catabolic regulatory) recognize and bind to opines, subsequently activating the expression of opine

365 catabolic genes (Von Bodman et al. 1992; Wang et al. 1992; Subramoni et al. 2014). The

366 production of LysR transcriptional regulators during bacterial growth in shake-flasks may prepare

367 the bacteria for the acidic environment of the host upon infection, suggesting adaptability of A.

368 tumefaciens in preparation for infection. We propose that future investigation through the

369 overexpression of these transcriptional regulators will influence infectivity of A. tumefaciens,

370 supporting opportunities to enhance target protein production.

371

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372 Conclusion.

373 In this study, we provide new insight into the biological processes and cellular remodelling

374 undergone by A. tumefaciens during shake flask vs. bioreactor growth. These observations may

375 impact the ability of A. tumefaciens to infect N. benthamiana cells and subsequent target protein

376 production during molecular pharming and are the focus of further investigations. Overall, we

377 observed a tolerable range of pH values that promote healthy bacterial growth, determined that

378 added fructose increases measurable bacterial growth by OD600nm measurements, and growth in

379 the dark positively impacts bacterial biomass through enhanced activation of flagellar proteins.

380 This process is mimicked over prolonged exposure to agroinfiltration medium, which may

381 influence bacterial virulence and ultimately, promote increased target protein yields. Several of

382 these observations are supported by our quantitative proteomic profiling, which underscores the

383 diversity of cellular remodeling between the growth conditions and emphasizes the importance of

384 transporters and flagellar proteins during bioreactor growth. Overall, our study provides novel

385 insight into fundamental biological processes of A. tumefaciens influenced by its growth

386 conditions, which may determine plant transformation efficiency and ultimately, the outcome of

387 biologic drug production by molecular pharming.

388

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389 Conflict of Interest

390 N.P., C.G.-H., E.A.-V. & J.G.-M. declare that the research is funded, in part, by PlantForm

391 Corporation. E. A.-V. is co-founder and CSO of NuBiyota LLC, a company that is working to

392 commercialize gut-derived microbial communities for use in medical indications. R.P. & W.-F.C.

393 are employees of PlantForm Corporation. M.D.M. & D.C. are original founders of PlantForm

394 Corporation and have a financial interest in the company.

395

396 Author Contributions

397 E.A.-V., M.D.M., D.C. & J.G-M. conceived the project; N.P., E.A.-V., M.D.M., D.C. & J.G-M.

398 planned experiments; N.P., C.G.-H., R.P. & W.-F.C. performed experiments; N.P., C.G.-H., E.A.-

399 V., M.D.M., D.C. & J.G-M. performed data analysis and interpretation; N.P., C.G.-H. & J.G.-M.

400 generated figures; N.P., C.G.-H., E.A.-V., M.D.M., D.C. & J.G-M. wrote and edited the

401 manuscript.

402

403 Funding

404 This work was supported, in part, by NSERC CRD (CRDPJ 539389 - 19), PlantForm

405 Incorporation, the University of Guelph, and the Canadian Foundation of Innovation (JELF 38798)

406 for J.G.-M, NSERC Engage (EGP 507653-16) for E.A.-V.

407

408 Acknowledgments

409 The authors wish to thank Dr. Jonathan Krieger of Bioinformatics Solutions Inc. for operating the

410 mass spectrometer and members of the Geddes-McAlister lab and PlantForm for their critical

411 reading and insightful comments during manuscript preparation. Support from members of Dr.

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412 Emma Allen-Vercoe’s lab at the University of Guelph, including Chris Ambrose, Caroline

413 Ganobis, and Jacob Wilde for operation and training on the bioreactor system.

414

415 Data Availability Statement

416 The mass spectrometry proteomics data have been deposited in the PRIDE partner repository for

417 the ProteomeXchange Consortium with the data set identifier: PXD018384

418 Reviewer account username: [email protected]

419 Password: JgAiDSBS

420

421

422

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516 The bases of crown gall tumorigenesis. doi:10.1128/JB.182.14.3885-3895.2000.

517

518 Figure legends

519 Figure 1: Optimization of A. tumefaciens bioreactor growth conditions. A) Comparison of

520 OD600nm of A. tumefaciens grown in bioreactor held at pH 6, 7.6, 8, 9, and standard bioreactor run

521 (pH 7). B) Comparison of OD600nm of A. tumefaciens grown in fructose-enriched media to standard

522 bioreactor run. Experiments performed in biological and technical duplicate. Two-tail Student’s t-

523 test performed: ***denotes p-value

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539 Fig. 4: Proteome remodeling by bacterial growth conditions. A) Volcano plot of shake-flask

540 vs. bioreactor growth condition samples. Student’s t-test p-value < 0.05; FDR = 0.05; s0 = 1. B)

541 1D annotation enrichment of Gene Ontology Biological Processes for growth conditions. C) 1D

542 annotation enrichment of Gene Ontology Cellular Compartment for growth conditions. For 1D

543 annotation enrichment, Student’s t-test p-value < 0.05; FDR = 0.05; score >-0.5 < 0.5. Score

544 represents the direction, which the proteins tend to deviate from the overall distribution of all

545 proteins (i.e., a positive or negative enrichment of the protein category).

546

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547 Table 1: A. tumefaciens proteins with significantly different changes in abundance between

548 shake-flask and bioreactor growth conditions.

Fold difference (log2)*GOBP Protein

IDsGene name Protein names Shake-

flaskBioreactor

Metabolic & Catalytic activityQ7CUT7 norQ Nitric oxide reductase 5.65Q7CUT2 nirK Copper-containing nitrite reductase 4.70Q7CUT9 norC Nitric oxide reductase 3.83A9CGJ7 norD Nitric oxide reductase 2.65A9CF99 bkdA1 2-oxoisovalerate dehydrogenase alpha subunit 2.34A9CF98 bkdA2 2-oxoisovalerate dehydrogenase beta subunit 2.06A9CGL8 gcdH Glutaryl-CoA dehydrogenase 1.50Q7CXU0 caiB L-carnitine dehydratase 1.44A9CGE9 soxA Sarcosine oxidase subunit alpha 3.98Q7D1S0 Atu0288 Peptidase_M75 domain-containing protein 3.64Q7CY41 cysJ Sulfite reductase 2.23A9CEY6 Atu3278 Aryl-alcohol dehydrogenase 2.06A9CHA2 Atu4878 SnoaL-like domain-containing protein 1.56Q8UH73 mqo Probable malate:quinone oxidoreductase 1.56

TransportQ7CUZ5 Atu4447 ABC transporter 4.03A9CLG5 dctP ABC transporter 2.37Q7CVB1 Atu4577 ABC transporter 2.34A9CGK7 Atu4400 Ferrienterobactin-like protein 2.05A9CGP2 Atu3170 ABC transporter 1.80A9CGL0 Atu4403 ABC transporter 1.63

Biosynthetic & Catabolic processes

A9CGK2 Atu4394 Uncharacterized protein 2.59A9CFI8 Atu3676 Putative siderophore biosynthesis protein 1.97Q8UER6 moaC Cyclic pyranopterin monophosphate synthase 1.87

TranscriptionA9CKM6 Atu0167 Transcriptional regulator, AraC family 2.70A9CFT1 Atu3889 Transcriptional regulator, LysR family 1.80

TranslationQ8U5M3 Atu8119 PRC domain-containing protein 2.72

UncharacterizedQ8UEE2 Atu1818 Uncharacterized protein 2.51Q8U5F3 Atu8146 Uncharacterized protein 2.50A9CGK8 Atu4401 Uncharacterized protein 1.68Q8U9H6 Atu3752 Uncharacterized protein 1.61A9CG22 Atu4094 DUF3597 domain-containing protein 1.49

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A9CHT0 Atu2469 Uncharacterized protein 1.44A9CIZ3 Atu1525 Uncharacterized protein 4.19A9CJ12 Atu1475 Uncharacterized protein 3.12Q7CY30 Atu2011 Uncharacterized protein 2.21A9CH98 Atu4874 Uncharacterized protein 1.84A9CJ17 Atu1468 DUF1775 domain-containing protein 1.70

549 * Fold difference represents increase in abundance of protein under shake-flask or bioreactor

550 conditions, relative to the other

551

552

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1

2

3

4

9 10 11 12 13 14

OD600nm

Time (h)

Added fructoseStandard run

0

1

2

3

4

10 11 12 13 14 15 16

OD600nm

Time (h)

pH6pH 7.6pH 8pH 9Standard run

A. B.***Page 29 of 32



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137 2074 81

A. B.

Component 1, 32.6%

Com

pone

nt 2

, 18.

6%

Shake flaskBioreactor

Bioreactor

Bio

reac

tor

Shak

e fla

sk

Shake flask

Replicate reproducibility94.5% 95.5% 96.5%

C.Page 31 of 32



DraftFold difference (log2)

-log 1

0p-

valu

eA. B.

Cellular protein metabolic processProtein metabolic processtRNA metabolic processncRNA metabolic processTranslationCellular metabolic processPrimary metabolic processCellular biosynthetic processCellular processNucleobase-containing compound metabolic processRNA metabolic processNucleic acid metabolic processCellular macromolecule metabolic processMacromolecule metabolic processMacromolecule biosynthetic processCellular macromolecule biosynthetic processFlagellar cell motilityCiliary or Flagellar motilityCellular component movementCell motilityBacterial-type flagella cell motility

Ann

otat

ions

(G

OB

P)

Shaker flask vs. Bioreactor

t-test difference-0.5 0 0.5

Score

C.OrganelleIntracellular organelleNon-membrane-bounded organelleIntracellular non-membrane-bounded organelleCytoplasmic partIntegral to membraneIntrinsic to membraneIntracellular partMacromolecular complexCytoplasmCell partPeriplasmic space Bacterial-type flagellum filament A

nnot

atio

ns (

GO

CC

)

Shaker flask vs. Bioreactor

t-test difference-0.5 0 0.5

Score

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Documents

Draft - tspace.library.utoronto.ca109 0.1% rifampicin solution (50 mg/ml) to maintain plasmids. 110 Shake-flask growth conditions 111 An overnight culture of A. tumefaciens EHA105