Bering Strait surface water and Chukchi Sea bottom water microbiome metaproteomics
Analysis of bacterial fraction collected on GF/F filters post pre-filtration on 1um filter. 15L were filtered from Bering Strait (BSt) surface water and Chukchi Sea (station 2) bottom waters.
Sample Processing Protocol
"Water samples were collected in August of 2013 from the Bering Strait (BSt) chlorophyll maximum layer (7 m depth, 65°43.44″ N, 168°57.42″ W) and from the more northern Chukchi Sea (CS) bottom waters (55.5 m depth, 72°47.624″ N, 16°53.89″ W) using a 24-bottle CTD (conductivity, temperature, and depth) rosette (10 L General Oceanics Niskin X). The measurement of integrated water column chlorophyll was 226.88 mg/m2 at station BSt and 2.64 mg/m2 at station CS. As our previous work has shown, to examine bacterial contributions, it is essential to remove the very high background contribution from algal inhabitants.(23) Also, oceanic marine bacteria are typically smaller than bacteria in gut biomes or freshwater systems, with the majority passing a 1.0 μm filter.(24, 25) Accordingly, a 15 L water sample was prefiltered through two high-volume cartridges (10 μm and then 1 μm) to remove larger eukaryotes, and the filtrate comprising the bacterial microbiome was then collected on a glass fiber filter (GF/F) with nominal pore size of 0.7 μm. Filters were flash frozen and stored at −80 °C until extraction. Filters were sliced, and DNA extraction was accomplished using the protocol developed for planktonic biomass on Sterivex filters, as described in Wright et al.(26) Briefly, DNA was extracted from the collected cells using phenol/chloroform and chloroform extractions. DNA was then purified using a cesium chloride density gradient. Extracted DNA was sheared to <1 kb, and excess salts were cleaned up using Agencourt AMPure XP purification (Beckman Coulter, Brea, CA). Library preparation was done with the Kapa Hyper Kit, following the manufacturer’s instructions (Kapa Biosystems, Wilmington, MA), and library quality was confirmed using the Bioanalyzer (Agilent, Santa Clara, CA). Libraries were sequenced in one lane on an Illumina HiSeq. The resulting 100 bp, paired-end sequencing reads were trimmed and filtered using SolexaQA,(27) with a minimum Phred quality score(28) of 20 on any base. GF/F filters with the bacterial fraction were placed in 1.5 mL tubes with 100 μL of 0.5 mm glass beads, 100 μL of 6 M urea, and 500 μL of nanopure water. Filters were shaken on a bead beater for 1 min and then placed in ice for 5 min. This process was repeated 10 times to ensure cell lysis and filter breakup. A needle was then heated by flame and used to create a <0.5 mm hole at the bottom of the 1.5 mL sample tube. The sample tubes were then placed atop an open 1.5 mL tube and centrifuged (3000g, 10 min). This process was completed to isolate protein lysate from extracted particles and glass beads. Protein concentrations were determined using BCA colormetric assay; 100 μg of total protein was used for digestion. Each 100 μg protein sample received 300 ng of purified human ApoA1 to monitor protein digesion. Samples were reduced, alkylated, enzymatically digested with trypsin, and desalted following Nunn et al.(29) Prior to MS injections, 50 fmol of the Pierce Peptide Retention Time Standard (ThermoFisher Scientific) was added to each autosample vial at 50 fmol per 2 μg of total protein. Peptides were separated using an inline NanoAquity HPLC with a 4 cm precolumn (5 μm; 200A; Magic C18) and 30 cm Reprosil-Pur Basic 3 μm C18 analytical column (Dr. Maisch GmbH, Germany). Peptides were eluted using a 2–30% ACN, 0.1% formic acid nonlinear gradient in 120 min at 300 nL/min. LC-MS/MS was performed with a Q-Exactive-HF (ThermoScientific) on technical triplicates for each sample. The instrument was operated in Top 20 data-dependent acquisition mode, collecting data on 400–1600 m/z range with a 5 s dynamic exclusion."
Data Processing Protocol
"All computation was performed on a Univa Grid Engine cluster with 1.90 GHz AMD Opteron processors. The MOCAT pipeline(30) was used to assemble a metagenome and predict genes as follows. Trimmed and filtered reads from both BSt and CS samples were aligned to the human hg19 reference using SOAPaligner v2.21, and aligned reads were removed. The remaining reads were assembled into contigs and scaftigs with SOAPdenovo v1.06. The assembly was revised, correcting for indels and chimeric regions, with SOAPdenovo v1.06 and BWA v0.7.5a-r16. Genes were predicted using Prodigal v2.60. We used three well-established gene fragment prediction tools to predict gene fragments directly from shotgun metagenomic sequencing reads from each sample: MetaGeneAnnotator (in multiple species mode), FragGeneScan version 1.2.0 (illumina_10 model parameters), and Orphelia (with Net300 prediction model). Separate metapeptide databases were constructed from the BSt and CS sequencing runs, from either predicted gene fragments or raw read sequences. When starting from raw read sequences, each read was translated in all six reading frames, and reading frames containing a stop codon were discarded. The results described in section 3 were obtained by starting with predicted gene fragments from MetaGeneAnnotator. Whether starting from gene fragments or raw read sequences, amino acid sequences from each nucleotide sequence were trimmed to the first and last tryptic cleavage site (or discarded if fewer than two sites), and the remaining ends were discarded (Figure 1A). This was done in order to remove partial tryptic peptide sequences that are unlikely to be detected by LC-MS/MS of a trypsinized metaproteome. The resulting candidate sequences were discarded if they were less than 10 amino acids long, if they contained no tryptic peptides with seven or more amino acids, or if the minimum Phred quality score over the length of the sequence was less than 30. Finally, metapeptide candidates meeting all the above criteria were discarded if they were represented by fewer than two reads. A FASTA database was constructed from the remaining metapeptides. For purposes of comparison, we also made use of a metagenome-derived database of translated genes from the metagenome described above and the NCBI nonredundant database of protein sequences from large environmental sequencing projects (‘env_nr’, downloaded from ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/env_nr.gz on December 1, 2015). All database searches were performed using Comet(31) version 2015.01 rev. 2, using a concatenated decoy database in which peptide sequences were reversed but C-terminal amino acids were left in place. Search parameters included a static modification for cysteine carbamidomethylation (57.021464) and a variable modification for methionine oxidation (15.9949). Enzyme specificity was trypsin, with one missed cleavage allowed. Parent ion mass tolerance was set to 10 ppm around five isotopic peaks, and fragment ion binning was 0.02, with offset 0.0. Peptide-spectrum matches (PSMs) from all technical replicates were combined into a single data set. As described previously,(32) after each unique peptide was associated with its top-scoring spectrum, irrespective of charge state, we used the widely used target–decoy search strategy of estimating the false discovery rate (FDR) associated with a given set of accepted peptides.(33) In this context, the FDR is defined as the proportion of the accepted peptides that are not responsible for generating observed spectra. We then empirically examined the trade-off between FDR and the number of accepted peptides, since in practice the mass spectrometrist is typically interested in accepting as many peptides as possible while maintaining an acceptable FDR. Note that this trade-off is similar to the distinction between precision (1 – FDR) and recall or sensitivity. Results of searches of individual samples against multiple databases were integrated as follows. PSMs from searches against all databases were combined into a single tab-delimited file of features for input to Percolator.(34) For each database, a new binary feature was added to the combined feature file indicating whether the PSM was derived from a search against that database. Percolator was then used to analyze the combined set, thereby computing a discriminant score for each PSM. For each scan with multiple PSMs (from multiple databases), all but the highest-scoring PSM were removed. Peptide-level FDR was then calculated as described above."
May DH, Timmins-Schiffman E, Mikan MP, Harvey HR, Borenstein E, Nunn BL, Noble WS. An Alignment-Free "Metapeptide" Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing. J Proteome Res. 2016 Aug 5;15(8):2697-705 PubMed: 27396978