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Nanolc 2d

Manufactured by Thermo Fisher Scientific

The NanoLC-2D is a high-performance liquid chromatography (HPLC) system designed for analytical separation and purification of complex samples. It features a dual-pump configuration and nanoflow capabilities, enabling precise and sensitive analysis of small sample volumes. The core function of the NanoLC-2D is to separate and purify analytes in a sample using a liquid mobile phase and a stationary phase within a chromatographic column.

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4 protocols using nanolc 2d

1

Nano-HPLC-MS/MS Proteomic Analysis

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Tryptic peptides were redissolved with 20 μL 1.5% acetic acid and 5% acetonitrile. Samples (10 μL each) were analyzed by online C18 nanoflow reversed-phase HPLC (Eksigent nanoLC·2D™) connected to an LTQ Orbitrap XL mass spectrometer (Thermo Scientific) as described previously [55 (link), 56 (link)]. Briefly, samples were loaded onto a 15 cm nanospray column (75 μm inner diameter, Magic C18 AQ, 3 μm particle size, 200Å pore size, Precision Capillary Columns) and separated at 300 nL/min with a 60-min gradient from 5 to 35% acetonitrile in 0.1% formic acid. MS data were acquired in a data-dependent manner selecting the fragmentation events based on the precursor abundance in the survey scan (350-1600 Th). The resolution of the survey scan was set at 30,000 at m/z 400 Th. Dynamic exclusion was 90 s. After each survey scan, up to ten collision-induced dissociation (CID) MS/MS spectra were acquired in the linear ion trap. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [57 (link)] with the dataset identifier PXD000776.
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2

Shotgun Proteomics Protocol for Identification

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Proteins were separated on a 2D gel and in-gel digested with trypsin (22 (link)). The resulting peptides were cleaned up using C18 OmixTips (Varian) and analyzed using an Eksigent nanoLC-2D coupled to a Thermo Orbitrap XL mass spectrometer (23 (link)). Proteins were identified from tandem mass spectra using complementary protein identification software systems, Bioworks (Thermo) and Scaffold (Proteome Software) as described by Sadygov (24 (link)).
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3

Characterizing Cryptic Proteins in øX174

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We linearized the genomes just following the stop codon of gene H and added 88 bp of the H/A-intergenic region and start of gene A to the 3′-end of each template. These modifications ensured that each linear genome contained uninterrupted sequences for all predicted ORFs (SI Appendix, Fig. S2). We added 8.6 nM of each genome and 1 U/µL Murine RNase inhibitor to the PURExpress expression system (New England Biolabs). Each genome was run in a separate reaction using either 12C/14N-, 13C/14N-, or 13C/15N-lysine (Thermo Scientific Pierce) at 25 °C for 16 h. All 3 reactions were pooled and digested with Lys-C, cleaned up, and run on nanoLC-2D and LTQ-Orbitrap Velos (ThermoFisher) followed by analysis with Byonic v2.0-25 software. The peptide search database contained all E. coli protein sequences pooled with the 11 known øX174 proteins and the 82 cryptic ORFs identified in this work.
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4

Metaproteomic Analysis of Fecal Microbiome

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Fecal specimens were thawed at 4°C and microbial cells extracted by differential centrifugation (Apajalahti, et al., 2002 (link), Verberkmoes, et al., 2009 (link)). Microbial cell pellets (~100mg wet weight) were washed then lysed via probe sonication in wash buffer with 0.25% w/v Rapigest. Lysed samples were centrifuged at 15K rcf for five minutes, and the protein concentration was determined via bicinchoninic acid colorimetric assay (Chutipongtanate, et al., 2012 (link)). Thirty mcg were fractionated via SDS-PAGE and in-gel digested overnight with trypsin. Tryptic peptides were purified via Varian C18 OMIX tips, and analyzed using an Eksigent nanoLC-2D coupled to a Thermo Orbitrap XL tandem mass spectrometer. Human and microbial proteins were identified from raw MS data using Proteome Discoverer and Scaffold software packages in conjunction with the UniProt human proteome and the METADB database, respectively. The METADB database has been previously described by Verberkmoes, et al. (Verberkmoes, et al., 2009 (link)).
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