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https://doi.org/10.37427/botcro-2020-021

Metabarcoding Cyanobacteria in coastal waters and sediment in central and southern Adriatic Sea

Anamarija Kolda ; Ruđer Bošković Institute, Zagreb, Croatia
Zrinka Ljubešić ; University of Zagreb, Faculty of Science, Department of Biology, Rooseveltov trg 6,Zagreb, Croatia
Ana Gavrilović ; University of Zagreb, Faculty of Agriculture, Department of Fisheries, Beekeeping, Game Management and Special Zoology, Zagreb, Croatia
Jurica Jug-Dujaković ; Sustainable Aquaculture Systems, Inc., Frenchtown, New Jersey, USA
Kristina Pikelj ; University of Zagreb, Faculty of Science, Department of Geology, Zagreb, Croatia
Damir Kapetanović ; Ruđer Bošković Institute, Zagreb, Croatia


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Sažetak

Seasonal sampling of the seawater column and sediment in Adriatic coastal areas affected by various anthropogenic activities, primarily aquaculture, was conducted during 2017. In total, 32 samples from two sites (central and southern Adriatic) were analysed by 16S rRNA amplicon sequencing. This approach was selected to test the possibilities of using metabarcoding in studying marine cyanobacteria, exploring their ecology and potential as an indicator group in anthropologically stressed coastal environments. Additionally, physico-chemical water column parameters, sediment granulometry and composition were assessed. Water column revealed a seasonal variation of amplicon sequencing variants (ASVs) closely related to Cyanobium PCC-6307, Prochlorococcus MIT9313 and Synechococcus CC9902, as well as seasonal grouping of physico-chemical parameters in PCA analysis. Sediment analysis uncovered greater community richness of 13 cyanobacterial genera and two uncultured groups. The most abundant in sandy gravels and gravelly sand type of sediments were ASVs closely related to Pleurocapsa PCC-7319 and Xenococcus PCC-7305. Furthermore, identified cyanobacterial ASVs predominantly displayed similarity to isolates from tropical areas (e.g. Neolyngbya, Chroococcidiopsis, Trichodesmium, etc.), which could indicate the tropicalization process already ongoing in the fish fauna of the Adriatic Sea.

Ključne riječi

Adriatic Sea, ecology, marine cyanobacteria, metabarcoding, sediment, water column

Hrčak ID:

238381

URI

https://hrcak.srce.hr/238381

Posjeta: 538 *




Introduction

Researching cyanobacteria brings several powerful facts into focus: they are (i) remarkably old organisms - as old as 3.5 billion years (Bellinger and Sigee 2015), (ii) the makers of the aerobic atmosphere in which life, as we know it, exists (Meriluoto et al. 2017), (iii) the main atmospheric nitrogen fixators in global oceans (Whitton and Potts 2012), (iv) one of the main primary producers in the oceans (Paerl 2012), (v) evolutionarily important for chloroplast origin through endosymbiosis (Margulis 1970), and finally, (vi) the creators of the oldest ecosystems - microbial mats (Green and Jahnke 2010). Although cyanobacteria are more commonly investigated in freshwater environments due to intensifying problems of eutrophication and production of cyanotoxins, cyanobacteria are an ecologically extremely important group in marine environments, both planktonic and benthic cyanobacteria. Their role in nutrient cycling, especially as primary producers and nitrogen fixators is of the essence (Whitton and Potts 2012).

Ecological monitoring of cyanobacteria includes many different methods such as the classical morphological counting method using light microscopy (Lund et al. 1958) and chemical methods e.g. HPLC (Colyer et al. 2005), flow cytometry (Casotti et al. 2000) and satellite remote sensing (Gons et al. 2005). However, in the last decade we have entered the era of “omics”, thanks to large advances in molecular methodology as well as in computational power and various bioinformatic tools (Heidelberg et al. 2010). The inability of standard culture techniques to isolate more than 99% of bacteria in the environment (Handelsman 2004) encourages the use of community sequencing approaches or metagenomics, which started to unveil a veritable black box of microbial diversity in marine science (Hugenholtz and Tysen 2008). Metagenomics requires only environmental samples of soil, water, etc., from which environmental DNA (eDNA) is isolated (Mandal et al. 2015). Therefore, cyanobacterial taxonomy has transitioned from dependence on morphological features/data to sequencing data. Although their taxonomic relationships are often confusing, and their nomenclature has been established by both botanists and microbiologists, there are efforts to overcome these issues through a polyphasic approach (Komárek 2016). The most popular phylogenetic marker in prokaryotic metabarcoding is 16S rRNA gene, due to its presence in all prokaryotes. 16S rRNA contains many variable but also highly conserved regions, More specific phylogenetic markers that can provide higher genetic resolution are widely used for Cyanobacteria, e.g. ITS, internal transcribed spacer region of 16S-23S rRNA (Huo et al. 2018). The combination of 2 markers, 16S rRNA and ITS, has been successfully applied in the identification of freshwater cyanobacteria in Croatia (Kolda et al. 2019). However, for metabarcoding studies, 16S rRNA is selected due to comprehensive public databases (i.e. SILVA, Greengenes, etc.) that do not exist for other (cyano-)bacterial markers.

The present study is conducted in the Adriatic Sea, a semi-enclosed basin in the northernmost part of the Mediterranean Sea, and distinctively subdivided into northern, central and southern Adriatic Sea. The eastern coast of the Adriatic Sea is marked by a high, rocky, and rugged coastline offering many habitats ideal for fisheries and aquaculture (Dragičević et al. 2017). Aquaculture is one of the fastest-growing industries in the world with 60 million tonnes of exported farmed aquatic organisms annually, which is a 245% increase in the last 40 years (FAO 2018). In the conditions of fish farming, nutrients, excretions of organisms, and food residues can cause eutrophication in the environment in which aquaculture is practised (Bentzon-Tilia et al. 2016). Only 13.9% of the nitrogen and 25.4% of the phosphorus from the fish feed is utilized, and the rest accumulates in the water and sediment (Zhang et al. 2014). In addition to these compounds leading to eutrophication, nitrogenous compounds such as ammonium and nitrite at high concentrations can be toxic to aquatic animals as well as damaging to human health (Zhang et al. 2014).

Investigations of marine cyanobacteria and other prokaryotes in the Adriatic Sea focused on modern molecular methods (the study of composition and dynamics of bacterial communities) are scarce. To our knowledge, these methods have not been employed in the investigation of aquaculture-impacted sites in the eastern Adriatic Sea. They have mainly addressed cyanobacteria as part of bacterioplankton in offshore waters in the southern Adriatic (Najdek et al. 2014, Babić et al. 2018, Mucko et al. 2018), and in wastewater-impacted coastal zones of the northern Adriatic (Paliaga et al. 2017). Bacterial communities of surface sediments are less researched, except sediments impacted by industry and tourism in the northern Adriatic (Korlević et al. 2015a, 2015b). Coastal areas are interesting to investigate, not just for the obvious anthropogenic influences, i.e. aquaculture, but for others that may be concealed (untreated wastewater) or seasonally impacted (effluents from agriculture or tourism pressures). Although influences are evident or assumed, it is difficult to discern whether there is a main stressor, and if so which, or whether they are working in sync at different times of the year. However, their influence evidently exists in the structure of a microbial community.

We hypothesize that the composition, diversity, and ecology of cyanobacteria can be changed rapidly in anthropogenically impacted coastal marine ecosystems. Recognizing these changes on this level could contribute to determining the ecological state of these human-impacted environments. In order to determine that, firstly we need to establish “what is there” using metabarcoding techniques and bioinformatics tools. Cyanobacteria are already widely used as eutrophication indicators in freshwater ecosystems, and highly eutrophicated marine ecosystems (e.g. Baltic sea) (Vahtera et al. 2007). Likewise, their importance is noted in the Marine Strategy Framework Directive under Descriptor 5: Eutrophication (Criteria: undesirable changes in algal community structure) (MSFD, 2008/56/EC). Therefore, we wanted to test the possibility of using specific marine Cyanobacteria as potential indicators of marine ecosystem ecological status in the highly impacted coastal zone, as they are in the freshwater environment. Lastly, our objective is to test the viability of metabarcoding as a standard monitoring method in investigating anthropogenically impacted coastal waters and sediments.

Materials and methods

Sampling

Samples of the water column and surface sediments were collected in the scope of the AQUAHEALTH project: from two sampling locations at the first site in central Adriatic (CA) and two sampling locations at the second site in the southern Adriatic Sea (SA). Both sites are in the coastal area affected by various anthropogenic influences (overpopulation, wastewaters, tourism, agriculture etc), but predominantly by aquaculture - European seabass cage farms. The site in the central Adriatic is characterized by more oligotrophic conditions and is under the effect of the open sea, while the southern Adriatic site is a moderately eutrophic enclosed bay with the strong freshwater influence of the Neretva River (Fig. 1). At both sites, two sampling locations were selected, first in the cage farm area (CA – Movar Cove N 43.509141, E 15.96268; SA – Mali Ston Bay N 42.922510, E 17.474728, respectively) and second, as a control point away from the farm (CA control N 43.504971, E 15.952208; SA control N 42.93022, E 17.49925, respectively). Sampling was conducted in all four seasons during 2017 (February, June, September, November).

Fig. 1 Study sites in the central and southern East Adriatic Sea (CA – central Adriatic, SA – southern Adriatic).
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Niskin sampler was used to collect water column composite samples (maximum depth 20 m) for molecular analysis in 1 L bottles from four depths (250 mL from surface, 5 m, 10 m and bottom layer). Seawater was collected in 250 mL bottles for water chemistry analysis from each depth. Physico-chemical parameters (salinity, dissolved oxygen and oxygen saturation, temperature, turbidity, pH, total dissolved solids) were measured in situ by probes: SevenGo pro/OptiOx, SevenGo pro pH/Ion (Mettler Toledo, Ohido, US). The water column transparency was determined by a Secchi disc. Immediately after sampling, samples for molecular analysis were filtered through 0.2 µm pore filters (Whatman, Sigma Aldrich, UK) in triplicate (300 mL per filter), frozen in liquid nitrogen until transported to the laboratory, where they were stored at -20 °C. Surface sediment samples were collected by a diver, stored on ice and transported to the laboratory, where they were stored at -20 °C.

Water column nutrients and granulometric analysis of sediment

Total nitrogen was determined by oxidative digestion with peroxydisulfate (ISO 11905-1: 1997); total phosphorus was determined with ammonium molybdate using Hach spectrophotometer DR/6000 (ISO 6878:2004); and the amount of silicon dioxide was determined by the Hach method 8186 - heteropoly blue (Hach, 1997), using a DR/6000 Hach spectrophotometer. All values were expressed in mg L-1.

To determine grain size, 100 g of dried sediment was weighed from each sample and sieved through 7 standard stainless sieves to separate coarse-grained (> 0.063 mm) and fine-grained (< 0.063 mm) fractions. The suspension with fraction < 0.063 mm was analysed using Micromeritics Sedigraph 5100. Sediment particles found in coarse-grained sediment (> 0.063 mm) were randomly separated from each fraction and microscopically examined under a binocular microscope for qualitative bulk identification. The sediment texture for the whole sediment fractions range (0.005-2.00 mm) was determined according to the Folk (1954) classification scheme.

DNA extraction and amplicon sequencing

Total DNA was extracted from filters and sediment samples by using DNeasy PowerSoil kit (Qiagen, Germany), following the manufacturer's instructions with minor changes. Modifications involved mechanical disruption on Vortex-Genie 2 (MoBio, USA) for 15 min at maximum speed and incubation at 37 °C for 30 min with the addition of 2 µL of lysozyme (0.5 mg mL-1 solution). Extracted DNA yield and quality were measured by spectrophotometry (BioSpec Nano, Shimadzu, Japan), while the integrity of DNA was checked on 1% agarose gel. Samples of total extracted DNA were sent for 16S rRNA gene library preparation and amplicon next-generation sequencing to Molecular Research LP (Shallowater, Texas, USA). Sequencing was performed on the Illumina MiSeq (Illumina, Chesterfold, UK) platform following the manufacturer’s guidelines (MR DNA; www.mrdnalab.com, Shallowater, Texas, USA). The 16S rRNA gene V1-V3 variable region was targeted by PCR primers 27F (5′-AGRGTTTGATCMTGGCTCAG-3′) and 519R (5′-GTNTTACNGCGGCKGCTG-3′), with a barcode on the forward primer. The PCR program included a 28 cycle PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, USA) under the following conditions: 94 °C for 3 minutes, followed by 28 cycles of 94 °C for 30 seconds, 53 °C for 40 seconds and 72 °C for 1 minute, with a final elongation step at 72 °C for 5 minutes. PCR products were visualized on 2% agarose gel to check the success of amplification and the relative intensity of bands.

Bioinformatics and statistical analysis

Reads were processed using QIIME 2 2019.4 (Bolyen et al. 2019). Pipeline included several steps: importing and demultiplexing of raw sequence data, quality filtering and denoising using DADA2 plugin (Challahan et al. 2016) and taxonomy assignment of the resulting amplicon sequencing variants (ASVs) using Naïve Bayes classifier pre-trained on the SILVA 132 database with 99% OTU identity threshold. From the total bacterial community, taxa filtering was performed to include only cyanobacterial ASVs and excluding chloroplast and mitochondrial sequences from the data. The cladogram was constructed using plugin q2-phylogeny: the MAFFT program was used to perform multiple sequence alignment, masking ambiguously aligned regions and applying FastTree for creating a cladogram from the masked alignment. The generated tree (On-line Suppl. Fig. 1) was visualized in iTOL 4.4.2. (Letunic and Bork 2019). Sequences that were poorly identified or defined as “uncultured” were searched in the NCBI GenBank database using the BLAST search tool, and those with low identity threshold were pruned from the tree. Sample frequency was added using FeatureTable[Frequency] (On-line Suppl. Fig. 1). Generated phylogenetic tree was visualized in iTOL using FeatureData[AlignedSequence] file generated from QIIME2. Leaf labels were automatically assigned by adding FeatureData[Taxonomy] file, and multi-value bar chart with sample frequencies was created with FeatureTable[Frequency] file. Downstream analysis and taxa bar plot visualizations were performed in RStudio version 1.2.1335, using qiime2R (Bisanz 2018), phyloseq (McMurdie and Holmes 2013) and ggplot2 (Wickham 2016) packages. Statistical analysis of physico-chemical parameters of seawater was conducted using Primer 5.2.9 and visualization further executed in GrapherTM version 8.2.460 (Golden Software, LLC, Colorado, USA). Raw sequences reads are deposited in European Nucleotide Archive (ENA) under project number PRJEB34935.

Results

Physico-chemical parameters of water column and sediment granulometry

The principal component analysis includes physico-chemical parameters of seawater (Tab. 1) in all sampling locations during all seasons in 2017 (Fig. 2). PC1 axis explains 31.9% of the variance in physico-chemical data (eigenvalue 3.50), while PC2 axis explains 23.7% (eigenvalue 2.61) (On-line Suppl. Tab. 1). By using PCA it was not possible to identify a clear pattern of grouping or separation of sampling sites. However, the seasonal pattern is easily identified for all sampling sites and locations. Winter samples have been grouped mostly in the negative part of the PC2 axis, positively correlated with dissolved oxygen. Most of the spring samples are grouped in the negative part of the PC1 and PC2 axis, correlating with pH, turbidity, transparency and percentage of O2 in the water column. Summer samples are described by temperature, SiO2 and total nitrogen in the positive area of the both the PC axis. Samples from the autumn were characterized by TDS, salinity and total phosphorus, and thereby grouped in the positive part of the PC1 and the PC2 axis.

Tab. 1 Median values for physico-chemical parameters of seawater in sampling sites during seasons in 2017. Site: CA – central Adriatic, SA – southern Adriatic, Aq – site type under the influence of fish farms, Co – control site type, S.disc – Secchi disc, Turb – turbidity, Sal – salinity, TDS – total dissolved solids, T – temperature, DO2 – dissolved oxygen, O2% – oxygen saturation, N – total nitrogen, P – total phosphorus, SiO2 – silicon dioxide, NA – not measured). Values for physico-chemical data
SeasonSiteS.disc
(m)
TurbSalTDS
(mg L-1)
T
(ºC)
pHDO2
(mg L -1)
O2
(%)
N
(mg L -1)
P
(mg L -1)
SiO2
(mg L -1)
WinterCA Co192.6333.1025.4713.837.8910.54102.02NANANA
CA Aq151.1033.4825.7814.138.0510.1398.73NANANA
SA Co144.9834.2826.4812.258.0910.95101.630.300.0080.12
SA Aq12.55.8833.7526.0512.017.8310.63101.630.600.010.26
SpringCA Co1527.5333.3025.6023.457.888.95103.730.900.021.46
CA Aq1244.6334.3026.0322.187.859.12102.651.130.062.38
SA Co9.554.3333.4025.5022.257.839.29105.830.600.021.08
SA Aq1023.9033.6825.7021.307.929.33104.650.680.051.19
SummerCA Co171.9836.4527.6321.87.959.01102.850.530.031.29
CA Aq121.7833.4827.2521.958.048.4696.980.830.051.50
SA Co150.7834.8526.4821.537.919.12102.91.000.020.80
SA Aq140.8334.6526.3321.357.938.97100.850.830.021.02
AutumnCA Co7NA34.526.4316.637.049.7299.680.230.060.14
CA Aq150.2534.6326.3316.807.299.4397.10.500.060.13
SA Co51.0034.1826.2015.707.959.7097.530.580.060.40
SA Aq90.5034.5326.5815.257.799.7898.030.680.070.61
Fig. 2 Principal component analysis ordination graph of physico-chemical parameters of seawater column (T – temperature, SiO2 – silicon dioxide, N – total nitrogen, P – total phosphorous, Secchi – Secchi disc, Turb – turbidity, Sal – salinity, TDS – total dissolved solids, DO2 – dissolved oxygen, O2% – oxygen saturation; WIN – winter, SPR – spring, SUM – summer, AUT – autumn) . Number of samples = 16.
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Analysed sediments were predominantly classified as gravelly sands with various and generally low proportions of gravel and mud (Tab. 2) Generally, CA sediments are mostly gravelly sands, with a muddy component present in aquaculture sites in autumn and summer. Sediment in the SA control location is, for the most part, sandy gravel, with more variation in the aquaculture location – gravelly sands with muddy gravel, with one sample of sandy gravel that was taken only in the vicinity of the fish cage due to inaccessibility. Most of the sediment samples were composed of biogenic carbonate clasts, generally, shell debris containing molluscan fragments with less present foraminifera tests, echinoid fragments, worm tubes and bryozoans. Textural characteristics did not show any regularity attributable to the sampling location or season.

Tab. 2 Textural characteristics of surface sediment samples after Folk (1954). CA – central Adriatic, SA – southern Adriatic, Aquaculture – site type under the influence of fish farms, Control – control site type.
Locality / Site typeSeasonClassification after Folk (1954)
CA AquacultureWinterSlightly gravelly sand – (g)S
SpringGravelly sand - gS
SummerGravelly muddy sand - gmS
AutumnSlightly gravelly muddy sand – (g)mS
CA ControlWinterSlightly gravelly sand – (g)S
SpringGravelly sand - gS
SummerGravelly sand - gS
AutumnGravelly sand - gS
SA AquacultureWinterGravelly sand - gS
SpringGravelly sand - gS
SummerMuddy gravel - mG
AutumnSandy gravel - sG
SA ControlWinterSandy gravel - sG
SpringSandy gravel - sG
SummerGravelly sand - gS
AutumnSandy gravel - sG

Cyanobacteria community relative abundance and diversity

Using the metabarcoding molecular approach, 32 samples were analysed with 10102 ASV assigned at 99% similarity threshold. Out of that number, 437 ASV were defined as “Cyanobacteria”. Additional filtering of sequences identified as “Chloroplast” was applied, resulting in the identification of a total of three cyanobacterial genera from the water column, and 13 genera and two uncultured groups in the surface sediments. Planktonic picocyanobacteria Cyanobium PCC-6307, Prochlorococcus MIT9313 and Synechococcus CC9902 were detected in the water column, as shown in Figs. 3 and 4. The difference in the community of marine picocyanobacteria was not due to sites (CA/SA) or location type (aquaculture/control point), but a seasonal pattern was observed. Although Prochlorococcus was absent from winter samples, the relative abundance of Synechococcus CC9902 can reach even 70%. However, Prochlorococcus MIT9313 had the highest abundance in autumn samples (up to 29%). Remarkably, the freshwater genus Cyanobium was represented in all samples, especially in summer samples (17%).

Fig. 3 Relative abundances (% of total sequence number) of cyanobacterial genera in all sampling points of coastal seawater (CA – central Adriatic, SA – southern Adriatic, Aquaculture – location under the influence of fish farms, Control – control location).
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Fig. 4 Relative abundances (% of total sequence number) of cyanobacterial genera in seawater during four seasons, including combined central and southern Adriatic samples.
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Sediment samples showed location and site type differentiation (Fig. 5). At the same time, there seems to be an indication of community structure connected to the type of sediment (Fig. 6). In total, 13 cyanobacterial genera closely related to strains (Arthrospira PCC-7345, Chroococcidiopsis PCC-6712, Crocosphaera WH0.03, Cyanobacterium CLG1, Geminocystis PCC-6308, Hormoscilla SI04-45, Pleurocapsa PCC-7319, Prochlorococcus MIT9313, Synechococcus CC9902 and Synechococcus PCC-7336, Trichodesmium IMS10, Xenococcus PCC-7305 and SU2 symbiont group) and 2 uncultured groups were detected. Samples from the SA showed a higher diversity of genera over CA samples (13 + 2 uncultured and 6 + 2 uncultured, respectively). Sediment characterized as sandy gravel contains the highest number of genera (11 + 2 uncultured), and it is most represented in SA. In general, control locations on both sites have higher richness (total of 12 + 2 uncultured cyanobacterial genera) than the sites near fish cages (7 + 1 uncultured).

Fig. 5 Relative abundances (% of total sequence number) of cyanobacterial genera in sediment, relating to locality and site type (combined central Adriatic aquaculture and control sites, and southern Adriatic aquaculture affected and control sites).
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Fig. 6 Relative abundances (% of total sequence number) of cyanobacterial genera in sediment, in relation to sediment type (combined central Adriatic aquaculture and control sites, and southern Adriatic aquaculture affected and control sites).
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Genera Xenococcus and Pleurocapsa (order Pseudocapsales) were represented and dominant in most samples. Planktonic cyanobacteria were also represented in sediment samples, e.g. Prochlorococcus and Synechococcus, but mainly in aquaculture locations. Interestingly, in the water column only Synechococcus CC9902 was detected, and not Synechococcus PCC-7336. Some genera, e.g., Crocosphaera, Cyanobacterium, Geminocystis (order Chroococcales) and Chroococcidiopsis PCC-6712 (order Chroococcidiopsidales) were only detected in the SA control location. Hormoscilla SI04-45 (Hormoscilla spongeliae (Gomont) Anagnostidis et Komárek) belonging to the order Oscillatoriales, was identified only in the summer sample in the SA control location, along with the unicellular SU2 symbiont group. Arthrospira PCC-7345 (Osciallatoriales, Phormidiaceae) was detected at both sites with 25% max. relative abundance in the CA aquaculture location. Only Prochlorococcus showed seasonal occurrence in the sediment. It was detected in autumn samples, in which it was the most abundant in the water column. The group “uncultured” contained ASVs of families Leptolyngbyanceae and Xenococcaceae, whilethe group “uncultured bacterium” comprised Cyanobacteriaceae and Melainabacteria.

Cladogram (On-line Suppl. Fig. 1) shows the genotypic diversity of cyanobacterial 16S rRNA gene sequences constructed from a total of 100 identified taxa or sequences, after removal of chloroplast, uncultured and poorly identified sequences. SILVA taxonomy is based on Bergey’s Taxonomic Outlines or, in cases of rapid taxonomy changes, on the “List of Prokaryotic Names with Standing in Nomenclature”. Topological differences between the SILVA Ref (NR 99) trees and other resources are expected, since SILVA taxonomy employs a phylogeny-based process using guide trees (Yilmaz et al., 2014).

Sample frequency bar plots visually demonstrate the separation of taxa found in the water column and in the sediment - planktonic and (mostly) benthic genera. Water column samples show lower number of taxa, but much higher sample frequency then ASVs from the sediment. Water column samples are mainly represented by the family Cyanobiaceae, consisting of the genera Synechococcus CC9902, Prochlorococcus MIT9313 and Cyanobium PCC-6307. Using BLAST, several unidentified sequences were re-assigned and showed a similarity to various coastal cyanobacterial strains. For instance, ASV found in autumn in SA is identified as Cyanobium sp. CSZ that was isolated in a eutrophic coastal lagoon in the Baltic coast. ASV detected in winter and spring on both sites was uncultured Synechococcus sp. clone KOTS4UC, isolated from the coastal Arabian Sea. A sequence detected in water and sediment at both sites shows a relation to Synechococcus Minos12 isolated from the Mediterranean Sea, which appears to be non-motile (clade III). Sequences similar to Atlantic strains were found in winter and spring waters (uncultured Synechococcus sp. clone DWH - surface water of the Gulf of Mexico and Synechococcus sp. WH 8020 - New England coastal strain).

Sediment samples show high diversity incorporating cyanobacterial families Xenococcaceae, Microcysteaceae, Cyanobacteriaceae, Phormidiacaeae and Rivulariaceae. Some of the ASVs determined as the “uncultured” strains, closely related to the non-photosynthetic cyanobacteria of the Melainabacteria group, are found in sediments in CA. Many other sequences similar to strains in tropical and subtropical regions (e.g. uncultured bacterium clone bac98c and uncultured bacterium clone bac129c) share similarities with bacteria isolated in oolitic sands of Highborne Cay (Bahamas). Sediments from autumn control samples in SA contain ASVs similar to Neolyngbya irregularis ALCB 114389 and Neolyngbya arenicola ALCB 114386, newly described filamentous benthic cyanobacteria from Brazilian coast. Sequences also showed similarities with uncultured cyanobacterium clone RII-OX103 isolated from subtidal surface sediments of Cíes Islands (NW coast of Spain). Some ASVs seems to belong to plankton, e.g. Chroococcidiopsis sp. CCMP2 that is classified as a saltwater strain isolated from Micronesia and similar habitats (Pavilion Beach, Sand Island, Midway Atoll, Midway Islands).

There are also ASVs pointing to nutrient cycling roles (nitrogen and carbon cycles), such as uncultured Chroococcales cyanobacterium clone D10, diazotrophic cyanobacteria isolated from salt marshes and uncultured bacterium clone OS02-CYA-1 from intertidal marine sediments with different organic substrate utilization. Another potential diazotrophic ASV found in both CA and SA (winter) is similar to the strain Trichodesmium erythraeum SERB 14, isolated from Great Nicobar Biosphere Reserve.

Some ASVs are hinting at biofilm and microbial mat formation in the sediments, e.g. Aphanocapsa sp. HBC6 and uncultured bacterium clone CI5cm.45 that have similarities with isolates from stromatolites of Highborne Cay in the Bahamas. ASVs from control location in SA during summer and autumn are similar to uncultured cyanobacterium clone AO26 found in anoxic and suboxic layers of permeable sediments from the South Atlantic Bight (Hunter et al., 2006), a shallow submarine hydrothermal system (Hirayama et al. 2007), a coral reef sediment (Sørensen et al. 2007, Gao et al. 2011). A sequence detected in sandy gravels of the SA control location during summer and autumn may be Romeria sp. (Synechococcales cyanobacterium LEGE 06003), isolated from Buarcos Beach in Portugal.

Lastly, the cyanobacterial propensity for symbiotic relationships is also shown, in ASVs from CA similar to Uncultured Calothrix sp. clone 10010_AA1_t7 and Uncultured cyanobacterium clone STX_22 isolated from the coral host in the Caribbean.

Discussion

The present study focused on discovering the composition, diversity, and ecology of cyanobacteria from the water column and sediment in rapidly changing and anthropogenically impacted coastal marine ecosystems. Using metabarcoding techniques and bioinformatics tools, we wanted to establish not only cyanobacterial taxa present in these ecosystems, but also whether they can have indicator value, as in freshwater ecosystems. Water column samples in this study display a seasonal variance, but do not show any difference between locations influenced by aquaculture activates and control locations, in contrast to sediment samples. The cyanobacterial community of the sediment seems to be affected by a muddy component, and to have a preference for a sandy gravel type of sediment away from aquaculture impacted locations. It is evident from the cyanobacterial composition that sediment samples have overall larger community richness than water samples, although the sampling frequency of ASVs is higher in seawater. Overall, detected Cyanobacteria in water column and sediment were not exclusively marine genera, and evidence of freshwater and coastal eutrophication was found from the cyanobacterial composition.

In the water column, although we expected to find a distinction between cyanobacterial assemblages in aquaculture impacted sites vs. control and variation between southern and central Adriatic locations, no significant difference was observed. This could be due to the similar physico-chemical parameters, as measured at both sites and locations. Additionally, it could indicate that these two marine aquacultures have well-managed systems which did not provoke triggers for dramatically different assemblages in the water column. However,a seasonal pattern is observed, both in physico-chemical parameters groupings in PCA (Fig. 2) and in the picocyanobacterial taxa from metabarcoding results (Figs. 2, 4). The ecological importance of picocyanobacteria in the world’s oceans cannot be stressed enough since they are one of the most important primary producers. They constitute over 50% of marine phytoplankton (Paerl 2012) and out of that percentage, Synechococcus and Prochlorococcus account for approximately half of primary production in the ocean (Flombaum et al. 2013, Dvořák et al. 2014). On a global ocean scale, the prevalence of Prochlorococcus or Synechococcus depends on their environmental preferences – for Prochlorococcus ecotypes and Synechococcus clades (Zwirglmaier et al. 2008). Investigations of picocyanobacteria in the eastern Adriatic Sea showed dominance in the abundance of Synechococcus over Prochlorococcus (Šantić et al. 2013, Paliaga et al. 2017, Mucko et al. 2018), which was also confirmed in this study (Fig. 4). Prochlorococcus MIT9313 strain belongs to an ecotype of low-light adapted Prochlorococcus, which could indicate occasional decreased light availability in the water column. This strain belongs to subclade IV and has one of the largest genomes, which indicates a higher ability to respond to environmental stress (Gómez-Baena et al. 2009). Moreover, it is shown that this particular strain has an important role in carbon cycling due to its carbon-concentration mechanism (Scott et al. 2007), and can utilize organic nitrogen compounds such as urea and amino acids (Zubkov et al. 2003, Scott et al. 2007) excreted by the fish in aquaculture facilities (Lazzari and Baldisserotto 2008). Investigating offshore oligotrophic southern Adriatic waters, Babić et al. (2018) also discovered la ow-light ecotype of Prochlorococcus, however, they were OTUs closely related to the Prochlorococcus NATL2A strain. This could demonstrate that Prochlorococcus MIT9313 is more adapted to the coastal, anthropogenically impacted water environment. Regarding its absence from the winter samples (Fig. 4), the explanation could be a combination of high light transparency (SA – 12.5 m and 14 m; CA – 19 m and 15 m) in the water column and lower temperature (SA 12.01-12.25 °C; CA 13.84-14.13 °C), presented in Tab. 1. This is in concordance with the reports by Zinser et al. (2002) from experimental data that involved growth rates depending on temperature and light, and Rocap et al. (2003) analysis of the Prochlorococcus MIT9313 genome, which established the loss of many genes encoding phycobilisome structural proteins and enzymes that are involved in phycobilin biosynthesis. With respect to salinity, values are lower in all sampling sites than the Adriatic Sea mean values, which clearly points to freshwater influence. As reported by Russo et al. (2012), depending on the season, salinity varies between 37.84 and 38.89, but in our sampling points, they range from 33.10 (min.) to 36.45 (max.). The proliferation of several freshwater genera, e.g. Cyanobium, Geminocystis, Cyanobium and Chrococcidiopsis, could signify that input throughout the year in both sites. In the SA site, this is definitely the freshwaters of the Neretva River coming into the Mali Ston Bay, while in CA it could indicate occasional submarine springs that are common for the karstic coast of the eastern Adriatic Sea (Pikelj and Juračić 2013). In agreement with this, Chroococcidiopsis cyanosphaera Komárek et Hindák (sub SAG 33.87), originating from mineral springs and pools was detected (On-line Suppl. Fig. 1). Cyanobium in coastal waters could not only signify freshwater influence but additional eutrophic conditions according to Pulina et al. (2011). In our samples, their highest abundances were found during the summer at aquaculture locations on both sites (SA – 17.81%, CA – 14.67%). Eutrophication generated or aided by aquaculture can have a negative impact on the productivity of the industry. It can be destructive to less tolerant species in the phytoplankton community and also lead to an increase of the cyanobacteria fraction (Pulina et al. 2011). Cyanobacterial blooms, most challenging in freshwater ecosystems, are also well documented in Mediterranean lagoons (Chomérat et al. 2007). They are reported in Ca’Pisani lagoons in the western coast of the Adriatic Sea (Sorokin et al. 2006), in conditions of intensive aquaculture in which a cyanobacterial bloom followed and surpassed the bloom of the potentially toxic dinoflagellate Alexandrium tamarense (Lebour) Balech. Therefore, the questions arise: in the face of global climatic perturbations, is there a possibility of picocyanobacterial blooms becoming a regular occurrence in the coastal bay areas (not just more secluded lagoons), especially areas affected by the additional pressure of aquaculture? In that sense, the advantages of having a long memory of sediment sample could be very informative. Some of the planktonic genera detected in sediments could be troublesome in the future, e.g. Trichodesmium erythraeum Ehrenberg ex Gomont. Specifically, this generally innocuous nitrogen fixator from tropical waters is forming potentially toxic blooms. Their decomposing blooms can affect aquaculture sites by creating anoxic conditions leading to mortalities (Negri et al. 2004). Furthermore, a large percentage of water column ASVs showed similarity to the eutrophic strain Synechococcus CC9902. OTUs similar to this strain were recorded in Croatia for the first time in the active bacterial community of the naturally eutrophic, marine meromictic Rogoznica Lake, situated in the coastal area of the central Adriatic Sea (Čanković et al. 2019). Furthermore, Synechococcus CC9902 (clade IV) was found to survive even in anoxic and dark conditions, and showed the highest abundances during the winter, as in this study (Fig. 4). The potential aquaculture-related concern could be that this strain was firstly isolated from coastal waters off California, where it can form extensive blooms (Hamilton et al. 2014). Experiments performed by Hamilton et al. (2014) on the native fish under the bloom concentration of the Synechococcus CC9902, showed a negative effect on the behaviour of the fish. This suggested the possibility of sublethal effects of Synechococcus blooms on coastal fish populations if climate change predictions come true since fish (regardless of the type of diet) absorb water through drinking, gills, eyes and skin (Flombaum et al. 2013, Hamilton et al. 2014).

Considering sediment, almost all samples contained the genera Xenococcus and Pleurocapsa, making them core genera in the cyanobacterial community. Unsurprisingly, they are microbial mat-forming cyanobacteria and first colonizers in marine sediments. Xenococcus forms colonies attached to any substrate, e.g. stones, alga etc., while Pleurocapsa is a unicellular, pseudofilamentous genus that can grow layers of cells on limestone substrate, and some species are endolithic (Goh et al. 2009). Results of the grain size analysis support the proliferation of these two genera - analysed sediments fit into the average coarse-grained carbonate biogenic sediment typical for the eastern part of the Adriatic Sea. While being composed mainly of biogenic shell debris, the grain size of the sampled sediment usually varies due to the presence of dominant organisms and the degree of biogenous skeletal detritus decomposition (Pikelj et al. 2016). The role of cyanobacteria in sediment is of importance, since they produce extracellular polymeric substances (EPS) (Golubic et al. 2000) that stabilize loose sediments, prevent erosion, and protect them from various biotic and abiotic stressors (Costa et al. 2018). These processes are active today, as they were in the ancient stromatolites (Margulis 1970, Bolhuis et al. 2014). EPS are produced by both filamentous cyanobacteria moving through sediment particles (Golubic et al. 2000) and unicellular non-motile cyanobacteria (Rossi and De Philippis 2015), many of whom are present in the sediment samples (Fig. 5). The sediment analysis of this study suggests that gravel and sand components create a higher number of niches for a large percentage of genera, while samples with muddy component contain 1-2 genera. This is close to the finding of Stal (2010) on cyanobacteria in intertidal coasts, where they appeared frequently in sandy sites, but did not “proliferate on muddy or wave-exposed sites” (Andersson et al. 2014). Moreover, cyanobacterial community richness in sediment samples from this study seems to be largely affected in aquaculture locations, even if they are determined as sandy gravel or gravelly sands (Figs. 5, 6). With the exception of the sandy gravel sample in SA aquaculture location in autumn, they all have an extremely low number of genera. This could indicate a continuing disturbance produced by the aquaculture activities on the community, but, in addition, the fish rearing probably generates a muddy component in the sediment (Tamminen et al. 2011). Aquaculture locations with muddy components also have higher abundances of planktonic Prochlorococcus and Synechococcus, which implies fish ingestion of picocyanoplankton and accumulation in sediment via fish excretion. In sediment, some members in the cyanobacteria community seem to indicate a light deprivation and anoxic condition that is at least intermittently occurring. Although cyanobacteria are oxygenic phototrophic organisms, Miyatake et al. (2013) confirmed that cyanobacteria and diatoms can survive in dark and anoxic conditions by glucose utilization, proposing a mixotrophic way of living for these organisms usually known as primary producers, e.g. Cyanobacterium CLG1 is known to synthesize both glycogen and starch (Kadouche et al. 2016). Geminocystis strain PCC-6308 can accumulate a large amount of phycoerythrin (Hirose et al. 2015), which could help it in light acclimation, and detection of a non-photosynthesizing group of cyanobacteria Melainabacteria (Di Rienzi et al. 2013) supports this claim. Additionally, sediment harbours ASVs similar to the benthic strain Synechococcus PCC-7336, clustering differently from other Synechococcus representatives (On-line Suppl. Fig. 1). Synechococcus PCC-7336 has an unusually large genome that contains type V polymerase proteins rarely found in other cyanobacteria, but common in plants. Additionally, Li et al. (2015) have found 107 kinases and regulators stimulating gene expression to environmental stress, making it highly adaptable to light/oxygen deficiency.

Finally, results markedly present a number of cyanobacterial ASVs related to the various strains from tropical areas (On-line Suppl. Fig. 1). They are present mostly in sediment samples (although some of them are planktonic), e.g. Neolyngbya, Chrococcidiopsis, Trichodesmium, Aphanocapsa, Cyanobacterium, Crocospaera, Xenococcus and many “Uncultured” cyanobacteria strains. Additionally, in seawater, there are ASVs closely related to Synechococcus strains from warm seas (the Gulf of Mexico, Arabian Sea). This tropical affinity or “tropicalization” is a trend most evident in wild fish composition in the Adriatic Sea within the last 2 decades, starting with the arrival of Lessepsian fish species from the Indo-Pacific (Dragičević et al. 2017). According to Ibarbalz et al. (2019), investigations in the temperate zone confirm the trend of tropicalization in marine plankton. It is not surprising that microbiota in our research, Cyanobacteria specifically, are mirroring a trend that is well underway throughout the food web.

Conclusion

This study was conducted to test the viability of marine cyanobacteria in human-impacted coastal zones as valuable indicators of ecological states, in the same way that they are used in freshwater ecosystems and the Marine Strategy Framework Directive. By using a metabarcoding approach, we wanted to circumvent the shortcomings of other methods, e.g. the light microscopy counting method. Although there are biases in metabarcoding method, especially if only a resident community is being investigated (DNA), it can deliver valuable information about “what was there”. By linking that knowledge with physico-chemical parameters in the water column and granulometric analysis of sediment, it allowed us the opportunity to hypothesise the ecological preferences of taxa found. Therefore, this study provides a starting point in the investigation of the cyanobacterial community in coastal waters and sediments in the Adriatic Sea impacted by aquaculture and proposes the metabarcoding method as a suitable monitoring tool.

Appendices

On line supplementaria materials

On-line Suppl. Fig. 1. Cladogram displaying grouping of 100 cyanobacterial ASVs, with addition of multi value bar chart of sample frequencies in different ASVs (CA – central Adriatic, SA – southern Adriatic, Aquaculture – site type under the influence of fish farms, Control – control site type; - sea water sample type, O – sediment sample type). Leaf labels are automatically assigned by SILVA database (black letters), or NCBI GenBank database BLAST search hit results (blue letters).

On-line Suppl. Tab. 1. Results of principal component analysis.

Acknowledgements

This research was supported by the project AQUAHEALTH (Aquatic microbial ecology as an indicator of the health status of the environment) funded by the Croatian Science Foundation (code: 3494). Assistance and advice from Lorena Perić, PhD in the statistical analysis is greatly appreciated, as well as help from Ines Sviličić Petrić, PhD for advice in DNA extraction and valuable suggestions for improvements on the manuscript. Likewise, we are thankful to the reviewers for their insightful comments, which helped us to improve our work. We appreciate Jakov Žunić for sampling effort and Zvjezdana Šoštarić Vulić for chemical analysis of seawater. We are grateful to AWS Educate for providing computational resources for analysis.

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