Supplementary Information for: Fitness and stability of obligate cross
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1 Supplementary Information for: 2 3 Fitness and stability of obligate cross-feeding interactions that 4 emerge upon gene loss in bacteria 5 6 Samay Pande1, Holger Merker1, Katrin Bohl1,2,3, Michael Reichelt4, Stefan Schuster2, Luís F. 7 de Figueiredo2,5, Christoph Kaleta3, Christian Kost1,6 8 9 1 Experimental Ecology and Evolution Research Group, Department of Bioorganic 2 10 Chemistry, Max Planck Institute for Chemical Ecology, Jena, Germany, 11 Bioinformatics, Friedrich Schiller University Jena, Jena, Germany, 12 Theoretical Systems Biology, Friedrich Schiller University Jena, Jena, Germany, 4 13 Department of Biochemistry, Max Planck Institute for Chemical Ecology, Jena, Germany, 5 14 Cheminformatics and Metabolism, European Bioinformatics Institute (EBI), Welcome Trust 15 Genome Campus, Hinxton, Cambridge, United Kingdom, 6 Institute of Microbiology, Friedrich 16 Schiller University Jena, Germany. Correspondence and requests for materials should be 17 addressed to C. Kost (email: christiankost@gmail.com) 18 19 20 21 22 23 24 25 26 1 3 Department of Research Group 27 a Amino acid production 29 30 31 32 ∆trpB ∆argH 2 28 ∆leuB ∆hisD 1 ∆hisD∆ppc ∆hisD∆mdh ∆hisD∆nuoN ∆leuB∆ppc ∆leuB∆mdh ∆leuB∆nuoN ∆trpB∆ppc ∆trpB∆mdh ∆trpB∆nuoN ∆argH∆ppc ∆ppc ∆nuoN ∆hisD ∆leuB ∆mdh *** Amino acid production 37 ∆argH∆mdh b 36 ∆argH∆nuoN 35 ∆trpB WT 34 ∆argH 0 33 38 39 40 41 42 43 44 Supplementary Figure S1. Amino acid production of wild type (WT) and all single and 45 double deletion mutants as quantified by using auxotrophs as biosensors. All 46 genotypes were cocultured together with each of four E. coli auxotrophs (1:1) and the 47 productivity of auxotrophs was determined as the number of CFUs x 107 after 24 h minus the 48 initial density. Combinations with matching amino acid auxotrophies were excluded. (a) 49 Amino acid production given as the mean productivity (95%CI) of auxotrophs. (b) Expected 50 versus observed amino acid production of cross-feeding mutants. The expected production 51 levels are the sum of the measurements of the two corresponding single gene deletion 52 mutants (i.e. auxotroph and overproducer) that were combined in one genetic background 53 (i.e. cross-feeder). Asterisks indicate a significant difference (***paired t-test, P=9x10-14, 54 n=8). 2 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Strain 1 Fitness Strain 2 0.1 Mutation 1 Mutation 2 Mutation 1 Mutation 2 ΔleuB ΔtrpB ΔargH ΔargH ΔtrpB ΔargH ΔargH ΔleuB ΔargH ΔargH ΔleuB ΔargH ΔtrpB ΔargH ΔargH ΔargH ΔargH ΔargH ΔleuB ΔargH ΔleuB ΔleuB ΔtrpB ΔleuB ΔtrpB ΔargH ΔleuB ΔleuB ΔargH ΔtrpB ΔargH ΔargH ΔargH ΔtrpB ΔargH ΔtrpB ΔargH ΔargH ΔleuB ΔargH ΔleuB ΔtrpB ΔtrpB ΔargH ΔargH ΔargH ΔargH ΔargH ΔtrpB ΔleuB ΔleuB ΔargH ΔargH ΔleuB ΔleuB ΔleuB ΔargH ΔleuB ΔleuB ΔleuB ΔhisD ΔhisD ΔhisD ΔtrpB ΔleuB ΔleuB Δmdh Δppc Δmdh Δppc Δmdh Δmdh ΔnuoN Δmdh Δppc Δmdh Δmdh ΔnuoN ΔnuoN Δmdh ΔnuoN Δppc Δppc Δppc Δmdh Δmdh Δppc Δppc Δppc Δppc Δmdh ΔnuoN Δmdh Δmdh Δppc ΔnuoN ΔnuoN Δmdh ΔnuoN Δmdh ΔnuoN Δppc Δppc Δppc Δppc ΔnuoN Δppc Δppc ΔnuoN Δmdh Δmdh Δppc Δmdh Δmdh Δmdh ΔnuoN ΔnuoN ΔnuoN ΔnuoN Δppc ΔnuoN ΔnuoN ΔnuoN ΔhisD ΔhisD ΔhisD ΔtrpB ΔtrpB ΔtrpB ΔhisD ΔtrpB ΔhisD ΔhisD ΔleuB ΔhisD ΔhisD ΔtrpB ΔtrpB ΔtrpB ΔhisD ΔtrpB ΔhisD ΔtrpB ΔhisD ΔhisD ΔleuB ΔhisD ΔtrpB ΔleuB ΔtrpB ΔhisD ΔhisD ΔhisD ΔhisD ΔtrpB ΔhisD ΔhisD ΔtrpB ΔhisD ΔhisD ΔhisD ΔleuB ΔleuB ΔtrpB ΔleuB ΔhisD ΔtrpB ΔhisD ΔleuB ΔleuB ΔhisD ΔhisD ΔhisD ΔleuB ΔtrpB ΔtrpB ΔtrpB 0.2 0.3 0.4 WT Aux ns Δppc ΔnuoN ΔnuoN Δppc Δmdh Δppc Δppc Δmdh Δppc Δmdh Δmdh Δmdh ΔnuoN Δppc Δppc Δppc ΔnuoN Δppc Δppc ΔnuoN Δmdh ΔnuoN Δmdh ΔnuoN ΔnuoN Δppc Δppc ΔnuoN Δppc ΔnuoN ΔnuoN Δmdh Δmdh ΔnuoN Δmdh Δmdh ΔnuoN ΔnuoN Δmdh Δppc Δmdh Δmdh Δmdh Δppc Δmdh Δppc ΔnuoN Δppc ΔnuoN ΔnuoN ΔnuoN Δmdh Δppc ΔnuoN Δmdh ΔnuoN Δppc Ov CF CF* 81 3 82 Supplementary Figure S2. Fitness of wild type and two-membered consortia. The table 83 (left) summarizes the genetic backgrounds of the two cocultured genotypes: wild type (WT), 84 auxotrophs (Aux), overproducers (Ov), cross-feeding consortia (CF), and cross-feeding 85 consortia (CF*) that included nuoNleuB. ‘Mutation 1’ specifies whether or not a strain is 86 auxotroph for either arginine (argH), tryptophan (trpB), leucine (leuB), or histidine 87 (hisD). ‘Mutation 2’ indicates whether a strain carries one of three deletion mutations 88 causing amino acid overproduction (i.e. nuoN, mdh, ppc). Four genotypes that cover a 89 representative spectrum of consortium-level fitness values are marked in bold and were used 90 for subsequent experiments. Shown is the median (range) fitness (i.e. Malthusian 91 parameter) of WT and all two-membered consortia. The region delimited by dashed lines 92 marks the range of WT fitness. All fitness values were significantly different from WT levels 93 (FDR-corrected two-sample t-test, P<0.05, n=8), except the one marked with ‘ns’. 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 4 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 Supplementary Figure S3. Competitive fitness of the four amino acid auxotrophic 127 mutants relative to WT. Single-gene deletion mutants were competed against WT for 24 h 128 in minimal medium, to which the one amino acid has been added (100 M) the auxotrophs 129 needed for growth. The dashed line indicates equality in fitness between WT and the 130 corresponding competitors. Asterisks indicate fitness values that were significantly different 131 from 1 (i.e. WT fitness, one sample t-test, ***P<0.001, n=10). 132 133 134 135 136 137 138 139 5 140 Supplementary Table 1. Meta-analysis and phenotypic predictions of 32 different 141 Escherichia coli strains to overproduce amino acids and/ or to be auxotroph for certain 142 143 amino acids. 144 145 146 147 148 149 150 Predicted phenotype AA auxotrophyc met leu arg tyr Total auxotrophies 6 4 3 3 2 1 5 4 3 3 2 1 1 1 1 1 1 F1299 1 EC2000623 1 EC970520 1 59243 0 LRH6 0 H4420 0 58212 0 97701 0 ECI-634 0 EDL933 0 Sakai 0 F5 0 R1797 0 EC20020119 0 FRIK1985 0 Total 6 12 7 7 5 3 3 3 2 2 1 (%) 19 37 22 22 16 9 9 9 6 6 AA overStrain IDa productionb ile pro trp FRIK 920 FRIK2001 LRH13 EC20000964 F1095 63154 lys his thr FRIK1990 FRIK1999 Zap0046 EC20000948 EC20000703 K12MG1655 71074 EC20030338 EC20011339 EC20000958 E2328 a 3 b Microarray-based comparative genomic hybridization; data from Zhang et al. (2007); Absence ()/ presence () of the ppc gene; c Strains predicted to be auxotroph () or prototroph () for certain amino acids. Predictions based on the presence/ absence of genes encoding key steps (Kanehisa & Goto 2000, Karp et al 2002) in the biosynthesis of amino acids as well as published in silico predictions (Tepper & Shlomi 2011) and experimental data (Bertels et al 2012, Joyce et al 2006, Orth et al 2011)(in the latter: essential biosynthetic genes (i.e. no growth in minimal medium) were declared as giving rise to an auxotrophy when lost). 6 151 Supplementary Materials and Methods 152 CASOP-GS 153 In order to identify target genes that can be knocked out to increase amino acid production 154 we used CASOP-GS. CASOP-GS (Bohl 2010) is based on the CASOP method (Hadicke & 155 Klamt 2010), which was extended for application to genome-scale metabolic networks. The 156 method provides a ranking of the reactions in a metabolic network with respect to how much 157 they contribute to the synthesis of a certain product of interest and is based on the concept of 158 elementary flux modes (Schuster et al 2000). Since the entire set of elementary flux modes 159 cannot be enumerated for most genome-scale metabolic networks (Klamt & Stelling 2002), 160 CASOP-GS uses a linear programming-based sampling procedure that computes a subset 161 of all elementary flux modes to obtain scores for reactions used in CASOP. Another 162 extension of CASOP-GS over CASOP is that a measure to assess the approximate increase 163 of production of a particular metabolite of interest for a given set of gene deletions is 164 provided. This measure thus allows one to rank gene deletions according to their increase in 165 the production of the target metabolite. 166 As metabolic network we used the most recent reconstruction of E. coli metabolism, 167 iAF1260 (Feist et al 2007) with external conditions corresponding to the cultivation media. 168 For taking into account regulatory interactions, we used the Boolean network provided by 169 Gianchandani et al. (Gianchandani et al 2009) For each production scenario we sampled two 170 sets of elementary flux modes: one that accounted for regulatory interactions and one that 171 did not. For each case, one million elementary flux modes were sampled. Regulatory 172 interactions were taken into account by testing for each elementary flux mode whether any of 173 the Boolean rules of the regulatory network were violated for the given external conditions. 174 Such a violation could be, for instance, that a reaction is used by an elementary flux mode 175 that is not active under the given environmental condition (i.e., it is set to off by the Boolean 176 regulatory network). 7 177 In order to identify potential deletion targets for amino acid overproduction we used 178 CASOP-GS as described before (Bohl 2010) (with γ=0.0 for the wild-type, γ=0.9 for the 179 production scenario and the weighting factor k=5). For each of the four amino acids 180 considered in this study (Arg, Trp, Leu, and His), all 1,260 genes of iAF1260 were ranked 181 according to how much their deletion would increase the production of the corresponding 182 amino acid (with and without considering regulation). Thus, we obtained eight lists of genes 183 with a total of about 80 knockout target predictions. From these lists, the ten top ranking 184 genes (excluding genes participating in the same enzyme complex) were chosen for each 185 scenario. Excluding genes with rather poor growth characteristics as documented in the Keio 186 collection (Baba et al 2006) led to a list of 54 candidate deletion targets for amino acid 187 overproduction. After inserting these 54 deletion alleles into a common genetic background 188 (E. coli BW25113), their production characteristics with respect to the wild type were 189 characterized by measuring amino acid concentrations in the supernatants as well as 190 determining cell count after 24 hours of growth. Subsequently, all mutants were ranked 191 according to total amino acid produced per cell and three of the top ranking mutants (nuoN, 192 mdh, ppc) were chosen for further examination. 193 194 Amino acid quantification 195 5 µl from a preculture diluted to OD600nm 0.1 were inoculated into 1 ml MMAB medium. To 196 quantify the amount of amino acids the overproducing strains released into the medium, 24 197 h-old cultures were sterile-filtered (0.2 µm) and amino acids in the supernatant analysed by 198 LC/MS/MS. 100µl of the culture was sampled before this step in order to estimate the 199 numbers of colony-forming units (CFUs) by plating on LB agar plates. These numbers were 200 used to normalize the total amino acid production of the culture in 1 ml of medium per 201 individual CFU. 202 Initially, we intended to analyse the non-derivatised amino acids in the culture medium 203 directly following a protocol modified from Jander et al (2004). However, it turned out that the 204 high concentrations of the media components led to a strong quenching effect in the 8 205 ionisation of the mass spectrometer due to coelution of a number of amino acids with the 206 media components. 207 Therefore, we first derivatised the amino acids with 9-fluorenylmethoxy-carbonyl chloride 208 (FMOC-Cl) (Fluka, Germany) in order to convert them into less polar derivatives. 100 µl of 209 the sterile-filtered medium was mixed with 100 µl of borate buffer (0.8 M, pH 8.0), spiked with 210 internal standards amino acid mix (13C-, 15N-labelled amino acids (algal amino acids 211 Isotec, Miamisburg, US) at a concentration of 20 µg of the algal amino acid mix per mL of 212 borate buffer). 200 µl of FMOC-Cl reagent (30 mM in acetonitrile) was added to the samples 213 and mixed. After 5 minutes, 800 µl of Hexane was added to stop the reaction and to remove 214 excess FMOC-Cl reagent, mixed and let stand for the separation of liquid phases. 200 µl of 215 the aqueous liquid phase was then transferred to a fresh 96 deep-well plate for 216 chromatographic analysis. 13 C, 15 N, 217 Chromatography was performed on an Agilent 1200 HPLC system (Agilent Technologies, 218 Böblingen, Germany). 10 µl of derivatised sample was injected and separation was achieved 219 on a Zorbax Eclipse XDB-C18 column (50x4.6 mm, 1.8 µm, Agilent Technologies, Germany). 220 Formic acid (0.05%) in water and acetonitrile were employed as mobile phases A and B 221 respectively. The elution profile was: 0-1 min, 90%A; 1-4.5 min, 10-90%B in A; 4.51-5 min 222 100% B and 5.1-8 min 90% A. The mobile phase flow rate was 1.1 ml min-1. The column 223 temperature was maintained at 20°C. The liquid chromatography was coupled to an API 224 3200 tandem mass spectrometer (Applied Biosystems, Darmstadt, Germany) equipped with 225 a turbospray ion source operated in negative ionization mode. The instrument parameters 226 were optimized by infusion experiments with pure FMOC-derivatized standards (amino acid 227 standard mix, Fluka, St. Louis, USA). The ionspray voltage was maintained at -4.5 keV. The 228 turbo gas temperature was set at 700°C. Nebulising gas was set at 70 psi, curtain gas at 35 229 psi, heating gas at 70 psi and collision gas at 2 psi. Multiple reaction monitoring (MRM) was 230 used to monitor analyte precursor ion → product ion (see Supplementary Table 2). 231 Both Q1 and Q3 quadrupoles were maintained at unit resolution. Analyst 1.5 software 232 (Applied Biosystems, Darmstadt, Germany) was used for data acquisition and processing. 9 233 Linearity in ionization efficiencies was verified by analyzing dilution series of FMOC- 234 derivatized standard mixtures (amino acid standard mix, Fluka plus Gln, Asn and Trp, also 235 Fluka). All samples were spiked with 236 concentration of the individual amino acids in the 237 been determined by classical HPLC-fluorescence detection analysis after pre-column 238 derivatisation with ortho-phthaldialdehyde-mercaptoethanol using external standard curves 239 made from standard mixtures (amino acid standard mix, Fluka plus Gln, Asn and Trp, also 240 Fluka). Individual amino acids in the sample were quantified by the respective 241 labelled amino acid internal standard, except for tryptophan, which was quantified using 242 15 13 C-, 15 N-labelled amino acids (see above). The 13 C-, 15 N-labelled amino acids mix had 13 C, 15 N- 13 C, N-Phe applying a response factor of 0.42. 243 244 Supplementary Table 2. Details for Multiple Reaction Monitoring (MRM) of FMOC- 245 derivatized amino acids on Triple quadrupole mass spectrometer (API3200, Applied 246 Biosystems, Darmstadt, Germany) in negative ionisation mode. 247 Amino acid Ala-FMOC Arg-FMOC Asn-FMOC Asp-FMOC Gln-FMOC Glu-FMOC Gly-FMOC His-FMOC Leu-FMOC Lys-FMOC Met-FMOC Phe-FMOC Pro-FMOC Ser-FMOC Thr-FMOC Trp-FMOC Tyr-FMOC Val-FMOC Retention Declustering time potential (min) (DP) 4.31 3.27 3.7 3.93 3.7 3.9 4.14 5.36 4.88 5.35 4.61 4.84 4.44 3.87 4.01 4.71 4.24 4.67 -25 -45 -35 -40 -35 -40 -30 -40 -30 -45 -30 -30 -30 -25 -35 -25 -35 -30 Collision energy (eV) MRM analyte (m/z of precursor ion > m/z product ion) MRM internal standard (m/z of precursor ion > m/z product ion) -10 -18 -12 -16 -12 -14 -10 -18 -10 -20 -18 -10 -10 -14 -14 -12 -10 -10 310 > 88 395 > 173 353 > 157 354 > 157.8 367 > 145 368 > 172 296 > 74 598 > 154 352 > 130 589 > 145 370 > 174 386 > 164 336 > 114 326 > 130 340 > 144 425 > 203 402 > 180 338 > 116 314 > 92 405 > 183 359 > 163 359 > 162.8 374 > 152 374 > 178 299 > 77 607 > 163 359 > 137 597 > 153 376 > 180 396 > 174 342 > 120 330 > 134 345 > 149 248 249 250 10 412 > 190 344 > 122 251 Cell viability assay 252 Both WT and four representative consortia (Supplementary Figure S2) were used to 253 determine whether the observed fitness advantage was due to altered mortality rates and 254 hence increased rates of amino acid liberation through lysis of cross-feeding consortia. The 255 cell viability of three replicates of each of these communities was determined by counting the 256 number of live and dead cells using flow cytometry after staining with SYBR green 257 (Eurogentech, Germany) and propidium iodide (PI, Sigma-Aldrich, USA) (Berney et al 2007). 258 For this, three replicates of WT and each cross-feeding consortium were inoculated at ~105 259 cells into 1 ml MMAB medium and samples were taken at 0 h, 16 h, and 24 h. Then, samples 260 were centrifuged and washed 2-times with PBS. After that, cells were stained with PI (0.3 261 µM) and incubated for 15 min. These cells were then again washed with PBS and counter- 262 stained with SYBR green (final concentration 1x), diluted from 10,000x stock solution 263 (Invitrogen). After 15 min of incubation in the dark, samples were measured on a Partec 264 CyFlow Space flow cytometer (Partec, Germany) with a 488 nm excitation from a blue solid- 265 state laser at 20 mW. Red fluorescence was measured at 610 nm (FL4) and green 266 fluorescence at 520 nm (FL1). The trigger was set for the green fluorescence channel FL1. 267 Data was analysed with the FlowMax software (Partec, Germany). 268 269 270 271 272 273 274 275 276 277 278 11 279 Supplementary Note 1. Causes for amino acid overproduction 280 The enormous complexity of metabolic networks and the various regulatory circuits 281 controlling it hampers a precise mechanistic explanation for an observed mutant phenotype. 282 Nevertheless, deleting one of three genes (i.e. nuoN, mdh, ppc) from the genome of E. coli 283 that have been predicted by CASOP-GS, the resulting mutants showed increased amino acid 284 production levels. Likely molecular causes for this observation are: 285 nuoN 286 The gene nuoN codes for a subunit of the inner membrane component NADH 287 dehydrogenase I, which is part of the respiratory chain. Deleting nuoN interrupts the 288 respiratory chain and impairs NADH oxidation. Since NADH is one of the principle products 289 of the TCA cycle, intermediates of this pathway might accumulate due to a feedback 290 inhibition from NADH concentration on TCA cycle flux (Vemuri et al 2006). Thus, 291 concentrations of substrates of amino acid biosynthesis increase, which may result in 292 increased amino acid production levels. 293 mdh 294 The gene mdh codes for the malate dehydrogenase. This enzyme is part of the TCA cycle, 295 the glyoxylate cycle, and the gluconeogenesis. It catalyses the reversible oxidation of malate, 296 which is then converted to oxaloacetate. Its knockout interrupts the TCA cycle and hence, 297 intermediates of this pathway accumulate, which may lead to an increased production of the 298 amino acids that derive from the TCA cycle (e.g. Ala, Ile, Leu, and Val). 299 ppc 300 The gene ppc codes for phosphoenolpyruvate carboxylase. It catalyses the carboxylation 301 from phosphoenolpyruvate to oxaloacetate. A deletion of this gene likely results in an 302 increased availability of phosphoenolpyruvate and pyruvate within the cell, which serves as a 303 precursor for many amino acids (e.g. Ala, Ile, Leu, Phe, Trp, and Tyr), which may account for 304 increased production levels of these amino acids. 12 305 References 306 Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M et al (2006). Construction 307 of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. 308 Mol Syst Biol 2: 2006 0008. 309 310 Berney M, Hammes F, Bosshard F, Weilenmann H, Egli T (2007). Assessment and 311 interpretation of bacterial viability by using the LIVE/DEAD BacLight Kit in 312 combination with flow cytometry. Appl Environ Microbiol 73: 3283-3290. 313 314 Bertels F, Merker H, Kost C (2012). Design and characterization of auxotrophy-based 315 amino acid biosensors. 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