Computational protocol: HIV infection results in metabolic alterations in the gut microbiota different from those induced by other diseases

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[…] Protein extracts were isolated as previously described, with small modifications. Briefly, equal amounts of enriched gut bacteria samples (108 cell in total per sample), obtained as described above, were re-suspended in 1.2 mL of BugBuster® Protein Extraction Reagent (Novagen, Darmstadt, Germany) for 30 min with shaking (250 rpm). Then, the samples were disrupted by sonication using a pin Sonicator® 3000 (Misonix, New Highway Farmingdale, NY, USA) for a total time of 2 min (10 watts) on ice (4 cycles × 0.5 min with 1.0 min ice-cooling between each cycle). The extracts were then centrifuged for 10 min at 12,000 g and 4 °C to separate cellular debris and intact cells, and the supernatants were then carefully aspirated (to avoid disturbing the pellet), transferred to new tubes, and stored at −80 °C until use.Protein extracts were thawed on ice, and 50 μg of each sample was precipitated using 1 mL 20% trifluoroacetic acid at 4 °C for 40 min followed by 15 min of centrifugation at 12,500 g and 4 °C. Protein pellets were washed twice in ice-cold acetone and dried using vacuum centrifugation at 30 °C. Prior to 1D-SDS-PAGE (12%, 60 min at 20 mA), pellets were solubilized in 15 μL Laemmli-buffer by 5 min of sonication followed by vortexing. Samples were incubated for 8 min at 95 °C to reduce disulphide bonds, and 12 μL of each sample was run on a gel. Proteins were separated by less than 1 cm, cut, and subjected to in-gel trypsin digestion overnight at 37 °C. The obtained peptides were purified and desalted using C18 StageTips, dried using vacuum centrifugation, and prior to LC-MS/MS measurements, they were reconstituted in 20 μL 0.1% trifluoroacetic acid/2% acetonitrile.Each sample of 8 μL was injected using an autosampler and concentrated on a trapping column (Pepmap100, C18, 100 μm × 2 cm, 5 μm, Thermo Fisher Scientific) with water containing 0.1% formic acid and 2% acetonitrile at flow rates of 4 μL min−1. After 10 min, the peptides were eluted into a separation column (PepmapRSLC, C18, 75 μm × 50 cm, 2 μm, Thermo Fisher Scientific). Chromatography was performed using 0.1% formic acid in solvent A (100% water) and B (100% acetonitrile). The solvent B gradient was set from 4 to 8% for the first 15 min and subsequently increased to 20% for the next 110 min. After this, solvent B was increased from 20% to 30% over 15 min, from 30% to 40% over 10 min, and finally switched to 90% solvent B for an additional 10 min using a nano-high pressure liquid chromatography system (Ultimate 3000 UHPLC, Thermo Fisher Scientific). Ionized peptides were measured and fragmented using a Q Exactive mass spectrometer (Thermo Fisher Scientific). For an unbiased analysis, continuous scanning of the eluted peptide ions was performed between 400–1200 m/z and then automatically switched to MS/MS higher energy collisional dissociation mode with twelve MS/MS events per survey scan. For MS/MS HCD measurements, a dynamic precursor exclusion of 30 s per peptide match and an apex trigger of 2 to 30 s were enabled.To achieve a more precise evaluation of protein abundances and to decrease the false discovery rate of peptide identifications, a two-step database search was used. Raw MS and MS/MS data were first processed using Thermo Proteome Discoverer software (v. and Mascot Server (v. 2.4.1) and then independently searched against the NCBInr databases (v. April 25, 2015) for bacteria. Oxidation of methionine was set as the variable modification, and carbamidomethylation of cysteine was set as the fixed modification. Precursor ion tolerance was defined at 10 ppm, and fragment ion tolerance was set to 0.02 Da. Furthermore, all peaks except for the top 12 peaks per 100 Da in each MS/MS were removed to remove noise from the spectra before identification. To extract a more specific protein Fasta database, the default settings of Thermo Proteome Discoverer were kept, including protein grouping with a minimum peptide confidence set to “medium” and a delta Cn of 0.15. A strict maximum parsimony principle was used.At the second step, the obtained in-house database was used for label-free protein quantification by applying the LFQ modality of the MaxQuant software (v. Cysteine carbamidomethylation was set as the fixed modification, and methionine oxidation was set as the variable modification. Re-quantification was enabled. Two missed cleavage sites were allowed for protease digestion, and peptides were required to be fully tryptic. Other parameters of the software were kept at the default settings. These included a peptide and protein FDR below 1%, at least 1 peptide per protein, a precursor mass tolerance of 4.5 ppm and a fragment ion mass tolerance of 20 ppm. The LFQ abundance values were subjected to statistical analyses using Primer 6 software (v. 6.1.16). Significant differences between treatment groups were determined by performing one-way Analysis of Similarities (ANOSIM) and Similarity Percentages (SIMPER) analyses. […]

Pipeline specifications

Software tools Proteome Discoverer, Mascot Server, MaxQuant
Databases STEPdb
Application MS-based untargeted proteomics
Organisms Escherichia coli, Homo sapiens, Clostridioides difficile
Diseases Lupus Erythematosus, Systemic, Metabolic Diseases, HIV Infections
Chemicals Amino Acids