Discovery and identi�cation of oxidative stress biomarkers in marine plankton using a new, open-source R package for lipidomicsJamie Collins 1,2 *, Bethanie Edwards 1,2,3, Kevin Becker 1, Helen Fredricks 1, and Benjamin A.S. Van Mooy 1

1 Department of Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution2 MIT/WHOI Joint Program in Oceanography; 3 School of Oceanography, University of Hawaii at Manoa

Introduction: ROS and the algal lipidome

Oxidative stress exerts a profound impact on the lives of marine microbes, including eukary-otic phytoplankton. Reactive oxygen species (ROS) can function as inter- or intracellular signals, alter various cellular metabolisms, and dramatically transform the individual mole-cules that make up the algal cell.

As in terrestrial plants, the polar lipids of marine algae are a “front line” barrier in the battle against oxidative stress. Because these lipids are primary structural components of cell and or-ganelle membranes, they are particularly susceptible to chemical transformation by both inter- and extracellular ROS. The lipids transformed by ROS can be exploited as biomarkers for speci�c types of oxidative stress.

We use lipidomics to analyze hundreds of these biomarkers simultaneously with their unoxi-dized parent compounds. Patterns in their distribution, abundance, and chemical structure can be used to:

• Identify new biomarkers for vari-ous biological & abiotic stressors

• Localize the impact of oxidative stress within an ecosystem or single cell

• Examine the e�ect of ROS on bio-chemical pathways, microorgan-isms, and ecosystem-scale biogeo-chemistry

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LOBSTAHS: An R package for lipidomics discovery

We developed LOBSTAHS1 (Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences) to assist in the discovery and identi�cation of new lipid biomarkers for oxidative stress. LOBSTAHS is an open-source pack-age for R that uses a unique screening criteria to make compound identi�ca-tions in HPLC-MS data. LOBSTAHS is available via Bioconductor or GitHub. The package can be downloaded via the QR code at right. Improvements, suggestions, and database additions are welcome – just submit a bug report.

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Screened & annotated “high con�dence” dataset

available for analysis

Assign con�dence scoresAnnotate possible iso-

mers and isobaric masses

Screen assignments using adduct ion hierarchy se-

quences

Screen assignments by re-tention time according to

parent lipid class

Initial compound assign-ments from database(s)

Initial mass spectralfeature discovery

Retention time correctionPeak grouping

Eliminate 2° isotope peaks

Data acquisition: HPLC-ESI-MS2; high mass

accuracy & resolution

Complex dataset: Multiple samples across experi-mental treatments or

environmental gradients

OOH

Lipoxygenase enzyme

Lipoxygenase enzyme

AbioticAbiotic

BiologicalBiological+ O2+ O2

1O2 •OH

H2O2

1O2 •OH

H2O2

OOH

Summary & signi�cance

Shifts in the distribution, abundance, and chemical structure of lipids in (a) P. tricornu-tum and (b) the microbial community at Sta-tion ALOHA can tell us about the e�ects and targets of oxidative stress within the algal cell and within ecosystems.

• Extensive lipidome remodeling followed treatment with 150 µM H2O2 in Phaeodacty-lum tricornutum Additionally, oxidative stress promoted reallocation of biomass to triacylglycerols (TAG)

• At Station ALOHA, UVB radiation induced wholesale change in the oxidation state of the metalipidome

Onboard, user-cus-tomizable databases: 14,068* unique IPL,

lyso-IPL, ox-IPL, oxylipins, TAG, and

photosyntheticpigments

1 Collins et al. 2016, Anal. Chem. 88:7154-7162. 2 Modi�ed method of Hummel et al. 2011, Front. Plant Sci. 2:54. 3 Smith et al. 2006, Anal. Chem. 78:779-787; Tautenhahn et al. 2008, BMC Bioinformatics 9:504. 4 Kuhl et al. 2012, Anal. Chem. 84:283-289. 5 Gra� van Creveld et al. 2015, ISME 9:285-395. In the study, cultures of P. tricornutum were treated with varying concentrations of hydrogen peroxide to induce an oxidative stress response. Experimental methods are described in the reference. 6 DGCC, diacylglyceryl carboxyhydroxymethylcholine; DGDG, digalactosyldiacylglycerol; DGTS & DGTA, diacylglyceryl trimethylhomoserine and diacylglyceryl hydroxymethyl-trimethyl-β-alanine; IPL, intact polar lipid; ox-IPL, oxidized intact polar lipid; MGDG, monogalactosyldiacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; SQDG, sulfoquinovosyldiacylglycerol; TAG, triacylglycerol 7 P≤0.05 (bold), P≤0.01 (*), P≤0.001 (**), and P≤0.0001 (***).. This research was supported by the Gordon and Betty Moore Foundation through Grant GBMF3301 to B.A.S.V.M. This research was also funded in part by a grant to B.A.S.V.M. from the Simons Foundation and is a contribution of the Simons Collaboration on Ocean Processes and Ecology. J.R.C. acknowledges support from a U.S. Environmental Protection Agency (EPA) STAR Graduate Fellowship (Fellowship Assistance Agreement No. FP-91744301-0).

https://github.com/vanmooylipidomics/LOBSTAHS/

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PEMGD

GPC SQ

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PG TAG

DGTS & A

DGD

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DGCC

Lipid class6

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Corrected retention time (min)

Within each lipid class6, certain molecules are associated only with oxidative stress

TAG

26 1114526 11145

MGDG

27 198627 1986

SQDG

23 239623 2396

PG

5 4285 428

PC

49 4510649 45106

PE

32 2211732 22117

DGTS & DGTA

10 75410 75419 136519 1365

DGDG

191 145703191 145703

All lipids DGCC

66

Healthy organism

Lipids observed only in 0 µM H2O2 control

StressedLipidsobserved only under 150 µM H2O2

Lipids observed in both treatments

Results: Oxidative stress in the marine diatom Phaeodactylum tricornutum

LOBSTAHS identi�es 1039 unique oxidized and unoxidized lipids that serve as potential biomarkers

Results in pure culture: Oxidative stress in the model marine diatom Phaeodactylum

We applied LOBSTAHS to lipid data from an experiment5 in which hydrogen peroxide (H2O2) was used to induce oxidative stress in the marine diatom Phaeodactylum tricornutum.

* ... and growing!

* @jamesrco; future: Moore/Sloan & WRF Data Science Fellow at University of Washington eScience Institute & School of Oceanography

New oxidized lipids are observed, but increase in oxidation state driven primarily by fall in concentration of unoxidized molecules

Results at Station ALOHA: E�ect of UVB exposure on a microbial metalipidome

We applied di�erent light screening treatments to large ( ~ 2 L ) volumes of un�ltered seawater collected at Station ALOHA, in the North Paci�c Ocean, during the SCOPE/C-MORE HOE-Legacy III cruise. LOBSTAHS was used to identify patterns & potential biomarkers of natural, daily-recurring UV radiation stress in HPLC-MS lipid data from three treatments. 1,747 di�erent oxidized and unoxidized compounds were identi�ed & quanti�ed.

0.8

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Time (hours)

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150 µM H2O2

Frac

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Stress induces TAG production

Initial + 9 h

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Dark− UVB+ UVB

UVB exposure leads to increase in overall oxidation state of metalipidome

Fold change from init. concentration

DGTS_DGTA 27:1 +3O, RT 10.87 min.DGTS_DGTA 30:5 +4O, RT 14.01 min.DGTS_DGTA 45:1 +1O, RT 19.51 min.SQDG 39:1 +1O, RT 19.67 min.DGDG 30:0, RT 19.68 min.DGTS_DGTA 43:0 +1O, RT 19.51 min.DGTS_DGTA 43:2 +1O, RT 18.78 min.DGTS_DGTA 34:1, RT 17.59 min.DGTS_DGTA 41:1 +1O, RT 18.76 min.DGTS_DGTA 43:1 +1O, RT 19.12 min.DGTS_DGTA 41:0 +1O, RT 19.09 min.DGTS_DGTA 39:0 +1O, RT 18.7 min.TAG 40:4, RT 18.65 min.PE 41:1, RT 17.47 min.PG 43:4 +3O, RT 12.51 min.PG 31:3 +3O, RT 8.98 min.PE 33:4 +1O, RT 18.15 min.TAG 39:4 +3O, RT 18.57 min.SQDG 46:8 +4O, RT 10.53 min.PG 30:4, RT 7.55 min.DGDG 32:1 +4O, RT 8.73 min.PG 37:8 +3O, RT 7.76 min.SQDG 39:7 +3O, RT 9.98 min.SQDG 42:6 +2O, RT 8.73 min.PE 31:3 +2O, RT 7.55 min.

Compounds exhibiting greatest relative increase in +UVB treatment

0 10 20 30

+ UVB

Dark control

− UVB

Notdetected

+ UVB

Dark control

− UVB

PG 41:5, RT 13.97 min.DGDG 36:9, RT 11.27 min.DGDG 28:1 +3O, RT 13.71 min.PC 38:6, RT 15.19 min.DGDG 34:7, RT 11.57 min.PG 41:5 +1O, RT 13.57 min.TAG 50:1, RT 21.84 min.TAG 50:2, RT 21.06 min.TAG 50:0, RT 22.77 min.TAG 52:1, RT 22.77 min.TAG 48:1, RT 21.06 min.TAG 48:0, RT 21.86 min.TAG 52:2, RT 21.83 min.SQDG 28:0, RT 12.07 min.PE 36:2, RT 17.2 min.DGDG 30:1, RT 14.07 min.SQDG 30:0, RT 13.32 min.MGDG 30:1, RT 14.92 min.SQDG 30:1, RT 12.3 min.PG 33:1, RT 14.81 min.PG 36:2, RT 15.24 min.SQDG 30:2, RT 11.36 min.

Fold change from init. concentration

-4 0 2 4-2

Compounds with greatest absolute change in concentration from t = 0

(at t +9 h)

(at t +9 h)