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. 2019 Dec 16;12(1):3647–3655. doi: 10.1093/gbe/evz278

Genome Sequences of 72 Bacterial Strains Isolated from Ectocarpus subulatus: A Resource for Algal Microbiology

Elham Karimi 1,✉, Enora Geslain 1,2, Hetty KleinJan 1,3, Gwenn Tanguy 2, Erwan Legeay 2, Erwan Corre 2, Simon M Dittami 1
Editor: Howard Ochman
PMCID: PMC6948157  PMID: 31841132

Abstract

Brown algae are important primary producers and ecosystem engineers in the ocean, and Ectocarpus has been established as a laboratory model for this lineage. Like most multicellular organisms, Ectocarpus is associated with a community of microorganisms, a partnership frequently referred to as holobiont due to the tight interconnections between the components. Although genomic resources for the algal host are well established, its associated microbiome is poorly characterized from a genomic point of view, limiting the possibilities of using these types of data to study host–microbe interactions. To address this gap in knowledge, we present the annotated draft genome sequences of seventy-two cultivable Ectocarpus-associated bacteria. A screening of gene clusters related to the production of secondary metabolites revealed terpene, bacteriocin, NRPS, PKS-t3, siderophore, PKS-t1, and homoserine lactone clusters to be abundant among the sequenced genomes. These compounds may be used by the bacteria to communicate with the host and other microbes. Moreover, detoxification and provision of vitamin B pathways have been observed in most sequenced genomes, highlighting potential contributions of the bacterial metabolism toward host fitness and survival. The genomes sequenced in this study form a valuable resource for comparative genomic analyses and evolutionary surveys of alga-associated bacteria. They help establish Ectocarpus as a model for brown algal holobionts and will enable the research community to produce testable hypotheses about the molecular interactions within this complex system.

Keywords: brown algae, holobiont, alga-associated bacteria, biosynthetic gene clusters, detoxification, metabolic networks

Introduction

Brown macroalgae are important primary producers and major ecosystem engineers on marine rocky shores, providing both shelter and nutrients for other forms of life (Brodie et al. 2017). They belong to the stramenopiles, an evolutionarily distinct lineage from the Achaeplastida, which comprise red and green algae as well as land plants (Charrier et al. 2008) and are of commercial importance in several regions of the world (Koru 2013; Raja et al. 2013; Venkatesan et al. 2015). Ectocarpus is a genus of brown algae that has been established as a laboratory model for this lineage (Peters et al. 2004) due to its small genome (Cock et al. 2010), the possibility of cultivation in the lab, and its short life cycle.

Like most if not all multicellular eukaryotes, brown algae, including Ectocarpus, are associated with bacteria (Paix et al. 2019). These interactions may be so intimate that the term holobiont has been suggested to describe the functional unit of a host and its associated microbiome (Zilber‐Rosenberg and Rosenberg 2008; Douglas and Werren 2016). For instance, it has been estimated that approximately half of all algae (including 49 out of 83 surveyed stramenopiles) rely on their bacteria associated to provide them with vitamin B12 (Croft et al. 2005; Tang et al. 2010). In Ectocarpus, associated bacteria are known to provide functions related to developmental transitions and growth of the algae (Pedersen 1968; Tapia et al. 2016). Furthermore, they may impact their capacity to tolerate environmental stressors (Dittami et al. 2016).

Collections of cultivable bacteria provide a valuable resource to study the mechanisms underlying these interactions, and in Ectocarpus three recent papers describe the generation of culture collections. In Ectocarpus siliculosusTapia et al. (2016) have reported the isolation of 9 bacterial strains, and in Ectocarpus subulatusKleinJan et al. (2017) cultivated 46 strains corresponding to 33 different bacterial genera from algal surfaces. An additional 95 strains corresponding to 27 different genera have also recently been isolated from field material of E. subulatus (Dittami et al. 2019).

In present study, we describe genomic resources for 72 of these cultivable Ectocarpus-associated bacteria. Sixty-two genomes were sequenced specifically for this study, plus ten previously sequenced genomes from the same culture collection (Burgunter-Delamare et al. 2019) were also included. These genomes constitute a valuable resource both to study the genomic adaptations of bacteria to life on the surface of brown algae, but also to generate hypotheses on potential beneficial interactions between the bacteria and their host, for example, via metabolic complementarity-based approaches (Frioux et al. 2018). They furthermore constitute a first step toward filling a big gap in our current knowledge: The fact that currently (September 2019), based on our research through Marine Metagenomics Portal (Robertsen et al. 2017; Klemetsen et al. 2018), only ∼100 draft and complete bacterial genomes isolated from algae/seaweed are publicly available in GenBank. Thus, the genomes from this study could add a great amount of information to algal microbiomes and will promote other studies aiming to decipher algal-microbial associations.

Materials and Methods

Bacterial Strains and DNA Extraction

Bacterial strains were isolated from a laboratory culture of E.subulatus (strain CCAP 1310/19; KleinJan et al. 2017) as well as from field samples of the same species (Dittami et al. 2019). Field samples were collected in March 2017 from two locations along the Hopkins River, Victoria, Australia, a few km upstream of Hopkins River falls, the original collection site of strain CCAP 1310/19 (West and Kraft 1996): Framlingham Forest reserve (–38.297064, 142.668291) and Kent's Ford (–38.191574, 142.698058). All bacterial strains were identified by Sanger-sequencing of the 16S rDNA gene using the 8F and the 1492R primer pair (Weisburg et al. 1991). Bacteria were grown on 90 mm Petri dishes with R2A medium (Reasoner and Geldreich 1985) Sigma–Aldrich at 19 °C for 4–7 days. Subsequently, a single colony was selected and grown at 25 °C in liquid R2A medium overnight. The bacterial genomic DNA was extracted using Promega Wizard Genomic DNA purification kit following the manufacturer’s instructions. The extracted DNA was quantified using a Qubit and its quality was determined using agarose gel electrophoresis.

Genome Sequencing, Assembly, and Annotation

Paired-end DNA libraries with an average insert size of 500 bp were prepared using the Nextera XT DNA library kit (library average size ∼1,100 bp). Libraries were then sequenced using the Illumina MiSeq technology (V3, paired-end, 2 × 300 bp reads) at GENOMER platform (Station Biologique de Roscoff), multiplexing ∼20 bacterial genomes per run. Raw reads were first examined using FastQC (Andrews 2010). Low-quality sequences were trimmed or removed using Trimmomatic v.0.38 and a sliding window with a quality score of 15 as well as a minimal read length of 36 bp as filters. Trimmed read pairs were used for genome assembly with SPAdes v.3.12.0 (Bankevich et al. 2012) using default parameters. Genomic sequences encoding parts of the ribosome were identified using Barrnap v. 0.8 (https://github.com/tseemann/barrnap) and 16S rDNA sequences used to search for complete reference genomes in the GenBank. These reference genomes were used for scaffolding with Medusa version 1.6. Finally, gaps in the scaffolds were filled wherever possible using GapCloser 1.12 (Li et al. 2010) and the resulting draft genomes were annotated and prepared for submission to public databases using the MicroScope platform (Vallenet et al. 2017). The genomes were deposited at the European Nucleotide Archive.

Phylogenomic Analyses

Phylogenomic relationships among all studied strains were confirmed by running genome clustering based on pairwise distances and Average Nucleotide Identity (ANI) between all selected genomes using the Neighbor-Joining algorithm in MicroScope. Furthermore, the closest genome has been provided for all genomes, based on their resulting Tetra-nucleotide signature correlation index via the JSpeciesWS tool (Richter et al. 2016).

In Silico Analysis of Bacterial Metabolism

Models of primary metabolism for each sequenced bacterium were generated using the Pathway tools pipeline implemented in the MicroScope platform. The output of this pipeline is a pathway completion value, that is, the ratio between the number of reactions for a specific pathway in a bacterium and the total number of reactions for that pathway defined in the MetaCyc (Caspi et al. 2018) or KEGG (Kanehisa et al. 2008) databases. In addition, secondary metabolite-related gene clusters were predicted using antiSMASH (Blin et al. 2017).

Results and Discussion

Genome Characteristics

Here, we report the sequencing of 62 and the analysis of 72 genomes of Ectocarpus-associated bacterial strains corresponding to 43 different genera and 16 different orders. The individual strains as well as key attributes of their genome sequences are listed in table 1. The genome size of all strains ranged from 2.4 Mb to 6.8 Mb. The largest genome was that of Imperialibacter sp. strain SDR9 from the Bacteroidetes and the smallest was that of Micrococcus sp. strain 11B from the Actinobacteria. The analyzed genomes showed diverse GC contents with strains belonging to the Bacteroidetes and Firmicutes exhibiting GC contents <40% (e.g., 30% in Flavobacterium sp. 9AF) contrary to Actinobacteria, where most strains exhibit GC contents over 70%. Overall, the GC content was positively correlated with genome size (Pearson correlation r = 0.73, P = 0.042). CheckM analyses (Parks et al. 2015) suggest that the sequenced genomes are nearly complete (>98%, table 1) and free of or with very low levels of contamination (<2.5%; supplementary table S1, Supplementary Material online). The only exception was Arthrobacter sp. strain 9V with 4.8% contamination (22 marker genes). This indicates that, overall, the presented genomes are suitable for downstream analyses such as comparisons of metabolic capacities.

Table 1.

Genome Features of Algal-Associated Bacteria Analyzed in This Study

Strain Complete-ness (%)a Genome Size (Mb) Coverage (X) N50 (Mb) %GC Scaffold Nb. CDS Nb. Mean CDS Length tRNA Nb. rRNA Nb. Closest Relative Accession Numbers
Actinobacteria
 Aeromicrobium sp. 9AM 99.7 4.2 144 2.98 68 9 4,422 897 46 3 Aeromicrobium sp. Root236 LR733303–LR733311
 Arthrobacter sp. 8AJ 99.7 4.3 88 4.22 66 4 4,228 944 51 5 Moraxella osloensis NCTC10465 LR733289–LR733292
 Arthrobacter sp. 9AX 99.7 4.4 230 4.41 66 7 4,453 918 50 6 Pseudarthrobacter siccitolerans 4J27 LR733289–LR733292
 Arthrobacter sp. 9V 99.7 5.1 221 4.82 62 158 5,091 925 62 9 Arthrobacter sp. EpRS71 LR732912–LR733069
 Citricoccus sp. K5 99.2 3.9 324 3.74 69 9 3,708 974 47 5 Citricoccus muralis DSM 14442 LR732817–LR732825
 Curtobacterium sp. 8I–2 99 3.6 109 2.80 71 5 3,767 911 47 6 Curtobacterium flaccumfaciens UCD-AKU LR732826–LR732830
 Frigoribacterium sp. 9N 98.5 3.3 151 2.53 71 16 3,339 926 45 5 Frigoribacterium sp. Leaf8 LR733390–LR733405
 Microbacterium sp. 8M 99.5 3.7 185 3.68 71 2 3,659 961 44 4 Microbacterium azadirachtae DSM 23848 LR733284–LR733285
 Micrococcus sp. 116 98.6 2.6 215 2.49 73 19 2,526 943 48 5 Micrococcus luteus 2385 LR732370–LR732388
 Micrococcus sp. 11B 98.1 2.4 450 1.89 73 52 2,398 952 48 5 Micrococcus luteus 2385 LR733070–LR733121
 Micrococcus sp. 80W 98.1 2.5 224 1.78 73 80 2,521 942 48 4 Micrococcus luteus 2385 LR732389–LR732468
 Nocardioides sp. AX2bis 98.7 4.2 221 3.96 73 37 4,397 915 45 4 Marmoricola aurantiacus DSM 12652* LR733215–LR733251
 Plantibacter sp. T3 99.5 4 287 3.98 69 3 4,131 924 48 4 Plantibacter flavus VKM Ac-2504 LR733286–LR733288
 Pseudoclavibacter sp. 8L 98.2 4.1 98 1.43 68 30 4,137 921 45 4 Microbacterium sp. TS-1* LR733185–LR733214
Bacteroidetes
 Imperialibacter sp. SDR9 100 6.8 111 0.96 47 65 5,767 1069 38 4 Arcticibacter pallidi-corallinus CGMCC 1.9313* LR701573–LR701637
 Marinoscillum sp. 108 99.1 5.2 83 3.73 46 12 4,489 1086 37 4 Marinoscillum furvescens DSM 4134* LR734808–LR734819
 Chryseobacterium sp. 8AT 100 4.7 114 4.43 34 31 4,483 931 70 7 Chryseobacterium scophthalmum DSM 16779 LR733314–LR733344
 Flavobacterium sp. 9AF 98.9 4.2 101 2.95 30 74 3,871 992 51 5 Flavobacterium sp. 316* LR733556–LR733629
 Flavobacterium sp. 9R 99.6 3.6 184 3.42 35 16 3,175 1006 42 6 Flavobacterium succinicans DD5b* LR733413–LR733428
 Maribacter sp. 151 99.7 4.4 59 4.35 36 4 3,857 1044 36 6 Maribacter litoralis SDRB-Phe2 LR733271–LR733274
 Sphingobacterium sp. 8BC 100 5.8 129 5.73 40 14 5,379 960 70 9 Sphingobacterium multivorum NCTC11343 LR733857–LR733870
Firmicutes
 Bacillus sp. 348 99.6 3.8 246 3.58 41 5 4,070 846 79 9 Bacillus stratosphericus LK33 LR732831–LR732835
 Bacillus sp. 349Y 99.3 4.5 114 0.12 48 85 4,616 839 97 9 Bacillus sp. Leaf406 LR733732–LR733816
 Bacillus sp. 71 99.3 5.7 116 5.69 35 14 6,092 796 98 18 Bacillus cereus HuA2-4 LR733376–LR733389
 Bacillus sp. 9J 99.6 3.8 179 3.74 42 76 4,109 834 86 9 Bacillus sp. Leaf49 LR732836–LR732911
 Exiguobacterium sp. 8A 99.3 3.1 184 2.87 48 77 3,234 868 63 13 Exiguobacterium sp. AT1b LR733630–LR733706
 Exiguobacterium sp. 8H 99.3 3 296 0.87 48 40 3,154 868 63 14 Exiguobacterium sp. AT1b LR733429–LR733468
 Exiguobacterium sp. 9Y 99.3 3 88 1.61 47 20 3,070 876 65 11 Exiguobacterium oxidotolerans JCM 12280 LR732308–LR732327
 Staphylococcus sp. 8AQ 99.2 2.5 269 2.49 31 4 2,501 886 62 9 Staphylococcus pasteuri BAB3 LR733871–LR733874
Proteobacteria
 Aeromonas sp. 8C 100 4.6 345 4.57 59 3 4,769 899 114 11 Aeromonas veronii TTU2014-115ASC LR732797–LR732799
 Aeromonas sp. 9A 100 4.8 105 4.70 59 11 4,590 925 114 16 Aeromonas salmonicida Y577 LR732779–LR732789
 Alteromonas sp. 38 100 4.7 209 4.70 44 3 4,324 975 62 6 Alteromonas stellipolaris LMG 21856 LR733300–LR733302
 Marinobacter sp. HK377 100 4.4 172 4.34 57 7 4,176 976 45 6 Marinobacter salarius R9SW1 LR701480–LR701486
 Marinobacter sp. N1 100 4.4 152 4.35 57 2 4,125 978 45 6 Marinobacter salarius R9SW1 LR733269–LR733270
 Burkholderia sp. 8Y 100 6.3 61 2.36 63 37 6,403 874 52 8 Burkholderia sp. MR1 LR733519–LR733555
 Limnobacter sp. 130 99 3.3 74 1.82 52 6 3,034 1007 37 3 Limnobacter sp. MED105* LR732328–LR732333
 Massilia sp. 9I 100 5.5 195 5.51 66 9 5,242 984 70 7 Massilia alkalitolerans DSM 17462 LR733275–LR733283
 Burkholderiales bacterium 8X 99.8 4.8 141 4.78 67 3 4,776 973 44 5 Variovorax sp. WDL1* LR732703–LR732705
 Brevundimonas sp. G8 99.7 3.3 375 3.32 66 1 3,308 927 47 3 Brevundimonas sp. Leaf280 LR732816–LR732816
 Oceanicaulis sp. 350 99.8 3.1 185 2.98 62 4 3,035 939 47 6 Oceanicaulis alexandrii DSM 11625 CABWMW010000001–CABWMW010000008
 Pantoea sp. 111 100 4.9 62 4.09 56 35 4,807 890 73 9 Pantoea brenneri LMG 5343 LR733469–LR733503
 Enterobacterales bacterium 8AC 100 5.3 134 4.81 53 63 4,858 936 74 10 Serratia oryzae J11-6 LR733916–LR733978
 Halomonas sp. 153 100 5.5 35 5.44 55 11 5,045 972 59 5 Halomonas titanicae BH1 LR733721–LR733731
 Halomonas sp. 98 100 5.5 109 5.43 55 14 5,029 975 59 6 Halomonas titanicae BH1 LR733707–LR733720
 Acinetobacter sp. 8BE 100 4.4 144 3.94 41 35 4,368 891 61 7 Acinetobacter sp. NIPH 809 LR732744–LR732778
 Acinetobacter sp. 8I-beige 100 3.5 138 2.08 41 7 3,452 895 73 7 Acinetobacter johnsonii DSM 6963 LR732790–LR732796
 Moraxellaceae bacterium 17A 100 3 194 2.75 43 37 2,973 897 41 6 Moraxella osloensis CCUG 57516 LR732269–LR732305
 Enhydrobacter sp. 8BJ 100 2.8 301 2.62 43 31 2,628 919 45 7 Moraxella osloensis NCTC10465 LR733345–LR733375
 Enhydrobacter sp. AX1 99.7 2.7 350 2.65 44 16 2,517 943 49 6 Enhydrobacter aerosaccus SK60 LR732800–LR732815
 Pseudomonas sp. 8AS 98.1 4.3 199 4.26 66 7 4,113 945 57 4 Pseudomonas alcaligenes NBRC 14159 LR733406–LR733412
 Pseudomonas sp. 8BK 100 4.5 145 4.38 60 11 4,205 960 63 9 Pseudomonas peli DSM 17833 LR733252–LR733262
 Pseudomonas sp. 8O 99.8 5.2 78 1.61 62 6 4,949 949 60 5 Pseudomonas pseudoalcaligenes AD6 LR733263–LR733268
 Pseudomonas sp. 8Z 99.4 4.8 144 1.12 61 12 4,625 935 61 8 Pseudomonas composti CCUG 59231* LR733824–LR733835
 Pseudomonas sp. 9Ag 100 4.7 136 4.62 60 4 4,465 946 52 4 Pseudomonas sp. 10B238 LR733836–LR733839
 Pseudomonas sp. 9AZ 99.7 4.5 235 4.46 60 4 4,260 961 60 8 Pseudomonas peli DSM 17833 LR733840–LR733843
 Bosea sp. 125 99.1 6.3 46 6.12 67 63 6,435 899 46 3 Bosea sp. Root483D1 LR733122–LR733184
 Bosea sp. 127 99.1 6.3 78 6.28 67 8 6,705 876 46 3 Bosea sp. Root483D1 LR733511–LR733518
 Bosea sp. 29B 99.1 6.3 137 6.32 67 7 6,422 904 46 3 Bosea sp. Root483D1 LR733817–LR733823
 Bosea sp. 62 99.1 6.3 154 6.28 67 7 6,411 905 46 3 Bosea sp. Root483D1 LR733504–LR733510
 Bosea sp. HK365B 99.1 6.3 133 1.03 67 18 6,738 876 46 3 Bosea sp. Root483D1 LR701663–LR701680
 Hoeflea sp. HK425 99.9 5.2 326 4.68 61 28 5,266 898 43 3 Hoeflea halophila KCTC 23107 LR701545–LR701572
 Rhizobium sp. SD404 100 4.2 148 4.22 62 18 4,192 920 42 3 Pararhizobium haloflavum XC0140* LR701442–LR701459
 Roseovarius sp. SD190 99.3 4.7 80 3.89 61 17 4,794 902 44 3 Roseovarius sp. TM1035 LR701460–LR701476
 Erythrobacter sp. HK427 99.1 3.1 157 3.12 63 3 3,097 947 45 3 Porphyrobacter sp. AAP60* LR701477–LR701479
 Novosphingobium sp. 9U 99.6 4.6 221 2.82 65 75 4,843 867 49 5 Novosphingobium resinovorum SA1* LR732469–LR732543
 Sphingomonas sp. 8AM 99.7 3.8 119 3.66 67 13 3,739 929 48 4 Sphingomonas phyllosphaerae FA2 LR733844–LR733856
 Sphingomonas sp. AX6 99.4 3 228 3.01 64 1 3,161 892 44 3 Sphingomonas echinoides ATCC 14820* LR733857–LR733870
 Sphingomonas sp. HK361 99.7 3.3 150 1.78 66 8 3,274 935 45 3 Hephaestia caeni DSM 25527* LR701487–LR701494
 Sphingomonas sp. SD391 99.5 4.6 114 4.15 66 34 4,682 903 49 5 Sphingomonas sp. Leaf28 LR701495–LR701528
 Sphingomonas sp. T1 99.3 4.5 243 3.83 66 41 4,647 900 50 3 Sphingomonas sp. Leaf30 LR733875–LR733915
 Sphingorhabdus sp. 109 99.2 3.6 97 3.56 58 5 3,585 928 45 6 Sphingorhabdus sp. M41* LR732707–LR732711
 Luteimonas sp. 9C 100 3.3 77 2.83 69 2 3,207 957 48 3 Xanthomonas sp. Mitacek01 LR733312–LR733313
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Note.—The closest relative with the similarity below Cut-off [z-score (<0.98)] is marked with asterisk. Nb, number; CDS, coding sequence.

a

Determined using the CheckM tool.

Phylogenomic Tree

Several of the sequenced bacteria in this study correspond to bacteria with no or only few closely related sequences in the databases. Notably, Enterobacterales bacterium 8AC, and Moraxellaceae bacterium 17A could be confidently identified only to the family level through RDP classifier (supplementary table S1, Supplementary Material online), making these strains candidates for new species or genera. Besides, fifteen strains including Imperialibacter sp. EC-SDR9, Marinoscillum sp. 108, Sphingomonas sp., AX6, and Novosphingobium sp., and Burkholderiales bacterium 8X have low similarity (z-score below cutoff < 0.989) with their closest genome-sequenced relatives (based on the tetra-nucleotide signature correlation index, table 1 and supplementary fig. S1, Supplementary Material online). This phylogenomic analysis yielded a tree generally grouping together bacteria from the same taxon (supplementary fig. S1, Supplementary Material online). However, Imperialibacter sp. EC-SDR9 and Sphingobacterium sp. 8BC from Bacteroidetes clustered with Firmicutes.

Secondary Metabolic Activities and Potentially Symbiosis-Related Metabolites

Algal-associated microbes are likely to interact with both the host and other microbes within the community. Secondary metabolites are metabolites not essential for normal growth of microorganisms, but they play a major role as chemical signals for interaction with other microorganisms (Netzker et al. 2015), restriction of pathogens (antimicrobial activities), and biofouling (Wiese et al. 2009; Nasrolahi et al. 2012; Susilowati et al. 2015). For instance, terpenes as the largest class of natural compounds have protective roles against competitors and are involved in interspecies signaling (Gershenzon and Dudareva 2007; Yamada et al. 2015). Similarly, bacteriocins, peptidic toxins produced by bacteria, have been suggested to play a role in pathogenesis by induction of cell lysis (Li and Tian 2012). The annotation of the 72 bacterial genomes with respect to genes involved in secondary metabolism obtained from AntiSMASH via the MicroScope platform showed that all analyzed strains except Oceanicaulis sp. strain 350, had at least one secondary biosynthetic gene cluster. Furthermore, 68% of genomes have at least one predicted terpene cluster gene, followed by bacteriocin (40.2%), nonribosomal Peptide Synthetases (NRPS, 36%), Type 3 polyketide synthases (PKS-t3, 33.33%), siderophores (23.6%), Type 1 polyketide synthases (PKS-t1, 20.8%), and homoserine lactone synthesis genes (16.6%; fig. 1 and supplementary table S1, Supplementary Material online). These genes are likely to be at least partially involved in the communication with the host and between microbes.

Fig. 1.

Fig. 1.

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—Heatmap of representative secondary metabolite clusters, detoxification-, and vitamin biosynthetic genes in the studied bacterial genomes. The dendrogram represents a whole-genome phylogeny, secondary metabolite gene clusters were predicted via AntiSMASH, detoxification genes were identified based on the MicroCyc database, and vitamin biosynthesis capacities were assess based on KEGG entries. The color code represents the number of genes per cluster (secondary metabolites) or the proportion of genes found in a particular organism and pathway.

Detoxification Role of Symbionts and Provision of Vitamins

In terms of detoxification mechanisms, one pathway that was complete in all studied genomes was the capacity to degrade superoxide radicals. Moreover, 46 strains of 72 possessed the complete pathway for glutaredoxin synthesis (fig. 1). This mechanism is important for the degradation reactive oxygen species (ROS), which are formed by the algae through metabolic processes and in response to different stressors (Cosse et al. 2007). ROS can cause significant damage to the cell; thus, microorganisms have developed defense systems to detoxify ROS in order to survive.

Furthermore, the cyanate degradation pathway was complete or semicomplete in all bacteria except in strains 8BE, 8AC, and 8AQ. Cyanate is a common compound in marine environments and may serve as both an energy source for marine microbes (Palatinszky et al. 2015) as well as a potential source of nitrogen (Kamennaya et al. 2008; Sáez et al. 2019). Whether this pathway also plays a role during the interactions of microbes with their algal host, for example, by enabling the microbes to provide nitrogen to their host, remains to be tested.

Finally, most genomes analyzed encoded nearly complete or complete pathways for production of B vitamins like biotin (B7), folate (B9), riboflavin (B2), thiamine (B1), and pyridoxine (B6) (fig. 1). They may thus be contributors of vitamin B for the algal host, as has previously been suggested for diatom-bacteria associations (Behringer et al. 2018). All in all, these studied metabolic features highlight the possible contributions of the alga-associated bacteria to maintain host fitness and survival.

The genomic resources provided here constitute a valuable resource for comparative genomic analyses and evolutionary surveys of alga-associated bacteria and will allow us to produce testable hypotheses about the molecular interactions between the microbes and their host. They may, among other uses, facilitate metabolic complementarity centered approach as proposed by Dittami et al. (2014), to identify potential beneficial interactions between the partners. They will also form the bases for more targeted molecular approaches, for example, gene knockouts or gene expression analyses once specific interactions are being targeted in coculture experiments.

Supplementary Material

Supplementary data are available at Genome Biology and Evolution online.

Supplementary Material

evz278_Supplementary_Data
Click here for additional data file. (1.2MB, zip)

Acknowledgments

We appreciate the LABGeM (CEA/Genoscope & CNRS UMR8030), the France Génomique and French Bioinformatics Institute national infrastructures (funded as part of Investissement d'Avenir program managed by Agence Nationale pour la Recherche, contracts ANR-10-INBS-09 and ANR-11-INBS-0013) for their technical support within the MicroScope annotation platform and thank to Sylvie Rousvoal for help with DNA extractions.

This study was supported partially by the CNRS Momentum call, the ANR project IDEALG [ANR-10-BTBR-04] “Investissements d’Avenir, Biotechnologies-Bioressources,” and the European Union’s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie grant agreement [624575 (ALFF)]. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Author Contributions

E.K. participated in the conception and design of the study, sample processing, genome sequencing and assembly, data analysis, writing the manuscript. E.G. participated in the genome assembling, submission of genomes to MicroScope, and helped with the preparation of the figures. H.K. participated in the isolation of bacteria and genome sequencing. G.T. and E.L. both contributed to the sequencing of the genomes. E.C. participated in the assembling protocol and revision. S.M.D. participated in the conception and design of the study, isolation of bacteria, genome assembly and writing the manuscript. All authors approved the final draft.

Data deposition: The projects PRJEB31339 and PRJEB34356 have been deposited at European Nucleotide Archive - European Molecular Biology Laboratory- EBI under the accession numbers given in Table 1 (http://www.ebi.ac.uk/ena/data/view/ <ACCESSION NUMBER>).

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