Open access
Environmental Microbiology
Research Article
22 June 2023

Distribution, abundance, and ecogenomics of the Palauibacterales, a new cosmopolitan thiamine-producing order within the Gemmatimonadota phylum

ABSTRACT

The phylum Gemmatimonadota comprises mainly uncultured microorganisms that inhabit different environments such as soils, freshwater lakes, marine sediments, sponges, or corals. Based on 16S rRNA gene studies, the group PAUC43f is one of the most frequently retrieved Gemmatimonadota in marine samples. However, its physiology and ecological roles are completely unknown since, to date, not a single PAUC43f isolate or metagenome-assembled genome (MAG) has been characterized. Here, we carried out a broad study of the distribution, abundance, ecotaxonomy, and metabolism of PAUC43f, for which we propose the name of Palauibacterales. This group was detected in 4,965 16S rRNA gene amplicon datasets, mainly from marine sediments, sponges, corals, soils, and lakes, reaching up to 34.3% relative abundance, which highlights its cosmopolitan character, mainly salt-related. The potential metabolic capabilities inferred from 52 Palauibacterales MAGs recovered from marine sediments, sponges, and saline soils suggested a facultative aerobic and chemoorganotrophic metabolism, although some members may also oxidize hydrogen. Some Palauibacterales species might also play an environmental role as N2O consumers as well as suppliers of serine and thiamine. When compared to the rest of the Gemmatimonadota phylum, the biosynthesis of thiamine was one of the key features of the Palauibacterales. Finally, we show that polysaccharide utilization loci (PUL) are widely distributed within the Gemmatimonadota so that they are not restricted to Bacteroidetes, as previously thought. Our results expand the knowledge about this cryptic phylum and provide new insights into the ecological roles of the Gemmatimonadota in the environment.

IMPORTANCE

Despite advances in molecular and sequencing techniques, there is still a plethora of unknown microorganisms with a relevant ecological role. In the last years, the mostly uncultured Gemmatimonadota phylum is attracting scientific interest because of its widespread distribution and abundance, but very little is known about its ecological role in the marine ecosystem. Here we analyze the global distribution and potential metabolism of the marine Gemmatimonadota group PAUC43f, for which we propose the name of Palauibacterales order. This group presents a saline-related character and a chemoorganoheterotrophic and facultatively aerobic metabolism, although some species might oxidize H2. Given that Palauibacterales is potentially able to synthesize thiamine, whose auxotrophy is the second most common in the marine environment, we propose Palauibacterales as a key thiamine supplier to the marine communities. This finding suggests that Gemmatimonadota could have a more relevant role in the marine environment than previously thought.

INTRODUCTION

Over the last three decades, the development of culture-independent techniques has allowed the study of many microbial taxa that had remained hidden due to culture limitations. Among these taxa, the phylum Gemmatimonadota was discovered in 2001 by two independent studies that used 16S rRNA gene clone libraries to explore the microbial diversity of a reactor sludge and coastal marine sediments (1, 2). Formerly designated as “candidate division BD” (or KS-B), this phylum was renamed in 2003 when the strain T-27T was isolated from a wastewater treatment plant and named Gemmatimonas aurantiaca (3). The phylum contains seven classes based on 16S rRNA gene phylogeny (Gemmatimonadetes, Longimicrobia, PAUC43f marine benthic group, BD2-11 terrestrial group, S0134 terrestrial group, AKAU4049, and MD2902-B12), but only the Gemmatimonadetes and Longimicrobia have cultured representatives. In fact, approximately 86% of all 16S rRNA gene sequences of Gemmatimonadota deposited in the SILVA database have been retrieved from uncultured members of the phylum.
Previous studies based on 16S rRNA gene sequences have highlighted Gemmatimonadota as a cosmopolitan phylum, as diverse as Actinobacteria or Proteobacteria (4), which maybe indicating a broad physiological diversity allowing this group to colonize a great variety of environments. Accordingly, Gemmatimonadota are present in many types of soils, where they constitute one of the eight most abundant phyla, accounting for up to 6.5% of total 16S rRNA gene sequences (5, 6). Recently, Bay and coworkers suggested the metabolic potential of soil Gemmatimonadota MAGs to oxidize CH4 and reduce N2O, both potent greenhouse gases (7). Indeed, in vitro experiments with G. aurantiaca had previously confirmed its ability to reduce N2O (8, 9). Gemmatimonadota are also present in the water column and sediments of freshwater lakes (10 - 13). These environments harbor both chemoorganotrophic and photoheterotrophic Gemmatimonadota, as revealed by cultures (14, 15) and metagenomics (10, 11). A recent study in Czechia and Switzerland’s freshwater lakes estimated that Gemmatimonadota could represent up to 1% of the planktonic microbial community, with the highest relative abundances in the hypolimnion (11). Finally, Gemmatimonadota have also been found in marine environments, such as seawater (16, 17), marine sediments (18 - 21), and sponges (4, 22, 23). Due to this ubiquity in marine environments, Hanada and Sekiguchi, in 2014, suggested that Gemmatimonadota may play an important role, albeit still unexplored, in the oceans (4).
PAUC43f is one of the most frequently detected classes of Gemmatimonadota in marine environments (4). However, although the first 16S rRNA gene sequence assigned to this class was discovered 20 yr ago (24, 25), and it is the third largest class of Gemmatimonadota in the SILVA database, very little is known about its ecology and physiology. Indeed, to date, PAUC43f members have been detected only through 16S rRNA gene sequences, and there is not a single isolate or metagenome-assembled genome (MAG) affiliated with this group. Published data suggest that PAUC43f members are salt-adapted, present in marine sediments, hydrothermal vents, sponges, and corals (19, 26 - 31) and also in ephemeral saline lake sediments (32, 33), although their phylogenetic breadth, metabolic potential, and ecological role remain unexplored.
In this work, we aim to fill the gap of information about the distribution, abundance, physiology, and ecological role of the Gemmatimonadota PAUC43f group. For this purpose, we retrieved all the PAUC43f 16S rRNA gene sequences from SILVA r138 and performed an extensive search for the group in 189,104 publicly available 16S rRNA gene amplicon datasets from the Sequence Read Archive (SRA). Several databases were also screened for PAUC43f MAGs that had been previously overlooked. Our results confirmed the widespread distribution of PAUC43f in salt-related environments (brackish to hypersaline, with the highest abundances in sponges and marine sediments) and also in soils. Based on 16S rRNA gene sequences, 16 new genera were defined and linked to different ecological niches. The characterization of the metabolic potential of some members of PAUC43f indicated that they may reduce N2O and thus be helpful for mitigating the harmful effects of this potent greenhouse gas. In addition, the potential capability to synthesize serine and vitamin B1 (thiamine) was found in most PAUC43f MAGs, suggesting that they might play an important role by supplying these compounds to the community.

MATERIALS AND METHODS

PAUC43f 16S rRNA gene analyses

A dataset was built with complete and partial 16S rRNA gene sequences, classified as “PAUC43f marine benthic group” retrieved from SILVA r138 database (34), marine invertebrates (corals and sponges from the Mediterranean Sea (35)), and sediments from the Mar Menor lagoon (SE, Spain (36)). For the following bioinformatic analyses, default settings were used unless otherwise noted. Sequences were aligned using SINA (37), in the ARB software (38), and introduced by parsimony into the SILVA 16S rRNA tree to check their taxonomy. Only 3,686 sequences, clustering within the PAUC43f group, were kept for further analyses. To avoid redundancy, sequences were clustered with cd-hit-est v4.8.1 (39) at 97% of identity, a threshold commonly used for species delineation (40), and 90% of coverage (-c 0.95 -aS 0.9). As a result, 384 groups were generated, and the longest sequence of each group was selected as the representative for subsequent analyses. The map with the global distribution of PAUC43f (Fig. 1A) was drawn in R with the ggplot2 v3.3.5 (41) and tidyverse v1.3.1 (42) packages, based on the type of environment and geographic coordinates provided in the 179 sequences, out of the 384 representatives, for which metadata were available.
Fig 1
Fig 1 Ecological distribution and abundance of PAUC43f based on 16S rRNA gene sequences. (A) Worldwide distribution and environments where PAUC43f sequences have been detected. (B) Boxplot, in logarithmic scale, of PAUC43f relative abundances in different environments, measured as the percentage of PAUC43f 16S rRNA gene sequences with respect to the total number of 16S sequences (see P-values for Wilcoxon pairwise comparisons in Table S2). Colors correspond to the sampled environment. Values above and below each boxplot indicate the maximum and minimum abundance, respectively, in each environment. Number of datasets per environment: sponge, n = 324; marine sediment, n = 1,997; soil, n = 1,049; estuary, n = 295; oyster, n = 63; coral, n = 195; marine sediment mat, n = 28; fish, n = 41; hypersaline, n = 6; seawater, n = 930; and hydrothermal, n = 30.
The presence of PAUC43f in different environments was estimated using the IMNGS software (43). The abovementioned 384 representative sequences were searched in a total of 189,104 16S rRNA gene amplicon datasets, available in the SRA repository, from 16 different environments (air, coral, estuary, fish, freshwater, human gut, human not gut, hydrothermal, hypersaline, marine sediment, marine sediment mat, seawater, oyster, skin, soil, and sponge) using a 97% identity cutoff. To obtain a more precise value of PAUC43f relative abundances, estimated by the percentage of total 16S rRNA gene sequences, those SRA datasets where PAUC43f was detected by IMNGS (4,965 datasets corresponding to 11 environments) were downloaded, BLASTN-queried (-outfmt “6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen”) against the 384 representative sequences, and only best hits (BlastTab.best_hit_sorted.pl) above 97% identity and 70% coverage were considered (awk “{if($4/$13>=0.7 && $3>=97)print$0}”). Since the sequences not meeting these criteria were not used for calculating PAUC43f relative abundance, the calculated values likely underestimate the true abundance of this group in the abovementioned environments.
For precise taxonomic studies, the 66 sequences longer than 800 bp (from the 384 representative sequences) were analyzed in the ARB software v6.0.6. SINA was used to align the sequences and, to exclude highly variable positions, a base frequency filter was applied prior to the tree construction. First, the tree was constructed with the 45 sequences longer than 1,200 bp with both neighbor-joining (Jukes-Cantor correction) and maximum likelihood (PHYML) algorithms (1,000 bootstraps). Then, 21 sequences, between 800 and 1,200 bp, were added by parsimony. Sequences from classes BD2-11, MD2902-B12, and Gemmatimonadetes were used as outgroups. A cluster representing a genus was defined when at least two sequences were monophyletic in both neighbor-joining and maximum likelihood trees (44, 45) and their identities were above 94.5% of the threshold for genus delineation (46). Finally, iTOL was employed to draw the tree (47). The environmental frequency and abundance of each genus were estimated as explained above for the 384 representative sequences. The frequency of each genus in each environment was defined as the number of samples where the genus was detected with respect to the total number of samples with presence of PAUC43f.

Metagenome-assembled genome analyses

MAGs belonging to Gemmatimonadota were searched in the GTDB release 207 (48) and GEM databases (49) as well as in other public sources (https://data.ace.uq.edu.au/public/sponge_mags/; (22, 23, 50)) and Mar Menor sediments samples described in (36). DNA from Mar Menor sediments (stations 2, 3, 5, 10, 13, 20, and 21) was extracted with the DNeasy PowerSoil kit (Qiagen) following the manufacturer’s indications, and metagenomes were sequenced on an Illumina Novaseq 6000 2×150 bp run in the CNAG (Barcelona, Spain). Raw reads were quality filtered and adapters removed using Trimmomatic v0.36 (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36) (51) and then, megahit v1.2.9 (52) was used to assemble reads. Then, contigs (>2 Kb) were binned using MaxBin2 v2.2.7 (53) and MetaBAT2 v2.15 (54) and, finally MAGs were refined with DAS_Tool v1.1.3 (55).
To identify PAUC43f MAGs from the pool of Gemmatimonadota genomes and MAGs, 16S rRNA gene sequences were extracted and classified in the online SILVA ACT service (https://www.arb-silva.de/aligner/). MAGs carrying a 16S rRNA gene sequence of PAUC43f were classified, using the whole genome classifier tool GTDB-tk v2.1.1 r207 (classify_wf) (48), within the order KS3-K002, in the Gemmatimonadetes class. For this reason, all Gemmatimonadota MAGs lacking a 16S rRNA gene sequence but assigned to order KS3-K002 were considered as PAUC43f.
MAGs considered as PAUC43f were manually curated by removing contigs with different sequencing depths or incongruent taxonomic affiliation of proteins, as previously proposed (56). To calculate sequencing depth, the metagenomic reads were mapped against the MAGs by BLASTn (-outfmt “6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen”), hits were filtered by best hit, 70% coverage breadth, and 95% identity, and finally the BlastTab.seqdepth.pl script of Enveomics calculated the sequencing depth values. Contigs with more than twofold change with respect to the mean sequencing depth were removed. Regarding the taxonomic affiliation, MAG’s proteins were queried against the nr database using DIAMOND BLASTp v0.9.21.122 (-outfmt “6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen stitle”), retaining only the best hit for each protein. The most common taxonomic classification was determined by visual inspection, and contigs that do not present proteins classified to these taxa were removed. Completeness and contamination were estimated using CheckM 1.1.3 (lineage_wf) (57). To calculate the estimated genome size, MAG assembly size was divided by CheckM completeness (ranging from 0 to 1) (58). ANOVA was used to test for statistically significant differences in genome size with regard to the origin using the aov function (R stats) and the HSD.test function of the agricolae package (unbalanced=TRUE, group=FALSE) (59). Phylogenomic tree for the 441 genomes and MAGs classified as Gemmatimonadota, which includes the orders Gemmatimonadales, Longimicrobiales, PAUC43f (=Palauibacterales), JACCXV01, and the classes Glassbacteria and GCA-2686955, was then constructed with PhyloPhlAn v3.0.58 (60) using Robiginitalea biformata HTCC2501 as outgroup. Phylogeny was inferred from the alignment of 400 marker genes by the RAxML maximum-likelihood algorithm. To calculate MAG abundances, metagenomic reads were mapped by BLASTn (-outfmt “6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen”), hits were filtered by best hit using the enveomics script BlastTab.best_hit_sorted.pl, coverage > 70%, and identity > 95% (awk “{if($4/$13>=0.7 && $3=95)print$0}”). Normalized abundance was calculated as the number of mapped reads divided by metagenome and genome size. Metabolic reconstruction was carried out using the annotation provided by KofamScan v1.3.0 (61) and Interproscan v5.57–90.0 (-appl CDD, Pfam, SMART, TIGRFAM) (62 - 66). Secondary metabolite biosynthetic gene clusters (BGCs) were identified by antiSMASH v6.1.1 (67) with the “strict” detection level. CAZymes were annotated against dbCAN V11 (68) by DIAMOND BLASTp v0.9.21.122 (identity > 40%, coverage > 50%) and HMMER v3.3.1 (e-value < 1e-15, coverage > 0.35) (69), considering only hits reported by both strategies. Statistically significant differences in abundance among environments and between the number of CAZymes and susCD genes per order were tested in R using the Kruskal-Wallis (kruskal.test) and Wilcoxon (pairwise.wilcox.test) tests. For the latter, p-values were corrected by Bonferroni afnd, to avoid biased results due to small group size, only groups with more than 16 samples were evaluated, as previously indicated (70).
To identify genomic and metabolic differences between the Gemmatimonadales, Longimicrobiales, and Palauibacterales orders, the 415 available Gemmatimonadota genomes and MAGs were dereplicated at 95% ANI (species threshold (71)) using dRep v3.2.2 (72), and only those with completeness above 80% and contamination below 5% were considered (dereplicate -comp 80 -con 5 -sa 0.95). As a result, 215 genomes and MAGs were obtained and analyzed by Anvi’o 7.1 to get the enriched KEGG modules (-module-completion-threshold 0.8 and qvalue < 0.01) (73).

Fluorescence in situ hybridization (FISH)

To get experimental information (presence, activity, morphology, and size) about PAUC43f, PCR primers and FISH probes were designed using DECIPHER (74) and PrimerQuest Design Tool (IDT, https://eu.idtdna.com/PrimerQuest/Home/Index). Since Mar Menor sediment samples (Murcia, Spain; 37°45'N 0°47'W), where PAUC43f had been previously detected, were readily accessible to our lab, we designed primers and probes against the 16S rRNA gene sequences of PAUC43f retrieved from these sediments (36). In silico quality control was performed using the OligoAnalyzer Tool (IDT, https://eu.idtdna.com/pages/tools/oligoanalyzer), searching for secondary structures and dimerization, while probe specificity was checked with TestProbe against the SILVA database (34). As a result, the probe PAUC43f_826 (5′- AGGGTCAATCCTCCCAACACCTAGTAC-3′), which covered 32.7% of the PAUC43f sequences from SILVA, was selected as the best candidate. To test the probe, a sediment sample from the Mar Menor lagoon (37°40'02.8"N 0°48'55.2"W) was collected in the summer of 2021 and fixed with 4% formaldehyde at 4°C for 4 h. Before hybridization, the presence of PAUC43f in these samples was confirmed by PCR with specific primers for this group (272F: 5′-GTAAGTCGGGTGTGAAATTC-3′; and 393R: 5′-TTCCCGATATCTACGCATTC-3′) which covered 11.2% of SILVA’s PAUC43f sequences. The hybridization was carried out on a filter, as previously described (75), and the probe was optimized using six different formamide concentrations (10%, 20%, 30%, 40%, 50%, and 60%). Briefly, hybridization was done at 46°C for 4 h, followed by two washing steps at 48°C for 15 min. Then, filters were stained with DAPI (1 mg/mL), washed with milli-Q water, dehydrated with absolute ethanol (1 min each step), and finally visualized in the Zeiss LSM800 confocal laser scanning microscope.

RESULTS AND DISCUSSION

Ecological distribution of PAUC43f

PAUC43f 16S rRNA gene sequences were detected in several marine environments (such as sediments, sediment mats, corals, sponges, oysters, estuaries, seawater, and hydrothermal vents), hypersaline lake sediments, and soils (Fig. 1A). A large proportion of the sequences (89 out of 179) were recovered from marine sediment samples. Regarding geographical distribution, PAUC43f has been detected around the world in almost every latitude and longitude, and in both shallow and deep aquatic environments.
To get more insights into the PAUC43f ecological distribution, its relative abundance (as a percentage of PAUC43f 16S reads from the total 16S reads) was estimated for each environment (Fig. 1B). PAUC43f was detected in 4,965 of the 189,104 16S rRNA gene amplicon datasets analyzed, mainly from the marine environment, supporting the definition of PAUC43f as an essentially “salt-related” group (see Table S1 for the available salinity values). The group is also present in soils, some of them saline. However, since metadata for most soil samples were not available, the presence of PAUC43f in non-saline soils cannot be ruled out. The highest mean relative abundances were in sponges, marine sediments, and soils, while the lowest values were found in seawater and hydrothermal vent samples (Fig. 1B; Table S2). The extremely high relative abundance of PAUC43f in some samples is remarkable, such as an arid saline soil in China (76) and petroleum-impacted sediments from a saline lake in the Egyptian Red Sea (77), reaching up to 34.3% and 19.3%, respectively.
Since PAUC43f reached significantly higher relative abundances in sponges, marine sediments, and soils (Table S2), its distribution in these environments was explored more deeply. PAUC43f was detected in at least 30 different sponge species, found most frequently in Coscinoderma matthewsi (87 samples, where it accounted for up to 5.4% of the 16S rRNA gene sequences), Xestospongia spp. (71 samples), Rhopaloeides odorabile (32 samples), and Suberites spp (16 samples). Regarding marine sediments, no clear pattern of distribution was observed in relation to latitude, water temperature, or water column depth above the sediment (Fig. S1A through C). However, the depth along the sediment did seem to be important since PAUC43f abundances were highest at the surface and they decreased with depth (Fig. S1D). For soils, the highest abundances were found in middle latitudes in the Northern Hemisphere (Fig. S2A), although it must be noted that this hemisphere presents a higher proportion of land than the Southern Hemisphere. As for sediments, the abundance of PAUC43f in soils was also higher at the surface (Fig. S2B). These observations were not influenced by the different number of samples available for each depth (Fig. S3).

Ecotaxonomy

The 16S-based phylogenetic tree revealed 16 PAUC43f genera, supported by both neighbor-joining and PHYML algorithms (Fig. 2), which included 62% of the total tree sequences. These genera, altogether with the rest of the sequences included in the tree (except AB305477.1.916), belong to the same order and the same family, based on previously proposed thresholds for these taxonomic ranks (82.0% and 86.5% identity of 16S rRNA gene sequence, respectively (46)).
Fig 2
Fig 2 Maximum-likelihood tree based on 66 PAUC43f 16S rRNA gene sequences longer than 800 bp. Monophyletic clusters in both NJ and maximum-likelihood trees with identities above 94.5%, the threshold for delineating genera, are displayed with different colors and numbers. The external grey circle indicates the sequences targeted by the FISH probe. The 16S rRNA gene sequences from MAGs are marked with stars. Star A: 3300025554_5; Star B: Bin_M15_27; Star C: 3300026127_2; and Star D: RHO2_bin_49.
To analyze the ecological distribution of these genera, their frequencies and abundances in different environments were calculated. As shown in Fig. 3A, the detection frequency of each genus differed across environments. Some genera, such as 1, 3, 4, 6, and 9, were generalists, displaying a wide environmental distribution, while others, such as genera 10, 11, 12, and 13, were more limited to a few environments and samples. All genera were detected in corals, seawater, marine sediments, and soils, whereas only a few were found in fish, hydrothermal vents, hypersaline lake sediments, and marine sediment mats. According to their relative abundances (Fig. 3B), PAUC43f genera might be included in the rare biosphere of many environments (relative abundances <0.1% (78)). However, in certain samples, some genera showed moderate to high relative abundances (>0.1%). For example, genera 6 and 10 were significantly more abundant in marine sediments and soils than in the other samples (Table S2). Genus 16, mostly host-associated, had significantly higher abundances in corals and sponges (Table S2), and genera 7 and 9 displayed abundances above 0.1% in hydrothermal vents and marine sediments. These observations suggest that each genus might be better adapted to specific environments, which implies that at least some genera could be genuine members of microbiomes of corals, sponges, marine sediments, hypersaline lake sediments, and soils. Other genera, due to their low abundances and frequencies in fish, marine sediment mats, and oysters, might likely be transient inhabitants of these environments.
Fig 3
Fig 3 Ecological distribution of 16 PAUC43f genera. (A) Percentage of samples per environment where each genus is detected with respect to the total number of samples where PAUC43f is present. Highest values are displayed in green and lowest in white. Beside each environment name, in parentheses, there are the numbers of samples where PAUC43f genera were detected out of the total number of samples analyzed for that environment. (B) Relative abundance of each genus in each environment, as percentage of PAUC43f 16S rRNA gene sequences with respect to the total number of 16S sequences. The horizontal dashed line indicates a relative abundance of 0.1%, as a threshold for abundant and rare biospheres. (see p-values for Wilcoxon pairwise comparisons in Table S2).

Phylogenomic analyses and description of order Palauibacterales

The search of genomes/MAGs in databases (GEM & GTDB r207) and recent publications (22, 23, 50) led to the identification of 37 PAUC43f MAGs: 19 from GTDB, 8 from the GEM database, and 10 from recent publications. Fifteen additional MAGs were recovered from Mar Menor sediments (see Methods). Out of these 52 PAUC43f MAGs (Table S3), 45 could be considered of good quality according to the published criteria (completeness above 80% and contamination below 5% [56, 71]); 15 of them also carried 16S rRNA genes (Table S3). The estimated MAG sizes ranged from 1.9 to 4.3 Mb, with GC contents between 52.8% and 71.7%. Regarding their origins, the MAGs were obtained from marine sediments, sponges, saline soils, sea water and ground waters (26, 21, 3, 1, and 1 MAGs, respectively, Suppl. Table 3). A statistically supported relationship between MAG origin and estimated genome size was observed, with the smallest genomes found in marine sediments and the largest in sponges (Suppl. Figure 4), independently of their completeness. In terms of relative abundance, most MAGs accounted for more than 0.1% (and up to 12.52%, Suppl. Table 3) of total reads in their original metagenomes and thus, belonged to the abundant biosphere.
A phylogenomic tree using all available Gemmatimonadota genomes and MAGs supported the monophyletic origin of PAUC43f within this phylum. Contrary to the SILVA and in agreement with GTDB classification, PAUC43f (=KS3-K002) is likely a new order within class Gemmatimonadetes rather than a new Gemmatimonadota class (Fig. 4A). Within the order, PAUC43f MAGs recovered from marine sediments, sponges, and saline soils clustered in three different subbranches, respectively. A similar result was obtained when the AAI among these MAGs was calculated (Fig. 4B). Thus, PAUC43f MAGs clustered according to their origin, in concordance with the 16S-based ecotaxonomy (Fig. 2 and 3). Indeed, the classification of 16S rRNA gene sequences retrieved from MAGs also showed that some genera were associated with specific environments (Fig. 2), supporting the specialization of these MAG lineages on specific ecological niches.
Fig 4
Fig 4 Taxonomic classification of Palauibacterales MAGs. (A) Phylogenomic tree with all available Gemmatimonadota MAGs. In Palauibacterales, the external circle indicates the environment where the MAG was recovered. The genome of Robiginitalea biformata HTCC2501 was used to root the tree. (B) Heatmap based on average amino acid identity (AAI) values for the Palauibacterales MAGs. Values above 95% of AAI, the threshold for species delimitation, are highlighted in dark blue. At the bottom, genera colors indicate the environment where the MAG was recovered following the same color schema as in A.
Both the phylogenomic tree and AAI values (Fig. 4B) indicated that the 52 MAGs represented 24 different species (AAI ≥ 95% (71, 79)), 10 of which were recovered at least twice from different metagenomes. MAGs from sponges belonged to 10 different species within the same genus, while the 14 species from saline soils and marine sediments fell into 8 different genera (AAI ≤ 65% (71)).
Based on these results and following the instructions of the recently published code for prokaryotes nomenclature from sequence data (SeqCode (80)), we propose renaming PAUC43f (=KS3-K002) as order Palauibacterales, in reference to the Republic of Palau, where the first 16S rRNA gene sequence of this group was retrieved. Thus, hereinafter, we will refer to PAUC43f as Palauibacterales. Additionally, we propose a name for the 7 genera and 16 species that meet the criteria of the SeqCode (Table 1; Table S3, SeqCode draft register list URL: seqco.de/r:0hkazsoc).
TABLE 1
TABLE 1 Proposed taxonomic classification for Palauibacterales ordera
OrderFamilyGeneraSpeciesSpecies name
PalauibacteralesPalauibacteraceaePalauibacterSp. 1Palauibacter polyketidifaciens
Sp. 2Palauibacter ramosifaciens
Sp. 3Palauibacter denitrificans
Sp. 4TPalauibacter soopunensis
Sp. 5Palauibacter irciniicola
Sp. 6Palauibacter rhopaloidicola
Sp. 7Palauibacter australiensis
Sp. 8Palauibacter scopulicola
Sp. 9Palauibacter poriticola
CarthagonibacterSp. 11Carthagonibacter metallireducens
BenthicolaSp. 13TBenthicola marisminoris
Sp. 15Benthicola azotiphorus
HumimonasSp. 17THumimonas hydrogenitrophica
CaribbeanibacterSp. 18TCaribbeanibacter nitroreducens
KutchimonasSp. 22TKutchimonas denitrificans
IndicimonasSp. 23TIndicimonas acetifermentans
a
Protologue description can be found in Table S3.

Core and niche-specific metabolic pathways in Palauibacterales MAGs

To shed light on the ecological role of Palauibacterales, the potential metabolic capabilities of each species were explored (Fig. 5; Fig. S5; Table S3). MAG annotation indicated that Palauibacterales coded for typical gram-negative cell walls, as expected, and lacked the genes for flagella assembly (except species 18). Regarding central carbon metabolism, complete or almost complete glycolysis and tricarboxylic acid cycle (TCA) pathways were found in almost all species, as well as sugar transporters, pointing to Palauibacterales as a chemoorganotrophic bacteria. In good agreement, genes related to carbon fixation or photosynthetic metabolism were not found. However, species from sediments and saline soils presented 1 c and 1 f hydrogenases (81), so they may potentially shift between chemoorganotrophy and chemolithotrophy. It is worth noting that hydrogenotrophy has been recently demonstrated in other Gemmatimonadota members (82).
Fig 5
Fig 5 Heatmap showing the presence/absence of metabolic pathways within the 24 Palauibacterales species. A pathway was considered present if at least 80% of genes were detected. More detailed information can be found in Fig. S5 and Table S3. MR: Metal resistance.
Members of the Palauibacterales are most likely facultative aerobes since genes for complex IV cytochrome oxidase, which transfers electrons to oxygen, were detected in almost all species. In addition, most of them also encoded genes for nitrate, nitrite, and/or nitrous oxide respiration, while the species retrieved from sponges were predicted to be able to respire thiosulfate, and species from sediments and saline soils might carry out acetate fermentation. The potential to reduce N2O by sediment and saline soils MAGs is in agreement with previous observations in other representatives of the phylum (8, 9, 83) and highlights Palauibacterales ecological relevance. Nitrous oxide is a potent greenhouse gas, which, due to human activities such as agricultural fertilization and combustion of fossil fuels (84), is increasing its atmospheric concentrations at a rate of 0.8 ppb per year (85), with some of the highest concentrations measured in coastal and estuarine waters (86, 87). Thus, N2O reducers, such as some Palauibacterales species may be, play a key role in mitigating the harmful effects of this gas. Furthermore, the Palauibacterales might have another restoring effect on the environment. In a recent study of Mar Menor (Spain) marine sediments, we observed a high relative abundance of PAUC43f in heavy-metal contaminated sediments (36). The most abundant PAUC43f OTU in these sediments shared 99.3% identity to the 16S rRNA gene sequence found in Carthagonibacter metallireducens (Sp. 11), which encodes for the hyaABCD NiFe hydrogenase that could act in hydrogenotrophic respirations using metals as electron acceptors, as previously described for Geobacter sulfurreducens (88). These observations suggest that some Palauibacterales species might obtain an ecological advantage by means of the respiration of metals, allowing them to thrive in these extreme environments. Furthermore, these species may be potential bioremediation agents in metal-contaminated areas.
With respect to amino acid biosynthesis, it is noteworthy that species from sponges were potentially able to synthesize more amino acids (12 - 15) than species from sediments and saline soils (5 - 12). The most common putative auxotrophies were found for lysine, tyrosine, phenylalanine, leucine, isoleucine, valine, and histidine. However, for some species these auxotrophies might be circumvented by acquiring amino acids from the environment using specific transporters (i.e., branched-chain amino acid transporters or transporters for oligopeptides). Since serine auxotrophy has been demonstrated for key marine microbes, such as Pelagibacter ubique (89), Palauibacterales may play a relevant ecological role in providing serine to the marine community.
Regarding the potential for vitamin B production, core biosynthetic genes for thiamine (vitamin B1) (thiC, thiG, and thiE), a cofactor of several essential enzymes (90), were detected in most species. Since B1 auxotrophy has been proposed as the second most common auxotrophy in marine environments (91), affecting both eukaryotes and prokaryotes (92 - 94), Palauibacterales might also be important suppliers of B1 to the marine communities. Genes for the complete biosynthetic operon of riboflavin (vitamin B2), a precursor of coenzymes FAD and FMN (95), and niacin (vitamin B3), a coenzyme in redox reactions, were also found in most species. The pathways for pantothenate (vitamin B5), a precursor of coenzyme A, and folate (vitamin B9), an important molecule in anabolic reactions, were partially present in these MAGs. If we assume that missing genes are a result of MAG incompleteness, Palauibacterales might also be capable of synthesizing these two vitamins. Biosynthetic pathways for vitamins B6, B7, and B12 were not found, and the presence of the bioY gene, which encodes a biotin (vitamin B7) transporter (96), and btuF and btuB, which are part of the cobalamin (vitamin B12) transporter (97), suggest that Palauibacterales may import these vitamins from the extracellular environment.
Secondary metabolites are usually involved in growth, development, and defense (98), and they are interesting molecules for medicine due to their potential uses as antibiotics, and antitumoral and cholesterol-lowering drugs. The search for BGCs with antiSMASH (67) revealed that sponge MAGs presented a higher number and diversity of BGCs (2, 9 BGCs per MAG) than those from sediments and saline soils (1, 2 BGCs per MAG) (Fig. S6A; Table S3). Despite the fact that most of the detected BGCs had no similarity to previously described BGCs, some T1PKS were similar to those known to synthetize azinomycin B, a potent antibiotic with antitumor activity (99, 100); cyphomycin, an antifungal compound (101); and vazabitide A and funisamine, both compounds with unknown biological properties (102, 103).
CAZymes involved in biosynthesis, degradation, or modification of poly- and oligosaccharides showed clear differences in both abundance and composition between marine sediments, sponges, and saline soils MAGs (Fig. 6B and C). For example, families GH29 and GH95, both acting on fucose, a common polysaccharide in the marine environment, were detected only in marine sediment species. These differences in CAZymes composition might be related to the adaptation of Palauibacterales to the variety of niches they inhabit.
Fig 6
Fig 6 Differences in the potential metabolic and biosynthetic capabilities inferred from genomic data between the orders Gemmatimonadales (n = 134), Longimicrobiales (n = 61), and Palauibacterales (n = 20). (A) Frequency of KEGG modules with a minimum completeness of 80% displaying statistically significant differences between orders. Colors indicate the percentage of genomes/MAGs of each order that codify a given module. (B) Percentage of genomes/MAGs of each order that carry BGC. (C) Boxplot of the number of CAZymes and susCD proteins, markers of PULs, in the genomes/MAGs of each order. Only statistically significant differences (P-value < 0.05) reported by Wilcoxon test are shown.
In addition, Palauibacterales MAGs encoded antibiotic resistance genes such as β-lactamases, tetracycline/H+ antiporters, and fosmidomycin and macrolide efflux pumps. Heavy metal resistance genes were also detected in sediment and saline soil species, including genes encoding efflux pumps for As3+, Zn2+, and Fe2+and bacterioferritin, an iron storage protein which protects cells from reactive Fe2+.

Order specific traits within the Gemmatimonadetes class

In an attempt to correlate the phylogeny with their metabolic potential, the main differences among the three largest Gemmatimonadetes orders (Gemmatimonadales, Longimicrobiales, and Palauibacterales) were explored. Notably, our results (Fig. 6) highlighted Palauibacterales as thiamine-producing bacteria, a trait with much less prevalence in the two other orders (Fig. 6A). This finding suggests either a higher limitation in the environment or a more relevant role of thiamine in Palauibacterales habitat. This vitamin is a coenzyme implicated in central metabolic processes such as the TCA cycle or the pentose-phosphate pathway and thus is essential for most living organisms (90). However, as mentioned above, previous studies have pointed to B1-auxotrophy as the second most common in the marine environment (91). About 25% of marine bacterial species require exogenous vitamin B1, a value that is notably higher in relevant marine taxa such as Flavobacteriales (76%) or Rhodobacterales (50%) (91). Furthermore, the number of B1 vitamin-requiring enzymes per genome is higher than for other vitamins, such as B7 or B12 (91), which is in agreement with the higher B1 uptake rates observed in coastal microbial communities (104). In addition (105), pointed out marine sediments, one of the main habitats of Palauibacterales, as sources of thiamine to the water column (105). Therefore, the literature highlights the relevance of B1-producing bacteria, such as Palauibacterales may be, in ecosystem functioning.
A second difference among the three orders was the presence in the Palauibacterales of BGC of ranthipeptide, betalactone, and proteusin, which were absent or less frequent in the other orders (Fig. 6B). Furthermore, polyketide synthase clusters were rare in Gemmatimonadales, whereas T1PKS were common in both Palauibacterales and Longimicrobiales and T3PKS in Longimicrobiales. These observations point to Palauibacterales as an interesting source of novel bioactive compounds with potential biotechnological applications.
Finally, the third main difference among orders has to do with the presence of polysaccharide utilization loci (PULs). These PULs are genomic loci that encode the necessary proteins to bind a given polysaccharide to the cell surface, cleaving it to oligosaccharides and importing them into the periplasmic region for their degradation to monosaccharides (106). They are typically composed of susCD genes, which transport the oligosaccharides from the extracellular to the periplasmic space, and CAZymes that catalyze polysaccharide degradation. Although PULs were thought to be restricted to Bacteroidetes, they were also observed in a few cultured genomes of Gemmatimonadota (106). Here, we show that PULs are widely distributed within this phylum and are not a rarity, as previously believed (Fig. 6C Fig. S7). The prevalence of PULs (based on the number of CAZymes and susCD genes) is significantly higher in the genomes of Gemmatimonadales and Longimicrobiales than in Palauibacterales (Fig. 6C). This observation might indicate that Palauibacterales present less potential for importing and degrading polysaccharides than its sister orders within the phylum.
Fig 7
Fig 7 FISH of Mar Menor sediment samples with a Palauibacterales specific probe. A and B show two different microscopic fields observed with two different color channels, red for probe and blue for DAPI. From left to right: PAUC43f_826, DAPI, and merged channels. Arrows indicate cells displaying signals in both FISH and DAPI channels.

Visualization of active Palauibacterales in marine sediments

To visualize Palauibacterales cells and evaluate their metabolically active state in the environment, a FISH probe was designed and tested. In silico analyses indicated that the probe matched 32% of the Palauibacterales sequences deposited in the SILVA database and sequences of genera 2, 3, 4, 5, 6, 7, and 8 (Fig. 2). Thus, the probe does not target the whole Palauibacterales order but rather a set of closely related sequences, mostly associated with marine sediments and saline soils. Since the probe also matched 16S rRNA gene sequences from MAGs recovered from Mar Menor sediments, FISH was performed with sediment samples from this lagoon. The best hybridization was obtained with 40% formamide and, as shown in Fig. 7, Palauibacterales cells displayed a small but wide rod morphology. Considering that the number of hybridized cells seemed to be higher than the cells stained with DAPI, we suspected DAPI might have been quenched by the probe fluorophore or by pigments present in the cells. With this assay, we provide experimental evidence of the presence and metabolically active state of the order Palauibacterales in marine sediments.

Final remarks

Based on the ubiquity of the Gemmatimonadota phylum in the marine environment, Hanada and Sekiguchi noted that this phylum may play an important but still unknown ecological role (4). The results presented here highlight the ecological relevance of a key unexplored order in that phylum, the Palauibacterales, within marine environments. This cosmopolitan order within the Gemmatimonadetes class displays a salt-related character and presents interesting potential metabolic features, such as N2O reduction and serine and thiamine biosynthesis, with the latter as a probable key trait of the group. The presence of PULs in most Gemmatimonadetes expands the capability for complex polysaccharide degradation beyond the well-known Bacteroidetes and Verrucomicrobia. With this work, we provide evidence that the influence of Gemmatimonadota on marine ecosystem functioning, despite having been overlooked to date, may be more significant than previously supposed.

ACKNOWLEDGMENTS

We thank Ramon Rosselló-Móra for his help with 16S rRNA gene tree construction and uncultured taxa naming, Fernando Nicolás Flores for his help with the etymology of names, and Heather Maughan and Karen Neller for their professional English editing and the critical reading of the manuscript.
B.A.-R., F.S., and J.A. conceived and designed the study. B.A.-R. performed the analyses under the supervision and guidance of F.S. and J.A. All authors discussed, wrote, read, and approved the manuscript.
This research was supported by the EU-H2020 MetaFluidics and Bluetools projects with grant agreement numbers 685474 and 101081957 (to J.A.). B.A.-R. is an ACIF fellow (Generalitat Valenciana).
The authors declare that they have no competing interests.

SUPPLEMENTAL MATERIAL

Fig S1 - msystems.00215-23-s0001.pdf
PAUC43f abundance based on 16S rRNA gene sequences in sediments as functions of A) latitude, B) temperature, C) water column depth above the sediment, and D) sediment depth.
Fig S2 - msystems.00215-23-s0002.pdf
PAUC43f abundance based on 16S rRNA gene sequences in soils as function of A) latitude and B) soil depth.
Fig S3 - msystems.00215-23-s0003.pdf
Dotplot between the mean abundance (% of 16S rRNA reads) and the number of samples per depth bin for sediments (A) and soils (B).
Fig S4 - msystems.00215-23-s0004.pdf
Boxplot of the A) estimated MAG size and B) completeness related to the environment from which they were recovered. Statistically significant p-values reported by ANOVA are shown above boxplots.
Fig S5 - msystems.00215-23-s0005.pdf
Predicted metabolic capabilities for the 24 Palauibacterales species. Each species is represented by a colored dot (see legend) named in the same order as in Figure 4. The annotation of MAGs used to reconstruct the metabolism can be found in Supplementary Table 4.
Fig S6 - msystems.00215-23-s0006.pdf
Differences within the Palauibacterales order with regard to the MAG origin. A) Secondary metabolite biosynthetic gene clusters (BGCs) predicted by antiSMASH for each MAG. The colored background of species’ names shows the origin of the species (green: marine sediment; red: saline soils; and orange: sponges) and colored dots indicate the number of each BGC per MAG (1: blue; 2: yellow; 3: orange; and 4: red). B) Boxplot of the number of annotated CAZymes per genome. C) NMDS plot based on Bray-Curtis distances calculated from a matrix of CAZymes composition and abundance in each genome.
Fig S7 - msystems.00215-23-s0007.pdf
Examples of PUL in the three orders of Gemmatimonadota. Colored dots at the left indicate the order, following the same color schema as in previous figures. PUL sequences were identified from MAG GCA_016713785.1 for Gemmatimonadales, 3300025924_14 for Longimicrobiales, and Bin_S212_14 for Palauibacterales.
Table S1 - msystems.00215-23-s0008.xlsx
Metadata of the 16S rRNA gene amplicon datasets where PAUC43f was detected.
Table S2 - msystems.00215-23-s0009.xlsx
First worksheet, pairwise Wilcoxon test between the abundance of PAUC43f in different environments (see Figure 1). P-values were corrected by Bonferroni. Only groups with more than 16 samples were tested to avoid biased results due to small sample size. Second and following worksheets, pairwise Wilcoxon test for the abundance of each PAUC43f genus in different environments (see Figure 3). P-values were corrected by Bonferroni. Only groups with more than 16 samples were tested to avoid biased results due to small sample size.
Table S3 - msystems.00215-23-s0010.xlsx
First worksheet, general characteristics of Palauibacterales MAGs. aStrain heterogeneity. bMAG abundance is shown as a percentage of recruited reads from the total metagenome reads. CMAG normalized abundance is shown as the number of recruited reads divided by metagenome and genome size. Second worksheet, protologue for the new described taxa within the Palauibacterales order. Third and following worksheets, annotation of predicted proteins from MAGs using the KEGG KO, Pfam, CDD, SMART, TIGRFAM, and AntiSMASH.
ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.

REFERENCES

1.
Hugenholtz P, Tyson GW, Webb RI, Wagner AM, Blackall LL. 2001. Investigation of candidate division TM7, a recently recognized major lineage of the domain bacteria with no known pure-culture representatives. Appl Environ Microbiol 67:411–419.
2.
Madrid VM, Aller JY, Aller RC, Chistoserdov AY. 2001. High prokaryote diversity and analysis of community structure in mobile mud deposits off French Guiana: identification of two new bacterial candidate divisions. FEMS Microbiol Ecol 37:197–209.
3.
Zhang H, Sekiguchi Y, Hanada S, Hugenholtz P, Kim H, Kamagata Y, Nakamura K. 2003. Gemmatimonas aurantiaca gen. nov., sp. nov., a gram-negative, aerobic, polyphosphate-accumulating micro-organism, the first cultured representative of the new bacterial phylum Gemmatimonadetes phyl. nov. Int J Syst Evol Microbiol 53:1155–1163.
4.
Hanada S, Sekiguchi Y. 2014. The phylum Gemmatimonadetes, . In Rosenberg E, EF DeLong, S Lory, E Stackebrandt, F Thompson (ed), The prokaryotes. Springer.
5.
Janssen PH. 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl Environ Microbiol 72:1719–1728.
6.
Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, Maestre FT, Singh BK, Fierer N. 2018. A global atlas of the dominant bacteria found in soil. Science 359:320–325.
7.
Bay SK, Dong X, Bradley JA, Leung PM, Grinter R, Jirapanjawat T, Arndt SK, Cook PLM, LaRowe DE, Nauer PA, Chiri E, Greening C. 2021. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat Microbiol 6:246–256.
8.
Chee-Sanford J, Tian D, Sanford R. 2019. Consumption of N2O and other N-cycle intermediates by Gemmatimonas aurantiaca strain T-27. Microbiology 165:1345–1354.
9.
Park D, Kim H, Yoon S. 2017. Nitrous oxide reduction by an obligate aerobic bacterium, Gemmatimonas aurantiaca strain T-27. Appl Environ Microbiol 83: e00502-17.
10.
Fecskeová LK, Piwosz K, Hanusová M, Nedoma J, Znachor P, Koblížek M. 2019. Diel changes and diversity of pufM expression in freshwater communities of Anoxygenic Phototrophic bacteria. Scientific reports 9: 18766.
11.
Mujakić I, Andrei A-Ş, Shabarova T, Fecskeová LK, Salcher MM, Piwosz K, Ghai R, Koblížek M. 2021. Common presence of phototrophic Gemmatimonadota in temperate freshwater lakes. mSystems 6: e01241-20.
12.
Song H, Li Z, Du B, Wang G, Ding Y. 2012. Bacterial communities in sediments of the shallow Lake Dongping in China. J Appl Microbiol 112:79–89.
13.
Zhang J, Yang Y, Zhao L, Li Y, Xie S, Liu Y. 2015. Distribution of sediment bacterial and archaeal communities in plateau freshwater lakes. Appl Microbiol Biotechnol 99:3291–3302.
14.
Zeng Y, Nupur Y, Wu N, Madsen AM, Chen X, Gardiner AT, Koblížek M. 2020. Gemmatimonas groenlandica sp. nov. is an aerobic anoxygenic phototroph in the phylum Gemmatimonadetes. Front Microbiol 11: 606612.
15.
Zeng Y, Selyanin V, Lukeš M, Dean J, Kaftan D, Feng F, Koblížek M. 2015. Characterization of the microaerophilic, bacteriochlorophyll a-containing bacterium Gemmatimonas phototrophica sp. nov., and emended descriptions of the genus Gemmatimonas and Gemmatimonas aurantiaca. Int J Syst Evol Microbiol 65:2410–2419.
16.
Nunoura T, Takaki Y, Hirai M, Shimamura S, Makabe A, Koide O, Kikuchi T, Miyazaki J, Koba K, Yoshida N, Sunamura M, Takai K. 2015. Hadal biosphere: insight into the microbial ecosystem in the deepest ocean on Earth. Proc Natl Acad Sci U S A 112:E1230–E1236.
17.
Nunoura T, Hirai M, Yoshida-Takashima Y, Nishizawa M, Kawagucci S, Yokokawa T, Miyazaki J, Koide O, Makita H, Takaki Y, Sunamura M, Takai K. 2016. Distribution and niche separation of planktonic microbial communities in the water columns from the surface to the hadal waters of the Japan Trench under the eutrophic ocean. Front Microbiol 7:1261.
18.
Durbin AM, Teske A. 2011. Microbial diversity and stratification of South Pacific abyssal marine sediments. Environ Microbiol 13:3219–3234.
19.
Bergo NM, Bendia AG, Ferreira JCN, Murton BJ, Brandini FP, Pellizari VH. 2021. Microbial diversity of deep-sea ferromanganese crust field in the Rio grande rise, Southwestern Atlantic Ocean. Microb Ecol 82:344–355.
20.
Marcial Gomes NC, Borges LR, Paranhos R, Pinto FN, Mendonça-Hagler LCS, Smalla K. 2008. Exploring the diversity of bacterial communities in sediments of urban mangrove forests. FEMS Microbiol Ecol 66:96–109.
21.
Schauer R, Bienhold C, Ramette A, Harder J. 2010. Bacterial diversity and biogeography in deep-sea surface sediments of the South Atlantic ocean. ISME Journal 4:159–170.
22.
Robbins SJ, Song W, Engelberts JP, Glasl B, Slaby BM, Boyd J, Marangon E, Botté ES, Laffy P, Thomas T, Webster NS. 2021. A genomic view of the microbiome of coral reef demosponges. ISME Journal 15:1641–1654.
23.
Engelberts JP, Robbins SJ, de Goeij JM, Aranda M, Bell SC, Webster NS. 2020. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME Journal 14:1100–1110.
24.
Hentschel U, Hopke J, Horn M, Friedrich AB, Wagner M, Hacker J, Moore BS. 2002. Molecular evidence for a uniform microbial community in sponges from different oceans. Appl Environ Microbiol 68:4431–4440.
25.
Webster NS, Wilson KJ, Blackall LL, Hill RT. 2001. Phylogenetic diversity of bacteria associated with the marine sponge Rhopaloeides odorabile. Appl Environ Microbiol 67:434–444.
26.
Hardoim CCP, Ramaglia ACM, Lôbo-Hajdu G, Custódio MR. 2021. Community composition and functional prediction of Prokaryotes associated with sympatric sponge species of Southwestern Atlantic coast. Sci Rep 11: 9576.
27.
Griffiths SM, Antwis RE, Lenzi L, Lucaci A, Behringer DC, Butler MJ, Preziosi RF. 2019. Host genetics and geography influence microbiome composition in the sponge Ircinia campana. Journal of animal Ecology 88:1684–1695.
28.
López-García P, Duperron S, Philippot P, Foriel J, Susini J, Moreira D. 2003. Bacterial diversity in Hydrothermal sediment and Epsilonproteobacterial dominance in experimental Microcolonizers at the mid-Atlantic ridge. Environ Microbiol 5:961–976.
29.
Radwan M, Hanora A, Zan J, Mohamed NM, Abo-Elmatty DM, Abou-El-Ela SH, Hill RT. 2010. Bacterial community analyses of two red sea sponges. Mar Biotechnol (NY) 12:350–360.
30.
Gerçe B, Schwartz T, Syldatk C, Hausmann R. 2011. Differences between bacterial communities associated with the surface or tissue of Mediterranean sponge species. Microbial Ecology 61:769–782.
31.
Cerqueira T, Pinho D, Froufe H, Santos RS, Bettencourt R, Egas C. 2017. Sediment microbial diversity of three deep-sea hydrothermal vents southwest of the Azores. Microbial Ecology 74:332–349.
32.
Genderjahn S, Alawi M, Mangelsdorf K, Horn F, Wagner D. 2018. Desiccation- and saline-tolerant bacteria and archaea in Kalahari pan sediments. Front Microbiol 9:2082.
33.
Aerts JW, van Spanning RJM, Flahaut J, Molenaar D, Bland PA, Genge MJ, Ehrenfreund P, Martins Z. 2019. Microbial communities in sediments from four mildly acidic ephemeral salt lakes in theYilgarn Craton (Australia) -terrestrial analogs to ancient Mars. Front Microbiol 10:779.
34.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The Silva ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596.
35.
Rubio-Portillo E, Ramos-Esplá AA, Antón J. 2021. Shifts in marine invertebrate bacterial assemblages associated with tissue necrosis during a heat wave. Coral Reefs (Online) 40:395–404.
36.
Aldeguer-Riquelme B, Rubio-Portillo E, Álvarez-Rogel J, Giménez-Casalduero F, Otero XL, Belando M-D, Bernardeau-Esteller J, García-Muñoz R, Forcada A, Ruiz JM, Santos F, Antón J. 2022. Factors structuring microbial communities in highly impacted coastal marine sediments (MAR menor lagoon, SE Spain). Front Microbiol 13:937683.
37.
Pruesse E, Peplies J, Glöckner FO. 2012. Sina: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823–1829.
38.
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar A, Buchner A, Lai T, Steppi S, Jobb G, Förster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, König A, Liss T, Lüssmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke M, Ludwig T, Bode A, Schleifer K-H. 2004. ARB: A software environment for sequence data. Nucleic Acids Res 32:1363–1371.
39.
Li W, Godzik A. 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinform 22:1658–1659.
40.
Stackebrandt E, Goebel BM. 1994. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int J Syst Evol Microbiol 44:846–849.
41.
Wickham H. 2016. Ggplot2, . In Ggplot2: elegant graphics for data analysis. Springer-Verlag New York.
42.
Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, Müller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H. 2019. Welcome to the tidyverse. JOSS 4:1686.
43.
Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, Clavel T. 2016. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep 6:33721.
44.
Pohlner M, Dlugosch L, Wemheuer B, Mills H, Engelen B, Reese BK. 2019. The majority of active Rhodobacteraceae in marine sediments belong to uncultured genera: a molecular approach to link their distribution to environmental conditions. Front Microbiol 10:659.
45.
Massana R, Castresana J, Balagué V, Guillou L, Romari K, Groisillier A, Valentin K, Pedrós-Alió C. 2004. Phylogenetic and ecological analysis of novel marine stramenopiles. Appl Environ Microbiol 70:3528–3534.
46.
Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer K-H, Whitman WB, Euzéby J, Amann R, Rosselló-Móra R. 2014. Uniting the classification of cultured and uncultured bacteria and Archaea using 16S rRNA gene sequences. Nat Rev Microbiol 12:635–645.
47.
Letunic I, Bork P. 2019. Interactive tree of life (iTOL) V4: recent updates and new developments. Nucleic Acids Res 47:W256–W259.
48.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. 2019. GTDB-TK: A Toolkit to classify Genomes with the genome Taxonomy database. Bioinform 36:1925–1927.
49.
Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, Wu D, Paez-Espino D, Chen I-M, Huntemann M, Palaniappan K, Ladau J, Mukherjee S, Reddy TBK, Nielsen T, Kirton E, Faria JP, Edirisinghe JN, Henry CS, Jungbluth SP, Chivian D, Dehal P, Wood-Charlson EM, Arkin AP, Tringe SG, Visel A, IMG/M Data Consortium, Woyke T, Mouncey NJ, Ivanova NN, Kyrpides NC, Eloe-Fadrosh EA. 2020. loe-Fadros of Earth’s microbiomes. Nat Biotechnol 39:499–509.
50.
Zheng X, Dai X, Zhu Y, Yang J, Jiang H, Dong H, Huang L. 2022. (Meta)Genomic analysis reveals diverse energy conservation strategies employed by globally distributed Gemmatimonadota. mSystems 7: e0022822.
51.
Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinform 30:2114–2120.
52.
Li D, Liu CM, Luo R, Sadakane K, Lam TW. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinform 31:1674–1676.
53.
Wu YW, Simmons BA, Singer SW. 2016. Maxbin 2.0: an automated Binning algorithm to recover genomes from multiple metagenomic datasets. Bioinform 32:605–607.
54.
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. 2019. Metabat 2: An adaptive Binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. Peerj 7: e7359.
55.
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3:836–843.
56.
Ramos-Barbero MD, Martin-Cuadrado A-B, Viver T, Santos F, Martinez-Garcia M, Antón J. 2019. Recovering microbial genomes from metagenomes in hypersaline environments: the good, the Bad and the Ugly. Syst Appl Microbiol 42:30–40.
57.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055.
58.
Rodríguez-Gijón A, Nuy JK, Mehrshad M, Buck M, Schulz F, Woyke T, Garcia SL. 2021. A genomic perspective across Earth’s microbiomes reveals that genome size in Archaea and Bacteria is linked to ecosystem type and trophic strategy. Front Microbiol 12:761869.
59.
de Mendiburu F, Yaseen M. 2020. Agricolae: statistical procedures for agricultural research, . In R package version 1.4.0
60.
Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, Zhu Q, Bolzan M, Cumbo F, May U, Sanders JG, Zolfo M, Kopylova E, Pasolli E, Knight R, Mirarab S, Huttenhower C, Segata N. 2020. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using Phylophlan 3.0. Nat Commun 11:2500.
61.
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, Ogata H. 2020. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinform 36:2251–2252.
62.
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, McWilliam H, Maslen J, Mitchell A, Nuka G, Pesseat S, Quinn AF, Sangrador-Vegas A, Scheremetjew M, Yong S-Y, Lopez R, Hunter S. 2014. InterProScan 5: genome-scale protein function classification. Bioinform 30:1236–1240.
63.
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A. 2021. Pfam: the protein families database in 2021. Nucleic Acids Res 49:D412–D419.
64.
Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. 2013. TIGRFAMs and genome properties in 2013. Nucleic Acids Res 41:D387–95.
65.
Letunic I, Bork P. 2018. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res 46:D493–D496.
66.
Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, Lanczycki CJ, Lu F, Marchler GH, Song JS, Thanki N, Wang Z, Yamashita RA, Zhang D, Zheng C, Bryant SH. 2015. CDD: NCBI’s conserved domain database. Nucleic Acids Res 43:D222–D226.
67.
Medema MH, Blin K, Cimermancic P, de Jager V, Zakrzewski P, Fischbach MA, Weber T, Takano E, Breitling R. 2011. AntiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res 39:W339–W346.
68.
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y. 2018. Dbcan2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46:W95–W101.
69.
Eddy SR. 2011. Accelerated profile HMM searches. PLoS Comput Biol 7: e1002195.
70.
Dwivedi AK, Mallawaarachchi I, Alvarado LA. 2017. Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Statistics in medicine 36:2187–2205.
71.
Konstantinidis KT, Rosselló-Móra R, Amann R. 2017. Uncultivated microbes in need of their own taxonomy. ISME Journal 11:2399–2406.
72.
Olm MR, Brown CT, Brooks B, Banfield JF. 2017. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME Journal 11:2864–2868.
73.
Shaiber A, Willis AD, Delmont TO, Roux S, Chen L-X, Schmid AC, Yousef M, Watson AR, Lolans K, Esen ÖC, Lee STM, Downey N, Morrison HG, Dewhirst FE, Mark Welch JL, Eren AM. 2020. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biol 21:292.
74.
Wright ES, Yilmaz LS, Corcoran AM, Ökten HE, Noguera DR. 2014. Automated design of probes for rRNA-targeted fluorescence in situ hybridization reveals the advantages of using dual probes for accurate identification. Appl Environ Microbiol 80:5124–5133.
75.
Snaidr J, Amann R, Huber I, Ludwig W, Schleifer KH. 1997. Phylogenetic analysis and in situ identification of bacteria in activated sludge. Appl Environ Microbiol 63:2884–2896.
76.
Xie K, Deng Y, Zhang S, Zhang W, Liu J, Xie Y, Zhang X, Huang H. 2017. Prokaryotic community distribution along an ecological gradient of Salinity in surface and subsurface saline soils. Sci Rep 7: 13332.
77.
Mustafa GA, Abd-Elgawad A, Ouf A, Siam R. 2016. The Egyptian Red Sea coastal microbiome: a study revealing differential microbial responses to diverse anthropogenic pollutants. Environmental pollution 214:892–902.
78.
Pedrós-Alió C. 2012. The rare bacterial biosphere. Ann Rev Mar Sci 4:449–466.
79.
Konstantinidis KT, Tiedje JM. 2005. Towards a genome-based taxonomy for prokaryotes. J Bacteriol 187:6258–6264.
80.
Hedlund BP, Chuvochina M, Hugenholtz P, Konstantinidis KT, Murray AE, Palmer M, Parks DH, Probst AJ, Reysenbach AL, Rodriguez-R LM, Rossello-Mora R, Sutcliffe IC, Venter SN, Whitman WB. 2022. SeqCode: a nomenclatural code for prokaryotes described from sequence data. Nat Microbiol 7:1702–1708.
81.
Søndergaard D, Pedersen CNS, Greening C. 2016. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep 6:34212.
82.
Islam ZF, Welsh C, Bayly K, Grinter R, Southam G, Gagen EJ, Greening C. 2020. A widely distributed hydrogenase oxidises atmospheric H2 during bacterial growth. ISME J 14:2649–2658.
83.
Jones CM, Spor A, Brennan FP, Breuil MC, Bru D, Lemanceau P, Griffiths B, Hallin S, Philippot L. 2014. Recently identified microbial guild mediates soil N2O sink capacity. Nat Clim Change 4:801–805.
84.
Reay DS, Davidson EA, Smith KA, Smith P, Melillo JM, Dentener F, Crutzen PJ. 2012. Global agriculture and nitrous oxide emissions. Nat Clim Change 2:410–416.
85.
Hofmann DJ, Butler JH, Dlugokencky EJ, Elkins JW, Masarie K, Montzka SA, Tans P. 2006. The role of carbon dioxide in climate forcing from 1979 to 2004: introduction of the Annual Greenhouse Gas Index. Tellus B Chem Phys Meteorol 58:614.
86.
Arévalo-Martínez DL, Kock A, Löscher CR, Schmitz RA, Bange HW. 2015. Massive nitrous oxide emissions from the tropical South Pacific ocean. Nat Geosci 8:530–533.
87.
Barnes J, Upstill-Goddard RC. 2011. N 2 O seasonal distributions and air-sea exchange in UK estuaries: implications for the tropospheric N 2 O source from European coastal waters. J Geophys Res 116.
88.
Coppi MV, O’Neil RA, Lovley DR. 2004. Identification of an uptake hydrogenase required for hydrogen-dependent reduction of Fe (III) and other electron acceptors by Geobacter sulfurreducens. J Bacteriol 186:3022–3028.
89.
Tripp HJ, Schwalbach MS, Meyer MM, Kitner JB, Breaker RR, Giovannoni SJ. 2009. Unique glycine-activated riboswitch linked to glycine-serine auxotrophy in SAR11. Environ Microbiol 11:230–238.
90.
Jurgenson CT, Begley TP, Ealick SE. 2009. The structural and biochemical foundations of thiamin biosynthesis. Annu Rev Biochem 78:569–603.
91.
Sañudo-Wilhelmy SA, Gómez-Consarnau L, Suffridge C, Webb EA. 2014. The role of B vitamins in marine biogeochemistry. Ann Rev Mar Sci 6:339–367.
92.
Paerl RW, Bertrand EM, Allen AE, Palenik B, Azam F. 2015. Vitamin B1 ecophysiology of marine picoeukaryotic algae: strain‐specific differences and a new role for bacteria in vitamin cycling. Limnol Oceanogr 60:215–228.
93.
Paerl RW, Sundh J, Tan D, Svenningsen SL, Hylander S, Pinhassi J, Andersson AF, Riemann L. 2018. Prevalent reliance of bacterioplankton on exogenous vitamin B1 and precursor availability. Proc Natl Acad Sci U S A 115:E10447–E10456.
94.
Tang YZ, Koch F, Gobler CJ. 2010. Most harmful Algal bloom species are vitamin B1 and B12 Auxotrophs. Proc Natl Acad SCI U S A 107:20756–20761.
95.
Averianova LA, Balabanova LA, Son OM, Podvolotskaya AB, Tekutyeva LA. 2020. Production of vitamin B2 (Riboflavin) by microorganisms: An overview. Front Bioeng Biotechnol 8: 570828.
96.
Finkenwirth F, Kirsch F, Eitinger T. 2013. Solitary bio Y proteins mediate Biotin transport into recombinant Escherichia coli. J Bacteriol 195:4105–4111.
97.
Van Bibber M, Bradbeer C, Clark N, Roth JR. 1999. A new class of Cobalamin transport Mutants (btuF) provides genetic evidence for a periplasmic binding protein in Salmonella Typhimurium. J Bacteriol 181:5539–5541.
98.
Gozari M, Alborz M, El-Seedi HR, Jassbi AR. 2021. Chemistry, biosynthesis and biological activity of terpenoids and meroterpenoids in bacteria and fungi isolated from different marine habitats. Eur J Med Chem 210:112957.
99.
Mari Ohtsuka SI, Irinoda K, Kukita K, Nagaoka K, Nakashima T. 1987. Azinomycins A and B, new antitumor antibiotics III antitumor activity. J Antibiot 40:60–65.
100.
Zhao Q, He Q, Ding W, Tang M, Kang Q, Yu Y, Deng W, Zhang Q, Fang J, Tang G, Liu W. 2008. Characterization of the azinomycin B biosynthetic gene cluster revealing a different iterative type I polyketide synthase for naphthoate biosynthesis. Chem Biol 15:693–705.
101.
Chevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE, Barns KJ, Book AJ, Cagnazzo J, Carlos C, Flanigan W, Grubbs KJ, Horn HA, Hoffmann FM, Klassen JL, Knack JJ, Lewin GR, McDonald BR, Muller L, Melo WGP, Pinto-Tomás AA, Schmitz A, Wendt-Pienkowski E, Wildman S, Zhao M, Zhang F, Bugni TS, Andes DR, Pupo MT, Currie CR. 2019. The antimicrobial potential of Streptomyces from insect microbiomes. Nat Commun 10: 516.:.
102.
Hasebe F, Matsuda K, Shiraishi T, Futamura Y, Nakano T, Tomita T, Ishigami K, Taka H, Mineki R, Fujimura T, Osada H, Kuzuyama T, Nishiyama M. 2016. Amino-group carrier-protein-mediated secondary metabolite biosynthesis in Streptomyces. Nat Chem Biol 12:967–972.
103.
Covington BC, Spraggins JM, Ynigez-Gutierrez AE, Hylton ZB, Bachmann BO. 2018. Response of secondary metabolism of hypogean actinobacterial genera to chemical and biological stimuli. Appl Environ Microbiol 84: e01125-18.
104.
Koch F, Hattenrath-Lehmann TK, Goleski JA, Sañudo-Wilhelmy S, Fisher NS, Gobler CJ. 2012. Vitamin B (1) and B (12) uptake and cycling by plankton communities in coastal ecosystems. Front Microbiol 3: 363.
105.
Monteverde DR, Gómez-Consarnau L, Cutter L, Chong L, Berelson W, Sañudo-Wilhelmy SA. 2015. Vitamin B1 in marine sediments: pore water concentration gradient drives benthic flux with potential biological implications. Front Microbiol 6:434.
106.
Terrapon N, Lombard V, Drula É, Lapébie P, Al-Masaudi S, Gilbert HJ, Henrissat B. 2018. PULDB: The expanded database of polysaccharide utilization loci. Nucleic Acids Res 46:D677–D683.

Information & Contributors

Information

Published In

cover image mSystems
mSystems
Volume 8Number 431 August 2023
eLocator: e00215-23
Editor: Thomas J. Sharpton, Oregon State University, Corvallis, Oregon, USA
PubMed: 37345931

History

Received: 5 March 2023
Accepted: 19 April 2023
Published online: 22 June 2023

Keywords

  1. Gemmatimonadota
  2. Palauibacterales
  3. PAUC43f
  4. KS3-K002
  5. cosmopolitan
  6. thiamine
  7. marine
  8. microbial ecology

Data Availability

The list of SRA 16S rRNA gene amplicon runs used in this study are available in Table S1. The accession numbers for the MAGs employed in this study can be found in Table S3.

Contributors

Authors

Department of Physiology, Genetics, and Microbiology, University of Alicante, Alicante, Spain
Author Contributions: Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, and Writing – review and editing.
Department of Physiology, Genetics, and Microbiology, University of Alicante, Alicante, Spain
Multidisciplinary Institute of Environmental Studies Ramón Margalef, University of Alicante, Alicante, Spain
Author Contributions: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, and Writing – review and editing.
Department of Physiology, Genetics, and Microbiology, University of Alicante, Alicante, Spain
Author Contributions: Conceptualization, Investigation, Writing – original draft, and Writing – review and editing.

Editor

Thomas J. Sharpton
Editor
Oregon State University, Corvallis, Oregon, USA

Notes

The authors declare no conflict of interest.

Metrics & Citations

Metrics

Note:

  • For recently published articles, the TOTAL download count will appear as zero until a new month starts.
  • There is a 3- to 4-day delay in article usage, so article usage will not appear immediately after publication.
  • Citation counts come from the Crossref Cited by service.

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. For an editable text file, please select Medlars format which will download as a .txt file. Simply select your manager software from the list below and click Download.

View Options

Figures and Media

Figures

Media

Tables

Share

Share

Share the article link

Share with email

Email a colleague

Share on social media

American Society for Microbiology ("ASM") is committed to maintaining your confidence and trust with respect to the information we collect from you on websites owned and operated by ASM ("ASM Web Sites") and other sources. This Privacy Policy sets forth the information we collect about you, how we use this information and the choices you have about how we use such information.
FIND OUT MORE about the privacy policy