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Title: Reprograming of Gut Microbiome Energy Metabolism by the FUT2 Crohn’s Disease
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Risk Polymorphism
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Authors: Maomeng Tonga, Ian McHardyb, Paul Rueggerc, Maryam Goudarzid, Purna C.
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Kashyape, Talin Harituniansf, Xiaoxiao Lif, Thomas G. Graebera, Emma Schwagerg, Curtis
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Huttenhowerg, Albert J. Fornace Jr.d, Justin L. Sonnenburge, Dermot P.B. McGovernf, James
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Bornemanc, Jonathan Brauna,b,1
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Author affiliation: aDepartment of Molecular and Medical Pharmacology,
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Department of
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Pathology and Lab Medicine, David Geffen School of Medicine, University of California Los
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Angeles, Los Angeles, CA 90095
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c
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d
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Washington, DC, NW 20057
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e
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CA 94305
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f
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Center, Los Angeles, CA 90048
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Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521
Department of Biochemistry and Molecular & Cellular Biology, Georgetown University,
Department of Microbiology &Immunology, Stanford University School of Medicine, Stanford,
F. Widjaja Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical
Biostatistics Department, Harvard School of Public Health, Boston, MA 02115
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To whom correspondence should be addressed. E-mail: JBraun@mednet.ucla.edu.
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Keywords: microbial ecology, glycan foraging, intestinal microbiome, multi’omic analysis
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SI Materials and Methods
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Genotyping. All genotyping of human subjects was performed at the genotyping laboratory of the
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General Clinical Research Center and Medical Genetics Institute at Cedars-Sinai Medical Center
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using the Illumina Immunochip platform with Infinium technology (protocol from Illumina, San
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Diego, CA) (Gunderson et al 2006a, Gunderson et al 2006b). Given the custom design of the
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Immunochip platform, variants were manually reviewed, and recalled when necessary, to ensure
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accurate allele-calling. Variants were reviewed based on several SNP statistic parameters (cluster
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separation, theta mean and deviation, heterozygous excess and frequency, call frequency, R
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intensity mean, and replicate and Mendelian errors). The average genotyping rate of samples was
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greater than 99.9%. Twenty-five control samples performed in replicate yielded 0.9999962%
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concordance for genotypes called. The heritability frequency of six control trios was 0.99982%.
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Metabolomic Profiling.
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Solid phase extraction: Each human lavage samples was subjected to solid-phase
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extraction to eliminate a polymeric contaminant believed to originate from the lubricant used
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during colonoscopy preparation. A 1-mL aliquot of each sample was diluted 1:2 in 2%
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phosphoric acid and loaded onto a MCX cartridge (Waters Corp, Milford, MA) after conditioning
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the cartridge with methanol and 2% phosphoric acid. Ample time was allowed for binding of
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sample to the mix-mode polymer sorbent in the cartridge. The polymeric contaminant was then
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washed off with 2% aqueous formic acid, and water. The metabolites which were retained on the
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cartridge were then eluted with 5% ammonium hydroxide. The eluate was finally dried and
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reconstituted in 2% acetonitrile in water prior to MS analysis.
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Mass spectrometry analysis: A 5 μL aliquot of extracted metabolites from each sample
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was injected onto a reverse-phase 50 × 2.1 mm ACQUITY 1.7-μm C18 column (Waters Corp,
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Milford, MA) using an ACQUITY UPLC system (Waters Corp, Milford, MA). The binary
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mobile phase components of the UPLC consisted of 2% acetonitrile in water containing 0.1%
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formic acid (buffer A) and 2% water in acetonitrile containing 0.1% formic acid (buffer B). The
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10-minute long gradient was carried out at 100% buffer A for 0.5 minutes with a ramping curve
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of 6 to 100% buffer B from 0.5 to ten minutes at a flow rate of 0.5 mL/min. A Waters Q-TOF
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Premier was operated in negative-ion (ESI-) or positive-ion (ESI+) electrospray ionization mode
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with a capillary voltage of 3200 V and a sampling cone voltage of 20 V in negative mode and 35
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V in positive mode. The desolvation gas flow was set at 800 liters/h, the temperature at 350 °C,
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the cone gas flow at 25 liters/h, and the source temperature at 120°C. Accurate mass was
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maintained by introduction of LockSpray interface of sulfadimethoxine (311.0814 [M+H]+ or
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309.0658 [M-H]-) at a concentration of 250 pg/μL in 50% aqueous acetonitrile and a rate of 150
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μL/min. Data were acquired in centroid mode with a mass window of 50 to 850 m/z, and
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processed using MassLynx software (Waters Corp, Milford, MA). The data was normalized to
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total protein using R. These metabolites were putatively identified using several online databases
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such as HMDB, MMCD, and Lipidmaps. The putative IDs of the biologically significant
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metabolites were then map out to metabolic pathways in KEGG.
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Meta-proteomic profiling and protein identification. To profile the meta-proteome of lavage
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samples, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-
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TOF MS) was performed using the soluble fraction of samples as described in (Li et al 2011).
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Protein identification was performed by in silico search, followed by two strategies:
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immunoprecipitation MALDI (when the antibodies against the target proteins were available); or,
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HPLC isolation followed by tandem MS/MS fragmentation. In silico search for putative protein
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identities was performed using the Empirical Proteomics Ontology Knowledge Base. When a
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specific antibody was available, the in silico identification was validated by immunoprecipitation
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of the original clinical isolate using an antibody specific to the protein of interest, and magnetic
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beads conjugated with either Protein A or G (Invitrogen, CA). After washing and desalting
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specifically-bound peptides were extracted with formic acid and mixed with matrix for spotting
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on a MALDI target. Protein identification was performed by confirming the expected m/z of the
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original peptide extracted by antibody, as well as examining the immunodepleted clinical sample
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to see if the same peptide/protein mass was quantitatively reduced. In the second strategy,
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MALDI MS-defined peptides of interest were fractionated and isolated by HPLC. Purified
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peptides smaller than 10 kDa were fragmented directly by LC-MS/MS coupled to a Nano2DLC
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pump (Eksigent, Dublin, CA) and LTQ-Orbitrap (Thermo Fisher Scientific, MA), or by MALDI
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MS/MS using an Ultraflex MALDI-TOF/TOF with LIFT technology (Bruker, MA). Purified
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peptides larger than 10 kDa were digested with trypsin in solution, or in a gel plug after running
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an SDS-PAGE gel, and the fragments analyzed by LC-MS/MS. Proteins were identified by
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searching against the SwissProt database using Mascot, and only proteins with P-values < 0.05
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were included in the results. The differences of peaks from MALDI-TOF mass spectrometer were
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analyzed using ANOVA.
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References
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Gunderson KL, Kuhn KM, Steemers FJ, Ng P, Murray SS, Shen R (2006a). Whole-genome
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genotyping of haplotype tag single nucleotide polymorphisms. Pharmacogenomics 7: 641-648.
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Gunderson KL, Steemers FJ, Ren H, Ng P, Zhou L, Tsan C et al (2006b). Whole-genome
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genotyping. Methods Enzymol 410: 359-376.
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Li X, LeBlanc J, Truong A, Vuthoori R, Chen SS, Lustgarten JL et al (2011). A metaproteomic
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approach to study human-microbial ecosystems at the mucosal luminal interface. PLoS One 6:
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e26542.