MS Workflow for Quantification of Mass Isotopologue

Subscriber access provided by Universiteit Utrecht

Article

SWATH MS/MS Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon Damini Jaiswal, Charulata B Prasannan, John I Hendry, and Pramod P. Wangikar Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b05329 • Publication Date (Web): 30 Apr 2018 Downloaded from http://pubs.acs.org on May 1, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

3

SWATH MS/MS Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon

4

Damini Jaiswal,1 Charulata B. Prasannan,1,2 John I. Hendry,1 Pramod P. Wangikar1,2,3*

1 2

1

Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

5 6

2

DBT-Pan IIT Center for Bioenergy, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

7

3

Wadhwani Research Center for Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

8 9

*Corresponding author: email – [email protected], telephone - +91 2225767232

10

ABSTRACT: Accurate quantification of Mass Isotopologue Distribution (MID) of metabolites is a prerequisite for 13 C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Cofragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic 13C labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

The use of stable isotope as a tracer combined with the measurement of transient mass isotopologue distribution (MID) of intermediate or terminal metabolites provides useful information regarding intracellular fluxes.1–6 Specifically, cultures grown with isotopically labeled 13C substrates such as glucose, glycerol, amino acids and sodium bicarbonate (in case of photoautotrophic organism) can yield qualitative and quantitative insights into the cellular metabolism via 13C-metabolic flux analysis (13C-MFA). 13C-MFA relies on a computational method to estimate the intracellular fluxes from the mass isotopologue distribution of intermediate or terminal metabolites7,8. Over the past two decades, 13C-MFA has evolved into a standard tool for analyzing the metabolic phenotype9,10. The conventional isotopic stationary 13 C-MFA uses the MIDs of proteogenic amino acids for flux estimation4 obtained by GCMS and NMR.

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

The more recently developed isotopic nonstationary 13C-MFA (INST-MFA) uses the labelling patterns of intermediate metabolites that are present at very low concentration in the cells11,12and are typically obtained via LCMS. The INST-MFA method greatly reduces the duration of the labelling experiments13 and is indispensable for estimating the intracellular fluxes during photoautotrophic metabolism14. LCMS based identification and quantitation of metabolites has been widely reported in targeted 15,16 and large-scale untargeted studies in a range of biological systems.17–20 The tandem MS generated via collision induced dissociation (CID) has been primarily used in identification of compounds rather than quantification of MIDs of the fragments. Interestingly, the positional information of carbon moieties provided by tandem MS data has been shown to enhance the precision of flux estimates

1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115

both theoretically21,22and experimentally.23–25 Notably, majority of these studies were based on GCMS analysis of highly abundant protein-bound amino acids for flux estimation with only a limited number of reports that investigated the impact of tandem MS data of metabolic intermediates using LCMS25,26. The MS2 acquisition techniques can be broadly categorized as (i) data independent acquisition (DIA) of fragments of all precursors, (ii) data dependent acquisition (DDA) of fragments of only the most abundant precursors and (iii) acquisition of only a selected fragment (termed as MRM) or all fragments (termed as PRM) of a predetermined number of precursors. MRM or multiple reaction monitoring has been recognized as the paragon of MS techniques due to its high sensitivity and selectivity.27–29 MRM-based quantification of product MIDs becomes challenging due to the limitation on the number of precursor-product transitions that can be analysed in a single run and the often-tedious process of data deconvolution (Figure 1A). These limitations have been partially addressed by strategically modifying the MRM method.25,26 Recently reported parallel reaction monitoring (PRM)30 permits simultaneous detection of all product ions generated from selected precursor ions thereby reducing the number of transitions to number of carbon atoms plus 1 (Figure 1B). The use of wide-isolation-window PRM further reduces the number of transitions, although the challenges associated with data deconvolution prevail since most of the current LCMS/MS systems place limitation on the width of the Q1 window31. Although the deconvolution step can theoretically be eliminated by using a sufficiently large isolation window such an approach has not been reported. ALL 32–

In the recent times, DIA methods such as MS and SWATH 35,36 (Sequential Windowed Acquisition of all Theoretical Fragment Ion Mass Spectra, here after SWATH) have gained considerable attention in the field of proteomics36–39 and metabolomics.35,40,41 Unlike the DDA approach where MS2 spectra are recorded only when a pre-set criterion is satisfied, DIA allows unbiased cyclic acquisition of high quality multiplexed MS2 spectra.42 The DIA method, however, disbands the link between the precursor and the corresponding fragments, which can be potentially restored by spectral deconvolution based on retention time alignment. SWATH has been shown to outperform MSALL in terms of sensitivity and quality of MS2 data.42 However, in the field of metabolomics, the use of SWATH technique has been limited only to 34

116 117

molecular identification35,41 and its application for 13 C-MFA remains unexplored.

141

Here we present a novel SWATH MS/MS workflow (Figure 1C) for quantification of precursor and product MIDs of intracellular metabolites from a dynamic 13C-labeling experiment on a QqTOF instrument (SCIEX, TripleTOF 5600+). Our strategy takes advantage of the co-fragmentation of all the precursor isotopologues in a mass window followed by measurement of MS2 spectra that obviates the need for data deconvolution to obtain MIDs (Figure 1C). The method has been demonstrated for the acquisition and quantification of MIDs of a larger number of precursor and product ions per injection as compared to traditional approaches of data acquisition (Figure 1). Our results show that SWATH achieves greater sensitivity in the detection and quantitation of precursor and product isotopologues of low abundance. The MIDs of metabolite fragments acquired with SWATH-MS were found to improve the precision of flux estimates. We argue that the proposed SWATH workflow has the potential to alleviate the limitations associated with conventional DDA, DIA and MRM based MS2 data acquisition techniques in the context of 13C MFA.

142

EXPERIMENTAL SECTION

118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140

143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168

13 Standards and Reagents. C sodium bicarbonate, metabolite standards and LC-MS reagents were purchased from Sigma-Aldrich (St. Louis, MO).

Strain and Culture Conditions. The strain Synechococcus sp. PCC 7002 was grown in BG-11 and ASN-III medium (50%, v/v)43 with the initial pH of 8.2 in New Brunswick Innova 44R shaker. The culture medium was supplemented with 10 µg L-1 of Vitamin B12 and trace metal mix. The culture was grown at a temperature of 38oC and an orbital shaking speed of 120 rpm. A continuous illumination of 300 µmol photons m-2 s-1 was provided PhotoBioSim solid state lamps. Sampling and Extraction. The culture was sampled at 0, 0.5, 1, 1.5, 2, 3, 5 and 10 minutes following the introduction of 13C sodium bicarbonate . The samples were quickly filtered and extracted using method described in Supplementary Information 1. Instrumentation. A Triple TOF 5600+ mass spectrometer (SCIEX, Framingham, MA) interfaced with Shimadzu Ultra Performance- Liquid Chromatography (UPLC) system (Shimadzu, Nexera LC -30 AD, Singapore) equipped with a binary pump, degasser, column oven and autosampler was


Page 2 of 12

Page 3 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191


used for the analysis. The instrument was operated in negative ion mode for detection of metabolites and the corresponding fragments. Additional details of chromatographic method, ion source parameters and SWATH programs is provided in Supplementary Information 1 (Table S1-S5). Generation of Product ion spectral library. A library of all possible precursor and product ions of a particular metabolite was created by injection of pure standard solution diluted to an appropriate concentration in 50/50 (v/v %) of methanol and water using the IDA method. Each precursor ion and its corresponding product ions were structurally annotated using .mol file of the respective analyte. The assignment of carbon positions to each product ion of a particular metabolite was based on elemental composition calculated from mass to charge ratio and matching of theoretical and observed fragmentation pattern. An inbuilt fitting algorithm running under PeakView 2.2 environment was used for this purpose. Out of total 114 precursor and product ions annotated (Figure S1-S19), the MIDs of 53

192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213

quantifiable carbon containing ions were estimated using SWATH (Supplementary Information 2). Data Processing. The extracted ion chromatograms (XIC) of precursor ions were plotted with PeakView2.2 software (SCIEX, Framingham, MA) by using an in-house metabolite library. The peak and fragment visualization was facilitated by MasterView1.0 (SCIEX, Framingham, MA). The PRM and SWATH data for precursor and the product ions were integrated using MultiQuant 3.0.1 (SCIEX, Framingham, MA). In the case of SWATH, the raw data for a precursor and its product mass isotopologues of a particular metabolite was integrated from the Q1 isolation window where all the mass isotopologues of that metabolite were present. These areas were directly used to estimate precursor and product MIDs after correction for natural labeling using IsoCor44. The PRM data sets were deconvoluted to obtain precursor and product MIDs of metabolites as described previously26 using equations described in Supplementary Information 1.

214



215

Figure 1: Comparison of the multiple reaction monitoring (MRM), parallel reaction monitoring (PRM) and SWATH mass spectrometry based workflows used to quantify mass isotopologue distributions (MIDs) of a 16 carbon metabolite ADP-glucose (precursor) and its 6-carbon fragment with m/z of 588 and 241, respectively, in negative ion mode. (A) The MIDs of the parent ion are obtained by summing areas of all the product isotopologue that result from a given precursor isotopologue. Likewise, the product MID is calculated by summing up areas of a given product isotopologue from all possible isotopologues of the precursor. A total of 77 MRM transitions would be required to quantify all the MIDs of the parent and the fragment ions in the present example. (B) For PRM based workflow, the number of transitions needed to quantify the MIDs of precursor and product ions are fewer although the deconvolution process is similar to that for MRM based workflow. The present example would require 17 transitions. (C) The SWATH (Sequential Windowed Acquisition of all Theoretical Ion Mass Spectra) method relies on co-fragmentation of all isotopologues of a precursor ion thereby alleviating the deconvolution process.

216 217 218 219 220 221 222 223 224 225 226 227 228 229 230

Metabolic Flux Analysis. The INST-13C MFA simulations were performed using the INCA software 45using the previously reported model for

231 232 233

Synechococcus sp. PCC 700243,46. The atom transition network was modified by adding reactions to account for the additional metabolites


Page 4 of 12

Page 5 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256


and their fragments that were quantified in this study (Table S6, Supplementary Information 1). Apart from the central carbon metabolism, the model accounted for biosynthesis of the nucleotides ATP and UTP, the amino acids aspartate, glutamate, glycine and serine, cofactor NAD and turnover of ATP. The biomass equation was also modified to directly account for the demand of the above metabolites. Previously, the demand for these metabolites were represented by the demand of their respective precursors in the central carbon metabolism. The fraction of A+0 isotopologue was significant at pseudo-isotopic steady state in case of 3PGA, PEP, RUBP and APDG due to the presence of inactive pools which do not get labeled but mix with the active (labeled) pools while sampling. Therefore, dilution parameters were introduced for these metabolites. The Sum of Squared Residual (SSR) for the final fit was within the 95% confidence interval determined by χ2-Statistical test. The 95% confidence interval for the individual fluxes were determined through the parameter continuation method.

266

Statistical Analysis. The data used for 13C-MFA was an average of MIDs obtained from 2 replicates. Replicate injections from a given sample were made using PRM (n=6) and SWATH (n=3) methods. The differences in MIDs obtained by these methods were analyzed using t-test at 5% significance level (α = 0.05) and a p-value < 0.05 was considered significant. The random error of PRM and SWATH were calculated from equal number of replicates of the same sample.

267

RESULTS AND DISCUSIONS

268

Co-fragmentation of precursor isotopologues . In addition to the precursor MIDs, it is of interest to quantify the product MIDs to obtain greater information on positional labeling in metabolites of interest. However, a product isotopologue can result from a number of precursor isotopologues (Figure 1A). Thus, to improve sensitivity in quantification of product MIDs, we designed Q1 isolation windows of 18 Da or 25 Da to enable cofragmentation of precursor isotopologues of metabolites. The m/z of 60-850 Da was used to cover the metabolite mass range . In proteomics, variable Q1 isolation window widths are often used with smaller windows in the dense m/z regions. However, in our workflow, the width of Q1 window was fixed to ensure that all precursor isotopologues fall in a single window (Table S2-S5). Figure 1C depicts SWATH method with window width of 25 Da, where all the isotopologues of the precursor and product ions of ADP-Glucose are obtained in the

257 258 259 260 261 262 263 264 265

269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287

288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306

MS2 scan of window 22. Likewise, the precursor and product ions of other metabolites can be quantitated from specified Q1 isolation windows in a given LCMS run. This is advantageous over MRM which would require 77 transitions to be recorded simultaneously for the 16 carbon containing (C16) ADP-Glucose using a C6 fragment (Figure 1A). We also designed an additional SWATH program with shifted Q1 windows to enable quantitation of MIDs of metabolites, the isotopologues of which may span two windows in the first program (Figure 2). It should be noted that the metabolites depicted in Figure 2 are only representatives to emphasize the need for the two SWATH programs with shifted Q1 windows. The strategy is designed taking into consideration the number of carbons present in metabolites in general. This allows untargeted analysis of MIDs from the collected data at later stage without the need for deconvolution.

307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324

Figure 2: The design of fixed-width SWATH programs for co-fragmentation of all isotopologues of metabolites. This enables quantification of precursor and product MIDs from a single SWATH window without the need for deconvolution. The metabolites whose isotopologues spill into two adjacent windows in SWATH Program 1 fall in a single window in Program 2 and vice versa. Abbreviations: act, acetate; akg, alphaketoglutarate; asc, ascorbate; chrst, chorismate; cit, citrate; e4p, erythrose-4-phosphate; fum, fumarate; gap, glyceraldehyde 3 phosphate; glycrt, glycerate; glyx, glyoxylate; g6p, glucose-6-phosphate; i3gp, indole3-glycerol phosphate; mal, malate; mev, mevalonate; 3pga, 3-phosphoglycerate; pyr, pyruvate; r5p, ribose5-phosphate; s7p, sedoheptulose-7-phosphate; thb, tetrahydrobiopterin.



325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355

Precursor and product MIDs from MS2 scans. The MS2 scans corresponding to each Q1 isolation window contain quantifiable peaks for precursors and fragments. In MRM and PRM based methods, collision energy (CE) is typically optimized for each precursor, which improves the quantitation of fragments of interest30. SWATH provides the option to automatically select the theoretically predicted optimal values of CE for each Q1 window. This is particularly useful for untargeted analysis where optimization of CE is not feasible. Thus, the precursor and product MIDs can be quantified from the MS2 scans as long as the metabolites get separated on chromatographic retention time (RT) or fall in different Q1 windows. As a first step, we created a library of precursor and product ions by injecting the standard compounds individually under the IDA mode at the collision energies (CE) of -20, -30 and -40 eV. An initial list of 114 precursor and product ions corresponding to 19 metabolites was generated in this process. Structural annotation of the ions suggested that 98 corresponded to carbon containing precursors or fragments, which are pertinent in this study (Figure S1-S19). As a next step, the relationship between precursor and product ions was reconfirmed for a cyanobacterial sample under the SWATH mode by RT alignment with MS-DIAL41 software. While the Total Ion Chromatogram (TIC) for the unlabeled sample shows a number of overlapping peaks (Figure 3A), noticeably cleaner TICs emerge for each of the Q1

356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385

isolation windows as exemplified for the window corresponding to m/z values of 546-572 (Figure 3B). Furthermore, the Extracted Ion Chromatograms (XIC) emerge significantly cleaner as shown for UDP-Glucose (m/z 565) obtained from Q1 window of m/z 546-572 (Figure 3C). Likewise, the precursor and product peaks align well (Figure 3D) for this metabolite as demonstrated with MS-DIAL41. This permits the quantification of the MIDs of a large number of metabolites and fragments directly from the MS2 scans and presents a significant improvement over the methods that are based on either MRM or MS1 scan. Additionally, the SWATH method substantially reduces the potential interference from co-eluting compounds provided that they fall in different Q1 windows. For example, ADP and ATP co-elute in our LC program and show fragmentation patterns that are similar to each other. However, due to the difference in their precursor ion masses, it was possible to separate ADP and ATP in different Q1 isolation windows and thus quantify their precursor and product MIDs. Finally, MIDs of 53 precursors and fragments were used in 13C-MFA which were unequivocally identified and showed high abundance in the samples (Table S7, MIDs obtained using SWATH are provided in Supplementary Information 2). We also quantified few additional metabolites putatively identified using MS-DIAL41 (Figure S20) that were not included in MFA simulations.

386


Page 6 of 12

Page 7 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3: SWATH data acquisition and deconvolution. (A) Total ion chromatogram (TIC) of unlabeled sample, (B) TIC of one of the SWATH windows (m/z 546 to 572) where UDP-glucose (m/z 565) is present, (C) Extracted ion Chromatogram (XIC) of UDP-Glucose, and (D)The deconvoluted chromatograms of fragments of UDPglucose obtained through MS-DIAL.41

387 388 389 390 391 392 393 394 395 396 397

398 13

Validation with a C labeling experiment

399

Dynamic 13C labeling experiment was performed with a model cyanobacterium Synechococcus sp. PCC 7002 using uniformly labeled sodium bicarbonate (NaH13CO3) as the sole carbon source. After the addition of the labeled substrate, biomass

400 401 402 403 404 405

samples were collected at predetermined intervals, quenched in cold methanol and extracted. We compared precursor and product MIDs of a few representative metabolites obtained via the SWATH method with those from the PRM method. The CE used for PRM transitions of the metabolites under study were derived from the corresponding Q1 windows in the SWATH method.

406

407 408 409 410 411 412 413 414 415 416 417

Figure 4: (A) The fractional labeling of ADP-Glucose (ADPG) and its two fragments ADPG 241 (m/z 241) and ADPG 346 (m/z 346). The fractional labeling of glucose containing fragment (m/z 241) is greater than that of the precursor (m/z 588) and the ADP containing fragment (m/z 346). The fractional labeling in the precursor would be the weighted average of labeling in fragments ADPG 246 and ADPG 346. (B) MID of ADPG from a partially labeled cyanobacterial sample obtained using the SWATH program compared to that obtained using PRM by deconvolution of transitions for the two fragments of m/z 241 and 346, separately. (C) MID of fragment ADPG 241 obtained by SWATH is compared to that obtained by PRM and (D) MID of fragment ADPG 346 obtained by SWATH is compared to PRM. The error bar represents standard error of mean of 3 (n=3) and 6 (n=6) replicates in the case of SWATH and PRM respectively. * indicates a p-value n-m and p equals to n-m+1 ( n is the number of carbons in the precursor ion and m is the number of carbons in the product ion). However, in the case of SWATH, all the mass isotopologues of the precursor are fragmented simultaneously in a single Q1 window and the respective product spectra are recorded together, thereby resulting in a lower random and systemic error. Our results with ADP-Glucose show that the random errors are indeed greater for MIDs quantified with PRM than those quantified with SWATH (Figures S21-S23). Furthermore, we find that the attempts to deconvolute MIDs from two adjacent SWATH windows leads to systematic error. To exemplify, the isotopologues of UDP-glucuronic acid fall neatly in Q1 window in SWATH program 1, but split into two windows in program 2. The later, which requires deconvolution results in underestimation of A+6, which falls on the boundary of two windows (Figure S24 and Supplemental discussions). Thus, the proposed workflow of co-fragmentation of all isotopologues in single Q1 window provides unique advantage over MRM, PRM and wide-isolation-window PRM.

476 477 478 479

Figure 5: Comparison of 95% confidence interval for flux estimates with and without fragment MIDs for selected reactions. The dark squares indicate the estimated flux while the vertical bars represent the upper and lower bounds of the estimated confidence interval. (A) Glyceraldehyde-3-phosphate dehydrogenase and Glycerate kinase


Page 9 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(Glyceraldehyde-3-Phosphate ↔ 3Phosphoglycerate) (B) Pyruvate kinase (Phosphoenolpyruvate ↔ Pyruvate) (C) Sedoheptulose-1,7-bisphosphatase (Sedoheptulose-1,7-bisphosphate ↔ Sedoheptulose-7-phosphate) (D) Fumarase (Fumarate ↔ Malate). (E) The flux map for Synechococcus sp. PCC 7002 obtained from INST-13C-MFA using the precursor and fragment MIDs obtained with the SWATH program. A labeling experiment was performed with 13 NaH CO3 and time profile of MIDs obtained for 53 precursors and fragments. The flux values are normalized for a net -1 -1 CO2 uptake of 100 mmol.gDCW .h . ‘*’indicates the metabolites for which the precursor or product MIDs were measured using LCMS.

480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518

519 520

Deconvolution Isotopologues

of

conflicting

Product

532

We observed several instances where the masses of isotopologues of two fragments of a precursor may be in conflict. For example, fragmentation of glycerate at a collision energy of -14.8 eV leads to two 2-carbon fragments of m/z 73 (F1) and 75 (F2). Thus, the area under the peak for m/z of 75 represents summed abundance of the isotopomers F1+2 and F2+0 thereby resulting in erroneous estimates for both. .This overlap was deconvoluted based on the observation that the relative intensity of F1 and F2 remains constant at 0.87 ± 0.01 for an unlabeled sample. With the assumption that the presence of heavier isotope does not affect this ratio and since the peaks for F1+0, F1+1, F2+1, and F2+2 do not have conflict, we use equation 3 to deconvolute the area for m/z of 75 into F1+2 and F2+0. Figure S25, shows the mass isotopomer distribution of fragment F1 of glycerate before and after deconvolution. The latter agrees well with that of the precursor ion glycerate of m/z of 73. We argue that this approach may be useful when the two fragments have comparable intensities. When the intensities of the two fragments differ significantly, the less dominant fragment may not interfere in the isotopologue estimation of the dominant fragment and deconvolution may not be required. On the downside, it may not be possible to use equation 3 to deconvolute the areas of the less dominant fragment. ∑ ∑

0.87 ± 0.01

531

(3)

533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563

where i and j represents the number of labeled carbons in F1 and F2 respectively

564 565 566

521 522 523 524 525 526 527 528 529 530

Improved Precision of Measured Fluxes using SWATH Data. Although majority of the 13C-MFA studies have been based on the labeling patterns of intact metabolites, the metabolite fragment data generated by performing the tandem LC-MS/MS has been shown to improve the precision of the fluxes estimated using 13C-MFA25,26. The increase in precision in these studies were attributed to the position specific labeling information contained in

the fragment MIDs25,26 (Figure 4A and S26). In this regard, our workflow, which provides precise MIDs of parent and product ions, contributes significantly to the field of MFA. We estimated the intracellular fluxes of Synechococcus sp. PCC 7002 with and without fragment data using INST-13C-MFA . We observed a significant improvement in the 95% confidence interval range for a number of the estimated fluxes when the fragment data was included, indicating an increase in precision of flux estimates (Table S8-S9). A comparison of the 95% confidence intervals for flux estimates of a few selected reactions with and without fragments is shown in Figure 5A-D. Reactions in the Calvin cycle, Sedoheptulose-1,7-bisphosphatase, Transaldolase and Transketolase showed significant reduction in the 95% confidence intervals when the fragment data was also used for the flux estimation. The flux map obtained from the analysis is shown in Figure 5E (refer Figure S27 for fits). Thus, the collisional fragmentation information contained in the metabolite fragment data significantly improves the precision of flux estimates when used with INST-13CMFA.

567 568 569 570 571 572 573 574 575 576

CONCLUSIONS The success of non-stationary 13C-MFA hinges heavily on accurate estimation of MIDs of intermediate metabolites. The already limited pool of a metabolite is further divided amongst its isotopologues when an isotopic tracer is introduced. Furthermore, the individual acquisition of all possible precursor and product isotopologues of a metabolite using conventional MRM based MS methods from a fixed chromatographic peak width drastically decreases the sensitivity of the measurements25. We describe a SWATH-MS workflow that enables quantification of MIDs of a large number of metabolites and their fragments in a single run with improved sensitivity and smaller error compared to the conventional techniques. We demonstrate the approach with the quantification of 53 precursor and fragment ions corresponding to 19 intermediate metabolites. Although a much larger number of chromatographic features show labeling, this study was limited to metabolites



594

which were confirmed using standards. We did not adopt the approach of untargeted metabolomics as a wrongly identified metabolite would lead to erroneous flux estimates. However, the SWATH data is collected in such a way that MIDs of many more metabolites can be quantitated subject to the availability of standards or matching of mass spectra with libraries (Figure S20) making the approach truly untargeted. We find that inclusion of the MIDs of fragment ions improve the accuracy of flux estimates. This is possibly because of the information on positional labeling provided by the fragment MIDs. We believe that this approach may have significant implications when selectively labeled substrates are used. The SWATH workflow described in this study provides a significant advancement and may change how the LC/MS/MS data is collected and analysed for 13C-MFA.

595

ASSOCIATED CONTENTS

596

Supporting Information: Additional supplemental figures, tables and data, Supplementary Information 1 (PDF), Supplementary Information 2 (XLSX) and Supplementary Information 3 (XLSX). The Supporting Information is available free of charge on the ACS Publications website.

577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593

597 598 599 600 601

602 603

606

†email:

630 631

(5)

632 633

(6)

634 635 636

(7) (8)

637 638

(9)

639 640

(10)

641 642

(11)

643 644

(12)

645 646

(13) (14)

649 650

(15)

651 652

(16)

653 654 655

(17) (18)

657 659

(19)

660 661

(20)

662

[email protected], Phone : +91 (22) 2576

7232

608

Author Contributions

609

612

DJ, CBP, and PPW designed research; DJ and CBP performed research; DJ and PPW analyzed the data; JIH performed INCA simulations; DJ, CBP, JIH, and PPW wrote the paper.

613

Notes

614 615

The authors interests.

616

ACKNOWLEDGEMENTS

611

(3) (4)

658

607

610

629

656

All correspondence should be addressed to P P. Wangikar*.

627 628

648

Corresponding Author

605

(2)

647

AUTHOR INFORMATION

604

625 626

663 664

(21)

665 666 667 668 669

no

competing

financial

(24)

670 671

(25)

672 673

declare

(22) (23)

(26)

674 675

(27)

676 677

617 618 619 620

678

This work was supported by a grant from Department of Biotechnology (DBT), Government of India, awarded to PPW towards DBT-Pan IIT Center for Bioenergy (Grant No: BT/EB/PAN IIT/2012).

680

(29)

681 682

(30)

683 684

621

685

622

REFERENCES

623

(1)

624

(28)

679

(31)

686

Antoniewicz, M. R.; Kraynie, D. F.; Laffend, L. A.; Gonza, J.; Kelleher, J. K.; Stephanopoulos, G. Metab.

687 688 689

(32)

Page 10 of 12

Eng. 2007, 9, 277–292. Antoniewicz, M. R.; Kelleher, J. K.; Stephanopoulos, G. Metab. Eng. 2007, 9, 68–86. Sauer, U. Mol. Syst. Biol. 2006, 2, 62. Zamboni, N.; Fendt, S.-M.; Rühl, M.; Sauer, U. Nat. Protoc. 2009, 4, 878–892. Wiechert, W.; de Graaf, a a. Adv. Biochem. Eng. Biotechnol. 1996, 54, 109–154. Young, J. D.; Shastri, A. A.; Stephanopoulos, G.; Morgan, J. A. Metab. Eng. 2011, 13, 656–665. Wiechert, W. Metab. Eng. 2001, 3 (3), 195–206. Alagesan, S.; Gaudana, S. B.; Sinha, A.; Wangikar, P. P. Photosynth. Res. 2013, 118, 191–198. Crown, S. B.; Antoniewicz, M. R. Metab. Eng. 2013, 20, 42–48. Crown, S. B.; Antoniewicz, M. R. Metab. Eng. 2013, 16, 21–32. Nöh, K.; Wiechert, W. Biotechnol. Bioeng. 2006, 94, 234–251. Nöh, K.; Wahl, A.; Wiechert, W. Metab. Eng. 2006, 8, 554–577. Nöh, K.; Grönke, K.; Luo, B.; Takors, R.; Oldiges, M.; Wiechert, W. J. Biotechnol. 2007, 129 (2), 249–267. Shastri, A. A.; Morgan, J. A. Phytochemistry 2007, 68 (16–18), 2302–2312. Bennette, N. B.; Eng, J. F.; Dismukes, G. C. Anal. Chem. 2011, 83, 3808–3816. Lu, W.; Bennett, B. D.; Rabinowitz, J. D. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2008, 871, 236–242. Cajka, T.; Fiehn, O. Anal Chem. 2016, 88, 524–545. De Vos, R. C.; Moco, S.; Lommen, A.; Keurentjes, J. J.; Bino, R. J.; Hall, R. D. Nat. Protoc. 2007, 2, 778– 791. Garcia, C. J.; García-Villalba, R.; Garrido, Y.; Gil, M. I.; Tomás-Barberán, F. A. Metabolomics 2016, 12, 138. Tautenhahn, R.; Cho, K.; Uritboonthai, W.; Zhu, Z.; Patti, G. J.; Siuzdak, G. Nat Biotechnol 2012, 30, 826– 828. Choi, J.; Antoniewicz, M. R. Metab. Eng. 2011, 13, 225–233. Tepper, N.; Shlomi, T. PLoS One 2015, 10, 7–9. Rantanen, A.; Rousu, J.; Kokkonen, J. T.; Tarkiainen, V.; Ketola, R. a. Metab. Eng. 2002, 4, 285–294. Jeffrey, F. M. H.; Roach, J. S.; Storey, C. J.; Sherry, a D.; Malloy, C. R. Anal. Biochem. 2002, 300, 192–205. Rühl, M.; Rupp, B.; Nöh, K.; Wiechert, W.; Sauer, U.; Zamboni, N. Biotechnol. Bioeng. 2012, 109, 763–771. McCloskey, D.; Young, J. D.; Xu, S.; Palsson, B. O.; Feist, A. M. Anal. Chem. 2016, 88, 1362–1370. Kitteringham, N. R.; Jenkins, R. E.; Lane, C. S.; Elliott, V. L.; Park, B. K. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2009, 877, 1229–1239. van Dongen, W. D.; Niessen, W. M. Bioanalysis 2012, 4, 2391–2399. Zakett, D.; Flynn, R. G. A.; Cooks, R. G. J. Phys. Chem. 1978, 82, 2359–2362. Li, Z.; Li, Y.; Chen, W.; Cao, Q.; Guo, Y.; Wan, N.; Jiang, X.; Tang, Y. J.; Wang, Q.; Shui, W. Anal. Chem. 2017, 89, 877–885. Hartl, J.; Kiefer, P.; Meyer, F.; Vorholt, J. A. Nat. Microbiol. 2017, 2 (May). Bateman, K. P.; Castro-Perez, J.; Wrona, M.; Shockcor, J. P.; Yu, K.; Oballa, R.; Nicoll-Griffith, D. A. Rapid Commun. Mass Spectrom. 2007, 21, 1485–


Page 11 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


690 691

(33)

692 693 694

(34)

695 696 697

(35)

698 699

(36)

700 701 702 703

(37)

704 705 706

(38)

707 708 709

(39)

1496. Tiller, P. R.; Yu, S.; Castro-Perez, J.; Fillgrove, K. L.; Baillie, T. A. Rapid Commun. Mass Spectrom. 2008, 22, 1053–1061. Wrona, M.; Mauriala, T.; Bateman, K. P.; MortishireSmith, R. J.; O’Connor, D. Rapid Commun. Mass Spectrom. 2005, 19, 2597–2602. Arnhard, K.; Gottschall, A.; Pitterl, F.; Oberacher, H. Anal. Bioanal. Chem. 2015, 407, 405–414. Röst, H. L.; Rosenberger, G.; Navarro, P.; Gillet, L.; Miladinović, S. M.; Schubert, O. T.; Wolski, W.; Collins, B. C.; Malmström, J.; Malmström, L.; Aebersold, R. Nat. Biotechnol. 2014, 32, 219–223. Gillet, L. C.; Navarro, P.; Tate, S.; Rost, H.; Selevsek, N.; Reiter, L.; Bonner, R.; Aebersold, R. Mol. Cell. Proteomics 2012, 11, O111.016717-1-17. Tsou, C.-C.; Avtonomov, D.; Larsen, B.; Tucholska, M.; Choi, H.; Gingras, A.-C.; Nesvizhskii, A. I. Nat. Methods 2015, 12, 258–264. Wu, J. X.; Song, X.; Pascovici, D.; Zaw, T.; Care, N.;

710 711 712

(40)

713 714

(41)

715 716 717

(42)

718 719

(43)

720 721 722 723

(44)

724 725 726 727 728

(45) (46)

Krisp, C.; Molloy, M. P. Mol. Cell. Proteomics 2016, 15, 2501–2514. Li, H.; Cai, Y.; Guo, Y.; Chen, F.; Zhu, Z. J. Anal. Chem. 2016, 88, 8757–8764. Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. Nat. Methods 2015, 12, 523–526. Zhu, X.; Chen, Y.; Subramanian, R. Anal. Chem. 2014, 86, 1202–1209. Hendry, J. I.; Prasannan, C.; Ma, F.; Möllers, K. B.; Jaiswal, D.; Digmurti, M.; Allen, D. K.; Frigaard, N.U.; Dasgupta, S.; Wangikar, P. P. Biotechnol. Bioeng. 2017, 114, 2298–2308. Millard, P.; Letisse, F.; Sokol, S.; Portais, J. C. Bioinformatics 2012, 28, 1294–1296. Young, J. D. Bioinformatics 2014, 30, 1333–1335. Hendry, J. I.; Prasannan, C. B.; Joshi, A.; Dasgupta, S.; Wangikar, P. P. Bioresour. Technol. 2016, 213, 190– 197.

729

730



731

732

For TOC Only

733

TOC Graphic


Page 12 of 12

MS Workflow for Quantification of Mass Isotopologue

Recommend Documents