Data from: A catalogue of proteins released from Asaia bogorensis under two growth conditions
Abstract
Bacterial secretion systems are essential for growth and responsible for a plethora of functions, ranging from nutrient acquisition to biofilm formation, and pathogenesis. Bacteria use Type II secretion systems (T2SS) to transport folded proteins from the periplasm outside of the cell in a two-step process. Opportunistic pathogens often secrete virulence factors through this pathway. In contrast, the Type I secretion system transports proteins across both the inner and outer membranes in a single step. The Type 4 secretion system also transports substrates across the entire cell envelope. In many instances, it also translocates substrates directly into target cells. Researchers have utilized the Gram-negative bacterium Asaia bogorensis for paratransgenesis. The bacterium was engineered to secrete anti-Plasmodium effector peptides to target and destroy the Plasmodium parasites that cause malaria in the mosquito vector through the T2SS. Here we identify a catalogue of proteins released by A. bogorensis under two growth conditions as well as predictions for the secretion systems that transport them using liquid chromatography with tandem mass spectrometry. These secretion systems can be utilized in the future to improve paratransgenesis. We propose utilizing the Type 1 and/or Type 4 secretion systems for more streamlined effector secretion directly out of the bacterium. Fusion of anti-parasitic effector peptides to the substrates encoded at these loci could also serve as an effective minimal paratransgenic strain not reliant upon plasmids.
https://doi.org/10.5061/dryad.4mw6m90nd
Description of the data and file structure
Files and variables
File: 5.zip
Description: This is a scaffold file containing all potentially secreted proteins identified using LC-MS/MS from A. bogorensis. In this study, samples were grown under two conditions, chocolate media agar (lysed red blood cell agar) and minimal media agar.
Samples and sample abbreviations: Headings Chocolate and Minimal refer to the growth media from which all bacterial proteins were collected. Subheadings Choc 1, 2, etc, refer to the individual replicates for each collection. "Chocolate and Minimal No" refers to the negative control samples. On these plates, no bacteria were grown, but proteins were collected in the same method as all other samples.
Each sample (Choc 1, Choc 2, Choc 3, Choc No, Min 1, Min 2, Min 3, Min No) represents collected secretions from A. bogorensis from a single Petri plate. These are each unique replicates for each growth media condition.
Dataset: This is a single scaffold file containing mass spectrometry protein results for each of the above samples.
Other variables:
- Protein IDs: Accession identifiers for the proteins in this group
- Alternate ID: Gene name
- Mol. weight [kDa]: Molecular weight of the protein
- Protein grouping ambiguity: groups consist of proteins that cannot be definitively distinguished based on the peptides identified
Missing or empty values:
There may be some missing values. This is a reflection of those values not being applicable to a particular sample or setting. Absent values reflect a particular protein not being identified from that individual sample of interest.
- Intensity values: Arbitrary units derived from MS signal; for relative quantification
- Percent of total spectra: percentage of spectra for individual protein / total spectra identified
- Sequence coverage [%]: Range from 0 to 100
- Protein identification probability: probability protein was correctly identified
- Molecular weight: in kiloDaltons (kDa)
- Spectrum count, total unique spectra, total exclusive spectra : number of spectra counts for that protein; used to identify protein
- protein sequence: amino acid sequence with unique peptides, exclusive spectra, and oxidation sites identified
- spectrum: x-axis: mass to charge ratio (m/z), y-axis: relative intensity (arbitrary units)
Code/software
Scaffold (version Scaffold_5.3.0, Proteome Software Inc., Portland, OR) was used to validate MS/MS-based peptide and protein identifications.
Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Percolator posterior error probability calculation (Käll, L et al, Bioinformatics, 24(16):i42-i48, Aug 2008). Protein identifications were accepted if they could be established at greater than 84.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Al et al Anal. Chem. 2003;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters.
Suggested software: Data can be viewed using the free Scaffold Viewer software.
Collection of samples
Asaia bogorensis SF2.1 was grown in mannitol broth (0.5% yeast extract, 0.3% peptone, 0.5% mannitol (w/v)) to saturation in triplicate. At this time 100 μL of each culture was placed onto Davis minimal agar (0.7% dipotassium phosphate, 0.2% monopotassium phosphate, 0.05% sodium citrate, 0.01% magnesium sulfate, 0.1% ammonium sulfate (w/v) with 15 g/L agar) supplemented with 10% glucose solution (v/v) as well as chocolate agar (Davis minimal media agar supplemented with 10% glucose solution (v/v) and 2.5% lysed defibrinated sheep blood (v/v)). After three days of growth at 30°C, all samples and secretions were gently scraped from the plates for 30 seconds in 1 mL of Davis minimal broth using a glass scraper and immediately transferred to microcentrifuge tubes. All samples were centrifuged at 9,000 xg for 5 minutes, and supernatants were transferred immediately to fresh tubes and saved as the secreted samples.
Sample preparation
Seventy-five μL of each supernatant was moved to new microcentrifuge tubes with 25 μL of 3x Laemmli buffer and boiled for eight minutes. Samples were allowed to cool and then placed at -80°C. A Bradford Assay using the Pierce Micro BCA Protein Assay Kit (product #23235) was then performed according to the manufacturer instructions to quantify the concentrations of proteins in all samples against a Pierce Albumin Standard (Product #23209). For the assay, 1.5 μL of each sample was added to 148.5 μL of Davis minimal broth and 150 μL of the Bradford working reagent into a 96-well plate using two replicates of each standard and sample. The absorbance was measured using the Spectramax ID3 plate reader at 562nm. The protein concentration of each sample was determined against the standard curve. Twelve μL of each boiled sample was loaded onto a 1D SDS gel and run for 5 minutes at 200V until samples were 2-3 mm below the bottom of each well. The gel was stained with Coomassie, and protein bands were excised from the gel and stored in 100 μL of 5% acetic acid, followed by immediate shipment to the Michigan State University Proteomics Core Facility for liquid chromatography with tandem mass spectrometry analysis.
LC-MS/MS
The Michigan State University Proteomics Core Facility performed protein sample extraction from the 1D SDS gel, sample preparation, and liquid chromatography with tandem mass spectrometry (LC-MS/MS). Charge state deconvolution and deisotoping were not performed. All MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.8.3). The Mascot search engine was set up to search the crap_20230111.fasta; UP_human_20220418.fasta; UP_A_Borogensis_20231003 database (unknown version, 81934 entries) assuming the digestion enzyme stricttrypsin. Mascot was searched with a fragment ion mass tolerance of 0.020 Da and a parent ion tolerance of 10.0 PPM. Carbamidomethyl of cysteine was specified in Mascot as a fixed modification. Oxidation of methionine was specified in Mascot as a variable modification.
Scaffold (version Scaffold_5.3.0, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Percolator posterior error probability calculation (Käll, L et al, Bioinformatics, 24(16):i42-i48, Aug 2008). Protein identifications were accepted if they could be established at greater than 84.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Al et al Anal. Chem. 2003;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters.
