Computational protocol: Evidence supporting dissimilatory and assimilatory lignin degradation in Enterobacter lignolyticus SCF1

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Protocol publication

[…] After 48 h of growth, cells grown in lignin-amended or unamended xylose minimal media (as detailed above) were harvested for proteomics and transcriptomics assays. This time point was chosen based on strong differences observed between lignin degraded and cell growth in amended vs. unamended conditions, with no further growth or significant lignin degradation observed after around this time. For this analysis, three biological replicates of cells grown in lignin-amended and unamended conditions were analyzed. A methanol/chloroform extraction was done on the supernatant to separate the protein, metabolites and lipids. Ice cold (−20°C) cholorform:methanol mix [prepared 2:1 (v/v)] was added to the sample in a 5:1 ratio over sample volume and vigorously vortexed. The sample was then placed on ice for 5 min and then vortexed for 10 s followed by centrifugation at 10,000 xg for 10 min at 4°C. The upper, water soluble metabolite phase and the lower, lipid soluble phase were collected into separate glass vials, and both samples were dried to complete dryness in a speed vac and then stored at −80°C until analysis. The remaining protein interlayer was placed in a fume hood to dry.The protein pellet was resuspended in 8M urea and assayed with Bicinchoninic acid (BCA) (Thermo Scientific, Rockford, IL) to determine the protein concentration. 10 mM DTT was then added to the sample, sonicated and incubated at 60°C for 30 min with constant shaking at 800 rpm. Samples were then diluted 8-fold for preparation for digestion with 100 mM NH4HCO3, 1 mM CaCl2 and sequencing-grade modified porcine trypsin (Promega, Madison, WI) was added to all protein samples at a 1:50 (w/w) trypsin-to-protein ratio for 3 h at 37°C. The samples were cleaned using Discovery C18 50 mg/1 mL solid phase extraction tubes (Supelco, St.Louis, MO), using the following protocol: 3 mL of methanol was added for conditioning followed by 2 mL of 0.1% TFA in H2O. The samples were then loaded onto each column followed by 4 mL of 95:5: H2O:ACN, 0.1% TFA. Samples were eluted with 1 mL 80:20 ACN:H2O, 0.1% TFA. The samples were concentrated down to ~30 μL using a Speed Vac and a final was performed to determine the peptide concentration. The samples were then vialed for mass spectrometric analysis.To generate the AMT database, pooled samples of equal mass from each biological replicate of the lignin and xylose samples were combined and run using a custom built 2D-LC system using two Agilent 1200 nanoflow pumps and one 1200 capillary pump (Agilent Technologies, Santa Clara, CA), various Valco valves (Valco Instruments Co., Houston, TX), and a PAL autosampler (Leap Technologies, Carrboro, NC). Full automation was made possible by custom software that allows for parallel event coordination and therefore near 100% MS duty cycle through use of two trapping columns and two analytical columns. All columns were manufactured in-house by slurry packing media into fused silica (Polymicro Technologies Inc., Phoenix, AZ) using a 1-cm sol-gel frit for media retention [a PNNL variation of Maiolica et al. ()]. Samples were run as 15 fractions separated in the 1st dimension by SCX fractionation and reversed-phase separation in the 2nd dimension. Mobile phases consisted of 0.05% ACN in Nano H20 (A) and 500mM Ammonia Formate (B) and 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) for the 1st and 2nd dimensions respectively. Supplemental Table describes the change in mobile phase for each fraction.MS analysis was performed using a Velos-LTQ-Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA) outfitted with a custom-built electrospray ionization (ESI) interface. Electrospray emitters were custom made using 150 um o.d. × 20 um i.d. chemically etched fused silica (Kelly et al., ). The heated capillary temperature and spray voltage were 300°C and 2.2 kV, respectively. Data was acquired for 100 min, beginning 65 min after sample injection and 15 min into gradient. Orbitrap spectra (AGC 1×106) were collected from 400–2000 m/z at a resolution of 60 k followed by data dependent ion trap CID MS/MS (collision energy 35%, AGC 3×104) of the ten most abundant ions. A dynamic exclusion time of 60 s was used to discriminate against previously analyzed ions.The quantitative samples were run using a custom HPLC system configured using 65 mL Isco Model 65D syringe pumps (Isco, Inc., Lincoln, NE), 2-position Valco valves (Valco Instruments Co., Houston, TX), and a PAL autosampler (Leap Technologies, Carrboro, NC), allowing for fully automated sample analysis across four separate HPLC columns. Reversed-phase capillary HPLC columns were manufactured in-house by slurry packing 5 μm Jupiter C18 stationary phase (Phenomenex, Torrence, CA) into fused silica (Polymicro Technologies Inc., Phoenix, AZ) using a 0.5 cm sol-gel frit for media retention [a PNNL variation of Maiolica et al. ()]. Mobile phases consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The mobile phase flowed through an in-line Degassex DG4400 degasser (Phenomenex, Torrance, CA). The HPLC system was equilibrated at 10k psi with 100% mobile phase A. Fifty min after sample injection the mobile phase was switched to 100% B, which created a near-exponential gradient as mobile phase B displaced A in a 2.5 mL active mixer. A 35 cm length of 360 μm o.d. × 15 μm i.d. fused silica tubing was used to split ~18 μL min−1 of flow before it reached the injection valve (5 uL sample loop). The split flow controlled the gradient speed under conditions of constant pressure operation (10 k psi). Flow through the capillary HPLC column when equilibrated to 100% mobile phase A was ~400 nL min−1. MS analysis was identical to that of the 2D system.The Accurate Mass and Time (AMT) tag (Hixson et al., ; Monroe et al., ) approach was applied to produce quantitative peptide abundance data. This method is an LC-MS approach which matches LC-MS features to a previously generated database using the metrics monoisotopic mass and normalized elution time (NET). Peptide sequences were identified using the SEQUEST v.27 (rev. 12) search engine and then rescored using MS-GF (Mass Spectum-Generating Function) (Kim et al., ). The feature database was populated using identifications having an MSGF Score ≤ 1E-9, partially/fully tryptic or protein terminal as well as a peptide prophet probability ≥ 0.5. Features from the 1-D analysis were matched to this database and filtered using a uniqueness probability ≥ 0.51 to ensure specificity of the match.Peak matching of the 1D data was performed against the AMT database for peptide identification and peptide abundance. Identifications which referenced multiple proteins were removed from the peptide list. The quantitative information was then analyzed using the analysis suite DanteR (Taverner et al., ). Within this framework the data were log2 transformed and normalized using median central tendency. Technical replicate abundances were averaged to get the abundance value for each biological replicate and required at least two abundance values to be used. Each protein had its member peptides fit to a linear model treating media and peptide as fixed effects to estimate the effect due to media and p-value significance. The generated p-values were then adjusted to compensate for multiple comparisons using Benjamini–Hochberg p-value correction (Benjamini and Hochberg, ). Proteins with a corrected p-value ≤ 0.05 were considered significantly differentially regulated. Additionally each peptide was fit to a simple model comparing the effect size and direction due to media and this was compared to that of the protein results to ensure reliability of the protein model.Metabolic pathway analysis was performed using Pathway Tools software version 16.5 (Karp et al., ). Pathway-Genome Database (PGDB) for SCF1 was previously generated (Khudyakov et al., ) based on the genome annotation from the Joint Genome Institute's Integrated Microbial Genomics (IMG) system (Markowitz et al., ), and supplemented with additional Enzyme Commission numbers from Rapid Annotation using Subsystem Technology (RAST) (Aziz et al., ). It has undergone minimal manual curation and may contain some errors, similar to a tier 3 BioCyc PGDB (Karp et al., ). Data visualization was performed using omics viewer on Pathway Tool (Paley and Karp, ). Proteomics data can be found in the public proteomics repository at via the link […]

Pipeline specifications

Software tools Comet, MS-GF, DanteR, Pathway Tools, RAST
Application MS-based untargeted proteomics
Organisms [Enterobacter] lignolyticus SCF1
Chemicals Adenosine Triphosphate, Carbon, Glutathione, Lignin, NAD, Oxygen, Xylose