Moving beyond the van Krevelen diagram: A new stoichiometric

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Moving beyond the van Krevelen diagram: A new stoichiometric approach for compound classification in organisms. Albert Rivas-Ubach, Yina Liu, Thomas Stephen Bianchi, Nikola Toli#, Christer Jansson, and Ljiljana Paša-Toli# Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 19 Apr 2018 Downloaded from http://pubs.acs.org on April 19, 2018

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Analytical Chemistry

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Moving beyond the van Krevelen diagram: A new stoichiometric approach for compound classification in organisms.

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Headline: Stoichiometric compound classification

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Albert Rivas-Ubach1†*, Yina Liu1,2†, Thomas S. Bianchi3, Nikola Tolić1, Christer Jansson1, Ljiljana Paša-Tolić1

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1. Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, 99354, WA, USA. 2. Geochemical and Environmental Research Group, Texas A&M University, College Station, 77845, TX, USA. 3. Department of Geological Sciences, University of Florida, Gainesville, 32611-2120, FL, USA.

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† Authors contributed equally to this manuscript.

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* Author of correspondence: Albert Rivas-Ubach Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA, 99354 Tel: 971 319 5962 e-mail: [email protected] / [email protected]

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Keywords: Compound Classification, Ecological Stoichiometry, High-resolution mass spectrometry, Mass Spectrometry, Metabolomics, van Krevelen,

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Abbreviations: vK = van Krevelen MSCC = Multidimensional Stoichiometric Constraints Classification HRMS = High Resolution Mass Spectrometry MS = Mass Spectrometry FT-ICR = Fourier Transform Ion Cyclotron Resonance ESI = Electrospray ionization CIA = Compound Identification Algorithm CRAM = Carboxyl-rich Alicyclic Molecules Lipidsc = Lipids category Proteinsc = Protein category A-Sugarsc = Amino-Sugars category Carbohydratesc = Carbohydrates category Nucleotidesc = Nucleotides category Phytochemicalc = Phytochemical compounds category CM = Correctly Matched compounds IM = Incorrectly Matched compounds NM = Not Matched compounds DM = Double Matches IM+DM = Incorrectly Matched compounds considering double matches (DM) as incorrect. CM-(NM+DM) = Correctly Matched compounds without considering the not matched (NM) and double matched (DM) compounds.

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Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract

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van Krevelen diagrams (O:C vs H:C ratios of elemental formulas) have been widely used in

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studies to obtain an estimation of the main compound categories present in environmental samples.

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However, the limits defining a specific compound category based solely on O:C and H:C ratios of

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elemental formulas have never been accurately listed or proposed to classify metabolites in biological

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samples. Furthermore, while O:C vs. H:C ratios of elemental formulas can provide an overview of the

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compound categories, such classification is inefficient because of the large overlap among different

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compound categories along both axes. We propose a more accurate compound classification for

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biological samples analyzed by high-resolution mass spectrometrybased on an assessment of the

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C:H:O:N:P stoichiometric ratios of over 130,000 elemental formulas of compounds classified in 6 main

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categories: lipids, peptides, amino-sugars, carbohydrates, nucleotides and phytochemical compounds

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(oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC)

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constraints showed a highly accurate categorization of elemental formulas to the main compound

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categories in biological samples with over 98% of accuracy representing a substantial improvement over

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any classification based on the classic van Krevelen diagram. This method represents a signficant step

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forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies,

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by providing a novel and robust tool to further our understanding the ecosystem structure and function

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through the chemical characterization of different biological samples.

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Introduction The role of nutrients in organisms, especially primary producers, has been a critical topic in

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ecosystem studies of biogeochemical cycles,1,2 trophic relationships,3 and ecological stoichiometry.4,5

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The most abundant elements in living organisms are the macroelements, C, H, O, N, P, S, K and, Ca,5

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which typically behave as constituents of organic compounds.6 Understanding how these elements are

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selectively allocated into different categories of organic compounds remains a key challenge in many

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biogeochemical and ecological studies.4,6,7 Although the ratios between these key macroelementshave

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proven useful in ecological studies, accurately characterizing the major compound categories in

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organisms still remains a significant challenge.8 For example, the N:P ratio has been valuable as an index

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of the protein:nucleic-acids (e.g., DNA, RNA) ratio, due to the high content of N in proteins and P in

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nucleic acids.5,9–11 However, this approach has many limitations since the same macroelements are also

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commonly found in other organic compound categories.

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The metabolome of an organism, defined as the entire suite of low-molecular weight

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compounds (metabolites; typically < 1200 Da) present at any given time and under specific conditions,12

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provides valuable information for understanding the ecophysiology of organisms. Metabolomes include

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primary cellular products such as carbohydrates, small peptides, nucleotides, and lipids, as well as

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secondary metabolites that participate in several diverse physiological processes. Most notably, changes

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in stoichiometric C:N:P ratios in organisms have been shown to be more linked with the overall

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metabolome structure rather than with specific compounds or groups of compounds.6,13 Several

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ecosystem studies have combined metabolomic, elemental, and stoichiometric data to examine linkages

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between C:N:P biomass stoichiometries with the overall metabolome composition,6,14–18 yet

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stoichiometry-metabolome relationships remains elusive. A key reason for this ambiguity is our inability

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to identify the majority of metabolites, in large part due to current technological limitations. For

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example, the number of identified metabolites in non-targeted studies is typically < 200,8,13,15,19–22 even

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when complementary analytical techniques are combined (e.g. mass spectrometry and nuclear magnetic

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resonance). This small proportion of characterized metabolome, combined with a lack of understanding

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of the dynamics of the major organic compound categories in organisms’ under different environmental

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conditions, remains a key obsatcle for understanding the mechanisms of the linkages between the C:N:P

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stoichiometry of organisms and their metabolomes.

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In the 1950s, van Krevelen developed a graphical representation of macroelemental ratios to

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evaluate the origin and chemical evolution of petroleum and kerogen samples.23 This graphical

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representation, the van Krevelen diagram (hereafter vK diagram), represents atomic O:C vs. H:C ratios of


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organic compounds,23,24 and has become an important tool for characterizing organic matter extending

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well beyond petrochemical applications.25–28 vK diagrams have been used in numerous studies to

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elucidate chemical reactions25,27,28 as well as to assess main compound categories in natural organic

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matter (NOM) commonly defined as lipid-like, protein-like, amino-sugar-like, cellulose-like, lignin-like,

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and condensed hydrocarbons.26,27,29 In addition, vK diagrams were also used to examine the chemical

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characteristics of carboxyl-rich alicyclic molecules (CRAM)30,31 and oxidized black carbon32 in NOM.

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However, the large overlap in vK diagram between compound categories, often leading to incorrect

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compound classification (Fig. 1), and the ambiguity of “compound-like” term make the compound

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classification based solely on O:C and H:C ratios inefficient for obtaining robust conclusions for

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ecological and biogeochemical studies. For example, using vK diagram alone to depict CRAM is

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problematic as these compounds significantly overlapped with the tradiationnaly-defined “lignin-like”

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region,33 but CRAM compounds may not be related to lignins derived from vascular plants. Decomposing

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organic matter, largely driven by microorganisms (eg. oxidation, methylation, and dehydration) and

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photo-oxidation, can result in substantial shifts of molecular O:C, H:C and N:C ratioscomparing to the

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original composition. Therefore, using compound-classification methods based on O:C and H:C ratios

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alone when studying complex mixtures of organic matter in different matrices could, thus, lead to

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inconclusive and/or confounding results – largely due to significant compound transformation

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processes. Besides the large compound overlapping and the transformation of original molecular ratios,

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the O:C and H:C boundaries defining a specific compound category in a vK diagram substantially differ

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among published studies (see D’Andrilli et al. 201534) and have never been accurately defined for a

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robust overall classification of compounds. Many metabolites contain other heteroatoms, such as N or

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P; therefore, including these heteroatoms in additional to O in multidimensional compound

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classification approaches, i.e., using more than two dimensions (e.g. O:C and H:C ratios) should provide

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a better performance in classifying compounds into their corresponding main category (e.g. Lipids,

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Protein, etc.) (Fig. 2).

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N and P have been widely considered as the pillars of the elemental ratios found in most ecological

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and ecophysiological studies, largely because of their significant roles in ecosystem structure and

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function, especially as they relate to the carbon cycle. For example, C:N, C:P or N:P ratios are important

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factors in ecological processes such as nitrogen fixation, litter decomposition, trophic relationships,

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organism growth rate, biodiversity, the responsiveness capacity of organisms, and ecosystems

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responses to stressors.4–6,35 Lipids, proteins, carbohydrates, and nucleic acids represent the four major

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building blocks of life and occur in different proportions in all living systems. In addition to these core-


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compound categories, plants, fungi, and bacteria, also produce an assortment of secondary metabolites,

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commonly oxy-aromatic compounds, with diverse elemental composition that typically participate in

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specific functions.36–38

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Here, we propose an optimized compound classification method based on the C:H:O:N:P

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stoichiometric ratios of more than 130,000 molecular formulas of known compounds classified in six

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main categories: lipids, peptides, amino-sugars, carbohydrates, nucleotides, and phytochemical

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compounds. The MSCC represents a substantial improvement over the classic vK approach (i.e., O:C and

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H:C ratios) for overall compound classification. We demonstrate that the MSCC vastly improves the

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exploration and interpretation of the overall molecular composition in biological samples, as applied to

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ecology, ecophysiology, ecological stoichiometry, organic chemistry, organic geochemistry, and

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biogeochemistry. While this novel compound classification approach is more tailored for plants, it can

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be also applicable to other organisms. To demostrate our method can readily characterize the different

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metabolite compositions in different organisms, we analzyed three model organisms from different

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kingdoms (Plantae, Fugi, and Animalia (insect)) with high resolution mass spectrometry and applied the

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proposed MSCC.

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Materials & Methods

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Metabolite databases.

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For the MSCC determination, we explored the C:H:O:N:P molecular ratios of a total of 132,209

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elemental formulas from compounds consisting of 30,729 lipids; 93,245 peptides (including

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phosphorylated peptides); 7,774 phytochemical compounds; 82 carbohydrates; 142 amino-sugars

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(including amino sugar phosphates); and 37 nucleotides (Table S-1). Elemental formulas from lipids,

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phytochemical compounds, carbohydrates, amino-sugars and nuclotides were obtained from different

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compound databases: LIPID MAPS Lipidomics Gateway database,39 KEGG COMPOUND,40,41 and ChEBI

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databases.42 Table S-1 includes the details of the compounds from each dataset included to each

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compound category. Elemental formulas from peptides were compiled from FASTA file representation

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of 2014 SwissProt snapshot. Over 78,000 peptide sequences within the mass range 50-1,200 Da were

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converted to molecular formulas and H2O was added for peptide termini. Subsequently, 15,000 peptides

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that could be phosphorylated according to their sequences were randomly selected and

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computationally supplied with 1, 2 or 3 HPO4, depending on the size of the peptide, to generate a large

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dataset of possible phosphopeptides. See pages S-2 and S-3 in the Supporting Information for a detailed


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description of some considerations of the used compound databases for the determination of the

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MSCC. See page S-4 of the Supporting Information for validation of MSCC determination.

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Determination of new compound category constraints. The proposed compound classification in the MSCC system includes six of the following major

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categories, according to the aforementioned databases: lipids category (hereafter Lipidsc), proteins

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category (peptides; hereafter Proteinsc), amino-sugars category (hereafter A-Sugarsc), carbohydrates

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category (hereafter Carbohydratesc), nucleotides category (hereafter Nucleotidesc) and phytochemical

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compounds category (hereafter Phytochemicalc).

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Pairwise, stoichiometric variable scatterplots were obtained between all compound databases

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to explore the spatial distribution of molecules along all C:H:O:N:P combinations (Fig. S-1). N, P, S, O,

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and, molecular weight (MW) variables were also considered to complement the stoichiometric

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constraints of certain compound categories. Boundaries for compound classification were established

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using the distribution of compounds from databases along all the examined stoichiometric and

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elemental variables. For compound databases showing overlap in all the examined variables, we

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considered the variable showing better separation (lowering the overlapping proportion) between the

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databases as the discriminant variable for their classification (Fig. S-2 to S-8). By keeping the minimum

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overlapping between compound categories and using diverse stoichiometries (e.g. O:C, H:C, N:C etc.),

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our MSCC ensures a wide recovery of compounds and minimal compound matching error. We,

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therefore, expect that the probability of matching compounds outside their category is minimal and

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most of the detected features will match within their corresponding category boundaries (Fig. 4).

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Additional specific information regarding the established boundaries of the MSCC are detailed in pages

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S-2 and S-3 of Supporting Information. A R (https://www.r-project.org/) script is included in S-5 to S-9 of

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the Supporting Information for compound classification of stoichiometry ratios using MSCC.

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Comparison of the classic vK compound classification with the MSCC. We obtained the O:C and H:C boundaries defining the different compound categories from 21

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published studies (citations shown in table S-2) using GetData Graph Digitizer 2.24 to compare the

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performance of MSCC versus the classic compound classification - based exclusively on O:C vs. H:C. Due

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to the vague definition of the “Lignin-like” and “condensed hydrocarbon” categories typically

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represented in vK diagrams and the lack of specific databases including characterized compounds within

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those categories, we only considered the lipids, proteins, amino-sugars, and carbohydrates (cellulose)


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databases to evaluate how the O:C and H:C boundaries of each compound category from all 21 studies

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performed in contrast to our MSCC.

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The proportion of compounds from each compound database (lipids, proteins, amino-sugars,

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and carbohydrates) that correctly matched (CM), not matched (NM), and incorrectly matched (IM) in

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their corresponding categories (e.g. proportion of lipids from the database that CM, NM or IM into the

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Lipidsc) were calculated for the compound boundaries based solely on O:C and H:C ratios from the 21

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studies and for our MSCC (Table S-2). Double matched (DM), or compounds that fit into two different

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categories, were also calculated. We considered all amino-sugars matching the Carbohydratesc as CM

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for those studies that did not present the A-Sugarsc but only Carbohydratesc (Table S-2). We did not

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consider the carbohydrate database for studies that did not show Carbohydratesc but only A-Sugarsc.

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Additionally, for each compound category (lipids, proteins, amino-sugars, and carbohydrates), we

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calculated the proportion of IM considering the double matches as incorrect (IM+DM) and the CM

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without considering the NM and DM (CM-(NM+DM)) (Table S-2). In order to assess the number of correctly

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matched compounds per incorrectly matched we calculated the CM/IM+DM ratio for each compound

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category. The CM/(IM+DM + NM) ratio was calculated and used to assess the efficiency of stoichiometric

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constraints for the databases. Due the large difference between databases in number of compounds,

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the overall performance of each study was evaluated using two approaches: i) considering the total

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absolute number of compounds of all databases together (heareafter, “absolute total”), and ii)

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considering the relative number of compounds for each database (hereafter, “relative total”).

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MSCC performance. The performance of MSCC was also evaluated alone considering all compound categories: lipids,

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peptides, amino-sugars, carbohydrates, nucleotides and phytochemical compounds. For each category,

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the CM, NM, IM, CM-(NM+DM), CM/IM, and CM/(IM + NM) were calculated. The absolute total and relative

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total values were also calculated for each of the compound categories. Additional performance

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validation of the MSCC was performed for Lipidsc, Phytochemicalc and Proteinsc; the main categories

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showing larger overlapping across their stoichiometry (see page S-5 of the Supporting Information).

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Compound extraction and FT-ICR-MS analyses of model organisms.

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To test the MSCC in real biological samples and show the contrasted profiles from organisms of

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different kingdoms, fresh material of Brachypodium distachon (plant), Saccharomyces cerevisiae (fungi),

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and Drosophila melanogaster (insect) were flash-frozen in liquid nitrogen, lyophilized, and then grinded


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for metabolite extraction43 (Fig. 3a). Briefly, thirty mg of lyophilized B. distachon, S. cerevisiae, and D.

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melanogaster powders were added separately to 2 mL glass vials followed by the addition of 1 mL of

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MeOH/water (80:20). All samples were then placed into a Thermomixer (Eppendorf, New York, NY, USA)

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and shaken for 30 min. at 1,200 rpm at 12 ᵒC. All vials were subsequently sonicated for 5 min. and then

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centrifuged at 6,000 × g for 5 min. Supernatants were collected and placed into labeled 2 mL HPLC vials,

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and kept frozen at at -80 ᵒC until analyses.

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Metabolic fingerprints from these organisms were obtained using a ultra-high resolution 15

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Tesla SolariX Fourier-transform ion-cyclotron resonance mass spectrometer (FT-ICR-MS; Bruker

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daltonics Inc, Billerica, MA, USA) equipped with electrospray ionization (ESI) and operated in negative

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ionization mode (Fig. 3b). Each sample was directly infused at a flow rate of 3 µL/min. A total of 200

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spectra at 4 Mword (corresponding to resolution > 400,000 at m/z = 400) of each sample were averaged

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for each sample. Mass measurement accuracy was < 1 ppm with external calibration, and < 0.2 ppm

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after internal calibration. Formula assignment was performed using the Formularity software44 based

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on the automated compound identification algorithm (CIA)45 (Fig. 3c). To avoid assignment ambiguity,

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formulas for ions with m/z > 70046 and assignments with > 0.2 ppm error were not considered.

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Results Table 1 shows the determined stoichiometric and elemental boundaries for each compound category of the MSCC.

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Contrasting vK vs. MSCC stoichiometric boundaries performance. When considering lipid, peptide, amino-sugar, and carbohydrate databases, for compound

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classification limits derived from the O:C vs. H:C ratios as shown in the different 21 published studies,

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total absolute CM compounds varied from the 14.63% to 63.64% (Fig. 5a, Table S-2), and the absolute

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total IM+DM compounds varied from 0.7% to 68.93%. The absolute total CM and IM+DM compounds for

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the proposed MSCC were, 99.19% and 0.42%, respectively (Fig. 5a; Table S-2). The relative total CM

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ranged from the 12.43% and 55.68% for the 21 published studies, in contrast to 98.5% using the MSCC

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(Fig. 5b).

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The absolute total CM/IM+DM ratio ranged from 1.19 to 21.01 for the 21 published studies while

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the MSCC yielded a ratio of 234.64 (Fig. 5c). The relative total CM/IM+DM ratio varied between 1.09 and

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17.74 among the 21 studies and the MSCC had a ratio of 234.05 (Table S-2). The larger relative matching

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error was generally attributed to lower proportions of NM compounds (Fig. 5d). Although NM


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compounds were not classified in any compound category, they did not introduce any error into the

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results. For the 21 published studies, the CM-(NM+DM) compounds ranged between the 53.38% and

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95.46% in absolute terms, and between the 56.74% and 96.18% in relative terms (Table S-2). The MSCC

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reached 99.58% and 99.57% of absolute and relative total CM-(NM+DM) compounds, respectively. The

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absolute and relative total CM/(IM+DM + NM) ratios varied between 0.17 and 1.75 and between 0.15 and

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13.6, respectively, for the 21 published studies, while the MSCC showed a ratio of 121.73 and 272.05,

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respectively (Figs. 3e-3f).

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Evaluation of the MSCC performance considering all compound databases. The evaluation of the MSCC results obtained considering the molecular formulas from the six

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databases (lipids, peptides, phytochemical compounds, nucleotides, amino-sugars, and carbohydrates)

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are shown in Table 2. We obtained 98.8%, 0.14% and 0.9% of the absolute total compounds as CM, NM

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and IM, respectively. Lipids, peptides, and phytochemical compound databases showed the highest

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overlap among them for all the elemental and stoichiometric variables. However, only a 2.5% of lipid

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formulas were IM to other groups (1.5% matched into Proteinc, 0.9% matched into Phytochemicalc,

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0.08% matched into Carbohydratesc and 0.06% matched into A-Sugarc). For the peptide formulas, 0.06%

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were IM (0.01%, 0.05% and 0.002% matched into A-Sugarc, Phytochemicalc and Lipidc respectively). An

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estimated 3.3% of the phytochemical compound formulas were IM as A-Sugarsc (0.33%), Lipidsc (2.7%)

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and Proteinc (0.3%). Amino-sugar and carbohydrate formulas did not show any IM although 1.4% (2

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compounds) and 1.22% (1 compound) of compounds, respectively, were NM according to MSCC. All

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nucleotide formulas CM to Nucleotidesc although we found few ones that also matched as Proteinc

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(40%) and A-Sugarc (5%).

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Due to the high recovery of compounds using the MSCC, the total relative proportions of CM

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(98.5%) did not show large changes from the total absolute proportions of CM (99.8%) (Table 2). The

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total absolute CM/IM ratio was 108.3, so 110 compounds from the databases were CM per each IM. The

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total absolute CM/(IM + NM) ratio was 95.82.

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Proportions of compounds based on MSCC for model organisms.

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Based on the metabolite extraction, performed with MeOH:H2O (80:20) and the ESI-FT-ICR-MS

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analyses in negative ionization mode, we obtained very distinct compound profiles between the three

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organisms analyzed (Fig. 6). We only found the 9.6%, 7.5% and 8.7% of the metabolic features for B.

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distachon (plant), S. cerevisiae (yeast), and D. melanogaster (insect), respectively, not matching any of


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the compound categories - as defined by the MSCC defined in Table 1. The 0.1%, 0.4% and 0.3% of

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variables from B. distachon, S. cerevisiae and D. melanogaster, respectively, matched into two different

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categories. Phytochemical compounds and lipids were the most abundant compound categories for B.

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distachon, representing 40.4% and 32.8% of the variables, respectively. S. cerevisiae also showed a large

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fraction of lipids (31%) and oxy-aromatic compounds (33.6%), followed by protein (12.3%) and amino

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sugars (10.6%), while D. melanogaster showed a very distinct profile with 25.0%, 24.3%, 17.8%, and

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10.1% of lipids, proteins, amino sugars, and carbohydrates, respectively.

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Discussion

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Rationale and performance of the proposed MSCC.

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Lipids, proteins, carbohydrates and nucleic acids are the main building blocks of life and are

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present in all living organisms. Although the MSCC was focused on plants, they could be applied to other

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organisms (see Extension of MSCC to different living systems section). For a more exhaustive chemical

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characterization of plant samples, we included the “Phytochemical compounds” category

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(Phytochemicalc), which has been delimited by the stoichiometry from thousands of molecular formulas

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of well-characterized plant secondary metabolites. These compounds are well known for the crucial

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roles they play in the physiological, developmental, and anti-stress processes in plants.47,48 We used

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thousands of elemental formulas from described compounds to accurately define the multiple

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stoichiometric boundaries for each compound category. By using large compound databases and

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multiple boundaries defining each compound category, the probability of matching metabolites outside

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their corresponding major category is minor (Fig. 4; Table 2). The application of the proposed MSCC for

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the classification of elemental formulas is thus held under the premise that the vast majority of the

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detected features by high-resolution mass spectrometry (e.g. FT-ICR-MS) in samples should match to the

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major compound categories (Figs. 4, S-3 to S-8), including Phytochemicalc, in the case of plants.

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The MSCC (Table 1) is primarily based on the examination of the H:C, O:C, N:C, P:C and N:P

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ratios of a substantial number of described metabolites covering different organic compound categories.

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The MSCC provides a new powerful approach for classifying different molecular compounds into their

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respective categories, with minimal error and exceptional coverage (Fig. 5; Tables 2 and S-2). The

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enlargement of the areas delimited by O:C and H:C ratios of any compound categories in a vK diagram

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increases the coverage of compound matching, but also increases the compound matching error (Fig.

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5d). As such, the use of MSCC allows for a more accurate classification of the different metabolites into

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six major compound categories - with reduced compound matching error. After the examination of large


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databases that included primary and secondary metabolites, we contend that the probability of

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incorrectly matching metabolites using MSCC is minimal (Figs 4, 5c, 5e-5f) and thus provides a significant

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improvement over the vK method for assessment of the overall spectrum of compounds in biological

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samples.

333 334 335

Practical limitations. Advanced mass spectrometry offers high resolution (up to 1,000,000) (Brown, Kruppa, &

336

Dasseux, 2005; Denisov, Damoc, Lange, & Makarov, 2012). Ultrahigh mass-measurement accuracy

337

(typically < 1 ppm after internal calibration) by Fourier transform mass spectrometry (FTMS) allows

338

assignment of elemental formulas for the majority of the thousands of compounds detected in a given

339

sample.49,50 The application of high-resolution FTMS (HR-FTMS) in environmental and biological research

340

has allowed assessment of the diversity of molecular compounds present in a particular sample,

341

assuming the presence of several different essential elements, such as O, S, N and P, and calculating

342

their stoichiometric ratios. Assigning elemental formulas to HRMS data can thus be a valuable tool for

343

simply comparing different stoichiometric metabolic profiles, or for exploring how organisms assign

344

different elements to different molecular compounds under certain environmental conditions, which is

345

an important aspect of ecological stoichiometry studies.4–6

346

HRMS provides unprecedented means for understanding molecular ecological stoichiometry in

347

complex samples, nevertheless there are few limitations. For instance, accurate formula assignments

348

can be hindered by (1) mass measurement accuracy achieved by the HRMS after routine external

349

calibration, (2) final mass measurement accuracy after internal calibration, and (3) assignment accuracy

350

from the formula assignment algorithm. Typically, mass measurement accuracyof < 1 ppm error is

351

expected using well-maintained,i.e. routinely cleaned and calibrated,HR-FTMS instruments,such as FT-

352

ICR-MS.51 Less than 0.2 ppm of mass error is often achieved with a HR-FTMS by employing internal

353

calibration,52,53 which is a crucial step for accurate formula assignmentby CIA45. In fact, CIA correctly

354

assigned 96.94% of the known molecular formulas from our compound databases (Table S-3), when

355

considering all formula types and full mass range (70 to 1200 Da) (see page S-6 and Table S-3 in

356

Supporting Information for a detailed assessment of the formula assignment error of the compound

357

databases using CIA). Furthermore, it is important to note that as molecular mass increases, the number

358

of possible formula assignments also disproportionally increases, especially when multiple heteroatoms

359

are considered.45,46 Hence, only lower mass metabolites have been typically considered for formula

360

assignment (e.g. < 500 Da) to reduce the number of false positives.46 Therefore, using formula


11


Page 12 of 26

12 361

assignments for compounds < 500 Da and mass measurement accuracy < 0.2 ppm is advised for more

362

robust compound classification in samples; formula assignments for compounds over 500 Da should be

363

treated with caution. It should be noted that in addition to mass measurement accuracy related issue,

364

another inherit limitation of direct infusion FT-ICR-MS is the inability in distinguishing between structural

365

isomers. For example, a lignin compound may have the same elemental formula as a CRAM compound,

366

while they have different structure and different origins in nature.33

367 368 369

Extension of MSCC to different living systems. The MSCC presented here can also be applied to classification of lipids, proteins, carbohydrates

370

and nucleotides in all other living systems. “Lignin-like” and “condensed hydrocarbon” compound

371

categories are often depicted in vK diagrams.34 However, these categories are not directly applicable for

372

metabolites in living systems. Any organic sample contains a significant proportion of molecules with

373

low H:C ratios (< 1.3), typically represented by aromatic or unsaturated compounds. Those metabolites

374

possess one or more aromatic rings and different functional groups (e.g. OH, -C(O)OH, and -NO2) making

375

up a large variety of compounds along the O:C axis, typically polyphenolics. Ligninconsists of large

376

polymers of phenolic compounds (C31H34O11)n and represents one of the main components of plant cell

377

walls. Several plant phenolic secondary metabolites are lignin precursors (lignins) but other non-lignin

378

related phenolics, such as flavonoids, are abundantsecondary metabolites,36 and cluster within the

379

“lignin-like” category in a vK diagram (Fig. 1). Phytochemicalc, which include all polyphenolic

380

compounds, should only be used for plant samples. Another category of phenolics formed by CRAM that

381

occur within the “lignin-like” region of the vK diagrams has also been described.29–31,54 However, not all

382

compounds within this area in a vK diagram are necessarily carboxyl-rich. On the other hand, condensed

383

hydrocarbons are usually considered to be derived from incomplete combustion or geo-condensation

384

such as char or petroleum, respectively.55 Since condensed hydrocarbons category is not directly

385

applicable to organisms and overlaps with our proposed Phytochemicalc (oxy-aromatics), this category

386

was not considered in the MSCC. For the analyses of samples other than plants, we propose the use the

387

general term of “oxy-aromatic compounds” to include all those compounds with low H:C, mainly

388

polyphenolics, applying the same MSCC as for Phytochemicalc.

389

The compound profiles of the analyzed model organisms were obtained through an MeOH/H2O

390

(80:20) extraction and the extracted composition will vary if using different solvents.56,57 Thus, it is

391

important to use the same procedures for sample comparison. Additionally, the ionization method

392

dictates the nature of compounds being observed with MS platforms. Electrospray ionization (ESI) is


12

Page 13 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


13 393

often used to resolve polar and semi-polar compounds, while other ionization methods such as

394

atmospheric pressure photoionization (APPI) or atmospheric pressure chemical ionization (APCI) could

395

be considered for non-polar matrices.

396

As expected, three model organisms studied herein display very different compound profiles (Fig. 6).

397

Based on the ESI-FT-ICR-MS analyses of the MeOH/H2O (80:20) extracts, over 90% of the signals

398

detected in all three cases were matched into one of the compound groups of the MSCC (Table 1)

399

demonstrating a reliable compound classification. Furthermore, the proportion of double assignations

400

was minimal, reaching the maximum values for D. melanogaster (insect) (0.4%).

401

Insects are protein-rich organisms58 with relatively low content of polyphenolic compounds

402

compared to plants or fungi. Our results clearly showed this trend with 24.3% of pepetides in D.

403

melanogaster vs. 12.3% in S. cerevisiae (fungi) and 5.7% in B. distachon (plant) (Fig. 6). Chitin, an

404

abundant amino-sugar biopolymer,59 represents a major component in insects.60 The highest

405

proportions of amino sugars was detected in D. melanogaster, especially compared to plants, may

406

directly relates to the high chitin content of the exoskeleton of insects. On the other hand, plants and

407

fungi produce a large diversity of secondary metabolites, a major part of them with oxy-aromatic

408

structures.37 From all the detected features, 40.4% and 33.6% matched into Phytochemicalc (oxy-

409

aromatic category) for B. distachon and S. cerevisiae, respectively, further corroborating the high

410

content of secondary metabolites in those organisms.

411 412 413

Conclusions The proposed multidimensional stoichiometric constraints classification (MSCC) (Table 1) exhibited a

414

substantial improvement over vK diagrams in classifying database compounds with a minimal error and

415

large coverage (Table 2) and it can be applied to different organisms. Additionally, the classification

416

method can serve as a strong starting point to further investigate other (i.e. nonliving) complex

417

environmental matrices, and start defining and optimizing multiple elemental ratios allowing for more

418

robust classification for environmental samples. Inherit limitations originating from the mass

419

measurement accuracy and formula assignment accuracy should be carefully considered when

420

interpreting the MS data in terms of compound classifications and molecular level stoichiometric

421

interpretations. This stoichiometric compound classification method represents a valuable tool for

422

environmental research, especially in the fields of ecometabolomics and ecological stoichiometry.

423


13


Page 14 of 26

14 424

Acknowledgments.

425

This research was performed at the Environmental Molecular Science Laboratory (EMSL), a DOE

426

Office of Science User Facility sponsored by the Office of Biological and Environmental Research at the

427

Pacific Northwest National Laboratory (PNNL). The research was funded in part by US Department of

428

Energy (DOE) Contract DE-AC05-76RL01830 with PNNL.

429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449


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18 545 546 547 548 549

Table 1. Proposed C:H:O:N:P stoichiometric and elemental constraints for each compound category. Protein category (Proteinc) is composed by two different series of constraints which have to be used at the same time. Molecular mass range is also proposed for nucleotide category (Nucleotidesc). Table cells with hyphens (-) indicate that the specific variable is not necessary to be used as discriminant for the specific compound category.

550

O:C

N:C

P:C

N:P

O

N

P

S Mass

≤5

-

-

-

-

-

-

-

≥1

-

-

-

-

-

≥1

-

-

-

≤ 0.6

≥ 1.32 ≤ 0.126 < 0.35

> 0.12 ≤ 0.6 > 0.6 ≤1

> 0.9 < 2.5 > 1.2 < 2.5

≥ 0.126 0.2 0.07 ≤ 0.2

< 0.3

≤2

≥3

≥1

-

-

-

Carbohydratesc

≥ 0.8

-

-

-

-

=0

-

-

-

Nucleotidesc *

≥ 0.5 < 1.7

≥ 1.65 < 2.7 >1 < 1.8

-

≥2 ≥1 =0

Phytochemicalc

≤ 1.15

< 1.32 < 0.126 ≤ 0.2

Lipidc Constraints 1 Proteinc Constraints 2

551

H:C

≥ 0.2 ≤ 0.5

≥ 0.1 > 0.6 < 0.35 ≤ 5 ≤3

-

-

-

-

> 305 < 523 -

* Double matches in Nucleotidesc should be considered as nucleotides.

552 553 554 555 556 557 558 559 560 561 562 563


18

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19 564 565 566 567 568 569 570 571

Table 2. Number of compounds from databases and proportions of database compounds that correctly matched (CM), not matched (NM) and, incorrectly matched (IM) with each proposed compound category according to the porposed constraints from Table 1. The absolute number of compounds is shown in brackets. Correctly matched excluding the NM and double matchings (DM) (CM-(NM+DM)) and the CM/IM and CM/(IM + NM) ratios are also shown. The total absolute and relative proportions are shown on the calculations based on the absolute number of compounds in databases and on the relative number of compounds, respectively. The percentages marked by asterisks indicate the possibility of DM and it is discussed in the manuscript.

572

Lipids (30,729)

CM

NM

97.1% (29,823)

0.4% (122)

IM 2.5% (784)

CM-(NM+DM)

CM/IM

CM/(IM + NM)

· 17 A-Sugarc (0.06%) · 24 Carbohydratesc (0.08%) · 271 Phytochemicalc (0.9%) · 472 Proteinc (1.5%)

97.4%

38.0

32.91

99.9%

1502.3

1397.4

100%

70

70

98.8%

80.96

81.0

100%*

∞

∞

96.7%

29.3

27.3

0.06% (62)

573 574

Peptides (93,245)

99.9% (93,142 )

0.04% (41)

Amino sugars (142) Carbohydrates (82)

98.6% (140) 98.8% (81)

1.4% (2) 1.2% (1)

0% (0) 0% (0) 0%*

Nucleotides (37)

100%* (37)

0% (0)

Double matching: · 15 with Proteinc (40%) · 2 with with A-Sugarc (5%)

· 10 A-Sugarc (0.01%) · 50 Phytochemicalc (0.05%) · 2 Lipids (0.002%)

3.3% (256)

Phytochemical compounds (7,774)

96.5% (7,499)

0.25% (19)

TOTAL Absolute (132,009) Total Relative

98.8% (130,401)

0.14% (390)

0.9% (1,204)

99.1%

108.3

95.82

98.5%

0.55%

0.97%

98.8%

-

-

· 26 A-Sugarc (0.33%) · 207 Lipidsc (2.7%) · 23 Proteinc (0.3%)

* Double matches in Nucleotidesc are not considered as incorrect matches since any peptide or amino sugar from the databases matched with all the constraints of the Nucleotidesc.

575 576 577 578 579


19


Page 20 of 26

20

580 581 582 583 584 585 586 587

Figure 1. van Krevelen diagram (O:C vs. H:C elemental ratios) of metabolite databases. Different compound cateogires are represented in different colors (blue, lipids; dark red, peptides; yellow, aminosugars; orange, carbohydrates; cyan, nucleotides; green, phytochemical compounds). Classic compound classification areas according to O:C and H:C elemental ratios are represented. Shown areas are an approximation based on previous compound classification of organic matter studies (citations shown in table S-2) and do not represent exactly any specific areas.

588 589 590 591 592 593 594 595 596 597 598 599


20

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21

600 601 602 603

Figure 2. Examples of different molecular structures from different compound categories with their O:C, H:C, N:C, P:C, and N:P ratios. Nitrogen (N) and Phosphorus (P) are shown in red and green, respectively.

604 605 606 607 608


21


Page 22 of 26

22

609 610 611 612 613

Figure 3. Diagram of the principal procedures to apply the multidimensional stoichiometric constraints classification (MSCC) on samples. a, b and c denote three of the procedures cited in the main text that have been performed before testing the MSCC on model organisms.

614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631


22

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23

632 633 634 635 636 637 638 639 640 641 642 643 644 645

Figure 4. Example of a 2-dimensional (2D) density plot of H:C vs. O:C elemental ratios (vK plot) for the peptide database (93,245 elemental formulas). Color gradient indicates distinct number of peptides within each squared area (red squares indicate the areas with higher density of peptides (up to 540-560 peptides); blue squares indicate the areas with lower density of peptides (1-20 peptides)). Contour lines including the 80% and 95% of peptides are shown in black. Boundaries of multidimensional stoichiometric constraints classification (MSCC) for O:C and H:C ratios are indicated with red dashed lines (see Table 1 for accurate stoichiometric thresholds). By probability, the major part of peptides detected in samples will be found within the high-density area (95%). The boundaries of MSCC (Table 1) are substantially extended with respect to the high-desity area and the classification is based on multiple stoichiometric constraints and not just 2 as shown in the figure making thus the probability of matching compounds outside their defined stoichiometric constraints using the MSCC minimal. See Figures S-3 to S-8 for all stoichiometric constraints from all compound categories.

646 647 648 649 650 651 652


23


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653 654 655 656 657 658 659 660 661

Figure 5. Matching results of lipids, protein, amino-sugar and carbohydrate databases to its corresponding category according to the proposed multidimensional stoichiometric constraints classification (MSCC) (0; star) and the O:C and H:C tresholds provided by different studies (1 to 21). Proportion of total correctly matched compounds in absolute (a) and relative (b) terms. Correctmatched:Incorrect-matched ratio in absolute terms (c). Not-matched versus incorrect-matched compounds (d). Correct-matched:(Incorrect-matched+Not-matched) ratio in absolute terms (e) and relative terms (f). References for each of the studies are shown in table S-2.

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672 673 674 675 676 677 678 679 680 681 682 683

Figure 6. Pie diagrams representing the relative abundance (%) of the compound categories defined by the proposed multidimensional stoichiometric constraints classification (MSCC) (Table 1) for Brachypodium distachon (plant), Saccharomyces cerivisiae (yeast) and Drosophila melanogaster (insect). Each compound category is represented by different color. Metabolic variables that did not matched to any of the compound categories are shown in grey. Variables that matches into two compound categories are represented in black. The number of variables representing the metabolic fingerpints for each organism are shown below each pie diagram. Phytochemical compounds category (Phytochemicalc) is shown only for B. distachon. Oxy-aromatic compounds category is shown for S. cerevisiae and D. melanogaster.

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Moving beyond the van Krevelen diagram: A new stoichiometric

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