Metabolic diversity and co-occurrence of multiple Ferrovum species at an acid mine drainage site

doi:10.21203/rs.3.rs-16414/v1

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Metabolic diversity and co-occurrence of multiple Ferrovum species at an acid mine drainage site

https://doi.org/10.21203/rs.3.rs-16414/v1

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published 18 May, 2020

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Background Ferrovum spp. are abundant in acid mine drainage sites globally where they play an important role in biogeochemical cycling. All known taxa in this genus are Fe(II) oxidizers. Thus, co-occurring members of the genus could be competitors within the same environment. However, we found multiple, co-occurring Ferrovum spp. in Cabin Branch, an acid mine drainage site in the Daniel Boone National Forest, KY.

Results Here we describe the distribution of Ferrovum spp. within the Cabin Branch communities and metagenome assembled genomes (MAGs) of two new Ferrovum spp.. In contrast to previous studies, we recovered multiple 16S rRNA gene sequence variants suggesting the commonly used 97% cutoff may not be appropriate to differentiate Ferrovum spp. We also retrieved two nearly-complete Ferrovum spp. genomes from metagenomic data. The genomes of these taxa differ in several key ways relating to nutrient cycling, motility, and chemotaxis.

Conclusions Previously reported Ferrovum genomes are also diverse with respect to these categories suggesting that the genus Ferrovum contains substantial metabolic diversity. This diversity likely explains how the members of this genus successfully co-occur in Cabin Branch and why Ferrovum spp. are abundant across geochemical gradients.

General Microbiology

Ferrovum

acid mine drainage

carbon fixation

iron oxidation

metagenome

Iron-oxidizing bacteria are common in mine-impacted water and acid mine drainage (AMD) environments which are typically characterized by low pH and high concentrations of dissolved metals [1–3]. Betaproteobacteria of the genus Ferrovum play important roles in biogeochemical cycling in AMD environments including carbon fixation and rapid oxidation of iron[4–9] and could be of value in bioremediation [10]. However, Ferrovum spp. are challenging and labor-intensive to culture and isolate because they often co-occur with heterotrophic Acidiphilium spp. or other Fe(II) oxidizers [7, 11, 12]. Indeed, Ferrovum myxofaciens strain P3 is the only Ferrovum spp. in culture to date, highlighting the value and need for characterization of this enigmatic genus via molecular techniques.

Ferrovum spp. occur in AMD environments with diverse geochemistry. Ferrovum spp. are abundant at sites with pH ranging from less than 3 to 7[4–6, 13, 14] and with iron concentrations ranging from 2 µM[5] to 71 mM [7]. High-throughput sequencing and ‘omics techniques have aided in characterizing the metabolic potential of Ferrovum via non-culture-based techniques[15–18] across these large gradients of Fe and pH. These studies indicate that Ferrovum is a diverse genus composed of six clades (Groups I - VI) of closely related species and strains [18]. Still, only four Ferrvoum genomes are publicly available and these are from Ferrovum Groups I and IV. No genomes have been reported for Groups, II, III, or V. The published genomes and culture studies suggest that Ferrovum spp. are autotrophs that pair Fe(II) oxidation with carbon fixation [7, 15, 16, 18, 19]. Some members of the genus also fix nitrogen and are therefore important to nutrient cycling [7, 19]. However, sequenced Ferrovum genomes have variable genomes especially with genes associated with motility, chemotaxis, biofilm formation, and nitrogen metabolism [15]. Furthermore, Ferrovum dominated communities exhibit morphological differences, including forming large streamers[5, 7, 14] or low extracellular polymeric substances[4] though it can be difficult to identify EPS in low pH environments [16]. These differences may help explain how Ferrovum is able to occupy a large range of pH and ferrous iron concentrations in AMD environments. However, the potential genetic and metabolic diversity within the genus is under-represented because there are no cultured representatives or genomes available for several Ferrovum clades.

In our previous studies of Cabin Branch, we reported the predominance of a single operational taxonomic unit (OTU; 97% similarity) in our 16S rRNA amplicon data most closely related to F. myxofaciens at this AMD site in the Daniel Boone National Forest in southern Kentucky. This taxon was abundant in diverse morphotypes including filament, floc, and ‘brain’ or ‘spongy’ mat biofilms over a pH range of 2.1 to 2.4 and Fe(II) concentrations of 448 to 5200 µmol/L [5]. Based on these data, we employed a cultivation-independent approach to characterize the genetic diversity and functional potential of this taxon using 16S rRNA and by examining the gene content of metagenome assembled genomes (MAGs) from the site.

2.1 Geochemistry. All aqueous geochemistry data are reported in Table 1. pH values were acidic, ranging from 2.90 to 2.97 from the source to Rose Pool. Temperatures were highest at the source (14.6 °C) and decreased down the outflow channel to 8.1 °C at the Rose Pool, consistent with warm groundwater cooling after emerging due to relatively cool ambient temperatures (~ 0 °C at the time of sampling). Conductivity increased from 940 µS/cm at the emergence to 2063 µS/cm at Rose Pool. Dissolved oxygen increased from a low of 77.5 µmol/L at the emergence sampling site to a high of 401 µmol/L (close to saturation) at Rose Pool. Anion concentrations in Cabin Branch were dominated by sulfate (9.51 to 10.08 mmol/L), with lower concentration of chloride (166 to 220 µmol/L). Cation concentrations were highest at the emergence and nearby first outflow sites, and lowest at the Rose Pool, including calcium (1.20 to 0.39 mmol/L), magnesium (0.69 to 0.20 mmol/L), potassium (0.18 to 0.03 mmol/L), and sodium (0.12 to 0.03 mmol/L).

Dissolved inorganic carbon (DIC, predominantly present as dissolved CO₂ due to the low pH) concentration were highest at the emergence (1.67 mmol/L) and decreased down the outflow channel to 0.30 mmol/L at the Rose Pool. DIC δ¹³C values were more negative at the source (δ¹³C = -15.96 ‰) and became more positive down the outflow channel with the most positive values at the Rose Pool (δ¹³C = -11.79 ‰), consistent with preferential loss of ¹²CO₂ through volatilization and uptake by autotrophs. Dissolved organic carbon concentration was consistently low at all sites (36.8 to 44.2 µmol/L) and had similar δ¹³C values (-22.50 to -23.95 ‰).

Potential nutrients measured included NH₄(T), P, Mn, Fe(II), and Fe_(total). NH₄(T) concentration was highest at the first outflow site (50.0 µmol/L) and lowest at Rose Pool (10.7 µmol/L). P concentration was highest at the emergence (9.55 µmol/L) and decreased to below detection limits at the Rose Pool. Mn concentration decreased from the highest value at the emergence (76.84 µmol/L) to the lowest at the Rose Pool (40.23 µmol/L). Total iron (Fe_total) exhibited a similar trend as Mn and P, with the highest concentration at the emergence (4684 µmol/L) and the lowest at the Rose Pool in the outflow (249.6 µmol/L), though Fe(II) had concentrations significantly lower than Fe_total with the highest concentration at the outflow site downstream of the emergence (882 µmol/L) and the lowest at the Rose Pool (11.2 µmol/L). The discrepancy in the Fe(II) data may be due to the colorimetric tests which are optimized for 20 °C, whereas Fe_total is measured via ICP-MS or ICP-OES with samples that are acidified with concentrated HNO₃ to keep metals in solution.

2.2 Inorganic carbon assimilation. Autotrophic incorporation of added ¹³CO₂ was quantified in mesocosm experiments using treatments carried out under either light or dark (wrapped in aluminum foil) to characterize photoautotrophic and chemoautotrophic uptake, respectively. At the emergence, both green and white biomass was observed and autotrophic incorporation of ¹³CO₂ was quantified in both biofilm types. Because we assumed the majority of uptake in the green biomass would be due to photoautotrophy and we could not control for oxygen production in this treatment, we performed one set of microcosms on the bulk biofilm in the light and then separated the biofilm types (green or white) for microcosms performed in the dark. In the

light treatments of full biofilms (green and white biomass) C-uptake rates averaged 32.547 (± 7.949) and 21.691 (± 6.36) µg C uptake/g C_biomass/hr after one- and two-hour incubations, respectively. Dark treatments of the green biomass returned averages of 1.632 (± 0.454) µg C uptake/g C_biomass/hr after one hour and 0.746 (± 0.174) µg C uptake/g C_biomass/hr after two hours, while dark treatments of the white biomass returned averages of 0.305 (± 0.403) µg C uptake/g C_biomass/hr after one hour and 0.662 ( ± 0.240) µg C uptake/g C_biomass/hr after two hours. Biofilms with both green and white biomass together from the limestone-lined channel immediately downstream of the emergence returned average C-uptake rates of 35.947 (± 5.516) and 15.085 (± 0.106) µg C uptake/g C_biomass/hr for light treatments of one and two hours respectively and 2.586 (± 0.148) and 1.245 (± 0.035) µg C uptake/g C_biomass/hr for dark treatments after one and two hours. Rose Pool sediment incubations returned average C-uptake rates of 1.372 (± 0.061) µg C uptake/g C_biomass/hr for light treatments of two hours and 0.066 (± 0.132) µg C uptake/g C_biomass/hr for dark treatments of two hours. All C-uptake results are reported in Supplementary Table S1.

2.3 16S rRNA. 32,124 quality-controlled sequences were retrieved from the emergence, 41,988 from the limestone-lined channel, and 37,546 from the Rose Pool. These sequences were affiliated with 82, 276, and 286 denoised sequence variants (DSVs) respectively. Cabin Branch contains nine DSVs closely related to Ferrovum myxofaciens (Figure 2). Four DSVs are present in the emergence sample, five in the limestone lined channel, and eight in Rose Pool (Figure 2, 3). DSVs affiliated with Ferrovum are most abundant in the limestone lined channel where they compose 32.8% of the community and least abundant in the Rose Pool where they compose 5.0% of the community. Ferrovum DSVs compose 29.6% of the emergence community. The abundance of each DSV varies between samples. DSVs 4, 10, 12, and 81 are the most abundant Ferrovum DSVs in the Emergence community, 4, 10, 12, 34, and 59 in the limestone lined channel, and DSVs 56 and 81 in the Rose Pool (Figure 3). The DSVs are phylogenetically placed within Ferrovum groups I, II, III and between Groups IV and V (Figure 2).

2.4 Metagenomics. 74,021,852 to 101,886,314 metagenomic reads were retrieved per sample with an average GC content of 55% (Supplementary Table S2). Fourteen bins were retrieved that were phylogenetically placed within the genus Ferrovum. The resulting MAGs ranged from low- to high-quality drafts based on quality and contamination, but lack 16S rRNA gene sequences [20]. Five of these bins were retrieved from individually assembled metagenomes and 9 from the combined assembly. Based on average nucleotide identity (ANI), each of the Ferrovum bins from the individual assemblies was also present in the combined assembly and one bin was represented in the emergence, terrace, and combined assemblies (Supplementary Table S3). For MAGs that were present in more than one assembly, the highest quality bin from that MAG was used for further analysis (Table 2). Nine unique Ferrovum MAGs were present in the dataset. These MAGs shared 68.5% to 88.5% ANI with each other and between 68.6% and 99.4% ANI with published Ferrovum genomes (Supplementary Table S3). Based on a concatenated tree of ribosomal protein genes, eight of the nine MAGs occupy the phylogenetic space between the Group I Ferrovum sp. JA12 and sp. PN-J185 and the Group IV Ferrovum sp. Z-31 and the type strain (Figure 4). Two MAGs, MAG-4 and MAG-7 are > 5% complete and have limited contamination. Therefore, we have described them in more detail below.
2.5 MAG-4. MAG-4 is represented by a single bin which was retrieved from the combined assembly and is the most complete MAG recovered in this study. It is 95.7% complete with 0.4% contamination. MAG-4 contains 2250 gene coding sequences, 1466 of which were annotated by GhostKoala (Table 2). MAG-4 shares 69 -74% ANI with the other MAGs and the published Ferrovum genomes (Supplementary Table S3). The MAG is phylogenetically positioned between the Group I Ferrovum spp. JA12 and PN-J185 and the Group VI type strain and sp. Z-31 (Figure 4)

2.5.1 Energy Metabolism. MAG-4 contains three homologs of the high molecular weight, Cyc-2-like protein previously found in Ferrovum spp. (Figures 5, 6). This protein is thought to facilitate the oxidation of Fe(II) to Fe(III)[16]. The MAG also contains homologs of the genes necessary for oxidative phosphorylation using a B/A type NADH dehyrodgenase (nuoA-nuoN) and succinate dehydrogenase (sdhA-D), a E/B/A type cytochrome c reductase, cbb₃ and bd type terminal oxidases, and an F-type ATPase (Figure 6).

2.5.2 Carbohydrate Metabolism. MAG-4 encodes the enzymes necessary to fix carbon via the Calvin-Benson cycle except the enzyme needed to form ribose 5-phosphate from sedoheptulose-7 phosphate. The MAG also contains the genes for a nearly-complete TCA cycle. It contains a nearly complete suite of genes for glycolysis.

2.5.3 Nutrient Acquisition. MAG-4 encodes the structural proteins necessary for nitrogen fixation (NifHDK) and denitrification (NasAB, NirBD), but lacks the genes necessary to import inorganic nitrogen in to the cell (Figure 6). The MAG contains many of the genes necessary for assimilatory sulfate reduction but is missing PAPSS which reduces ammonium persulfate (APS) to 3-phosphoadenosine-5-phosphosulfate (PAPS). It also contains homologs of DsrA and DsrB which can be used to reduce sulfite to sulfide or to oxidize sulfide to sulfite (Figure 6). The MAG also contains the genes necessary to import inorganic phosphate and use it in biosynthetic pathways (PST, ppx, ppk).

2.5.4 Motility. MAG-4 contains all the genes necessary to construct flagella except motXY which encode proteins are involved in flagellar rotation and fliJT. The MAG also contains the genes necessary for chemotaxis (cheABCD, cheR, and cheVWZ)

2.6 MAG-7. MAG-7 is represented by bins retrieved from the emergence, limestone lined channel, and co-assembled metagenomes (Table 1). The highest quality bin is from the limestone lined terrace and is 86.7% complete with 0.63% contamination. The other two bins are 72.6% and 70.9% complete with 0% contamination. Because the most complete bin is only 86.7% complete, when genes were absent from that bin, we searched the other two less complete bins

2.6.1 Energy Metabolism. MAG-7 contains one homolog to the high-molecular weight Cyc2 type protein found in other Ferrovum taxa (Figure 5, 6). The Cyc2 sequence is phylogenetically placed as a sister group to those from the type strain and F. sp. Z31 (Figure 5). MAG-7 also contains homologs of the genes necessary for oxidative phosphorylation with a B/A type NADH dehydrogenase (nuoA-nuoN) and succinate dehydrogenase (sdhA-D), an E/B/A type cytochrome c reductase, bo, bd, and cbb₃ type terminal oxidases, and an F-type ATP-ase.

2.6.2 Nutrient Acquisition. MAG-7 encodes the genes necessary for reducing nitrate to nitrite (nasAB), but not those to further reduce the nitrate to ammonia (nirBD; Figure 6). The MAG also contains the genes necessary to import inorganic phosphate and use it in biosynthetic pathways (PST, ppx, ppk). MAG-7 does not encode protein necessary for nitrogen fixation and lacks the genes necessary to transform urea to ammonia.

2.6.3 Carbohydrate Metabolism. MAG-7 contains most of the genes necessary for carbon fixation via the Calvin-Benson cycle but like MAG-4 does not encode the enzyme needed to form ribose 5-phosphate from sedoheptulose-7 phosphate. The MAG encodes the proteins necessary for a complete TCA cycle. It contains a nearly complete suite of genes for glycolysis.

2.6.4 Motility. MAG-7 contains all the genes necessary to synthesize a functional flagellum except motX and motY which are also missing from MAG-4. The MAG also contains genes involved in bacterial chemotaxis including mcp, aer, tar, cheABCD, cheR, and cheVY, but it does not appear to contain cheW and cheZ which are present in MAG-4.

3.1 Co-occurrence of Ferrovum spp. in Cabin Branch Communities. All known Ferrovum spp. are obligate, Fe(II) oxidizing autotrophs that use O₂ as a terminal electron acceptor [7, 16, 17]. Therefore, co-occurring Ferrovum spp. would presumably compete for common resources if they exploited identical resources, possibly including Fe(II) and oxygen, essential nutrients (e.g., N and P), and trace elements. At Cabin Branch, Fe(II) is present in mmol/L amounts constantly supplied by mine-impacted sources and is unlikely to be limiting. Dissolved oxygen is also present although the concentration is lower at the emergence and increases downstream. Ammonia and phosphate were detected at all sites. Alternately , gene content differences could explain co-existence of Ferrovum populations at Cabin Branch. Co-occurring strains of the same species have been found in a variety of environments including cyanobacterial blooms [21], and solar saltern/crystallization ponds [22, 23]. In some communities, successful co-occurrence results from genomic variation or differential gene expression between the strains of the co-occurring species. For example, six co-occurring strains of the Epsilonproteobacteria Lebetimonas acquired new functional genes via lateral gene transfer in sea mounts in the Mariana Arc [24]. Similarly, strains of Salinabacter ruber contain hypervariable regions in their genome which are associated with differences in surface properties [22]. Co-occurring strains of S. ruber also express different metabolite pools [23]. These differences likely allow co-occurring strains to avoid competing within the environment [22, 24]. Like these communities, the Ferrovum spp. at Cabin Branch may be able to co-exist due to variations in their genomes or in gene expression.

Ferrovum spp. compose over 25% of the bacterial community in the emergence and limestone lined channel communities and 5% of the Rose Pool community, based on the 16S rRNA amplicon libraries. The relative abundance of Ferrovum spp. appears to increase with the rate of non-photosynthetic inorganic carbon assimilation (Figure 7). These data suggest that these populations are important primary producers in the environment because Ferrovum spp. are the only abundant chemoautotroph and therefore, likely the main contributor to dark carbon fixation. Our previous work on samples collected in 2015 reported a single Ferrovum OTU in Cabin Branch. However, that analysis relied on a definition of 97% identity in 16S rRNA sequences [5] which is the common cutoff for delineating species based on 16S rRNA gene sequence identity level, but likely underestimates bacterial diversity [25]. Currently, methods using unique sequences are becoming more common and may be more robust [26, 27]. Using a DSV approach on samples collected in 2017 as part of this research, we recovered nine unique Ferrovum taxa: four Ferrovum DSVs co-occur in the emergence community, five in the limestone lined channel, and eight in the Rose Pool sediments (Figures 2, 3). Genome-based studies using average nucleotide identity (ANI) and multilocus phylogenetic analysis are more robust than 16S rRNA-based analyses and are the best practice for assigning species affiliations in genomes [28]. Metagenomic data from Cabin Branch support the presence of multiple Ferrovum spp. at each site, suggesting that the diversity of Ferrovum spp. at AMD sites globally may be underestimated because studies have traditionally used an OTU approach [4, 5].

To co-occur successfully, the strains may partition the niche to use different resources as is seen in closely-related taxa or intraspecific competitors in eukaryotic systems [29–31]. If phylogenetic distance correlates with metabolic diversity, we might expect co-occurring strains of Ferrovum would be distantly related. Indeed, those that co-occur in the Cabin Branch communities span the Group I, II, and III Ferrovum clades and the limestone lined channel and Rose Pool also include DSVs that are not placed within a clade (Figure 2). However, some co-occurring Ferrovum spp. are closely related to one another. Multiple DSVs from the same Ferrovum group are found in the same microbial community. For example, two Group II DSVs are found in all three communities sampled and the Rose Pool community contains three closely related DSVs phylogenetically placed basally to the Group V Ferrovum (Figure 2). Therefore, phylogenetic distance may not determine whether taxa can coexist. However, published Ferrovum taxa vary in their gene content with respect to genes associated with motility and nitrogen cycling among others, likely due to lateral gene transfer [15, 16, 18]. Therefore, we can examine how genome content may allow co-occurring taxa to partition resources and avoid competition.

3.2 Relating two new Ferrovum spp. (MAG-4 and MAG-7) to published species. Here, we compared our Ferrovum MAGs to two published Ferrovum species from Ferrovum Groups I (F. myxofaciens sp. P3G) and IV (Ferrovum JA12) to explore if genomic variability could contribute to the co-occurrence of these taxa. Our two Ferrovum MAGs and the Group I and Group IV genomes share numerous features, but differ in key ways relating to energy metabolism, nitrogen cycling, and motility. Because the MAGs are incomplete, we only consider a feature to be missing if all of genes that code for key enzymes related to that feature are absent from the MAG.

3.2.1 Energy metabolism. All cultured Ferrovum spp. can grow autotrophically via iron oxidation [7, 15, 16, 18, 19] and we predict that the two MAGs presented here share that ability. Each genome contains high molecular weight, Cyc2-like proteins found in other Ferrovum taxa [16]. In the acidophilic Fe oxidizers Acidithiobacillus ferrooxidans and a Leptospirillum spp. Cyc2 is an Fe oxidase [32, 33]. Cyc2-like proteins are also highly expressed in neutrophilic Fe(II) oxidizers [34]. Therefore, this protein likely plays a role in Fe(II) oxidation in Ferrovum. In Acidithiobacillus ferrooxidans and Gallionella ferruginea, Fe(II) oxidation is paired with carbon fixation. The MAGs encode a nearly full suite of genes for carbon fixation via the CBB pathway but differ in the types of terminal oxidases they use for oxidative phosphorylation (Figure 6). MAG-7, F. myxofaciens sp. P3G, and Ferrovum sp. JA12 all contain cytochrome bo₃ type oxidases, but MAG-4 does not contain this type of cytochrome. Similarly, MAGs 4 and 7 and Ferrovum JA12 contain a cytochrome bd-type quinol oxidase which is not present in F. myxofaciens sp. P3G (Figure 6). Terminal oxidases differ in their affinity for oxygen and are differentially expressed depending on the concentration of oxygen [48, 49]. For example, E. coli express cytochrome bo₃ under oxic conditions, and cytochrome bd, which has a higher affinity for O₂, under oxygen limiting conditions [35]. MAG-4 lacks genes for cytochrome bo₃ which may indicate that this taxon does not colonize well-oxygenated environments that may be more suitable for MAG-7, F. myxofaciens sp. P3G, and Ferrovum sp. JA12. Similarly, MAGs 4 and 7 and Ferrovum sp. JA12 may be able to colonize microaerobic environments that are not ideal for F. myxofaciens sp. P3G which lacks high affinity terminal oxidases. In Cabin Branch, these differences may lead to MAGs 4 and 7 preferentially inhabiting different portions of the biofilm with MAG-7 inhabiting the better oxygenated surface of the biofilms and MAG-4 residing in more microaerobic niches within the biofilm.

3.2.2 Nitrogen Cycling. Ferrovum myxofaciens sp. P3G can fix nitrogen, but this trait is not conserved within the genus. Neither Ferrovum Z-31 nor PN-J185 are capable of nitrogen fixation [15, 18]. At Cabin Branch, MAG-4 encodes the enzymes necessary for N-fixation (NifH, D, and K) and the hydrolysis of urea (Figure 6) while MAG-7 does not. The ability to fix nitrogen would allow MAG-4 to colonize low NH₄environments. Additionally, it may influence where MAG-4 resides in the biofilm community as nitrogenase proteins are oxygen sensitive [36]. Therefore, MAG-4 may preferentially reside in low-oxygen environments that provide enough oxygen to perform Fe(II) oxidation, but at low enough concentrations to protect nitrogenase from oxidative damage. This idea is supported by the presence of cbb₃ and bd type oxidases in MAG-4 that are preferentially expressed in microaerobic conditions as discussed above.

3.2.3 Motility. Ferrovum spp. vary in their ability to be motile. Ferrovum sp. JA-12 and the closely related Ferrovum sp. PN-J185 lack genes both for flagella and for chemotaxis whereas F. myxofaciens sp. P3G contains the genes necessary for a functional flagellum [9, 15–17, 19]. The MAGs from Cabin Branch are more similar to F. myxofaciens sp. P3G in this respect, because they encode the genes necessary for the synthesis of a functional flagellum. MAG-7 and F. myxofaciens sp. P3G also contain homologs of tar, a chemotaxis related protein that responds to the presence of aspartate or maltose and aer which produces a protein that allow a chemotactic response to redox state [38, 39] potentially indicating that these taxa can respond to different environmental cues than MAG-4 which lacks these genes.

We recovered co-occurring Ferrovum taxa with distinct metabolisms from the emergence and outflow of an AMD site. A DSV-based approach suggested multiple Ferrovum taxa co-occur which contrasts to previous OTU-based studies [5]. These data and the Ferrovum genomes recovered from metagenomic data highlight the limits of using OTU clustering approaches in identifying discrete species. We also identified the metabolic diversity among closely related Ferrovum spp. that likely facilities the co-occurrence of these taxa. Specifically, the differences in nutrient cycling, motility, and chemotaxis may facilitate co-occurrence without direct competition for resources. These physiological differences may also help to explain why Ferrovum spp. are nearly ubiquitous in AMD environments despite the geochemical diversity of these environments. These data help fill in gaps of missing diversity in the Ferrovum clade which are ubiquitous in AMD environments but difficult to culture. Finally, our data highlight genetic diversity necessary for closely-related co-occurring species.

5.1 Site location and sampling. Cabin Branch is a site in the Daniel Boone National Forest in Kentucky near the border with Tennessee. Limestone was added to the channel as a passive remediation strategy and groundwater flows out from an emergence and across a limestone-lined channel before entering a pond, the Rose Pool (Figure 1). The microbial communities within Cabin Branch are predominantly the Fe(II)-oxidizing taxon Ferrovum myxofaciens [5] Samples were collected on March 16, 2017 between 10 am and 2 pm. Sample collection was approved by and performed in collaboration with the staff at Daniel Boone National Forest.

5.2 Geochemical analyses

5.2.1 Aqueous geochemistry. Temperature and pH were measured onsite using a WTW 330i meter and probe (Xylem Analytics, Weilheim, Germany) and conductivity was measured with a YSI 30 conductivity meter and probe (YSI Inc., Yellow Springs, OH, USA). Total ammonia (NH₄(T) = NH₃ + NH₄⁺) and ferrous iron (Fe(II)) concentration were determined using Hach DR1900 Portable Spectrometers (Hach Company, Loveland, CO) using the salicylate method (Hach Method 8155).

Water samples were collected with a 140 mL syringe directly after rinsing with sample water three times before filling. Water was filtered through 0.8/0.2 μm Supor^® syringe filters and dispensed into the appropriate sample bottles following a 10 mL flush to minimize contamination from the filter. Samples for ion chromatography (IC) analysis of major anions (Thermo Scientific Dionex ICS 5000+ ion chromatography system) were stored in 15 mL polypropylene centrifuge tubes. Samples for inductively coupled plasma optical emission spectroscopy (ICP-OES) analysis (Thermo Scientific iCAP 6000 series ICP-OES) of major cations and for inductively coupled plasma mass spectrometry (ICP-MS) analysis (Thermo Scientific X Series 2 ICP-MS) for trace elements were stored in acid-washed (3 day soak in 10 % TraceMetal Grade HNO₃ (Fisher Scientific, Hampton, NH, USA) followed by triple rinsing with 18.2 MΩ/cm deionized water) 15 mL polypropylene centrifuge tubes and acidified with 400 μL of concentrated, OmniTrace Ultra™ concentrated HNO₃ (EMD Millipore, Billerica, MA, USA). Field blanks were taken using 18.2 MΩ/cm deionized water transported to the field in 1-liter acid washed Nalgene bottles. IC, ICP-OES, and ICP-MS analyses were carried out by the Analytical Geochemistry Laboratory in the Department of Earth and Environmental Sciences at the University of Minnesota.

Samples for dissolved inorganic carbon (DIC) analysis were filtered into a gas-tight syringe and then injected into Labco Exetainers® (Labco Limited, Lampeter, UK) pre-flushed with He, with excess He removed following introduction of 4 mL of filtered sample with minimal agitation. Samples were inverted and stored on ice until returned to the lab, where 1 mL of concentrated H₃PO₄ was added and the samples shipped to the Stable Isotope Facility (SIF) at the University of California, Davis for analysis. DIC analysis for concentration and ¹³C isotopic signal using a GasBench II system interfaced to a Delta V Plus isotope ratio mass spectrometer (IR-MS) (Thermo Scientific, Bremen, Germany) with raw delta values converted to final using laboratory standards (lithium carbonate, δ¹³C = -46.6 ‰ and a deep seawater, δ¹³C = +0.8 ‰) calibrated against standards NBS-19 and L-SVEC. Samples for dissolved organic carbon (DOC) analysis were filtered through a 0.2 µm polyethersulfone syringe filter that had been flushed with ~ 30 mL of sample and then ~ 40 mL put into a 50 mL centrifuge tube and then immediately flash-frozen on dry ice and kept frozen in the dark until analysis at the SIF. DOC analysis for concentration and ¹³C isotopic signal were carried out using O.I. Analytical Model 1030 TOC Analyzer (O.I. Analytical, College Station, TX, USA). The standard deviation for DIC is determined from variation in check standards run by SIF. The standard deviation for DOC is determined from replicate analyses of each sample.

5.2.2 CO₂ assimilation. A microcosm-based approach was employed to assess the potential for inorganic carbon uptake in situ through the addition of NaH¹³CO₃. In situ microcosms were performed between noon and 4 PM under full or partial sun. Samples were collected using pre-sterilized spatulas or forceps and ~ 500-mg was placed into pre-combusted (12 h, 450°C) serum vials. Samples were overlaid with spring water from the collected site and serum vials were capped with gas-tight black butyl rubber septa. Assays were initiated by addition of NaH¹³CO₃ (100 µM final concentration) (Cambridge Isotope Laboratories, Inc., Andover, MA, USA). All assays were performed in triplicate. At the emergence, both green and white filaments were present. We performed assays on each by sampling predominantly green filaments and predominantly white filaments. We assumed the majority of uptake in the green biomass would be due to photoautotrophy and thus performed one set of microcosms on the bulk filaments/biofilm (green + white) in the light and then separated the biofilm types (green or white) for microcosms performed in the dark.

We assessed the potential for photoautotrophic (light) and chemoautotrophic (dark) NaH¹³CO₃ uptake. Assays were stopped by flash freezing vials on dry ice. All vials were stored at -80 °C until processed (described below). Reported values of ¹³C-labelled DIC uptake (carbon fixation rates) reflect the difference in uptake between the biomass in the assays that received NaH¹³CO₃ and the natural abundance biomass samples described below. For comparisons between mean ¹³C uptake rates, a one-way ANOVA followed by post hoc pairwise comparisons between treatments was conducted using a Turkey honest significant difference (HSD) within the R software package (R version 3.3.2). Mean rates with p-values < 0.05 were considered significantly different.

5.2.3 C and N concentration and stable isotope signals. Natural abundance samples were collected at the time of sampling, flash frozen on dry ice, stored in liquid N₂ for transport and stored at -80 °C until processed. Assays were thawed and biomass was washed with HCl (1 M) to remove any extra ¹³C-labelled DIC, triple washed with 18.2 MΩ/cm deionized water, then dried (60°C for three days). Dried biomass was ground/homogenized with a cleaned mortar and pestle (ground with ethanol silica slurry, triple rinsed with 18.2 MΩ/cm deionized water, dried). Dried and ground samples for determination of C and N concentration and stable isotope signal were weighed and placed into tin boats, sealed, and analyzed via an Elementar pyrocube elemental analyzer (EA) periphery connected to an Isoprime 100 continuous flow IRMS (IR-MS) at the University of Minnesota. Linearity corrections were made using NIST Standard 2710, and δ¹³C values were calibrated using reference standards USGS-40 and USGS-41 and checked with a laboratory working standard (glycine). Total uptake of DIC was calculated using DIC numbers determined for the source water (described above). All stable isotope results are given in delta formation expressed as per mil (‰). Carbon stable isotopes are calculated as:

δ¹³C= [((R_a)_sample/(R_a)_standard)-1] × 10³, (1)

where R_a is the ¹³C/¹²C ratio of the sample or standard and are reported versus the Vienna Pee Dee Belemnite (VPDB) standard.

Precautions were taken to minimize cross contamination of natural abundance samples with incubation assays. Natural abundance samples were processed and analyzed in turn, prior to incubation assays. All laboratory processing and weighing equipment was cleaned with 80% ethanol between each sample. Standard checks and blanks were run within each analysis batch (49 samples and standards per batch) to check for memory effects or cross contamination of samples, with none detected.

Assimilation rates were calculated from the following parameters: Absolute isotopic stable carbon ratios were used to determine the difference between the total amount of ¹³C in natural abundance samples and incubation assay replicates. The difference represents total mass of ¹³C-labelled DIC taken up during incubations. Incubation duration times were recorded in the field. Using the organic carbon content, the uptake rate is then calculated from the total µg C taken up divided by the grams of organic C per gram of sediment, and that was divided by the number of hours the incubation was carried out over (typically ~ 2 hours).

5.3 Molecular analyses

5.3.1 DNA extraction. Samples for DNA extraction were collected at the time of sampling using sterile spatulas. Samples were flash frozen on dry ice, stored in liquid N₂ for transport and stored at -80 °C until processed. Triplicate samples were collected from each sampling site. DNA was extracted from ~250 mg of biomass using a DNeasy PowerSoil Kit (Qiagen, Carlsbad, CA, USA) according to the manufacturer’s instructions. DNA was extracted from each replicate sample (n=3) and the concentration of DNA was determined using a Qubit dsDNA HS Assay kit and a Qubit 3.0 Fluorometer (Invitrogen, Burlington, ON, Canada). The concentration of DNA in the replicates was within 5% for each sample. Equal volumes of each extraction were pooled, and the concentration of the pooled extract was determined as described above. As a negative DNA extraction control, DNA was extracted from the filter used for the field blank water sample (described above). No DNA was detected in the control and sequencing failed to generate amplicons (see below for amplicon sequencing details).

5.3.2 DNA sequencing. Amplicons were sequenced at the University of Minnesota Genomics Center (UMGC) with MiSeq Illumina 2 × 300 bp chemistry using the primers 515F (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3’) and 806r (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3’) targeting V4 hypervariable region of bacterial and archaeal 16S SSU rRNA gene sequences [40]. Amplicon libraries were created by UMGC following their improved protocol for library preparation which enables detection of taxonomic groups that often go undetected using existing methods [41].Each sample was sequenced once. For metagenomic sequencing, total DNA was submitted to the UMGC and sequenced using HiSeq2500 High-Output 2 x 125 bp chemistry. The three samples were samples on a single lane.

5.3.3 16S rRNA analysis. 16S rRNA sequences from Cabin Branch were used to examine community composition. Primers and unpaired sequences were removed using trimmomatic [42]) The surviving reads were processed in dada2 (v.1.4) following the pipeline tutorial [27]. Briefly, forward reads were trimmed to 210 bp and reverse reads were trimmed to 120 bp based on their quality profiles. Sequences with ambiguous bases and those with more than 2 expected errors were removed. Error rates were estimated using the learnErrors command. Sequences were dereplicated using the derepFastq command and the unique sequence variates were inferred using the dada command. Forward and reverse reads were merged using mergePairs. Contigs shorter than 250 or longer than 256 bp and chimeric sequences were removed. The surviving unique, denoised sequences are referred to as denoised sequence variants (DSVs). Taxonomy was assigned using the Silva training set [43] Eukaryotic sequences and those unclassified at the domain level were removed. The closest cultured and environmental relatives were identified using BLASTN [44].

We retrieved 1,589 aligned, nearly-full length Ferrovum sequences from the Silva non-redundant database to serve as a reference alignment for the analyses described below. A 16S rRNA phylogeny was constructed by retrieving the sequences used by Ullrich et al. [18] with Ferritrophicum radicicola, Nitrospovibrio tenuis, and Nitrospira lenta used as an outgroup.. These sequences were aligned using MAFFT on the Cipres Science Gateway [45] using the aligned Ferrovum sequences from the Silva database as a reference alignment. A tree of the smaller subset of sequences was constructed in RAXML-HPC2 on XSEDE [46] also on the Cipres Science Gateway. Sequences of DSVs classified as Ferrovum were added to the alignment using the –addfrags option in MAFFT [47]. Non-full-length sequences were added to the tree using the evolutionary placement algorithm. Trees were rooted and visualized in the interactive tree of life [48]. Raw sequences were uploaded to the NCBI Sequence Read Archive under BioProject PRJNA554371.

5.3.4. Metagenomic analysis. Individual metagenomes were assembled and a co-assembly of all metagenomes was constructed following the “tutorial on assembly-based metagenomics” [49].Trimmed, quality-controlled sequences were assembled using MegaHit [50] using default parameters except minimum contig length, which was set at 1000 base pairs. Reads were mapped to the assembly using bowtie2 [51] and depth was calculated using the jgi_summarize_bam_contig_depths command in Anvi’o [52]. Contigs were binned using default parameters in metabat using [53]. Bin completeness was determined with CheckM [54]. Protein coding regions were identified with prodigal (within CheckM)[55] and GhostKoala was used to annotate protein coding sequences [16, 56]. Pathways were considered to be absent from the taxon when none of the key genes associated with that pathway were present in the MAG.

Each bin was uploaded to KBASE [57] and annotated with Prokka using the “Annotate Assembly and Re-annotate Genomes with Prokka (v1.12)” app. A concatenated gene tree containing each bin and four published Ferrovum spp. genomes was constructed using the “Insert Set of Genomes Into Species Tree 2.1.10” app. This app uses up to 49 ribosomal and single-copy genes to construct a phylogenetic tree. MAGs that were more closely related to Ferrovum spp. than other taxa were selected for further analysis. The pairwise ANI between each bin and published Ferrovum genomes was calculated using anvi-compute-ani in Anvi’o. If MAGs shared >98% pairwise ANI and were phylogenetically cohesive, we considered them to be representing the same populations and the bin with the highest completeness was chosen for further analysis. Raw reads, assemblies, and MAGs were uploaded to the NCBI Sequence Read Archive under BioProject PRJNA554371. The abundance of Ferrovum in each metagenomes using One Codex [58].

16S rRNA. A custom blast database containing the 16S rRNA gene from the Ferrovum myxofaciens type strain (NR_117782.1) using the makeblastdb command in BLAST+. Each of the Ferrovum bins were searched for 16S rRNA genes using BLAST and a cutoff value of E-30.

Cyc-2. A custom blast database of the high molecular weight Cyc-2 like proteins was constructed using the methods described above. Each of the Ferrovum bins were searched for the Cyc-2-like protein identified by Ullrich et al.,[16] by blasting translated nucleotide sequences against the blast database with an E-value of E-120. The retrieved sequences, database sequences, and a sequence for Cyc-2 from an Acidithiobacillus sp. (AVK42811.1) were aligned using MAFFT and a maximum likelihood tree was constructed as described above. The tree was rooted on Acidithiobacillus and visualized in iTOL.

5.3.5. Comparison with other Ferrovum genomes. Existing Ferrovum genomes are located within either the Group I or Group IV Ferrovum clades [18]. The genome for Ferrovum sp. JA12 (Group I) and Ferrovum myxofaciens (Group IV) were downloaded from NCBI. CheckM was used to determine genome completeness, and within CheckM, prodigal was used to identify protein coding sequences. Ghost KOALA was used to annotate protein coding sequences using the genus_prokaryotes database. Protein coding sequences were also annotated with prokka. We then manually compared the gene content of MAGs to that of existing Ferrovum genomes.

AMD

Acid mine drainage

ANI

Average nucleotide identity

APS

Ammonium persulfate 3-phosphoadenosine-5-phosphosulfate

DIC

Dissolved inorganic carbon

DSV

Denoised sequence variants

ICP-MS

Inductively coupled plasma mass spectrometry

ICP-OES

Inductively coupled plasma atomic emission spectroscopy

MAG

Metagenome assembled genomes

PAPS

3-phosphoadenosine-5-phosphosulfate

TCA

Tricarboxylic acid cycle

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

Availability of data and materials

All analyses tools used in the study are publicly available. 16S rRNA and functional genes of closely related species and outgroups were downloaded from NCBI databases and accession numbers are provided for these sequences. Raw reads and assembled scaffolds for the metagenomes and MAG sequences as well as the amplicon libraries have been deposited in the NCBI under BioProject PRJNA554371.

COMPETING INTERESTS

The authors declare that they have no competing interests.

FUNDING

This work was supported by the University of Cincinnati and the University of Minnesota. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. The funders had no role in the design of the study and collection, analysis, and interpretation of data.

Authors’ contributions

JRH, CLG, and TLH contributed to the conception of the project, data analysis interpretation of results, creation of figures, writing and editing the manuscript. JRH and TLH performed sample collection and sample analysis.

ACKNOWLEDGEMENTS

We are grateful to the staff of the National Forest Service and Daniel Boone National Forest, especially Margueritte Wilson and Claudia Cotton, for the advice and insight regarding mine locations. We thank A. Gangidine, M. Berberich, R. Jain, and C. Schuler for assistance in field sampling and processing samples in the laboratory.

Supplemental Table S1. Carbon uptake experiment results.

Supplemental Table S2. Sequencing and assembly statistics for metagenomes used in this research.

Supplemental Table S3. Average nucleotide identity of MAGs retrieved from the Cabin Branch metagenomes.

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Metabolic diversity and co-occurrence of multiple Ferrovum species at an acid mine drainage site

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