Phytoplankton group identification with chemotaxonomic biomarkers: In combination they do better

Peltomaa, Elina; Asikainen, Harri; Blomster, Jaanika; Pakkanen, Hannu; Rigaud, Cyril; Salmi, Pauliina; Taipale, Sami J.

doi:10.1016/j.phytochem.2023.113624

Cited by 8 publications

(12 citation statements)

References 76 publications

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“…Daphnia was able to efficiently use both brassicasterol and campesterol (methyl Δ5‐sterol) as cholesterol precursors. Brassicasterol is also common in cryptophytes (Martin‐Creuzburg and Merkel 2016, Peltomaa et al 2023). Two of the three studied golden algae strains belonged to synurophytes ( Synura sp.…”

Section: Discussionmentioning

confidence: 99%

“…Dinoflagellates, which are rich in the physiologically important long‐chain PUFA eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), contain complex di‐ and trimethyl sterols (LeBlond and Chapman 2002, Peltomaa et al 2023). In addition, dinoflagellates often contain cholesterol (Peltomaa et al 2023), which is also the main body sterol in Daphnia and thus well suited to support growth and reproduction (Martin‐Creuzburg et al 2014). Previous studies have shown that diatoms can contain various sterols, of which chalinasterol, cholesterol, campesterol and brassicasterol are the major sterols (Dunstan et al 2005, Rampen et al 2010, Martin‐Creuzburg and Merkel 2016, Peltomaa et al 2023).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, dinoflagellates often contain cholesterol (Peltomaa et al 2023), which is also the main body sterol in Daphnia and thus well suited to support growth and reproduction (Martin‐Creuzburg et al 2014). Previous studies have shown that diatoms can contain various sterols, of which chalinasterol, cholesterol, campesterol and brassicasterol are the major sterols (Dunstan et al 2005, Rampen et al 2010, Martin‐Creuzburg and Merkel 2016, Peltomaa et al 2023). Golden algae (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Eukaryotic microalgae contain a wide variety of phytosterols often differing only slightly in their chemical structure (Kritchevsky and Chen 2005, Taipale et al 2016, Martin‐Creuzburg and Merkel 2016). Generally, sterol composition of algae differs among chemotaxonomical groups, but unlike fatty acids and carotenoids, sterol composition also varies within phytoplankton groups (Martin‐Creuzburg and Merkel 2016, Peltomaa et al 2023). Most green algae species contain mainly Δ7‐sterols (Taipale et al 2016, Martin‐Creuzburg and Merkel 2016, Peltomaa et al 2023), which are thought to be used less efficiently by Daphnia than the Δ5‐sterols commonly found in cryptomonads, diatoms and golden algae (Volkman 1986, Martin‐Creuzburg et al 2014, Peltomaa et al 2023).…”

Section: Introductionmentioning

confidence: 99%

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Sterol limitation of Daphnia on eukaryotic phytoplankton: a combined supplementation and compound‐specific stable isotope labeling approach

Benjami Laine,

Martin‐Creuzburg,

Litmanen

et al. 2024

Oikos

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Essential biomolecules can critically influence the performance of consumers. A deficiency in dietary sterols has been shown to constrain the food quality of prokaryotic food sources for aquatic consumers. Here, we assessed the importance of dietary cholesterol for life history traits (survival, growth and egg production) of the freshwater herbivore Daphnia magna in supplementation experiments with various sterol‐containing eukaryotic phytoplankton diets (dinoflagellates, diatoms, and golden algae). We combined cholesterol supplementation via liposomes with 13C‐labelling of the phytoplankton diets and traced the origin of cholesterol in Daphnia using compound‐specific stable isotope analysis. All phytoplankton strains used here were rich in polyunsaturated fatty acids (PUFA) but differed in their phytosterol composition. We show that growth and reproduction of D. magna can be limited by sterols even when feeding on sterol‐containing eukaryotic phytoplankton diets. The impact of cholesterol supplementation on growth and reproduction of D. magna differed among phytoplankton diets (strains). The positive effect of cholesterol supplementation was most pronounced on diatom diets. Estimation of source proportions using stable isotopes revealed that D. magna preferentially assimilated the supplemented cholesterol rather than synthesizing it from dietary phytosterols. Our experiments suggest that the different responses to cholesterol supplementation on the various phytoplankton diets were unrelated to the suitability of dietary phytosterols to serve as cholesterol precursors but were caused by other biochemical or morphological food quality constraints. The combination of methods applied here could be very useful for uncovering nutritional constraints and thus for assessing the importance of essential biomolecules for the performance of herbivorous consumers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sterol limitation of Daphnia on eukaryotic phytoplankton: a combined supplementation and compound‐specific stable isotope labeling approach

Benjami Laine,

Martin‐Creuzburg,

Litmanen

et al. 2024

Oikos

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“…2). In addition to phycocyanin, aphanizophyll is a biomarker of cyanobacteria (Peltomaa et al 2023). In the dataset of all the lakes and samplings, phycocyanin concentration correlated with aphanizophyll concentration ( r = 0.6, p < 0.001) and with Chl a concentration ( r = 0.7, p < 0.001), suggesting that in addition to phycocyanin analytics other sources contributed to the variation.…”

Section: Discussionmentioning

confidence: 99%

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Salmi,

Pölönen,

Beckmann

et al. 2023

Limnology & Ocean Methods

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Motivated by the need for rapid and robust monitoring of phytoplankton in inland waters, this article introduces a protocol based on a mobile spectral imager for assessing phytoplankton pigments from water samples. The protocol includes (1) sample concentrating; (2) spectral imaging; and (3) convolutional neural networks (CNNs) to resolve concentrations of chlorophyll a (Chl a), carotenoids, and phycocyanin. The protocol was demonstrated with samples from 20 lakes across Scotland, with special emphasis on Loch Leven where blooms of cyanobacteria are frequent. In parallel, samples were prepared for reference observations of Chl a and carotenoids by high‐performance liquid chromatography and of phycocyanin by spectrophotometry. Robustness of the CNNs were investigated by excluding each lake from model trainings one at a time and using the excluded data as independent test data. For Loch Leven, median absolute percentage difference (MAPD) was 15% for Chl a and 36% for carotenoids. MAPD in estimated phycocyanin concentration was high (102%); however, the system was able to indicate the possibility of a cyanobacteria bloom. In the leave‐one‐out tests with the other lakes, MAPD was 26% for Chl a, 27% for carotenoids, and 75% for phycocyanin. The higher error for phycocyanin was likely due to variation in the data distribution and reference observations. It was concluded that this protocol could support phytoplankton monitoring by using Chl a and carotenoids as proxies for biomass. Greater focus on the distribution and volume of the training data would improve the phycocyanin estimates.

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Integrating pigment and fatty acid profiles for enhanced estimation of seston community composition

Litmanen,

Perälä,

Vuorio

et al. 2024

Ecosphere

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Climate change, nutrition pollution, and land use alterations influence the primary production of lakes. While light‐microscopy counting remains the standard for estimating phytoplankton community composition, its expense and time‐consuming nature necessitate cost‐effective alternatives for seston analysis. Furthermore, estimating the contribution of seston constituents other than primary producers, or non‐algal particles, is not possible with light‐microscopy counting. Biotracer approach using computational methods and chemotaxonomic biomarkers such as carotenoid pigments and fatty acids have been used as an alternative in seston analysis when species‐level taxonomy is not required. However, a comprehensive testing of how well carotenoid and fatty acids can be used in estimating a wide range of seston phytoplankton communities using different estimation methods is lacking. To assess the accuracy of a suite of state‐of‐the‐art biotracer‐based computational methods, namely CHEMTAX, FASTAR (Fatty Acid Source‐Tracking Algorithm in R), MixSIAR, and QFASA (Quantitative Fatty Acid Signature Analysis), lake water samples were collected in 2016, 2018, 2019, 2020, and 2021 for seston composition analysis in a boreal eutrophic lake with light‐microscopy counting serving as the reference for seston composition. Absolute errors between the biotracer‐based estimates were calculated to evaluate method performance. A small laboratory experiment to assess the reliability of estimating the contribution of non‐algal particles using the computational methods with fatty acids was also conducted. The closest alignment to light‐microscopy counting in terms of absolute error was achieved when both carotenoids and fatty acids were used together in the QFASA method. For CHEMTAX, FASTAR, and MixSIAR, using carotenoids alone produced the closest results. Additionally, the estimation methods accurately assessed the proportion of non‐algal particles in the seston when using fatty acid profiles, a capability not possible with light‐microscopy counting. Our findings demonstrate that the biotracer approach provides a viable and cost‐effective alternative to light‐microscopy counting when group‐level information of phytoplankton community composition suffices. Furthermore, we show that non‐algal particles can be effectively estimated together with phytoplankton when using fatty acids.

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Phytoplankton group identification with chemotaxonomic biomarkers: In combination they do better

Cited by 8 publications

References 76 publications

Sterol limitation of Daphnia on eukaryotic phytoplankton: a combined supplementation and compound‐specific stable isotope labeling approach

Sterol limitation of Daphnia on eukaryotic phytoplankton: a combined supplementation and compound‐specific stable isotope labeling approach

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Integrating pigment and fatty acid profiles for enhanced estimation of seston community composition

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