TITLE: METABOLIC PATHWAY ANALYSIS BY LIQUID CHROMATOGRAPHY (UHPLC) COUPLED TO HIGH RESOLUTION MASS SPECTROMETRY (LC/MS)
AUTHORS: Angelica S. Gouveia, Lucas L. Lima, Flaviane S. Coutinho, Juliano M. Rodrigues, Valquiria J. M. Pinheiro, Maria Eduarda S. Ramos, Camilo E. Vital, Claudia S.L. Pontes, Daniella P. Pinheiro, Pedro M. Vidigal, Edvaldo Barros, Humberto J O Ramos

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><div class = "justify" style = "text-align:justify">Plants produce a variety of compounds with diverse structure and abundance that play an important role in the development, growth and response to the environment. Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems.Metabolite profiles can be obtained from several samples by LC/MS (liquid chromatography coupled to mass spectrometry). However, the large number of ions detected for each run from different treatments and replicates require automatic processing for both spectra (metabolite identification and quantification) and global analysis of the metabolome. Thus, this protocol describes in detail, step-by-step, the comparison of the metabolite profiles from LC/MS data for determination of dysregulated pathways, as well as for Exploratory Statistical and Functional Enrichment Analyzes.                                  . </div><style>
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[STEPS]
?. [DATA PROCESSING AND LC/MS ALIGNMENT]
1) Install the Proteowizard package for your operational system (32 bits or 64 bits).2) Convert the data to mzXML format using ProteoWizard by executing MSConvertGUI.exe.3) Locate the directories to input all spectra and output; choose filters Peak Picking and MS level 1, click add and repeat this step for MS levels 1-2; Add samples to Browse and click Start. Wait for all jobs to be completed 4) Go to the output directory and rename the spectrums ****.mzXML and group the replicates in subdirectories according to the treatments.5) Go to the XCMS online website https://xcmsonline.scripps.eduand create an account.6) To enter your data click Dataset >>>> Add and click upload (Browse) the ***.mzXML spectra from biological replicates for each treatment one at a time, this guarantees that all the replicates will be grouped in a directory for each treatment
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							7) Rename the Dataset Name according to the treatment (use an abbreviation in English related to the treatment). Wait for the upload to finish and click Save.8) Repeat this procedure for all the treatments.9) Run the pairwiseor multigroup method:Click Create job >>> Pairwise. 
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							10) Click Select dataset: click in the directory for the first treatment (example “IAC17 NI – non infested).11) Click Next >>>> select the second treatment >>>> Next.12) Select the Parameters >>>> click HPLC/Q-TOF>>>> click Customized >>>> click View and Edit >>>>.Note: estimate the m/z accuracy and the maximum peak width for your LC/MS system. In the module “identification”, you need search for the presence of the metabolite library of the organism in study.13) Click Save. Back to the module “identification” and verified if the metabolite library of the organism in study was selected!! Click and save using a name “myXCMS parameter” for your customized method.Click Next>>>> click Submit. The job will start14) Go to View Results >>>> You will see the job progress. If you see the percentage values, the job is running without error. Wait and click View result to see the results of the completed job15) Download your full results using the link in the upper of the page. Click System Biology Results >>>> click the column Overlapping putative metabolites to classify in descending order.>>>> click the column Overlapping putative metabolites to classify in descending order. You could click in the link to see the metabolite informationfor each pathway enriched.Or select and copy the all table and edit in Excel and Word Editor for include the more significant and relevant informations such as:   ABCD1  Pathway    Overlapping putative metabolites1    All metabolites2*    p-values  2  flavonoid
  biosynthesis (in equisetum)    9    10    3.4e-4  3  kaempferol
  glycoside biosynthesis (Arabidopsis)    9    9    3.3e-4  4  sanguinarine and
  macarpine biosynthesis    8    13    9.9e-4  5  brassinosteroid
  biosynthesis I    7    17    2.2e-2  6  cytokinins
  7-N-glucoside biosynthesis    6    10    2.2e-3  7  flavonoid
  biosynthesis    6    8    7.6e-4  8  2,3-cis-flavanols
  biosynthesis    5    5    5.0e-4  9  cytokinins
  9-N-glucoside biosynthesis    5    8    2.8e-3  10  gibberellin
  inactivation I (2β-hydroxylation)    5    19    5.5e-1  11  leucopelargonidin
  and leucocyanidin biosynthesis    5    6    8.0e-4  12  matairesinol
  biosynthesis    5    9    1.2e-1  13  gibberellin
  biosynthesis I (non C-3, non C-13 hydroxylation)    4    9    3.0e-2  14  glucosinolate
  biosynthesis from hexahomomethionine    4    4    8.1e-4  15  leucodelphinidin
  biosynthesis    4    6    3.8e-3  16  luteolin
  biosynthesis    4    5    1.7e-3  17  luteolin glycosides
  biosynthesis    4    4    8.1e-4  18  quercetin glycoside
  biosynthesis (Arabidopsis)    4    6    3.8e-3  19  steviol glucoside
  biosynthesis (rebaudioside A biosynthesis)    4    7    5.4e-2  20  aliphatic
  glucosinolate biosynthesis, side chain elongation cycle    3    4    6.0e-3  21  anthocyanin
  biosynthesis (cyanidin 3-O-glucoside)    3    4    6.0e-3  22  arginine
  biosynthesis II (acetyl cycle)    3    5    1.5e-2  23  brassinosteroid
  biosynthesis II    3    14    3.5e-1  24  indole
  glucosinolate breakdown (active in intact plant cell)    3    3    5.4e-2  25  flavonol
  biosynthesis    3    4    6.0e-3  26  gibberellin
  biosynthesis II (early C-3 hydroxylation)    3    7    5.4e-2  27  glucosinolate
  biosynthesis from dihomomethionine    3    4    6.0e-3  
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							16 )Unzip the “result.rar” that you downloaded and open the file “XCMS-diffreport-MultiClass.xlsx”using the OpenOffice.  Delete the all column “A” and maintain “B”. Also, delete the columns from “C” to “Z”. Now you will maintain the information of the XIC area from the all ions detected and aligned by XCMS. 17) Edit the table for the use in the MetaboAnalyst plataform. You need inset the lines “name”, “sample” and “label” to indicate the treatments and replicates. Use appropriated codifications for the treatments because it will be used in the exported figures for publication!!!18)Save as a txt (csv) file (click in “edit filter configuration and maintaining the format), such as “XCMS-diffreport-MultiClass_yourprofile.csv”, using the following parameters:
ABCD1  Pathway    Overlapping putative metabolites1    All metabolites2*    p-values  2  flavonoid
  biosynthesis (in equisetum)    9    10    3.4e-4  3  kaempferol
  glycoside biosynthesis (Arabidopsis)    9    9    3.3e-4  4  sanguinarine and
  macarpine biosynthesis    8    13    9.9e-4  5  brassinosteroid
  biosynthesis I    7    17    2.2e-2  6  cytokinins
  7-N-glucoside biosynthesis    6    10    2.2e-3  7  flavonoid
  biosynthesis    6    8    7.6e-4  8  2,3-cis-flavanols
  biosynthesis    5    5    5.0e-4  9  cytokinins
  9-N-glucoside biosynthesis    5    8    2.8e-3  10  gibberellin
  inactivation I (2β-hydroxylation)    5    19    5.5e-1  11  leucopelargonidin
  and leucocyanidin biosynthesis    5    6    8.0e-4  12  matairesinol
  biosynthesis    5    9    1.2e-1  13  gibberellin
  biosynthesis I (non C-3, non C-13 hydroxylation)    4    9    3.0e-2  14  glucosinolate
  biosynthesis from hexahomomethionine    4    4    8.1e-4  15  leucodelphinidin
  biosynthesis    4    6    3.8e-3  16  luteolin
  biosynthesis    4    5    1.7e-3  17  luteolin glycosides
  biosynthesis    4    4    8.1e-4  18  quercetin glycoside
  biosynthesis (Arabidopsis)    4    6    3.8e-3  19  steviol glucoside
  biosynthesis (rebaudioside A biosynthesis)    4    7    5.4e-2  20  aliphatic
  glucosinolate biosynthesis, side chain elongation cycle    3    4    6.0e-3  21  anthocyanin
  biosynthesis (cyanidin 3-O-glucoside)    3    4    6.0e-3  22  arginine
  biosynthesis II (acetyl cycle)    3    5    1.5e-2  23  brassinosteroid
  biosynthesis II    3    14    3.5e-1  24  indole
  glucosinolate breakdown (active in intact plant cell)    3    3    5.4e-2  25  flavonol
  biosynthesis    3    4    6.0e-3  26  gibberellin
  biosynthesis II (early C-3 hydroxylation)    3    7    5.4e-2  27  glucosinolate
  biosynthesis from dihomomethionine    3    4    6.0e-3
?. [METABOLITE EXTRACTION AND ANALYSIS BY LC/MS]
1) Collect samples of plant tissues, immediately freeze in liquid nitrogen and store them in freezer -80°C until use.2) Macerate the samples in liquid nitrogen using mortar and pestle. Do not allow to thaw. Weigh approximately 150mg of each sample into microtubes (2ml) and annotate the weight (used for normalization ). Note: Always use microtubes and tips of good quality.3) Add 500 µl of extractive solution (80% methanol/ 0,1% formic acid) and vortex for 20 seconds and centrifuged by 14,000 g for 20 min.4) After centrifugation, filter the supernatant using a membrane of PVDF 0.22 um and transfer 80 μL of the solution to a vial. 5) Inject an aliquot of 10 μL for analysis through Nano Liquid Chromatography – Mass Spectrometry (nanoLC/MS) using the nanoACQUITY UPLC system (Waters, Milford, MA, USA), containing a trap column and a capillary column ProteCol GHQ303 C18 3,0 μm – 300 μm × 150 mm, operating at a flow rate of 5.0μL.min-1, online mode with a microESI ionization needle. This step consisted of mobile phase solutions used for the gradient program, as follows: (A) water and 0.02% acetic acid (v/v) and (B) acetonitrile and 0.02% acetic acid (v/v). The following gradient program was used: a linear rising ramp starting at 5%, increasing to 50% (B) for 30 min, 50% (B) for 5 min; linear rising ramp starting at 50%, increasing to 90% of (B) for 3 min; 90% (B) for 2 min, linear gradient descent starting at 90%, decreasing to 10% (B) for 3 min, followed by a steady condition at 10% (B) for 3 min.6)Ion scanning for MS1 spectra in positive or negative mode can be carried out for masses ranging between 100 and 1000 m/z, and between 50 and 1000 m/z for the MS2 spectra. The data were acquired over 57.0 min in each LC-MS/MS analysis, using the Hystar software program, version 3.2 (Bruker Daltonics, Bremen, Germany) and the spectra were processed through the DataAnalysis software program, version 4.0 (Bruker Daltonics, Bremen, Germany), using the default settings for metabolomics. The mass spectrometer was operated in Auto-MSn mode, which collected MS2 spectra for the most intense ions in each whole scan spectrum.
?. [METABOLITE DATA ANALYSIS]
1) Go to https://www.metaboanalyst.ca/faces/ModuleView.xhtml
Click in Statistical Analysis>>> “Select “Peak Intensity Table”and format “sample in columns (unpaired)”.Select the file “XCMS-diffreport-MultiClass_yourprofile.csv”Click in submit >>> click in skip. In the next window choice the method “Standard Deviation” for remove low quality results. Click in “process”2) The next module is “Data Normalization”.
Note: Now, the aligned raw data can be normalized using different statistical methods. During normalization process the “Data transformation” and “data scaling” have been recommended when some requirement of the parametric statistic, such as normality of the error distribution, homogeneity of variance and etc., are not met. The MetaboAnalyst pipeline enable to choice the better normalization options, indicating the result after and before the procedure. Data normalization by media or median also show satisfactory results and could be used. Use data transformation by “ParetoScaling”.See the results before and after normalization to verify if the data is under normal distribution and if the variability were reduced. Now, you can choice the analysis according with your experimental design and hypothesis. It convenient start with “Cluster Analysis” such as PCA enable view the general behaviors of the treatments and replicates, identifying the effect of specific treatments or genotype relative to the metabolite abundances. Adjustment for differences among your sample, data transformation and scaling are two different approaches to make individual features more comparable. You can use one or combine them to achieve better results. For details, see documentation on the MetaboAnalyst site (https://www.metaboanalyst.ca/faces/docs/Tutorial.xhtml).
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							Click in Normalization by median and Pareto Scaling>>>>>submitand see the results……..
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							3) Analysis by MetaboAnalyst plataformNote: Now, you can choice the analysis according with your experimental design and hypothesis. It convenient start with “Cluster Analysis” such as PCA enable view the general behaviors of the treatments and replicates, identifying the effect of specific treatments or genotype relative to the metabolite abundances. 
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							“Cluster Analysis” by PSDLA and heatmap enable also identify which metabolites showed higher variations for each group (the output indicate the relative concentrations of the corresponding metabolite in each group under study). ANOVA
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							Click in ANOVA one-wayClick in “red filled circles >>>> you will see the dysregulated compounds
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							Or click in the link to see the table containing all ANOVA resultsYou could also search the most significant ions listed from the PSDLA and heatmap above.To export: click in picture icon and select TIF and 600dpi and full page.
?. [METABOLITE IDENTIFICATION FROM MS/MS SPECTRUM]
Note: Metabolite identification based in MS and MS/MS spectra is a challenging task. To obtained high confidence putative identifications require the use of multiple approaches. Currently, this process relies on tandem mass spectrometry (MS/MS) spectra generated separately for peaks of interest identified from previous MS runs. Frequently, the start point is compare the query spectrum against a database of MS/MS spectra of reference compounds and rank the candidates based on their similarity or identity to the query spectrum. This approach allow also select putative compounds sharing chemical groups that could be useful for structure elucidation. Complementary approaches can be combined to confirmate the identifications, such as the use of isotope pattern analysis for detecting the molecular formula and further analyses the fragmentation pattern of a compound using fragmentation trees (Sirius Package) and/or of Competitive Fragmentation Modeling-ID (CFM-ID) to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures under investigation                                             . 
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							The followed steps describe the use of the NIST package for searches of the MS/MS spectrum of ions selected as dysregulated using the XCMS and Metaboanalyst plataforms. The fragmentation spectra (ESI MS/MS and LC/MS) were download from MassBank of North America (MoNA) repository (https://mona.fiehnlab.ucdavis.edu/downloads), imported and formatted for the use by the NISTdemo.exe. Use the trial version or purchase a commercial license for the NIST packages and libraries.Download the “Tutorial_LCMS.rar” from https://figshare.com/s/952a2e51cc79592deb9d containing the trial versions, videos and .mgf files as example. 1) Unzip the DataAnalysis.rar and install ….use the evaluation license for 30days or purchase a definitive license from Bruker Daltonics.Note: other packages could be used to export the MS/MS peak list. Verify the input formats for the NIST.exe.2) Run the DataAnalysis.exe. 3) Go to “file” >>>> “open” your LC/MS spectrum file. You will see the MS and MS/MS profiles.4)Go to “Method” >>>> “open” >>> in the directory “processing” choice the appropriated method, such as “DataAnalysis micrOTOF Default_HUM_altaIntensidade100.m” to detect and process for 100 higher intensity ions.5) Use the mouse right-click over the “TIC +all MSn” Windows and select “find compounds Auto MSn” to generate the compound listUse the mouse right-click over the spectrum window >>>> select “Display Parameters” configure “mass precision” for 4. High accuracy mass spectrum will be displayed. 6) Click over the LC/MS spectrum file on the upper left side to select all compounds and chromatograms>>>>> go to “File” >>> export>>>> mass spectrum>>> select mgf format >>> maintain the file name “myfilename.mgf” click in OK to export.......Note: You could export a file .mgf containing a spectrum of interest or a file containing multiple MS/MS spectrum.
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							7) Unzip the NIST.rar and install in your PC. Click in cancel when was required to search for the library in your PC.8) Unzip the “libraries.rar” and paste the directory library in “D:\NIST14\MSSEARCH\”9) Run the NIST “nist ms search.exe”.10) NIST configuration:Go to “options” >>> “library search options”Note: The NIST package enable search your MS/MSspectra against the MSM/MS spectra present in the libraries. There are three types: GC M/MS library (main lib), GC RI (retention index) and ESI - LC MS/MS. Thus, select all libraries except “mainlib, raplib and nist_riGo to “MS/MS” and configure for Q-TOF and low mass molecules. When you are performing the a search the option “Ignore the Precursor” could be unselect for enable the spectrum match with related compounds containing similar chemical groups. For example, flavonoids core with different glicoconjugates. The “Score Threshold” could also be modified to enable matching with spectra generated from different mass spectrometers using lower and higher collision energies;11) Import and open the mass spectrum exported as .mgf12) Process a double-click on the MS2file and the search will be start....Note: this search was performed using the options “Identity” and MS/MS and the option “precursor ion m/z” “in spectrum” as selected. Thus, enable hits with higher similarities for the fragments and precursor ions over the mass accuracy and scores thresholds!!!If this options fail to find compounds sharing the same accuracy mass, try the “Similarity” “simple” search option!In this example, the ion 625.1743 before the search using these option return with a matching for Quercetin 3-methyl ester (Isorharmnetin) (317.0656), thus could be a Methyl quercetin glycosilated.The search was performed again using the high constrains options such as options “Identity” and MS/MS and the option “precursor ion m/z” “in spectrum”as selected and not ignore the precursor (MS/MS options). Observe that the identify compound sharing the same high accuracy mass as a Rutin methyled or Isoharmnetin 3-rutinosideThus, if the libraries contain the MS/MS spectrum for your compound will see a matches for your spectrum (Remind that libraries contain repeated spectra for the same compound, however generated with different instruments and energy from different labs!!!). Thus, the similarity and identity searches will generate informations about a putative chemical groups and will be useful when applying other methods for putative compounds identification from MS data.
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?. [REFERENCES]
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 84:5035-9. https://doi: 10.1021/ac300698c. Epub 2012 May 10.Chong, J., Wishart, D.S. and Xia, J. (2019)Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. Current Protocols in Bioinformatics 68, e86
?. [ACKNOWLEDGMENTS]
The authors would like to thank the Núcleo de Análises de Biomoléculas (NuBioMol) of the Universidade Federal de Viçosa for providing the facilities for the data analysis. The authors also acknowledge the financial support provided by the following Brazilian agencies: Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – Finance code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), and Sistema Nacional de Laboratórios em Nanotecnologias (SisNANO)/Ministério da Ciência, Tecnologia e Informação (MCTI).