Ultrahigh reolution mass spectrometry data on lysis products following predation by micropredators from samples generated in 2022-07 by laboratory microcosm experiments and data collected in 2022-07 and 2023-04

Website: https://www.bco-dmo.org/dataset/922678
Data Type: experimental
Version: 1
Version Date: 2024-03-18

Project
» Excellence in Research: Assessing the Control by Multiple Micropredators on Bacterial Communities in Estuarine Environments and Characterization of Prey Lysis Products Resulting from Each Predator (Predators of bacteria)
ContributorsAffiliationRole
Williams, Henry NealFlorida A&M University (FAMU)Principal Investigator
Chen, HuanFlorida State University - National High Magnetic Field Lab (FSU - NHMFL)Co-Principal Investigator
Cobb-Abdullah, AhkinyalaVirginia Union University (VUU)Co-Principal Investigator
Kranz, SvenFlorida State University (FSU)Co-Principal Investigator
Stukel, MichaelFlorida State University (FSU)Co-Principal Investigator
York, Amber D.Woods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

Abstract
This dataset contains potential metabolites identified by 14.5T and 21T FT-ICR MS analyzed by MSDial. Heterotrophic protists and bacteriophages are considered to be major contributors to bacterial mortality in the microbial loop. Although evidence shows a similar role for the predatory bacterium, Halobacteriovorax (HBx), it has been largely ignored. Since the predation mechanism and utilization of prey cellular compounds differ for each predator, we examine by ultra-high resolution mass spectrometry the molecular-level composition of dissolved organic matter released at lyses of the prey. Equal volumes of prey bacteria suspension in artificial seawater were dispensed into three flasks and tested against a single strain of protist, bacteriophages, or HBx. Following incubation and prey lysis, each predator-prey culture was filtered sequentially to remove cell debris, leaving extracellular metabolites in the filtrate. Filtrates were acidified and extracted by solid phase extraction, followed by reverse-phase liquid chromatography coupled with positive and negative electrospray ionization (ESI) orbitrap and FT-ICR mass spectrometry. Results show that predation by protists, HBx, and phages, resulted in separate, distinguishable products. Prey lysis products unique to each micropredator were identified by comparing metabolite profiles. The structural information of these predator-specific signatures was further validated by tandem MS analysis. The chemical composition assessment of the lysis products by micropredators provides insights into how each predator may contribute in different ways to nutrient cycling in the ocean.


Coverage

Temporal Extent: 2022-07-07 - 2023-04-01

Dataset Description

This dataset includes two forms of the data, one as plain-text file 922678_v1_balo_metabolites_15t_21t_msdial.csv (See "Data Files" section), and one in Microsoft Excel (.xlsx) format sheets from MSDial software (See "Supplemental Files" sections).


Methods & Sampling

The first experiment was done using one strain of Vibrio vulnificus as prey. Equal volumes of the prey bacterial suspensions in artificial seawater were dispensed and tested against a single strain of protist, bacteriophages, and HBx. Predator-free control was also included in the analysis. Predator-prey dual cultures were filtered sequentially through 0.8µm, 0.45µm, 0.2 µm, and 0.01µm membrane filters to obtain extracellular metabolites. Filtrates were acidified and extracted by solid phase extraction (Dittmar et al. 2008) using the C18 cartridges, followed by reverse-phase liquid chromatography coupled with positive and negative electrospray ionization (ESI) FT-ICR MS.  

Instrument summary:

Liquid Chromatography
Reverse phase LC separations were performed online with a C18 custom packed (ReprosSil_Pur 120 Å C18-AQ, 5 μm., 360 μm o.d. X 75 μm i.d., 7.5 cm) outfitted on our nano-HPLC system (ACQUITY M-class, Waters, Milford, MA). An analytical gradient was modified from published gradients to fit our LC-MS/MS set-up (Soule et al. 2015). Analytical gradient going from 5% B to 100% B was carried out (A: 0.1% formic acid in water B: 0.1% formic acid in methanol). Samples were run in triplicates with 4 µL injections at a flow rate of 0.3 uL/min for the analytical separation.

Tandem Mass Spectrometry
Electrospray ionization (ESI) voltage was set to positive 3.5 kV with a source temperature of 300 ºC. MS analysis was performed on the NHMFL 14.5 T and 21 T FT-ICR MS mass spectrometer outfitted with a Velos Pro linear ion trap (Thermo Fisher Scientific) at the front end (Hendrickson et al. 2015; Schaub et al. 2008). Precursor spectra were obtained in the ICR cell with a 1E6 automatic gain control (AGC), sum 4 transient acquisition, and resolving power of 600k at 400 m/z. CID fragmentation spectra were collected from the Velos Pro Ion Trap on the top 3 most abundant ions in the precursor scan with sum 3 transient acquisition and collision energy of 40. Precursor and fragment spectra were collected in the 150-1500 m/z dynamic range. 

Location Summary:

Samples were generated in 2022-July by laboratory microcosm experiments, and the data were collected in 2022-07 and 2023-04.

In vitro microcosm experiments to isolate and characterize the prey cellular organic matter released as lysis products as a result of micropredators were conducted at PI Williams lab at Florida A&M University. 

FT-ICR MS data collection and analysis was conducted by co-PI Chen in the ICR user facility at the National High Magnetic Field Laboratory. 

Orbitrap MS data were collected at the Systems Mass Spectrometry Core at Georgia Institute of Technology in collaboration with Dr. Facundo Fernandez's lab. 


Data Processing Description

First, MSDial 4.90 (RIKEN), a freely available analysis software, was used for metabolite analysis loaded with a public library of tandem mass spectrometry experiments (13,303 compounds: positive mode) (Tsugawa et al. 2015). MSDial was used due to the ability to identify unique metabolites as well as compare relative abundance of metabolites within each sample. Data was organized by predator types for comparisons. Precursor parameters were set for accurate mass tolerance for MS and MS/MS (0.001 Da and 0.025 Da) and a minimum peak detection of 10k amp. Eight analyte adducts were selected, ([M+NH4]+, [M+Na]+ , [M+Na]+ , [2M+NH4]+ , [M+2H]2+, [M+H+NH4]2+, [M+H+Na]2+, [M+3H]3+) based on previous studies.

The identification score was set to 80% in MSDial for identifications and to limit false positives. A standard mixture was made with B12, ferrichrome, and ferrioxamine E, each at 5 mM. The standard mix runs were used for the alignment of all spectra for comparison. MSDial output includes metabolite retention times, m/z, compound name, and relative abundance within each sample set.

The dataset was also analyzed using Compound Discoverer (ThermoFisher Scientific, version 3.3 SP1).

After analysis, the data were exported from MSDial and saved as .xlsx. The pdf files (*_CompoundDiscovererReport.pdf) were exported from Compound Discoverer. See the "Supplemental Files" section for access to the .xslx and .pdf files.

Excel files contain sheets named by treatment:
'BALO VV' =  Samples from the Microcosm with Halobacteriovorax as the predator and Vibrio vunificus as the prey.; 
'Protist VV' = Samples from the Microcosm with Protist as the predator and Vibrio vunificus as the prey.; 
'Phage VV' =  Samples from the Microcosm with viruses as the predator and Vibrio vunificus as the prey.
'VV' = Samples from the Microcosm with Vibrio vunificus as the prey and no present of the predator. 


BCO-DMO Processing Description

One primary data table for this dataset was constructed by combining multiple tables within multiple Excel sheets from two separate Excel files. Details below of how that was performed and imported into BCO-DMO. Additional columns were added to the combined data table to contain metadata provided in the original excel sheets. The Excel sheets were added as supplemental files.

Source files for the combined table "922678_v1_balo_metabolites_15t_21t_msdial.csv":

* 15T_FT-ICR_NegESI_20220421_MSDIAL_Results.xlsx (see supplemental files)
* BALO_Metabolites_21T_MSDial.xlsx (see supplemental files)

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Sheets "BALO VV", "Protist VV", "Phage VV", "VV" of 15T_FT-ICR_NegESI_20220421_MSDIAL_Results.xlsx contained multiple tables and lists within each sheet. This csv file contains the combined tables (two per sheet) that were originally next to each other horizontally, each with a label and number of compounds above the table header. The label and number of compounds was added as columns in combined table.

Each table had the exact same header:
ID RT m/z type metabolite comment height area gaussian s/n
additional columns added"
Treatment (e.g. "BALO VV") - this was the sheet name in original excel file explained to be the treatement.
Label (e.g. "2022225_HC_Pos_4_Vv_01") - assumed to be run ID but confirming with the original submitter.
num_compounds , this was provided as text above each subtable in each sheet, assumed to be per run, confirming with submitter.

---

Sheets "BALO VV" "Protist VV" "Virus VV" and "VV" from BALO_Metabolites_21T_MSDial.xlsx were imported into the BCO-DMO data system and combined into one table as was done with the 15T results. The columns were not named but appeared to be the same format as 15T data which did have headers so assumed to be the same and confirming with the submitter before publishing.

15T and 21T data were combined with additional column "analysis_type" with values "14.5T FT-ICR MS
" or "21T FT-ICR MS"

There were no "num compound" and identifiers at the tops of the tables like in the 15T data so I added "1" or "2" as "Label" which allows separation of the two tables within each sheet.

-------

In the combined "primary" table for this dataset, a non-standard whitespace character was removed:
"Pseudolaric Acid A-O-beta-D-glucopyranoside" which is right before the A. That value appears in BALO_Metabolites_21T_MSDial.xlsx sheet Virus VV row 122.
"Pseudolaric Acid A-O-beta-D-glucopyranoside" changed to :
"Pseudolaric Acid A-O-beta-D-glucopyranoside"

-------

Each sheet contained an additional suitable to the right of the two other subtables. This list was assumed to be a list of metabolites (needs confirmation from the data submitter). They were all concatenated and added as supplemental file "Metabolites List".

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* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores.  Can not start with a number]

* column "comment" was removed since empty.


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Related Publications

Dittmar, T., Koch, B., Hertkorn, N., & Kattner, G. (2008). A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnology and Oceanography: Methods, 6(6), 230–235. doi:10.4319/lom.2008.6.230
Methods
Hendrickson, C. L., Quinn, J. P., Kaiser, N. K., Smith, D. F., Blakney, G. T., Chen, T., Marshall, A. G., Weisbrod, C. R., & Beu, S. C. (2015). 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: A National Resource for Ultrahigh Resolution Mass Analysis. Journal of the American Society for Mass Spectrometry, 26(9), 1626–1632. https://doi.org/10.1007/s13361-015-1182-2
Methods
Kido Soule, M. C., Longnecker, K., Johnson, W. M., & Kujawinski, E. B. (2015). Environmental metabolomics: Analytical strategies. Marine Chemistry, 177, 374–387. doi:10.1016/j.marchem.2015.06.029
Methods
Schaub, T. M., Hendrickson, C. L., Horning, S., Quinn, J. P., Senko, M. W., & Marshall, A. G. (2008). High-Performance Mass Spectrometry: Fourier Transform Ion Cyclotron Resonance at 14.5 Tesla. Analytical Chemistry, 80(11), 3985–3990. https://doi.org/10.1021/ac800386h
Methods
Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M. (2015). MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods, 12(6), 523–526. https://doi.org/10.1038/nmeth.3393
Methods

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Parameters

ParameterDescriptionUnits
analysis_typeAnalysis type ("14.5T FT-ICR MS " or "21T FT-ICR MS") unitless
TreatmentTreatment, (e.g. 'BALO VV'), see Methods & Sampling. These are treatment names are also the sheet names within the original excel files (see Supplemental Files). unitless
LabelUnknown (assumed to be run identifiers). These were identifiers above each subtable in the originally provided Excel files). unitless
num_compoundsUnknown (assumed to be count of compounds per run). unitless
IDUnknown unitless
RTUnknown. Assumed to be retention time. unitless
m_to_zMass to charge ratio (m/z) unitless
typeUnknown. Assumed to be Precursor type (e.g. "[M+H]+") unitless
metaboliteUnknown. Assumed to be the name or identifier of the metabolite identified. (e.g. "w/o MS2:Flavone base + 2O, 1Prenyl; PlaSMA ID-1724" ) (e.g. "Unknown (carbon number 18); PlaSMA ID-2556") unitless
heightUnknown unitless
areaUnknown unitless
gaussianUnknown. Assumed to represent the fit score, with values closer to 1 indicating a better fit to a Gaussian shape. unitless
s_to_nUnknown. Assumed to be signal to noise ratio (s/n, SNR) unitless

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Project Information

Excellence in Research: Assessing the Control by Multiple Micropredators on Bacterial Communities in Estuarine Environments and Characterization of Prey Lysis Products Resulting from Each Predator (Predators of bacteria)


NSF Award Abstract:

Microbes are the most abundant organisms on Earth and play an important role as degraders, cycling nutrients in the environment. Too many or too few bacteria may disrupt a sensitive ecological balance and proper functioning of environmental processes such as carbon, nitrogen and phosphorus cycles. The abundance of bacteria populations in any given environment is controlled by various biological, chemical and physical mechanisms. Among the biological agents are microscopic predators, or micropredators, of bacteria. The most studied of these are protists, viruses that infect bacteria, and a group of bacteria collectively known as the Bdellovibrio and like organisms (BALOs). These micropredators prey upon certain bacteria to obtain required nutrients or other cellular material for their replication. In the process, cellular products from the prey bacteria are released into the environment and utilized as nutrients by other microbes. Although the micropredators co-occur, and likely interact, in nature, most experimental studies have investigated their activities individually, rather than collectively. As a result, little is known about their collective role in controlling bacteria populations and the cycling of nutrients. The goal of the proposed research is to address this gap in knowledge by investigating all three as a collective group under simulated natural conditions representing a range of temperature, salinity and abundance of prey. This project is conducted at two Historically Black Universities (HBCUs) with strong records of training and mentoring students and postdocs from underrepresented populations in science. The project benefits up to 100 students by providing unique and meaningful educational and research training experiences at the undergraduate and graduate student levels and for early-career scientist. Specific activities include courses on scientific writing and presenting results at annual project workshops as well as national and international scientific meetings. Graduate students are being trained in modern advanced methodologies in chemistry and microbiology. There is an ongoing assessment module to document education and training outcomes.

Up to now, the two mainly accepted mechanisms of mortality in bacterial populations are heterotrophic protist grazing and viral infection. Increasingly, it has become evident that an understudied group of predatory bacteria, BALOs, can also contribute to bacterial mortality. Yet, the mechanisms underlying the dynamics of BALO-prey interactions are poorly understood, as are the interactions among the micropredators, BALOs, protists and bacterial viruses. Ultimately, these processes may have contrasting influences on the structure and functioning of the microbial loop, including impacting higher trophic levels and biogeochemical cycles. The investigators hypothesize that environmental factors significantly influence how mortality in bacterial populations is partitioned among the micropredators. To test this hypothesis researchers are (1) investigating the interactions amongst the micropredators, (2) examining the molecular-level composition and dynamics of dissolved organic matter as the result of the different mortality processes by the NMR/ FT-ICR mass spectrometry (MS) hybrid approach, and (3) modeling these tri-trophic dynamics. Intellectual Merit: Results from this research will define a new mechanistic understanding of mortality dynamics that influence the microbial loop and oceanic biogeochemical cycles.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.



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Funding

Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)

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