
==== Front
FEMS Microbiol Ecol
FEMS Microbiol Ecol
femsec
FEMS Microbiology Ecology
0168-6496
1574-6941
Oxford University Press

39122657
10.1093/femsec/fiae112
fiae112
Research Article
AcademicSubjects/SCI01150
Exploring modes of microbial interactions with implications for methane cycling
https://orcid.org/0000-0002-4312-237X
Brenzinger Kristof Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Validation Visualization Writing - original draft Writing - review & editing Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany

Glatter Timo Data curation Formal analysis Validation Writing - review & editing Core Facility for Mass Spectrometry and Proteomics, Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043 Marburg, Germany

Hakobyan Anna Data curation Formal analysis Methodology Writing - review & editing Research group of Methanotrophic Bacteria, and Environmental Genomics/Transcriptomics, Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043 Marburg, Germany
Institute of Crop Science and Resource Conservation (INRES) , Molecular Biology of the Rhizosphere, Nussallee 13, 53115 Bonn, Germany

Meima-Franke Marion Data curation Formal analysis Investigation Methodology Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands

Zweers Hans Data curation Formal analysis Methodology Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands

Liesack Werner Conceptualization Supervision Writing - review & editing Research group of Methanotrophic Bacteria, and Environmental Genomics/Transcriptomics, Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043 Marburg, Germany

Bodelier Paul L E Conceptualization Funding acquisition Investigation Project administration Supervision Writing - original draft Writing - review & editing Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands

Corresponding author. Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands. E-mail: P.Bodelier@nioo.knaw.nl
9 2024
09 8 2024
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100 9 fiae11206 5 2024
29 7 2024
08 8 2024
03 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.
2024
https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Methanotrophs are the sole biological sink of methane. Volatile organic compounds (VOCs) produced by heterotrophic bacteria have been demonstrated to be a potential modulating factor of methane consumption. Here, we identify and disentangle the impact of the volatolome of heterotrophic bacteria on the methanotroph activity and proteome, using Methylomonas as model organism. Our study unambiguously shows how methanotrophy can be influenced by other organisms without direct physical contact. This influence is mediated by VOCs (e.g. dimethyl-polysulphides) or/and CO2 emitted during respiration, which can inhibit growth and methane uptake of the methanotroph, while other VOCs had a stimulating effect on methanotroph activity. Depending on whether the methanotroph was exposed to the volatolome of the heterotroph or to CO2, proteomics revealed differential protein expression patterns with the soluble methane monooxygenase being the most affected enzyme. The interaction between methanotrophs and heterotrophs can have strong positive or negative effects on methane consumption, depending on the species interacting with the methanotroph. We identified potential VOCs involved in the inhibition while positive effects may be triggered by CO2 released by heterotrophic respiration. Our experimental proof of methanotroph–heterotroph interactions clearly calls for detailed research into strategies on how to mitigate methane emissions.

The interaction between methanotrophs and heterotrophs can have strong positive or negative effects on methane consumption and growth, depending on the species interacting with the methanotroph.

bacterial interaction
heterotrophs
methane oxidation
methanotrophs
protein composition
volatile organic compounds
DFG 10.13039/100004807 5535/1–1 Dutch Research Council 10.13039/501100003246 870.15.073
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pmcIntroduction

Methane (CH4) is Earth’s second-most important greenhouse gas (GHG) after CO2. The current atmospheric CH4 concentration of 1.896 parts per million (ppmv) is the highest in at least 80 000 years (Masson-Delmotte et al. 2021) and in recent years, the increase has even accelerated (Fletcher and Schaefer 2019, Masson-Delmotte et al. 2021). Global CH4 emissions contribute 15%–35% to total global radiative forcing (Masson-Delmotte et al. 2021) of which the largest part originates from agricultural (i.e. rice paddies) and natural wetlands (Saunois et al. 2020). In fact, the recent increase in atmospheric CH4 concentrations is likely associated to higher biological CH4 production and emission from wetlands due to warmer and wetter climate (Peng et al. 2022). These biological feedbacks to global warming, often involving microbially driven biogeochemical reactions, are poorly understood, necessitating further investigations of the controlling factors of the main processes involved (Cavicchioli et al. 2019, Gedney et al. 2019). The only biological sink for CH4 globally are CH4-oxidizing microbes (Bodelier et al. 2019), which exert a profound influence on CH4 dynamics in various environments. Because of their specialized lifestyle, utilizing CH4 as their primary carbon and energy source, they are called methanotrophs.

Methanotrophs, encompassing diverse taxa capable of degrading CH4 with and without oxygen (O2), have garnered considerable attention due to their role in mitigating CH4 emissions from natural and anthropogenic sources (Bodelier et al. 2019, Guerrero-Cruz et al. 2021). These microbes employ unique enzymes, including CH4 monooxygenase (MMO), to oxidize CH4 to methanol, serving as the foundation for their metabolic activities. There are two types of MMO: the soluble CH4 monooxygenase (sMMO) and the particulate CH4 monooxygenase (pMMO). The sMMO is found in the cytoplasm of methanotrophic bacteria and is notable for its ability to oxidize a broad range of substrates besides CH4. It contains a nonheme iron center and operates under a wide range of environmental conditions. The pMMO is embedded in the cellular membranes of methanotrophic bacteria. It primarily oxidizes CH4 to methanol with high specificity and contains copper centres that are crucial for its catalytic activity. Concurrently, heterotrophic microorganisms, comprising a vast array of bacteria and fungi, derive their energy from organic carbon substrates, displaying remarkable metabolic versatility and ecological adaptability across diverse habitats.

However, recent research has unveiled a complex interplay between methanotrophs and heterotrophic microorganisms. While shedding light on their intricate interactions and their broader ecological significance, this intriguing area of research revealed a spectrum of symbiotic, competitive, and syntrophic relationships. (Stock et al. 2013, Ho et al. 2014, 2017, Padmavathy 2017). Earlier paradigms predominantly viewed these two functional guilds as operating independently, but mounting evidence suggests intricate metabolic exchanges and mutual dependencies among them (Stock et al. 2013, Ho et al. 2014, Veraart et al. 2018). For instance, heterotrophs can utilize intermediates produced during CH4 oxidation by methanotrophs or vice versa (e.g. cobalamin, organics, or CO2), thereby fostering a dynamic network of cross-feeding interactions within microbial consortia (Stock et al. 2013, Ho et al. 2014, Yu et al. 2017). Understanding the mechanisms underpinning the mutualistic or antagonistic relationships between methanotrophs and heterotrophs holds immense significance in elucidating the biogeochemical cycles of carbon and energy fluxes within ecosystems. These interactions potentially influence CH4 consumption rates, substrate availability, and community structure, consequently impacting ecosystem resilience and functioning (Ho et al. 2017).

A potential way of communication between the methanotrophic and heterotrophic guilds, especially in case of environmental physical separation, could be via volatile organic compounds (VOCs) (Audrain et al. 2015, Schmidt et al. 2015, Tyc et al. 2015, Veraart et al. 2018). Plants and soil microorganisms in the rhizosphere can produce and release various VOCs such as ethylene, CH4, and volatile terpenes (Diyapoglu et al. 2022, Srikamwang et al. 2023). Microbial VOCs are known to act as signalling molecules, facilitating communication, and modulating microbial behaviour (Korpi et al. 2009, Weisskopf et al. 2021), which can even act over cm’s distances in soil (Schulz-Bohm et al. 2018). These VOCs play important roles in plant–microbe interactions, defence mechanisms, and signalling processes (Ortíz-Castro et al. 2009, Bitas et al. 2013), but can also have ecological consequences for biogeochemical cycles (de la Porte et al. 2020).

In high CH4 environments like rice paddy soils, landfills, or chemoclines of lakes, with a high turnover of CH4, VOC-mediated interactions between methanotrophs and heterotrophs can have profound influence on CH4 cycling and emission to the atmosphere. Rice paddies or stratified lakes are major sources of CH4 due to anoxic conditions that are conducive to the production of CH4 (Minami and Neue 1994, Cao et al. 1996, Schubert et al. 2010, Fuchs et al. 2022). Methanotrophs can help to mitigate these emissions by consuming the CH4 that is produced, thus reducing the amount that escapes into the atmosphere, but can also play a key role in the biogeochemical cycling of carbon and other nutrients (Guerrero-Cruz et al. 2021, He et al. 2023). However, the extent to which interactions with other microbes modulate methanotrophic activity as well as the underlying mechanisms have sparsely been investigated. Considering the crucial environmental role of methanotrophs, it is therefore necessary to assess and understand the modes of interaction involved. In this context, we aimed to assess the interplay between methanotrophs and heterotrophs, integrating various analytical techniques, including proteomics, volatile analysis, and measurements of bacterial growth and CH4 oxidation. This multidisciplinary approach aims at identifying and disentangle the complex volatolomes and elucidate the impact of the volatolomes on the methanotroph activity (process measurements) and proteome. Volatile analysis was employed to investigate the production and consumption of specific VOCs during microbial interactions. Proteomics, a powerful tool in systems biology, was employed to elucidate the protein expression patterns of methanotrophs and heterotrophs when confronted with each other. By comparing the proteomes of these organisms, we identified key proteins involved in CH4 oxidation, carbon metabolism, and interspecies communication. This allowed for a better understanding of the molecular basis of the interactions between methanotrophs and heterotrophs and will shed light on our earlier, more descriptive findings (Veraart et al. 2018). The findings of this research will contribute to our knowledge of CH4 cycling in various environments, ultimately helping to develop strategies for mitigating CH4 emissions and understanding the ecological roles of methanotrophs and heterotrophs in microbial communities.

Materials and methods

Experimental design

In this study, we analysed the interaction of heterotrophs with our model methanotroph organism (Methylomonas spp. LL1; de Assis Costa et al. 2021), as well as the effect of elevated CO2 concentrations on the LL1 strain, on its activity, volatile production, and growth. In addition to Methylomonas spp. LL1, we used two heterotrophs (Microbacterium oxydans H1 and Exiguobacterium undae H5; details about isolation see Veraart et al. (2018) and Krause et al. (2015). The heterotrophs were isolated from methanotrophic enrichment cultures, which were inoculated with wetland sediment and hence, they co-occur naturally with methanotrophs. Methylomonas spp. LL1 was also isolated from a wetland soil (Bodelier et al. 2012, 2013, de Assis Costa et al. 2021) and was used on the basis of environmental origin. Exiquobacterium was chosen because of the positive effect on methanotrophic growth and activity while Microbacterium was chosen from our collection of wetland enrichment isolates because these strains have been demonstrated in pilot incubation to have an inhibitory effect on methanotrophic growth (Cordovez et al. 2018). Methylomonas sp. LL1 was incubated either alone or together with one of the heterotrophs. In addition, we incubated Methylomonas sp. LL1 under elevated CO2 (5%) atmosphere (Vorobev et al. 2011). An overview is provided in Table 1. We used a similar incubation approach as in a previous study (Veraart et al. 2018) in which methanotrophs were grown in nitrogen mineral salts (NMS) medium, while heterotrophs were incubated in dilute tryptic soy broth (0.1% TSB) medium. In brief, preincubations were conducted at 25°C with shaking at 145 r m−1 in the dark using 50 ml Erlenmeyer flasks with either NMS medium for the methanotroph (Whittenbury et al. 1970) or TSB medium for the two heterotrophs (Johnson and Kelso 1983). From these precultures two parallel experiments were conducted, one for the measurement of CH4 uptake rates and the other one for the measurement of volatile production, bacterial growth, and proteomics analyses of the methanotroph. In total the experiment was run for 2 weeks.

Table 1. Experimental setup with all used incubation combinations.

	Plates compartment 1 NMS agar	Plates compartment 2 TSB agar	Headspace	
1	Methylomonas sp. LL1	Empty	Air + 20% CH4	
2	Methylomonas sp. LL1	M. oxydans H1	Air + 20% CH4	
3	Methylomonas sp. LL1	E. undae H5	Air + 20% CH4	
4	Methylomonas sp. LL1	Empty	Air + 20% CH4/ + 5% CO2	
5	Empty	M. oxydans H1	Air + 20% CH4	
6	Empty	E. undae H5	Air + 20% CH4	
7	Empty	Empty	Air + 20% CH4	

CH4 oxidation assay

CH4 oxidation was measured using two-compartment Petri dishes (Greiner, catalogue number 635102), one side containing 12.5 ml 0.1% TSB-agar, the other side containing 12.5 ml NMS-agar. Nutrient composition of the agar was as described in Veraart et al. (2018), but with 15 g l−1 agar added (Bacto agar, for NMS, Merck agar for TSB). In total we prepared 28 plates, with four replicates for each combination (Table 1). In treatments which contained heterotrophs, 50 µl of the respective liquid preculture [diluted to optical density at 600 nm (OD600) of 0.5] was spread on the TSB side of the plates. Following, 50 µl of the Methylomonas sp. LL1 preculture was spread on the NMS side of the plates (diluted to OD600 of 0.5). Plates were preincubated at 25°C in gas-tight jars for 5 days with a headspace containing 20% CH4 in air. After preincubation, individual plates were opened, and vented for 30 min in a fume hood to remove all gas from the previous incubation. Afterwards plates were sealed again and placed in closed flux chambers (V: 172 ml) (Ho et al. 2011), containing a sampling port with a silicon rubber septum. For the CH4 oxidation measurement alone one % of CH4 (10 000 ppmv) was added to the headspace of each chamber (treatment 4 (Table 1) received 5% additional CO2), after which CH4 concentrations were measured by sampling the headspace using a Vici precision sampling 250 µl syringe equipped with a side port needle (BGB Analytik, Schloßböckelheim, Germany) in a period of ∼29 h. CH4 measurements were conducted using an Ultra gas chromatograph (GC) (Interscience, Breda, The Netherlands) equipped with a flame ionization detector and a Rt-Q-Bond (L; 30 m, ID; 0.32 mm, Restek, Interscience) capillary column. Helium was used as a carrier gas, and oven temperature was set at 80°C. Chromeleon™ Chromatography Data System 7.1 (CDS, Thermo Fisher Scientific) Software was used to analyse the gas chromatograms obtained from the GC. CH4 oxidation rates were calculated using linear regression of headspace CH4 concentrations retrieve between 2.5 and 28.5 h, to avoid variation in measurements directly after starting incubation and subsequently capturing maximum rates.

Volatile analysis

Volatiles were analysed for all three strains alone, the combinations of Methylomonas sp. LL1 with both heterotrophs and the combination of Methylomonas sp. LL1 with additional CO2. The same treatments as for the CH4 oxidation measurements were applied (Table 1), each with four replicates. Split plates as described above were inoculated with precultures growing in the mid-exponential phase, spread with 50 µl of each heterotroph on one side and spotted with seven droplets of 8 µl of Methylomonas sp. LL1 on the other side. Plates were preincubated for 5 days in gas-tight jars containing 20% CH4 in the headspace and additional 5% CO2 in the Methylomonas sp. LL1 + CO2 treatment. After preincubation, the plates were placed in volatile-trapping chambers (Ho et al. 2011, Veraart et al. 2018), closed with a butyl septum through which CH4 and CO2 could be injected, and a steel volatile trap containing 150 mg Tenax TA and 150 mg Carbopack B (Markes International, Ltd., Llantrisant, UK) was inserted (Tyc et al. 2015). Plates containing traps were incubated in the dark at 25°C for 48 h, after which volatile traps were removed, rapidly capped, and stored at 4°C until further analysis within 2 weeks. Volatile analyses were performed on a Quadrupole Time of Flight GC/MS (hereafter GC-Q-TOF, Agilent 7890B GC, Agilent 7200A/B Q-TOF) as described in Veraart et al. (2018). For the volatolomics analysis, the acquired raw mass spectrometry (MS) data was extracted to m/z format using MassHunter Qualitative Analysis Software V B.07.00 (Agilent Technologies, Santa Clara, CA, USA). The m/z data was processed with MZMine V 2.36 (Copyright © 2005–2012 MZmine Development Team) to create a m/z and peak intensity table that could be used as input file for MetaboAnalyst 4.0 software (http://www.metaboanalyst.ca/MetaboAnalyst) (Pluskal et al. 2010, Lankenau et al. 2015, Chong et al. 2018). Before the statistical analysis, the data was filtered using Interquartile range (IQR) and normalized by the log transformation with automatic scaling (Ossowicki et al. 2020). The VOCs raw data can be received upon request due to the size of the dataset (∼650 GB).

The data displayed in Figs 4 and 5 are normalized and display relative mass intensity values of compounds detected, relative to the empty plates. Compounds detected in the empty plates should also appear in the treatments. However, since the ‘contaminating/background’ compounds are in most cases (see Supplementary Table 6) in low intensities present they will be either detected or not. Whether a nonmicrobiologically produced compound is detected or not may depend on the experimental handling but also potential degradation by the microbes in the inoculated plates. Especially, when amounts are low this can lead to zeros which will not happen in the empty plates. These inconsistencies are probably due to a combination of replication problems (i.e. degree of contamination is difficult to control in our experimental handling), caused measuring around detection limits in combination with degradation by the microbes in the experiment.

Determination of bacterial growth and proteome analyses

Immediately following the incubation for the volatile experiment, cell material from the methanotroph and the two heterotrophs were harvested by flushing the plates with 3 ml of NMS medium or TSB medium, respectively. The cells were gently scraped off using a cell-scraper, and an aliquot of the resulting cell suspension was diluted for OD600 measurement. Afterwards the cell suspension of each sample was centrifuged, and the pellets were resuspended and transferred to 2 ml vials. Then all suspensions were centrifuged again and washed twice with 1x PBS after which the cell pellets were frozen using liquid N2 and stored at −80°C till further processing for proteomics analysis. Since the growth of Methylomonas sp. LL1 in the combinations with M. oxydans H1 was inhibited by the heterotroph, this combination was excluded from the proteomics analyses.

Extraction, solubilization, and digestion of proteins

The proteomics sample preparation was done according to a previous study (Hakobyan et al. 2018). In brief, the frozen cell pellets were resuspended and sonicated (Hielscher Ultrasound Technology) in 2% sodium deoxycholate buffer (SDC, dissolved in 100 mM ammonium bicarbonate) in the presence of 5 mM tris[2-carboxyethyl]phosphine and subsequently incubated at 95°C for 60 min. The samples were subjected to additional 20 s rounds of sonication after 15 and 30 min of incubation. Upon the SDC treatment, all the protein samples were allowed to cool followed by incubation with 10 mM iodoacetamide at 25°C for 30 min. The resulting crude lysates were used for further digestion as described below. The concentration of proteins in each sample was measured with BCA protein assay kit (Thermo Fisher Scientific) according to the manufacturer’s instructions.

For protein digestion step, 50 μg of total solubilized protein from crude lysate samples was used. For in-solution digestion, the samples were diluted to 0.5% of solubilizing (SDC) agent in 100 mM ammonium bicarbonate buffer. LysC (0.5 µg, Wako Chemicals GmbH) was added directly to the protein extract and incubated for 4 h at 30°C, followed by trypsin (1 μg, Promega) digestion overnight at 30°C. Before LC–MS analysis, traces of SDC were precipitated using 2% trifluoroacetic acid, and all protein digests were desalted using C18 microspin columns (Harvard Apparatus) according to the manufacturer’s instructions.

LC–MS/MS analyses, peptide/protein identification, and LFQ quantification

The LC–MS/MS analysis of protein digests was performed on a Q-Exactive Plus mass spectrometer connected to an electrospray ion source (Thermo Fisher Scientific). Peptide separation was carried out using the Ultimate 3000 nanoLC-system (Thermo Fisher Scientific), equipped with an in-house packed C18 resin column (Magic C18 AQ 2.4 µm, Dr. Maisch). The protein digest (1 µg) was first loaded onto a C18 precolumn (preconcentration set-up) and then eluted in backflush mode with a gradient from 98% solvent A (0.15% formic acid) and 2% solvent B (99.85% acetonitrile, 0.15% formic acid) to 25% solvent B over 105 min, continued from 25% to 35% of solvent B up to 135 min. The flow rate was set to 300 nl/ min. The data acquisition mode was set to obtain one high-resolution MS scan at a resolution of 60 000 (m/z 200) with scanning range from 375 to 1500 m/z followed by MS/MS scans of the 10 most intense ions (Top10 DDA). The dynamic exclusion duration was set to 30 s. The ion accumulation time was set to 50 ms (both MS and MS/MS). The automatic gain control was set to 3 × 106 for MS survey scans and 1 × 105 for MS/MS scans. For label-free quantitative analysis MS raw files were imported into Progenesis (Nonlinear Dynamics, version 2.0) and the output data (MS/MS spectra) were exported in mgf format. MS/MS spectra were then searched using MASCOT (v.2.5, Matrix Science) against a decoy database of the predicted proteomes from Methylomonas sp. LL1, Exiguobacterium sp. RIT341, and M. oxydans. The protein databases were downloaded from uniprot and evaluated for protein sequence redundancy. The following search parameters were used: full tryptic specificity required (cleavage after lysine or arginine residues); two missed cleavages allowed; carbamidomethylation (C) set as a fixed modification; and oxidation (M) set as a variable modification. The mass tolerance was set to 10 ppm for precursor ions and 0.02 Da for fragment ions for high energy-collision dissociation (HCD). Results from the database search were imported back to Progenesis to map peptide identifications to MS1 features. The peak heights of all MS1 features annotated with the same peptide sequence were summed, and protein abundance was calculated per LC–MS run. Next, the data obtained from Progenesis were evaluated using SafeQuant R-package version 2.2.2 (Glatter et al. 2012). Hereby, 1% FDR of identification and quantification as well as intensity-based absolute quantification (iBAQ) values were calculated.

Significantly up- or downregulated proteins were defined as having values of the log2 ratios of >1.0 or <−1.0, respectively, with q-value < 0.05. The 75 proteins with the highest order of significance were also visualized on a heat map in which clustering was based on Euclidian distance, using Ward’s algorithm. For visualization of similarity between the different proteomics profiles we used a principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) analyses was performed to compare the differences between groups based on multivariate data (Supplementary Table 7). ClusterProfiler version 3.12.0 was used to perform functional enrichment analyses based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Here, the gene set enrichment analysis (GSEA) function (Subramanian et al. 2005) was applied to determine whether an a priori defined set of genes based on KEGG shows statistically significant, concordant differences between the different treatments.

Statistical analysis of growth and CH4 oxidation

All statistical analyses were done using R version 4.2.0 (R Development Core Team 2008). The CH4 uptake rate and bacterial growth from the different treatments were tested for normality by Kolmogorov–Smirnov test and homogeneity of variance by Levene’s test. If necessary, normal distribution was achieved by log-transformation of the data. Mean differences were assessed using one-way analysis of variance ANOVA followed by Tukey’s post hoc test. All levels of significance were defined at P < .05.

Statistical analysis of the volatile data was performed on the resulting peak intensity table using the MetaboAnalyst platform (Chong et al. 2018). We filtered peak intensities based on IQR, then log transformed and autoscaled them (mean-centred and divided by the standard deviation of each variable) to obtain a normal distribution. We used one-way ANOVA with Tukey’s post hoc test, to identify significant differences in peak intensities between samples, for each observed compound. PCA was used to visualize maximum separation between groups based on their peak intensities and permutational multivariate analysis of variance (PERMANOVA) analyses was performed to compare the differences between groups based on multivariate data (Supplementary Table 7). We constructed heat maps of peak intensities of the detected compounds in the samples to visualize differences between treatments, in which clustering was based on Euclidian distance, using Ward’s algorithm.

Data deposition

The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD051964, which can be accessed with Username: reviewer_pxd051964@ebi.ac.uk and Password: bkeJRW0q.

Results

Bacterial growth and CH4 oxidation

Methylomonas sp. LL1 performed best on plates with the addition of CO2 (OD600 = 5.3), while no growth was observed for Methylomonas sp. LL1 in combination with M. oxydans H1 (Fig. 1; Supplementary Table 1). Even though the growth of Methylomonas sp. LL1 together with Exiguobacterium sp. H5 (OD = 2.8) was ∼47% lower compared to its growth with CO2, it was still 71% higher than Methylomonas sp. LL1 (OD = 0.8) growing alone. The two heterotrophs showed a similar growth performance, no matter whether they were incubated with or without the addition of Methylomonas sp. LL1.

Figure 1. Cell growth of the six different treatments based on OD600 measurements. When a strain grew alone on the two-compartment agar plate, only its name is stated. If the strain grew with another strain, the name of the other strain is mentioned in parentheses below the name of the measured strain. Different letters indicate significant differences (P < .05) in the mean cell growth of each sample. Mean ± SD (n = 4). See Table 1 for further details on the six treatments.

The highest CH4 uptake rate (Fig. 2; Supplementary Table 2) of Methylomonas sp. LL1, was observed when growing together with Exiguobacterium sp. H5 (mean: 275 ppm h−1), which was almost 30 times higher as compared to growing alone (mean: 15 ppm h−1). The CH4 consumption rate with additional CO2 added (mean: 180 ppm h−1) tended to be lower than the rate within the presence of Exiguobacterium sp. H5. However, due to the high variability between replicate samples this difference was not significant (P > .05).

Figure 2. CH4 uptake rate for Methylomonas sp. LL1 incubated alone, in combination with the two heterotrophs, and with the addition of 5% CO2 in the headspace. Since the growth of Methylomonas sp. LL1 was inhibited by M. oxydans, no CH4 uptake rate was measurable. Same letters indicate similarity in CH4 uptake rate, different letters indicate significant differences (P < .05). Mean ± SD (n = 4.)

Proteomic analyses

Overall, we identified 2605 proteins for Methylomonas sp. LL1, 1586 proteins for M. oxydans H1, and 1590 proteins for Exiguobacterium sp. H5. For Methylomonas sp. LL1 we found 115 proteins that were upregulated (log2 > 1 and P-values < .05) when grown together with Exiguobacterium, while 178 proteins were downregulated (Supplementary Table 3). The incubation with elevated CO2 led to the detection of 40 upregulated proteins and 46 downregulated proteins. After the growth together with Methylomonas, the proteome of Exiguobacterium sp. H5 only revealed eight upregulated and seven downregulated proteins. Similarly, the proteome of Microbacterium only showed seven upregulated proteins and no downregulated proteins after growth together with Methylomonas. Given these results, it was not possible to identify or predict any proteomic interplay between the two heterotrophs and Methylomonas. (Supplementary Tables 4 and 5). All three Methylomonas sp. LL1 proteomes were clearly separated in the PLS-DA analyses (Fig. 3A), with the proteome of Methylomonas sp. LL1 grown under elevated CO2 being more similar to the proteome of Methylomonas sp. LL1 grown alone than in combination with one of the heterotrophs. Since the incubation of Methylomonas sp. LL1 in combination with M. oxydans H1 showed no growth, these samples could not be used for a proteomic analysis. The heat maps (Fig. 3B) of the TOP75 proteins significantly differed (P < .05) between the treatments. In particular, the proteome of Methylomonas sp. LL1 grown in the presence of Exiguobacterium sp. H5 was remarkably different from those of Methylomonas sp. LL1 grown alone or with CO2 amendment. A pathway analysis (GSEA) of the differentially expressed proteins showed that in cultures of Methylomonas sp. LL1 grown together with Exiguobacterium sp. H5, the CH4, fatty acid and pyramidine metabolisms had been downregulated relative to cultures in which Methylomonas sp. LL1 was grown alone (Table 2). In Methylomonas sp. LL1 cultures supplemented with additional CO2, the expression of genes involved in the biosynthesis of valine, leucine and isoleucine, and the 2-Oxocarboxylic acid metabolism were significantly downregulated, while genes involved in CH4 and glutathione metabolism, bacterial chemotaxis, degradation of aromatic compounds and two-component systems were upregulated relative to cultures in which Methylomonas sp. LL1 was grown alone (Table 2).

Figure 3. PCA and comparative heatmap visualization of the proteomics approach. (A) PCA plots of the proteomics composition between three different treatments: (i) Methylomonas sp. LL1 (red and triangle symbols), (ii) Methylomonas sp. LL1 + Exiguobacterium H5 (blue and x), and (iii) Methylomonas sp. LL1 with the addition of 5% CO2 in the headspace (green and cross). (B) Heatmap visualization and clustering analysis of the TOP 75 proteins that significantly differ in their expression between the three treatments shown in the PCA.

Table 2. KEGG-based proteomic GSEA shown for the Methylomonas sp. LL1 treatments with Exiguobacterium (H1) and 5% CO2 in comparison.

Treatment	Pathway1	Gene size	NES2	NOM3  P-value	FDR4  q-value	
Methylomonas sp. LL1 alone versus with Exiguobacterium H1	Carbon metabolism	94	−2.23	<.001	0.011	
	CH4 metabolism	52	−2.07	<.001	0.011	
	Microbial metabolism in diverse environments	153	−2.07	<.001	0.011	
	Fatty acid biosynthesis	13	−1.59	<.001	0.111	
	Fatty acid metabolism	13	−1.59	<.001	0.089	
	Pyrimidine metabolism	27	−1.5	<.001	0.180	
Methylomonas sp. LL1 alone versus with + CO2	Valine, leucine, and isoleucine biosynthesis	12	−1.88	<.001	0.054	
	2-Oxocarboxylic acid metabolism	24	−1.83	<.001	0.046	
	CH4 metabolism	52	2.23	<.001	0.018	
	Nitrogen metabolism	18	1.75	<.001	0.043	
	Bacterial chemotaxis	51	1.67	<.001	0.076	
	Carbon metabolism	94	1.63	<.001	0.073	
	Microbial metabolism in diverse environments	153	1.62	<.001	0.064	
	Glutathione metabolism	12	1.62	.019	0.057	
	Degradation of aromatic compounds	8	1.55	<.001	0.078	
	Two-component system	106	1.52	<.001	0.080	
	Phenazine biosynthesis	3	1.51	<.001	0.078	
	ABC transporters	32	1.42	<.001	0.140	
1 Upregulated pathways have a positive NES score, while downregulated pathways have a negative score in the first mentioned strain, respectively. Pathways with a P-value <.05 and an FDR q-value < 0.25 are shown.

2 NES = normalized enrichment score; 3NOM = nominal; 4FDR = false discovery rate.

Volatolome analyses

Negative effects of the volatolome on the growth of Methylomonas

The volatolome differed between all the treatments (Fig. 4A, Supplementary Table 6), with 14 volatiles been identified as significantly different between treatments (Fig. 4B). Dimethyl disulphide and dimethyl trisulphide were only present in samples with M. oxydans H1, while cyclohexane was only detected when Methylomonas sp. LL1 was grown alone. In addition, a phenol-like compound and an unknown compound were detected on plates where M. oxydans H1 and Methylomonas sp. LL1 were grown together, while these compounds were not present when M. oxydans H1 and Methylomonas sp. LL1 were grown alone.

Figure 4. PCA and comparative heatmap visualization of the volatile analysis results for Methylomonas sp. LL1 incubated (i) alone (blue and cross) and (ii) in combination with M. oxydans H1 (green and triangle), as well as for (iii) M. oxydans H1 alone (red and circle) and (iv) an empty Petri dish plate that acted as a negative control (light blue and x). (A) PCA plots of the volatile composition between the different treatments. (B) Heatmap visualization and clustering analysis of all the volatiles that significantly differed between the treatments.

Positive effects of the volatolome on the growth of Methylomonas

The volatile compounds emitted from plates where the growth of Methylomonas was stimulated were not as distinct as those from plates where growth was inhibited (Fig. 5A). Still, we observed 13 significantly different VOCs between the treatments (Fig. 5B). We found an unknown compound that was only present when Methylomonas sp. LL1 was growing together with Exiguobacterium sp. H5. By contrast, 2,4-di-tert-butylphenol was only detected when the methanotroph was growing in the presence of additional CO2.

Figure 5. PCA and heatmap analysis of the volatile analyses from Methylomonas sp. LL1 alone (blue and cross) and in combination with Exiguobacterium sp. H5 (green and triangle) and additional 5% CO2 in the headspace (light blue and x) as well as from Exiguobacterium H5 alone (red and circle), and an empty Petri dish plate as a negative control (pink and diamond). (A) PCA plots of the volatile composition between the different samples. (B) Heatmap visualization and clustering analysis of all significantly different volatiles between the different samples.

Discussion

This study explored the mechanisms and effects of the interaction between methanotrophic and heterotrophic bacteria, which occur naturally together in the environment (Stock et al. 2013, Ho et al. 2014). Our findings suggest that the release of VOCs by heterotrophic bacteria can affect the growth and CH4 uptake of methanotrophs positively or negatively depending on the heterotrophic species present.

The coexistence and interactions of these microbial groups, once considered separate entities in ecological models, have emerged as pivotal drivers influencing the fate of CH4, a potent GHG, and the flow of carbon in diverse habitats. Methanotrophs, by virtue of their unique metabolic capability to oxidize CH4, interface directly with heterotrophic communities reliant on alternative carbon sources by exuding methanol and other metabolites (Stock et al. 2013). The resulting interactions, ranging from cooperative mutualism to competitive antagonism, yield a complex network of metabolic exchanges and dependencies (Ho et al. 2016, Veraart et al. 2018). Unravelling the dynamics and mechanisms of these relationships holds profound implications for ecosystem functioning, carbon cycling, and global climate regulation.

Next to exchange of primary metabolites. Volatile compounds derived from secondary metabolism have been shown to play a role in the interaction of several methanotrophic and heterotrophic species (Veraart et al. 2018, Puri 2019, Kilic 2021). We aimed to underpin our earlier investigations (Veraart et al 2018) with more mechanistic information by exploring this interaction further using a proteomic approach to observe the impact of these VOCs on the metabolic activities of the methanotrophs and heterotrophs. We also wanted to investigate whether the effect of an interaction between heterotrophs and methanotrophs is mainly due to the additional CO2 provided by the heterotrophs or is actually triggered by the release of VOCs. We obtained very different results depending on the interacting species, but also a difference between the addition of solely CO2, as proxy for a respiring heterotroph, and the presence of a real respiring microbe. While growth of Methylomonas sp. LL1 was inhibited in the presence of Microbacterium’s volatolome, growth and activity were stimulated when exposed to E. undae. Addition of CO2 caused a similar stimulation of Methylomonas sp. LL1 on growth and CH4 uptake, but the accompanying proteomics data of Methylomonas sp. LL1 differed significantly between addition of CO2 and presence of Exiguobacterium, suggesting different underlying mechanisms of the stimulation observed.

Inhibition of growth of Methylomonas by Microbacterium

We observed a complete inhibition in growth of Methylomonas directly from the beginning of the incubation when exposed to the volatolome of a growing Microbacterium, without direct contact. Volatolome analyses led to the conclusion that sulphur compounds [dimethylsulphide (DMS) and dimethyldisulphide (DMDS)] were the agents inhibiting Methylomonas. Microbacterium strains have been observed to produce DMDS and DMS, eliciting e.g. plant growth promoting effects, like increased root biomass and a more extensive root system as well as increased shoot biomass (Cordovez et al. 2018, Ballot et al. 2023). It was suggested that the VOCs emitted by Microbacterium spp. act as signalling molecules, triggering physiological responses in plants that enhance growth and nutrient uptake. DMS and DMDS are well-known and ubiquitous bacterial volatiles (Ryu et al. 2020) and have also been demonstrated to influence methanotrophic growth and activity, albeit with mixed effects on different methanotrophs (Veraart et al. 2018). While Methylobacter luteus showed a stimulation in CH4 uptake, but no effect on growth, after growing together with the DMS and DMDS producing heterotroph Pseudomonas mandelii, Methylocystis parvus exhibits a contrasting effect with a stimulation in growth, but a decrease in CH4 uptake. However, a complete inhibition of growth and activity as in our study was not observed (Veraart et al. 2018). The volatile analyses in this study clearly showed that specifically sulphur compounds like DMS and DMDS were produced by P. mandelii and seem to play a role in the interaction. Methylophaga sulphidovorans is able to convert DMS to carbon dioxide and thiosulfate. This heterotrophic bacterium, which uses the ribulose monophosphate (RuMP) route for carbon assimilation, can also use sulphide as an additional energy source (Zwart et al. 1996). Furthermore, a large variety of bacteria are capable of oxidizing DMS to DMSO, provided an additional carbon source is present (Zhang et al. 1991). The oxidation can be carried out for example by the ammonium monooxygenases, with its similarity to the pMMO there could be potentially also methanotrophic oxidization of DMS (Fuse et al. 1998). While DMS might be beneficial for certain bacteria, our results show that, at least for Methylomonas, the presence of DMS and DMDS has negative effects. In a recent study analysing the methanotrophic community in landfill cover soils, a negative effect of higher DMS concentrations was observed (Wang et al. 2023). The relative abundance of Methylocaldum decreased and the CH4 oxidation rate was reduced, while at the same time the relative abundance of Methylobacter and Crenothrix increased. In the same study, a metagenomic analysis of these soils demonstrated a reduction in the mmo and mxa genes involved in CH4 uptake and CH4 assimilation. Since the growth of Methylomonas was completely inhibited in our study, we could not perform a proteomic analysis to determine possible inhibition of specific metabolic pathways such as sulphur oxidation or reduction. To understand how DMS, DMDS or other sulphur compounds can interact with different bacteria strains needs to be further analysed to understand their impact in the environment.

However, important to keep in mind is that in the natural context, there are multiple microbes around producing and possibly consuming volatiles which makes that our results of one on one interaction have to be put in context. With respect to this, when looking at relevant scales to microbes, e.g soil pore or aggregate scale, than the numbers of cells really interacting with each other can be very small and even one to one (Amelung et al. 2024), strengthening the environmental realism of our results. On the other hand, volatiles in soil can travel over cm's distance and can even obvious one to one interaction may be influenced from elsewhere (Schulz-Bohm et al. 2018).

Stimulation of activity of Methylomonas by Exiguobacterium and increased CO2 concentrations

The results of our study clearly showed an increase in growth and CH4 uptake rates by Methylomonas sp. LL1 growing together with Exiguobacterium undea or by addition of CO2 solely. To investigate the mechanisms behind these stimulatory effects we conducted a proteomics analysis. In a recent study, growth of Methylocystis parvus was also promoted by additional CO2 and the presence of heterotrophs, but not CH4 uptake rates (Veraart et al. 2018). In the same study Methylobacter luteus showed the complete opposite effect, with an increase in CH4 uptake rates, but a constant growth compared to the control. In both, Veraart et al. (2018) and the current study the effects of the presence of a heterotroph or the addition of CO2 seem to be quite similar. However, the proteomics data in this study differ significantly when Methylomonas is grown alone, with the addition of extra CO2 or together with Exiguobacterium. The latter results in a significantly different proteome of Methylomonas than the first two treatments, leading to the assumption that the stimulation by the volatolome of a heterotroph is not solely caused by the presence of CO2, but probably by other VOCs.

Stimulation of growth by CO2 maybe caused by fixation using the reductive glycine cycle, a pathway which in theory can also be used by some methanotrophs (Tveit et al. 2019, Claassens 2021, Nguyen et al. 2021). However, in our proteomics analyses the enzymes involved in the reductive glycine cycle did not show differences between the different samples, but all involved enzymes were detected in all samples (Supplementary Table 3). Nevertheless, there were two very clear differences in the proteome analyses in the CH4 processing pathways. While the sMMO of Methylomonas is upregulated after the addition of CO2, it is downregulated growing together with Exiguobacterium. An opposite trend is observed for the RuMP pathway, which is upregulated alongside Exiguobacterium and downregulated with additional CO2. This suggests a regulation of pMMO versus sMMO expression, possibly in response to higher CO2 concentrations and changing redox conditions, similar to the regulation seen with copper. The downregulation of RuMP in the presence of Exiguobacterium might indicate a shift to directing more CH4 carbon into the electron transport chain for additional energy generation, especially when essential compounds are provided by the heterotroph (Holmes et al. 2018). Additionally, there is also a possibility that methanotrophs (Verrucomicrobia) can grow as autotrophs together with H2 as a sole electron source (Mohammadi et al. 2019). It was shown that the NiFe-hydrogenase that is needed for this conversion is also present in our Methylomonas strain (de Assis Costa et al. 2021). However, we did not detect an upregulation of the NiFe-hydrogenase in our proteomics data, even though it was detectable. There are Methylomonas strains which are capable of fixing CO2 through the PEP carboxylase, pyruvate carboxylase, and acetyl-CoA carboxylase (Nguyen et al. 2018). However, it seems that our Methylomonas strain only harbours the first two carboxylases but not the latter one.

Remarkably, there are multiple studies in rice paddies and grasslands showing that elevated CO2 (eCO2) concentrations have a positive impact on growth of methanotrophs and the rate of uptake of CH4 (Yu et al. 2018, Qian et al. 2020, 2022). In these studies type I methanotrophs were the benefactors of this eCO2 compared to type II (Liu et al. 2022). The authors of these studies hypothesized that there is an indirect effect of CO2 driving an increase in DOC and root growth, providing a more friendly environment for methanotrophs by an increase in available CH4 and increase of oxic zones. However, our data point to a direct effect driving the observed results, of which the principal mechanisms of the direct effect of CO2 on the sMMO expression in Methylomonas, we cannot explain. Speculative, the effect of CO2 could be through reducing equivalents that the sMMO needs to initiate the first step of CH4 metabolism. To date the exact source of reducing equivalents for sMMO is still not fully understood, but it is thought to involve flavoproteins and iron–sulphur proteins that participate in electron transfer reactions. It utilizes a cofactor called a dinuclear iron center, often referred to as the diiron center, which plays a crucial role in the catalytic reaction (Whittington and Lippard 2001). The reducing equivalents generated through cellular metabolism are utilized by MMO to activate molecular O2 and initiate the oxidation of CH4. There are some studies that show that formate dehydrogenase (FDH) enzymes, cytochrome c, or succinate dehydrogenase flavoprotein subunit enzymes are important in the uptake and reduction of CO2 in the cell (Wan et al. 2016, Ruiz-Valencia et al. 2020, Alothaim 2023). Even though we detect these enzymes in the proteome of Methylomonas, they do not show a specific regulation in the incubation with CO2. The handling of the samples can influence the proteomics analyses. The setup of these experiments may introduce bias in the analyses due to the examination of cells in various growth stages within a single colony. So maybe it is a matter of timing, since for example for the FDHs it was shown that this could be used as a precursor for methanol production from CH4 by methanotrophic bacteria (Ruiz-Valencia et al. 2020), which speeds up the whole first step in CH4 oxidation, we may have missed this with our sampling approach. Besides the indications from other bacterial species there are no studies for methanotrophs about transport or uptake of CO2, therefore more studies are needed that specifically focus on this interaction, especially since under natural conditions, CO2 can reach up to 5% in in soils and especially in rhizosphere environments (Button et al. 2023, Bereswill et al. 2024).

The impact of Exiguobacterium on the function and growth of methanotrophs appeared to exhibit similarities to the effects of CO2, as previously mentioned. While both stimulate the growth, this effect was more than doubled with the added CO2, with the same CH4 uptake rates. This also hint into the direction that the CO2 was directly used as a carbon source. The amount of CO2 respired by Exiguobacterium is probably nowhere near the amount of CO2 added directly. Unfortunately, the CO2 production and consumption was not measured during the course of the experiment, therefor, the sudden exposure to high and gradually increasing CO2 is different, which may lead to different responses of which we do not known what consequences it may have on methanotrophic growth and activity. However, our analysis of volatolomics and proteomics data revealed also divergent indications from those associated with CO2. While we successfully detected specific VOCs in the volatolome analysis, these compounds were found exclusively in the interaction between Methylomonas and Exiguobacterium, yet their precise identification could not be determined. However, the proteomics analysis provided insights into the upregulation observed in this interaction, revealing that it was not the sMMO like the effect with CO2, but rather the RuMP pathway that exhibited upregulation. The RuMP pathway represents one of the three primary pathways employed by methanotrophic bacteria to assimilate CH4 (Hanson and Hanson 1996, Chistoserdova and Lidstrom 2013, Chistoserdova and Kalyuzhnaya 2018). In this pathway, CH4 is oxidized to formaldehyde, which is subsequently converted to ribulose-5-phosphate through a series of enzymatic reactions. Ribulose-5-phosphate can then enter the pentose phosphate pathway, providing the cell with energy and biosynthetic intermediates. The RuMP pathway is widely utilized by methanotrophs and plays a critical role in the global carbon cycle by converting CH4 into biomass. However, the mechanisms causal of the stimulation of the RuMP pathway through the interaction with Exiguobacterium remain uncertain. Additionally, we observed an upregulation of the pyrroloquinoline quinone (PQQ)-dependent methanol dehydrogenase and an increase in PQQ production. This suggests that the pathway from methanol to formaldehyde, and subsequently to the RuMP pathway, may be influenced by the growth of both heterotrophic bacteria and methanotrophs. It is possible that the heterotrophs provide reducing equivalents at an accelerated rate, enabling the methanotrophs to gain an advantage in the initial uptake and oxidation of CH4, consequently leading to higher metabolic activity downstream. However, further investigations are needed to elucidate the precise mechanisms involved, as the optimal sampling time or missing connecting information may have influenced the measurements.

Outlook

The current study explored the interaction between methanotrophic and heterotrophic bacteria, as well as high concentrations of CO2, and their effects on the functionality of the methanotrophs (growth, CH4 oxidation, and carbon cycling). Understanding the dynamics of the interaction between methanotrophic and heterotrophic bacteria can have implications for ecosystem functioning, carbon cycling, and global climate regulation. Methanotrophs play a crucial role in mitigating the effects of CH4, a potent GHG, by oxidizing it. The study highlights that the interaction between these bacteria can influence the fate of CH4 and the flow of carbon in diverse habitats. The findings suggest that the release of VOCs by heterotrophic bacteria can affect the growth and CH4 uptake of methanotrophs. Different VOCs, such as dimethyl-sulphide and dimethyl-disulphide, were found to have varying effects on different methanotroph species. Some VOCs stimulated growth and CH4 uptake, while others inhibited them. These findings indicate that the presence and composition of VOCs in the environment can impact the activity of methanotrophs and, consequently, the rate of CH4 oxidation. Moreover, the study highlights the role of CO2 in the interaction between bacteria. Elevated CO2 concentrations were found to have a positive impact on the growth of methanotrophs and their CH4 uptake rates. This suggests that increased CO2 levels, such as those associated with climate change, can potentially enhance the activity of methanotrophs and contribute to the reduction of CH4 emissions. Overall, the study emphasizes the importance of understanding the interactions between methanotrophic and heterotrophic bacteria in order to comprehend their influence on CH4 oxidation, carbon cycling, and ultimately, global climate regulation. These insights can inform strategies for mitigating GHG emissions and managing carbon in the environment.

Supplementary Material

fiae112_Supplemental_File

Author contributions

Kristof Brenzinger (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing), Timo Glatter (Data curation, Formal analysis, Validation, Writing – review & editing), Anna Hakobyan (Data curation, Formal analysis, Methodology, Writing – review & editing), Marion Meima-Franke (Data curation, Formal analysis, Investigation, Methodology), Hans Zweers (Data curation, Formal analysis, Methodology), Werner Liesack (Conceptualization, Supervision, Writing – review & editing), and Paul L.E Bodelier (Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing)

Conflict of interest

None declared.

Funding

K.B. was financially supported by the grant from the German DFG BR 5535/1–1 and by a grant from the Dutch Research Council (NOW) number 870.15.073.
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