Phytool v3: a annoted reference barcoding library for algae

[Lien vers la fiche source] Algae and cyanobacteria are an important biological compartment in the biosphere in terms of biomass ((Bar-On et al., 2018), (Bar-On & Milo, 2019)) and they present an exceptional diversity, with more than 170 000 taxa registered in worldwide taxonomical databases (Guiry & Guiry, 2023). They can be both the cause of the degradation of some ecosystem services and sentinels of the impact of anthropogenic degradations. For instance in freshwaters, human activities cause major eutrophication problems in rivers (Le Moal et al., 2019) and lakes (Jenny et al., 2020). There are regularly massive developments of algae with potentially toxic cyanobacteria which make water unusable for consumption (Jacquet et al., 2005). The diversity and composition of their communities can be strongly modified by nutrient level in lakes (e.g. (Anneville et al., 2018), (Thackeray et al., 2013)) and organic matter concentrations in rivers (Rimet, 2012). In the marine realm, human activities also eutrophicate costal ecosystems (Smith, 2003), causing for instance toxic blooms of Dinophyta which make shellfish inedible (Wells et al., 2015), or can cause the regression of seagrass meadows (Duarte, 2002). Terrestrial habitats host also particular and diverse microalgal communities (Metting, 1981), and these communities are also affected by human activities such as the use of pesticides ((Bérard et al., 2004), (Megharaj et al., 2000)) and agricultural practices (Antonelli et al., 2017). Therefore smart public policies to protect and restore the environment are needed to maintain ecosystem services (Carvalho et al., 2019). In order to implement these policies and carry out effective restoration actions, managers need to be able to assess the functioning and state of the environment, using tools based on ecological indicators. Algae and cyanobacteria are choice indicators, thanks to their diversity and ubiquity. For example, phytoplankton is a required indicator by European directives in lakes, rivers and marine environments ((European commission, 2000) (European Commission, 2008)). Several biotic indices have been developed for lake phytoplankton ((Thackeray et al., 2013) (Laplace-Treyture & Feret, 2016)), river microphytobenthos (Hering et al., 2006) and marine phytoplankton (e.g. (Tett et al., 2008), (Lugoli et al., 2012)). These indices are usually based on presence probability of species along a nutrient gradient. The stenoecy degree and the sensitivity value are the key metrics of these indices. The classical and standardized method to identify algae and cyanobacteria is based on optical devices and in particular microscopy for microalgae (e.g. (Utermohl, 1958) (CEN, 2006) (CEN, 2004)). But these methods require highly skilled taxonomists and are tedious (Kermarrec et al., 2014). An alternative method is based on molecular identification techniques using high-throughput sequencing and short DNA-sequences, such as DNA metabarcoding (Pompanon et al., 2011). For phytoplankton, methodologies have now been tested and validated on mock and natural samples, and enable to detect prokaryotic cyanobacteria as well as eukaryotic algae with a single pair of primers. These methodologies cover the different steps: sampling (Domaizon et al., 2019), primer design and test on mock communities for 16S and 23S (Canino et al., 2023), bioinformatics pipeline and comparison to microscopy with natural samples (Nicolosi Gelis et al. 2023), and the development of a dedicated reference barcoding library called Phytool (Canino et al., 2021). The development of a curated DNA reference library, such as Phytool, is crucial to make the methodology usable in the future by end-users. Phytool, as described in (Canino et al., 2021), encompass 30 575 sequences of algae and cyanobacteria (16 654 sequences for 18S, 8479 for 16S, 1996 for 23S, 3446 for rbcL). The sequences were gathered from different databases (such as SILVA NCBI, µGreenDB …) and an essential step has been carried out to homogenize the taxonomy of the sequences, which originate from these different databases. An important point for reference barcoding libraries is that the library must be as complete as possible in order to decipher correctly the species present in the field (Weigand et al., 2019). Therefore, a gap analysis was realized for Phytool for the phytoplankton of French alpine lakes (Nicolosi Gelis et al. 2023). A significant proportion of the taxa present in the plankton of these lakes was not present in the library (80%). However, Phytool enabled to decipher 8 times more taxa than microscopy: these additional taxa were mainly picoalgae (hardly visible in the microscope) and species difficult to distinguish from each other (e.g. taxa identified as Chlorella sp. in microscopy were identified with DNA into several genera of Eustigmatophyceae and Xanthophyceae). However, deciphering the taxonomic diversity in the environment is not the only important point. For most end-users (hydrobiologists and limnologists), the taxonomic names of the microalgal and cyanobacterial species do not give them any idea of the types of habitat these taxa are likely to be found, or do not inform about the type of environmental pressures that may be present. Furthermore, in many cases end-users probably do not have the necessary literature to know e.g., whether the algal taxa found are benthic or planktonic, if they are mobile, what their ecology is (freshwater, marine, terrestrial, polluted or pristine), how big they are, what their toxicity is. Knowing the proportion of these different traits in an microalgal community is important to know: for instance, functional groups and morphological traits of benthic micro-algae are strongly related to nutrients concentrations and physical turbulence in rivers ((Passy, 2007), (Berthon et al., 2011), (Marcel et al., 2013)) and lakes ((Rimet et al., 2015), (Rimet et al., 2019a)). Similarly, in lakes several authors also showed the interest to study the relative abundance of several key traits to assess grazing pressure and nutrients level. For instance size of microalgae is related to grazing pressure (Makarewicz et al., 1998) and/or to physico-chemical conditions (Kamenir & Morabito, 2009). Functional groups ((Reynolds et al., 2002), (Padisak et al., 2009)) as well as the morpho-functional groups ((Kruk et al., 2010), (Salmaso & Padisak, 2007)) are related to nutrient level (Salmaso et al., 2014) and biomonitoring tools based on traits were even set up (Padisak et al., 2006). Similarly, microalgae physiological, morphological traits and functional groups are also often used for marine phytoplankton studies (Litchman & Klausmeier, 2008) and these metrics are also well predictable with seasonality and environmental factors (e.g. (Edwards et al., 2013), (Ramond et al., 2021)). Phytool end-users need a turnkey tool that enables them to not only measure taxonomic diversity, but also ecological processes, therefore there is a need to annotate Phytool reference library with morphological, functional and physiological traits information for each taxa: this is the aim of this new version of Phytool v3. As mentioned above, previous versions of Phytool host several markers (16S, 18S, 23S, rbcL). Since the use of the 23S rRNA gene gives the best results to decipher phytoplankton diversity (Nicolosi Gelis et al. 2023), we selected this gene and annotated the sequences for this marker in Phytool based on existing databases of traits and based on literature. We present here the content of Phytool v3.