Contrasting microbial communities drive iron cycling across global biomes

Contrasting microbial communities drive iron cycling across global biomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Contrasting microbial communities drive iron cycling across global biomes Dimitar Epihov, Casey Bryce This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4248419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The global iron (Fe) cycle governs important aspects of biosphere function by defining Fe availability thus supporting productivity of terrestrial and ocean ecosystems. However, the link between soil microbiome function to global patterns in terrestrial iron cycling remains poorly investigated. Here, we developed a novel database termed IR on cyc le A nnotation (IRcyc-A) targeted at discovering and annotating Fe cycle genes within omics data that we validated against known localized patterns of iron cycling. We leveraged this new tool to analyse the Fe cycle of over 220 publicly available soil metagenomes and metatranscriptomes encompassing a wide range of biomes on Earth. We show that the greatest abundance of Fe(III)-reduction and Fe(II)-oxidation genes were attributed to Acidobacteriota and were most abundant in the microbiomes of peatlands and iron sulfide soils, respectively. This is consistent with the high levels of dissolved Fe recorded in rivers draining such areas. In contrast, genes encoding the biosynthesis of siderophores deployed in iron sequestration in response to Fe deficiency peaked in agroecosystems with the majority assigned to Actinomycetota. Siderophore synthesis genes were negatively correlated with Fe(III)-reduction and Fe(II)-oxidation genes, supporting the view of divergent communities under low and high iron availability. Our findings highlight how iron availability shapes terrestrial microbial communities and how microbial processes can in turn contribute to global patterns in terrestrial Fe and C cycling. Earth and environmental sciences/Biogeochemistry Biological sciences/Microbiology/Environmental microbiology/Soil microbiology global iron cycle Fe cycle soil microbiome iron oxidation iron reduction Acidobacteria siderophores Actinomycetes biomes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Iron is indispensable to terrestrial and ocean productivity as many important biochemical reactions utilize iron-containing co-factors 1 . Iron is an element essential to chlorophyll biosynthesis, biological nitrogen fixation and its nitrogenase enzymes, cellular respiration and its cytochrome proteins 2 . Despite being the most abundant redox-active element in Earth’s crust, the modern oxygen-rich atmosphere of our planet renders iron highly unavailable due to oxidation and subsequent precipitation of iron into insoluble iron hydroxide and oxide phases. Although iron metabolism is believed to be among the most ancient and prevalent forms of metabolism among early life on Earth, particularly prior the Great oxidation event, in modern ecosystems the scarcity of soluble iron has had profound impact to the biosphere 1 . First, low levels of available iron likely necessitated aerobic organisms to independently evolve the ability to strongly chelate iron by producing high-affinity organic ligands (siderophores of bacteria, siderophores of fungi and phytosiderophores in plants) 3 , 4 , capable of chelating and delivering iron at diminishingly low concentrations. Secondly, it limited iron metabolizing organisms capable of performing Fe(III)-reduction and Fe(II)-oxidation to niches, where low pH and/or low redox condition make the solubility and metabolism of iron thermodynamically favourable 1 , 5 , 6 , 7 . On land, iron limitation, particularly in systems underlain by calcareous lithologies of high pH limit plant productivity in natural and agricultural ecosystems 8 , 9 . In tropical forests, although iron is rarely limiting due to acid reaction of most tropical soils, phosphate is frequently limiting due to its absorption onto the surface of Fe-oxyhydroxides. Thus the importance of iron cycling to arboreal flora is driven by its ability to unlock phosphate by the process of Fe(III)-reduction that contributes to the availability of limiting phosphate otherwise adsorbed on the surface of Fe-oxyhydroxides 10 . Fast-growing legume trees in tropical forests, requiring high levels of phosphate to accommodate for their N 2 -fixing ability, may take advantage of that mechanism by containing a specialized belowground microbiome with greater phosphate solubilization capacity via increased Fe(III)-reduction potential 11 . Globally, iron limitation is reported for many parts of the world ocean 12 . Consequently, the extent of riverine fluxes of dissolved iron are known to positively correlate with enhanced coastal productivity 13 , 14 . The global average of riverine exports of dissolved iron (dFe) to the ocean are relatively low, in the range of 0.1 µM to 1.5 µM for temperate rivers 15 , 16 and 0.6–5.5 µM dFe for tropical rivers 16 (Fig. 1 ). However, rivers draining peatlands, flooded grasslands and marshes could often contain greater concentrations of dFe with ranges of 3.2–42.7 µM and 14.8–43.7 µM for temperate and tropical peatland-draining rivers 16 , 17 , respectively (Fig. 1 ). This has been linked to the typically high organic matter, high soil water content, and low redox conditions of peatlands that favour anaerobic metabolism and oxidation of organic matter utilizing iron as the final electron acceptor. The reduced ferrous iron [Fe(II)] is more mobile than its ferric counterpart, [Fe(III)] and can freely leach and be carried away together with counterbalancing negatively charged organic anions (e.g. acetate, phenolates) 18 , 19 . This co-leaching, particularly, when coupled with complexation as in the case of phenolics with galloyl and catechol-moieties acts to stabilize the resulting Fe(II)-organic complex, shielding it from Fe(II)-oxidation thus enabling its unconstrained transport to rivers draining such lands 19 . Areas underlain by iron sulfide (pyrite) lithology frequently exhibit discharge of some of the most acidic and highest in dFe waters on Earth, with ranges between 794 and 2,000,000 µM 20 , 21 (Fig. 1 ). The high solubility of iron sulfide through oxidation of dissolved Fe(II) to Fe(III) and of sulfide to sulfuric acid by iron-oxidizing microorganisms lowers pH to negative values (pH ≤ -2) under which conditions Fe(III) is fully soluble even in the most oxic, high redox conditions. However, it remains unclear to what degree the belowground microbial soil communities are coupled to recorded patterns of global Fe cycling derived from geochemical empirical and model data, as well as from incubation studies relying on a few culturable model Fe(III)-reducing and Fe(II)-oxidizing bacteria. As soil represents one of the largest pools of primary and secondary iron minerals, investigation of the processes taking place within its hosted microbial communities is necessary to better understand the role of the biosphere in the modern iron cycle as well as the role of the modern iron cycle in shaping soil microbiomes. To address these and dissect the Fe cycling potential of diverse soil microbiomes, we have created a functional marker database of iron cycling genes aptly termed IR on cyc le gene A nnotation ( IRcyc-A ). IRcyc-A contains ~ 680,000 gene entries from across the bacterial tree of life (Fig. 2 A) and is designed to: (i) discover and generate count data for Fe cycling genes in microbiomes (three broad functional categories: Fe(III)-reduction genes, Fe(II)-oxidation genes, siderophore biosynthesis genes involved in Fe(III)-chelation); (ii) limit false positive and double-counting hits that would otherwise affect the calculated abundance of particular iron cycling pathways, and (iii) perform normalization for number of genomes (to derive relative abundance) and provide taxonomic information by using the single copy marker-gene rpoA present in > 99% of all bacteria analysed to date. The database can be used as a standalone tool using any search algorithms (BLAST, blat, Diamond) in both user-defined protocols and pre-existing bioinformatics processing platforms. Furthermore, existing tools such as FeGenie 22 require use of python coding and access to external processing power. In contrast, IRcyc-A can be deployed in a user-friendly manner, utilized by end-users without any prior coding experience and/or without access to supercomputing local hubs. For instance, IRcyc-A can be used in the Galaxy platform ( https://usegalaxy.eu/ ) that provides free access to a graphical-interface environment with in-built functionality and processing power. Here, using IRcyc-A we have analysed 222 publicly available metagenomes and metatranscriptomes representing from all the main biomes around the world to (i) directly assess the hypothesis that the abundance of iron cycling microorganisms (derived by functional gene marker counts) in soil microbial communities will follow Fe cycle dynamics as reported in geochemical data and (ii) demonstrate the utility of IRcyc-A to provide novel insights into the function and main players of the iron cycle both globally and locally. Results and Discussion Benchmarking IRcyc-A against other tools We benchmarked IRcyc-A utilized in the Galaxy Europe server environment (Fig. 2 E) against the existing metagenomics platforms MG-RAST 23 and MGX 24 that both, similar to IRcyc-A, provide a code-free graphic user interface without the necessity for user-provided access to supercomputing hubs. For the comparative analysis, we used a set of six publicly available soil metagenomes from US agroecosystems. The functional annotation against the SEED database in MG-RAST is capable of assigning reads to Fe(III)-reduction genes based on functional homology to the decaheme cytochrome and porin cluster of genes ( MtrA-F ) of Shewanella oneidensis . MGX on the other hand has the capacity to functionally annotate metagenomic reads against the TIGRFAM domain database 25 thus also assigning reads to Fe(III)-reduction genes based on homology to the conserved domains TIGR03507 ( OmcA/MtrC family decaheme c-type cytochrome), TIGR03508 ( DmsE family decaheme c-type cytochrome including MtrA and MtrD ), and TIGR03509 ( MtrB/PioB family decaheme-associated outer membrane protein). Since both the MG-RAST-SEED database and TIGRFAM database used in MGX lack entries to Fe(II)-oxidation genes, we only compared the potential of the three databases to functionally annotate short reads to Fe(III)-reduction genes. Reads annotated by MGX as TIGR03508 ( DmsE/MtrA decaheme) domain resulted in ~ 42% false-positive hits that did not match any of the so-far discovered MtrA genes provided by the exhaustive list found in the IRcyc-A database. The great number of false-positive hits found in functional annotation of metagenomic reads against the TIGRFAM domain database in MGX most likely results from a significant number of reads that belong to paralogous genes not included in the original TIGRFAM list of domains. Annotation against the SEED database in MG-RAST resulted in a small number of false positive hits – only 2% in line with their smaller number of matches. False-positive hits from the MGX and MG-RAST outputs were excluded for further downstream analyses. Our findings show that IRcyc-A discovered 13-times greater number of reads mapping to the decaheme protein MtrA/DmsE/MtoA (1650 reads) compared to MG-RAST-SEED (132 reads), and 1.6-times more relative to the MGX-TIGRFAM (1059 reads) algorithm (Fig. 2 B). That pattern was also apparent for other iron cycling genes (Fig. 2 B). The greater discovery potential of IRcyc-A results from the more exhaustive list of full-length iron cycling protein gene sequences available within the IRcyc-A database. For instance, the IRcyc-A database contains MtrA/DmsE/MtoA entries from 1435 bacterial genomes compared to just one full length protein sequence, that of Shewanella oneidensis , in the SEED database of MG-RAST and just 13 used in the original seed alignment behind the conserved structure of the TIGR03508 domain. Next, we performed taxonomic analyses of the MtrA reads reported by the three algorithms and found that in general the greatest number of taxa contributing to the MtrA pool in these soil metagenomes was found in searches using IRcyc-A followed by TIGRFAM-MGX, with the least found in SEED-MG-RAST searches (Fig. 2 C). Phylum- and class-level taxonomic profiling revealed similar patterns between outputs of IRcyc-A and MGX-TIGRFAM with those of MG-RAST-SEED being most divergent, with heavy bias towards Proteobacteria, particularly Gammaproteobacteria (Fig. 2 D). This bias is consistent with the fact that the SEED database version used in MG-RAST only contains MtrA-F sequences of the gamma-proteobacterium Shewanella oneidensis . This stands to demonstrate that the size and diversity of the database of iron cycling genes used in alignment searches ultimately determines the reliability and accuracy of downstream analyses. Functional validation: local patterns in iron cycling predicted by IRcyc-A • Acid sulfide soils in Finland Acid sulfide soils are characterised by high amount of pyrite (FeS 2 ) minerals found either as primary mineral inclusions in the underlying lithology or formed as a result of diagenesis of the soil substrate during coastal flooding and its subsequent reduced conditions 26 . They cover an estimated area of 17 million hectares, mainly in coastal regions of Australia, North America, Europe, and Asia 27 , 28 . The pyrite present in lower horizons (Fig. 3 A) is unreactive under anoxic conditions. However, in contact with the oxygen atmosphere permeating upper soil horizons, pyrite interacts with oxygen in an oxidizing weathering process to form sulfuric acid and release ferrous iron (Eq. 1). The resulting highly acidic conditions allow for iron to remain in the ferrous Fe(II) form and not become oxidized to Fe(III) despite the more oxic environment of the upper soil horizons 29 , 30 . Under these conditions, biological iron Fe(II)-oxidation by acidophilic bacteria 6 and not abiotic oxidation is the dominant process converting Fe(II) to Fe(III) species 30 . The acid sulfate soils at the Risöfladan experimental site in Finland are the result of a diagenesis process likely resulting from the rise in sea levels during the last 7500 years of the current Holocene era and subsequent rise of iron sulfide rich sediments accumulated in the anoxic sea zone to raise up to 100 m above sea level 31 . Inspection of the soil profiles at the site 32 indicates the presence of three distinct types of sub-soil. The uppermost layer of the sub-soil contains an already oxidized zone (lowest pH, low ferrous iron) followed by an actively oxidizing zone (acidic pH slightly greater than that of the oxidized zone, highest measured ferrous iron levels) and lastly finishing in an unoxidized zone containing pyrite parent material (highest pH, lowest ferrous iron, Fig. 3 A). To check if these recorded patterns in soil chemistry were aligned to changes in microbial iron cycling, we analysed the publicly deposited but as of yet unanalysed metagenomic and metatranscriptomic datasets associated with this experimental site 32 . Using the IRcyc-A database tool, we find that reads annotated to Fe(II) oxidation genes in the metagenomes predominantly mapped to the cytochrome c gene cyc2 utilized by iron oxidizers 33 , 34 . The total summed relative abundance of Fe(II) oxidation genes was greatest in the metagenomes of the oxidized and the actively oxidizing zones, whereas their abundance significantly dropped in the alkaline, unoxidized zone (Fig. 3 A). Metatranscriptomic data available from this site contains information on the overall pattern of gene expression across the entire microbial community. Its analysis through the IRcyc-A database tool shows that the expression of Fe(II)-oxidation genes peaks in the actively oxidizing zone in line with the greatest levels of ferrous iron necessary for the lithoautotrophic metabolism of iron-oxidizing bacteria. Unlike their abundance in metagenomes, the relative expression of iron oxidation genes was relatively low in the metatranscriptomes of the uppermost oxidized zone. This apparent contrast between the two types of omics data likely results from the history of the oxidized zone. Iron oxidation was high in this zone at some point in the past. The subsequent exhaustion of ferrous iron substrate for oxidation consequently led to the cessation of the process of microbial iron oxidation but nevertheless left a transcriptionally inactive but metagenomically present potential for iron oxidation. The expression of Fe(III)-reduction genes was greatest in the actively oxidizing zone. This apparent synchrony between Fe(II)-oxidation and Fe(III)-reduction may be explained by the increasing concentrations of Fe(III) required by ferric reducers that is derived from the activity of iron oxidizers. This freshly generated Fe(III) will likely form more amorphous hydroxides and thus be more readily available for reductive dissolution by Fe(III)-reducers than the Fe(III) that had been oxidized in the past and that would already have achieved a certain level of crystallinity in its hydroxide minerals. Locally, the microsites enriched in Fe(III) with actively on-going pyrite and ferrous iron oxidation (see Eq. 1) would also consume great amounts of available oxygen perhaps creating microniches of anoxic conditions necessary for microbial Fe(III)-reduction. Interestingly, the expression of siderophore biosynthesis genes, in this case exemplified by the most abundant group in these microbiomes – that of the catecholate class of siderophores – was greatest in the metatranscriptomes of the bottommost unoxidized layer. These findings support the view that siderophore production and their iron-chelating activities are most favoured under conditions limiting the supply of iron available for immediate biological uptake by microorganisms – such as those of lowest levels of Fe(II) and alkaline pH as observed in the unoxidized zone. • Intact versus drained peatlands in French Guiana Peatlands cover an estimated 422 million hectares of land area worldwide 35 and act as important sinks for carbon 36 . The peat found in peatlands contributes between a third and a half of the global reservoir of soil organic carbon – with temperate peatlands contributing 10–30% 36 and tropical peatlands another ~ 20% 37,38 . Peat is a complex mixture of organic matter in different stages of decomposition formed under the anoxic conditions of these lands. Anoxic conditions develop owing the combined effects of high microbial respiration consuming oxygen and high soil water content limiting oxygen diffusion. Under these conditions, the processes of anaerobic microbial respiration including Fe(III)-reduction are favoured as alternative electron acceptors different to atmospheric oxygen (O 2 ) have to be utilized 39 . The dissimilatory reduction of Fe(III)-containing minerals such as oxides goethite and ferrihydrite, phosphates such as vivianite and strengite, and carbonates such as siderite by microorganisms consumes electrons generated from the oxidation of electron donors – either organic (typically fermentation products such as lactate, acetate) 40 or inorganic (hydrogen gas) 39 . Draining is a method employed for converting peatlands for the purposes of agriculture, peat extraction, forestry, and human settlement 41 . Here we utilized a set of publicly available metagenomes from a study site in French Guiana 41 to investigate the effects of peatland draining on the microbial Fe cycle. In terms of soil chemistry, the effects of draining resulted in predictable decreases in soil water content and soil organic carbon (Fig. 3 B). The latter was likely caused by the increase of aerobic respiration, which allowed for faster decomposition of materials typically recalcitrant to decomposition under anoxic conditions. Using the IRcyc-A database, we reveal that draining was associated with a decrease in the relative abundance of Fe(II)-oxidation and Fe(III)-reduction genes in the microbial community (Fig. 3 B) consistent with a more limited role for biogeochemical Fe cycling in response to the transition from anaerobic to aerobic metabolism. In contrast, transition from intact to drained peatlands was associated with a significant increase in the relative abundance of biosynthesis genes for all three major siderophore classes including the hydroxamate, catecholate, and mixed ligand class (Fig. 3 B). These findings likely reflect the increased need of Fe(III)-chelating siderophores under more oxic conditions that would transition iron speciation from the highly mobile and directly available for microbial transport Fe(II) towards the Fe(III) forms intractable to direct biological utilization. Using IRcyc-A, we demonstrate that factors decreasing peatland soil water content such as draining but also probably climate change-driven drought would affect the biogeochemical cycling of iron and decrease the microbiome potential for iron oxidation and reduction while increasing the abundance of iron-chelating siderophore genes. These changes in the microbiome would likely be associated with a decline in the export of dissolved Fe to rivers from drained or dried peatlands. • Biological iron oxidation as a mechanism driving silicate weathering and pedogenesis in tropical forest ecosystems in Australia The Atherton Tablelands in Northeast Australia is a plateau developed on deep basaltic soils, the natural climax vegetation of which is luxuriant tropical rainforests. Basalt bedrocks in this system are geologically young, deposited mainly during the Quaternary period 42 . Mineralogically, these basalts are iron-rich 42 , with iron mostly in a ferrous form [Fe(II)] and contributed mainly by olivine and clinopyroxenes 43 . The oxidation of basaltic ferrous iron to ferric iron during weathering could contribute to pedogenesis and the substantial amount of secondary iron oxyhydroxide (mostly hematite 44 ) minerals and acidity in these tropical soils. This is exemplified by the dissolution equations of the silicate iron-rich end-member of the olivine group – fayalite: To investigate the role of biological iron oxidation of basalt as an acidogenic factor contributing to rock weathering and soil formation (pedogenesis) in this system, we deposited mesh bags containing fresh unweathered basalt grains (basalt bags, n = 16) or unweathered olivine (olivine bags, n = 17) in the top 0–10 cm of the secondary forest soils and left them to weather 45 (Fig. 3 C). After a year of in situ weathering, we recovered the bags and collected soil samples from the immediate surroundings of the deposited bags ( n = 2). Next, we extracted total DNA from the collected weathered rock grains and soils and performed shotgun metagenome sequencing 45 . Here, we run these publicly available metagenomic sequencing data through the IRcyc-A pipeline. Our analyses show that both weathered basalt and soils exhibited significantly greater relative abundance of the Fe(II)-oxidation cyc2 genes than the microbial communities of weathered olivine (Fig. 3 C). Interestingly, both weathered basalt and olivine samples exhibited clear evidence for alteration, colour change, and development of iron oxide-rich rind (surface layer – see Fig. 3 C). Comparing the pH of the three substrates in our study, it is clear that the weathering of basalt increased pH from 4.2 (surrounding soil) to 4.8 (inside basalt bags) but not to the same extent as olivine at pH 6.6 (inside olivine bags). These findings show that, in agreement with geochemical theory, the pH, but not total ferrous iron content (assuming majority as FeO and not Fe 2 O 3 in fresh rock, basalt = 6.8% vs. olivine = 10.3% wt.) determined the extent of biological iron oxidation which is favoured by low pH 29 . In soils, however, both acidity and total Fe are appropriate for biological iron oxidation (Fig. 3 C), but most of the iron is present in ferric form as hematite. Consequently, the high abundance of cyc2 genes in these soils possibly reflect the redox fluctuations experienced during the wet season 46 . After prolonged wet weather, the water content of these tropical soils becomes conducive to microbial iron reduction of Fe(III) to Fe(II) 46 . During oxic conditions the thus accumulated Fe(II) can become biologically re-oxidized to Fe(III) by cyc2 -carrying bacteria. However, given the large amounts of ferrous iron needed to support autotrophic growth by iron oxidation 47 , it seems likely that most iron oxidizers in highly weathered tropical forest soils (where most iron is ferric) are mixotrophs or heterotrophs that only partially complement their energy needs by cyc2 -enabled iron oxidation – in an analogy to carboxydotrophic bacteria in soil 48 . In further support of that, the acidobacterial genus Ca. Acidoferrum , recovered as a MAG from soil in Panamanian tropical forests 11 , does contain cyc2 and cyc1 homologues but no carbon fixation pathway. In contrast, during the weathering of basalt, there is no necessity for ferrous iron to be supplied by microbial iron reduction since most of the iron released from weathering is ferrous. At the pH of 4.8 (inside basalt rock bags), ferrous iron is relatively stable 29 and locally abundant therefore allowing for biological iron oxidation by autotrophs 49 . Our generated data allowed us to examine this hypothesis by isolating several metagenome-assembled genomes (MAGs) from weathered basalt bags recovered from these tropical forest soils. One of these MAGs was identified to belong to a new species within the GJ-E10 lineage – a lineage of unnamed Burkholderiales bacteria previously shown to carry out iron oxidation under acidic conditions 50 . Genomic analysis of our GJ-E10-related MAG revealed the presence of homologues to cyc2 and cyc1 of Acidithiobacillus ferrooxydans and a full set of genes required for the Calvin cycle of autotrophic carbon dioxide fixation (Fig. 3 C). Here, we propose a new taxonomic nomenclature for this generically named iron-oxidizing autotrophic genus GJ-E10 – Ca. Ferrobasaltibacterium , gen. nov. and for our newly discovered species – Ca. Ferrobasaltibacterium australiensis , sp. nov. Taxonomic analysis of the universal single copy marker gene rpoA from these microbiomes through the IRcyc-A database show that Ca. Ferrobasaltibacterium was 10-fold more abundant in weathered basalt communities relative to those of soils and weathered olivine (Fig. 3 C). These results strongly indicate that biological iron oxidation of basalt in these tropical soils requires basalt specialist autotrophic microbial assemblages and that iron oxidizing bacteria are important factor for silicate weathering and soil development under tropical conditions. Global biome patterns in iron cycle genes We selected soil microbiomes from a wide range of publicly available datasets with the final collection containing 193 soil shotgun metagenomes and 29 soil metatranscriptomes, representative of all major terrestrial biomes (Fig. 4 A). A sub-collection of soil metagenomes ( n = 15) representing specific treatment conditions were separated and the remaining metagenomes exemplifying untreated soil conditions were used for across-biome comparisons of iron cycling gene abundance. The abundance of genes involved in the process of Fe(III)-reduction was highest (ANOVA, P < 0.001, post-hoc multiple comparisons) in soil metagenomes of peatlands and flooded grasslands (Fig. 4 B). On average, the abundance of Fe(III)-reduction genes in the soil metagenomes of peatlands and flooded grasslands (mean = 0.79, SE = 0.04) was ~ 3.0-fold greater than the unweighted average across biomes (mean = 0.27, SE = 0.05). To establish the robustness of this observation, we compared the peatland metagenomes of several locations across the globe against the global average. Metagenomes of peatlands located in the United Kingdom, USA, Sweden, and French Guiana all exhibited means greater than the global unweighted average across biomes. Peatlands may thus represent a hotspot for biological Fe(III)-reduction consistent with their role in exporting larger amounts of soluble iron compared to non-peaty catchments (Fig. 1 ). From a metabolic perspective, organic matter oxidation coupled to the reduction of Fe(III) is most energetically favourable when alternative electron acceptors (O 2 , NO 3 − ) are exhausted upon prolonged growth under anaerobic, low-redox conditions created by the high soil water content limiting oxygen diffusion and the high demand for O 2 under the organic-rich peat. In line with these, the abundance of Fe(III)-reduction genes in soil metagenomes were lowest in desert and arctic deserts where available soil water and organic matter are at their lowest. The abundance of genes involved in Fe(II)-oxidation was greatest (ANOVA, P < 0.001, post-hoc multiple comparisons) in soil metagenomes of acid iron sulfide soils (Fig. 4 C). On average, the metagenomes of acid iron sulfide soils (mean = 0.74, SE = 0.15) exhibited 4.6-fold greater abundance in Fe(II)-oxidizing genes relative to the global across-biome average (unweighted mean = 0.16, SE = 0.06). This observation held for comparisons made between the two sites of acid iron sulfide soils – one in China and one in Finland – against the global average. Both of these acid sulfide soil sites exhibited mean Fe(II)-oxidation gene abundance 2.5-6.6-fold greater than the mean abundance in metagenomes across biomes. Iron(II)-oxidation is most commonly associated with acidic conditions under which Fe(II) remains stable even under highly oxic conditions. Iron sulfide soils thus provide the ideal conditions for biological Fe(II)-oxidation as the dissolution of their contained pyrite produces ferrous iron and the acidity (Eq. 1) necessary to maintain Fe(II) stable in oxic environments. In the absence of pyrite, acidophilic Fe(II)-oxidizers will be dependent upon the external acidity of the substrate (e.g., basalt weathered in tropical soils as in Fig. 3 C) and natural fluctuations in soil redox conditions that (e.g., highly weathered oxisols in the tropics – Fig. 3 C) generate Fe(II) from soil Fe(III)-oxyhydroxides through microbial Fe(III)-reducing activity. These conditions are best exemplified by the soils of tropical rainforests, peatlands, and tundra where high soil acidity and/or fluctuations in soil redox are commonplace. Indeed, our global biome analyses confirm that soil metagenomes from these three biomes exhibited some of the greatest abundance of Fe(II)-oxidizing genes after that hereby reported for acid sulfide soils (Fig. 4 C). Next, we analysed the across-biome patterns in the metagenomic gene abundance of different classes of siderophore biosynthesis genes. Genes involved in the biosynthesis of catecholate siderophores (e.g., enterobactin, vibriobactin, bacillibactin etc.) were greatest in the soil microbiomes of tropical grasslands (savannas), acid iron sulfide soils, peatlands, and tropical forests, while agroecosystems and temperate grasslands showed lower abundances (Fig. 4 D). Lower yet were the catecholate gene abundances of soil microbiomes of tundra, deserts, and arctic deserts. These results may highlight the dual role of catecholates serving as Fe(III)-chelating agents produced in response to iron deficiency 51 , 52 as well as anti-oxidant molecules protecting against reactive oxygen species (ROS) 53 . ROS are commonly produced in soil as a result of the respiratory activity of soil microorganisms 54 . Biotic ROS undergo a catalytic reaction with soil Fe(II) and Fe(III)-(oxyhydr)oxide minerals that generate the highly reactive OH • radicals. These Fenton and Fenton-like reactions are particularly enhanced in soils experiencing high levels of microbial activity, fluctuations in redox state, Fe(II) production via microbial Fe(III)-reduction, and are rich in poorly-crystalline amorphous iron minerals 54 . These findings help explain the high abundance of genes involved in the production of ROS-scavenging catecholate siderophores in microbiomes typically associated with high biological availability of iron – e.g., peatlands, acid iron sulfide soils, tropical forest soils etc. or large fluctuations in redox state such savannas and to a lesser extent tropical forests experiencing alternating dry and wet seasons (Fig. 4 D). These provide a novel insight between Fe(II)-oxidizers and Fe(III)-reducers on the one hand affecting the levels of reactive iron species and Fenton-based ROS generation 54 and catecholate siderophore producers on the other hand that can inactivate the produced ROS species 53 . In addition, the Fenton-based chemistry of iron cycling and the role of catecholates in these cases will ultimately determine the amount of ROS species that can impact soil organic matter stability and decomposition 55 . These findings help us gain further insight into the coupling between the global Fe and C cycling. The metagenomic relative abundance of genes implicated in the production of hydroxamate siderophores (e.g., desferrioxamine B and E, ferrichrome, aerobactin, malleobactin 56 etc.) was highest in agroecosystems and deserts closely followed by temperate forest, temperate grasslands, tropical grasslands, and tropical forests (Fig. 4 E). The lowest abundance in hydroxamate biosynthesis genes was observed in the soil metagenomes of acid iron sulfide soils, peatlands, tundra, and arctic deserts. Genes involved in the synthesis of mixed ligand siderophores (e.g., pyoverdine, mycobactin, yersiniabactin etc.) followed a similar pattern to that of hydroxamate siderophores in that agroecosystem soil metagenomes had among the greatest abundance, whereas lowest abundances were recorded for the soil microbiomes of arctic deserts, deserts and tundra. Furthermore, we performed correlational analyses that revealed a cluster of strong positive correlations between the relative abundance of Fe(III)-reducing, Fe(II)-oxidation, and catecholate biosynthesis genes across biomes (Fig. 4 F). A second cluster of positive correlations was formed between the relative abundance of genes implicated in the biosynthesis of hydroxamate, mixed ligand, and carboxylate siderophores (Fig. 4 F). The two clusters were negatively correlating with each other. Our findings can be understood as a result of microbiomes being actively shaped by iron availability and ROS species. For instance, catecholate siderophores, Fe(II)-oxidation genes, and Fe(III) reduction genes are highest in abundance in soil microbiomes subjected to high biological availability of iron in soil allowing for extensive biogeochemical cycling of iron and ROS species evolution. This is consistent with previously suggested views that the processes of iron reduction and oxidation frequently occur simultaneously or cyclically 7 . In contrast, the genes for the majority of siderophore classes (hydroxamates, mixed ligands, carboxylates) peak in metagenomic abundance where Fe availability is more limited as indicated by their negative association with Fe(II)-oxidation and Fe(III)-reduction genes. These findings are in line with a major role of siderophores in ameliorating Fe deficiency 57 . Taxonomic profiling of target Fe cycling gene groups across biomes (Fig. 5 ) suggest that members of the Acidobacteriota, many of which uncultured 58 , dominate the gene pools involved in biogeochemical cycling of iron (oxidation and reduction; Fig. 5 A,B) in topsoils, whereas Actinobacteriota dominate the production of hydroxamate siderophores (Fig. 5 C). Actinobacteria and Acidobacteriota together with the group of Proteobacteria are among the most important phyla in terms of iron cycling and chelation (Fig. 5 A-C). Actinobacteria and Acidobacteria are also among the most abundant bacterial phyla in soil microbiomes across the globe, making up to ~ 60% of all bacteria in soil (Fig. 5 D, panel 1 ). We observed a strong negative correlation between the abundance of these groups in soil microbiomes across biomes (Fig. 5 D, panel 1 ). Furthermore, as expected based on their contribution to the pools of Fe cycling genes, the abundance of Acidobacteriota in the metagenome correlated positively with the abundance of Fe(III)-reduction and Fe(II)-oxidation genes (Fig. 5 D, panels 2 and 3 ). Similarly, the abundance of Actinobacteriota correlated positively with the abundance of hydroxamate siderophore biosynthesis genes in the metagenome (Fig. 5 D, panel 4 ). These findings suggest that the abundance of these two keystone phyla in soil microbiomes is associated with iron availability, chelation and redox cycling, pointing to iron as an overlooked factor in shaping soil microbial communities. Conclusions We show that the novel database tool IRcyc-A can be reliably used for functional annotation of iron cycling and chelation genes in soil omics data with results from our benchmarking tests demonstrating its utility compared to existing metagenomics pipelines. We further validate IRcyc-A output by showing its capacity to detect shifts in central Fe cycling genes within the soil community in response to known localized gradients in Fe cycling across soil depths, water regimes, and substrates. Using IRcyc-A to test for patterns in iron cycling potential using metagenomes from all major terrestrial biomes, we show that Fe(III)-reduction potential was greatest in peatland microbiomes in line with greater fluxes of dFe(II) in rivers draining peatlands relative to rivers draining non-peaty areas. Fe(II)-oxidation genes exhibited their greatest abundance in soil microbiomes of acid sulfide soils in line with the greatest concentrations of dFe observed in the highly acidic red streams draining areas of pyrite lithology. These data show that global trends in microbial Fe cycling correlates well with observed fluxes of dFe in rivers. Hydroxamate, mixed ligand, and carboxylate siderophore genes were greatest in abundance in ecosystems with low bioavailability of iron such as agroecosystems in line with major role of siderophores in sequestrating iron under drier, low organic and thus more iron-deficient conditions. In contrast, the abundance of biosynthesis genes for catecholate siderophores in agreement with the dual role of catecholates as iron chelating agents and antioxidants did not follow trends of low Fe availability but instead was highest in areas with high amount of Fe(III)- and Fe(II)-oxidation associated with extensive Fenton-like reactions generating reactive oxygen species. Furthermore, using IRcyc-A, we expand our understanding on the major players in microbial Fe cycling in soils and provide evidence for the major role of iron bioavailability as a force shaping the soil microbiome. Our analyses highlight the important role biology plays in determining the biogeochemical cycling of iron as well as the ability of iron to affect key aspects of soil microbiome composition and function. In addition, improving our understanding of the localized and global patterns in iron cycling in soil may help elucidate the complex interactions and coupling between dynamic processes in iron and soil organic matter transformations that can inform future policies for soil carbon conservation. Methods Selection of publicly available soil metagenomes and metatranscriptomes Publicly available metagenomes were selected to include topsoils (0–15 cm soil depth) and their respective treatments, and any other associated metadata (geographic coordinates, country, biome association, soil chemistry, etc.) were acquired from primary publications, archived manuscripts, and/or public server metadata files. Omics samples recovered from greater soil depths were also obtained and analysed but those were used for localized patterns functional validation and not in the global analyses that only utilized topsoil data. Similarly, omics samples from non-soil substrates found in soil (e.g., mesh bags containing crushed rocks deposited in soil as shown in Fig. 3 C and the associated section in the Main Text) were analysed through the IRcyc-A database for functional validation but were not used in the global analyses of the terrestrial iron cycle. Public servers storing omics data used in this study included MG-RAST and NCBI. Biome association was used as indicated by the data uploaded with slight modifications with existing biome classification. List of all of the metagenomic and metatranscriptomic data used in this study and their unique database identifiers is available in Supplementary Information File 1 . Development of IRcyc-A The IR on cyc ling gene A nnotation ( IRcyc-A ) database was developed based on the principle of including pre-annotated functional marker genes exhaustively from across the prokaryotic tree of life. To do so, specific TIGRFAM protein domains 25 and Kyoto Encyclopaedia of Genes (KEGG) orthologues (KO) numbers ( https://www.genome.jp/kegg/kegg2.html ) with a known role in Fe cycling were searched against the pre-populated genome annotations available as part of the AnnoTree project 59 (GTDB database Release R95, http://annotree.uwaterloo.ca/annotree/app/ ). All homologues of Fe(III)-reduction genes of Shewanella oneidensis decaheme cytochrome c protein genes ( MtrA,C ) and porin protein gene ( MtrB ) were downloaded from across all publicly available genomes using matches against the TIGRFAM domains DmsE/MtrA family decaheme c-type cytochrome (TIGR03508), decaheme c-type cytochrome, OmcA/MtrC family (TIGR03507), and decaheme-associated outer membrane protein, MtrB/PioB family (TIGR03509). These included known homologues from other Fe-cycling microorganisms including the MtoAB genes of Siderooxydans lithotrophicus and PioAB from Rhodopseudomonas palustris . The cyc2 gene of Acidithiobacillus ferrooxydans directly implicated in the process of Fe(II)-oxidation was searched against the pre-populated AnnoTree annotation using the KO number K20150, cyc2 , iron:rusticyanin reductase [EC:1.16.9.1]. The universal single-copy marker gene rpoA encoding the DNA-directed RNA polymerase alpha subunit 60 is present in most known bacteria, therefore making it a good taxonomic marker. All of the rpoA annotated hits across the prokaryotic tree of life were downloaded from AnnoTree annotated genomes using the KO number K03040. Matches against the rpoA gene when using IRcyc-A on metagenomic and metatranscriptomic data were utilized for studying the taxonomic composition of samples. Note that the FASTA heading for each of the described iron cycling and rpoA full-length protein coding sequences present in IRcyc-A are formatted to have a taxonomic identity tag containing detailed information on their taxonomic lineage (domain to species as acquired from the GTDB taxonomy 61 - https://gtdb.ecogenomic.org/tree ) as shown in the example below: “ >MtrA/DmsE_NZ_KB892868.1_95[d_Bacteria;p_Proteobacteria;c_Gammaproteobacteria;o_Burkholderiales;f_Rhodocyclaceae;g_ Uliginosibacterium;s_Uliginosibacterium gangwonense]_NZ_KB892868.1_95 MSIIRKLLVACMLVAGVSGAPLVWAADNIQLPDLSPAKSKAELAQDTLKKDAICTRCHDETEPAPVLSIYQTKHGMRGDARSPS… ” The structure of the heading is as follows: a. gene name (“MtrA/DmsE” followed by “_”), b. unique sequence identifier as acquired from AnnoTree/GTDB database (NZ_KB892868.2_95), c. taxonomic identity tag (from domain to species, divided by “;” as a delimiter and “[“ and “]” as delimiters to separate it from the rest of the header), and d. unique sequence identifier repeated at the end. Unfolding taxonomic tags in downstream analyses allows for taxonomic assessment of whole microbiomes (using rpoA since it is present in almost all bacteria) and the iron cycle specifically. Siderophores are a diverse set of iron-chelating molecules and multiple pathways are involved in their biosynthesis. In addition, their orthologues are not well mapped in the KEGG database (except catecholates and some mixed ligand siderophores). To address that, key siderophore biosynthesis genes from organisms in which the given type of siderophore biosynthesis was first discovered/described were acquired from pathways available in MetaCyc 62 ( https://metacyc.org/ ). Acquired key genes were then used as queries in pBLAST searches against the UniProt 63 database ( https://www.uniprot.org/ ). Hits matching certain cut-off scores ( a. close matches : positive matches ≥ 70%, identical matches ≥ 45%, length < ± 15% of original query length, b. -like matches : identical matches ≥ 45%, length < ± 25% of original query length, and c. similar to siderophores : three sub-groups – c.1 : positives ± 25%, c.2 : positives ≥ 70% but length > ± 25%, and c.3 : id < 45%, with c.1-c.3 included as a filter to sieve out false-positive hits but not counted towards siderophore biosynthesis genes) were all selected and added to the developing IRcyc-A database. It was aimed that only one central gene within each siderophore biosynthetic pathway was included so that over-assignment to particular siderophore types is avoided. In order to enable the use of IRcyc-A as a stand-alone database tool for Fe cycle gene annotation and microbiome taxonomy, filter sequences had to be supplied to limit false-positive hits. To do so, two strategies were deployed. First, a general filter set of sequences were added in the form of the SwissProt Uniprot set of protein gene sequences. The latter was added after removal of matching sequences (identical matches > 70%) against the so-far assembled IRcyc-A core functional genes. Second, a specific filter set of sequences containing KO paralogs of Fe cycling genes, rpoA , and siderophore biosynthesis genes (including group c. similar to siderophores hits from the performed pBLASTs as described above) were added to the IRcyc-A database. All genes present in the resulting IRcyc-A database were included as amino acid FASTA (FAA) protein sequences with a summarised breakdown shown in Fig. 2 A and a more detailed breakdown available in Supplementary Information File 2 . Benchmarking IRcyc-A A central driver in our motivation to develop IRcyc-A was the premise of easy non-programmatic access and use. Consequently, we compared IRcyc-A against other existing tools that do not require coding experience and contain a user-friendly graphical interface. Those included MGX 24 and MG-RAST 23 . The comparison was based upon the TIGRFAM domain annotation in MGX (particularly, domains Fe(III)-reduction gene-specific domains TIGR03507, TIGR03508, TIGR03509) and the Fe(III)-respiration pathway of Shewanella available in the Subsystem-database annotation output from MG-RAST (Fig. 2 B,C). Taxonomy was assigned against the IRcyc-A database using the provided taxonomic identity tags. Since the Mtr -genes available in the MG-RAST Subsystem database are only derived from the gamma-proteobacterium Shewanella oneidensis, the taxonomy of Fe(III)-reduction genes in MG-RAST outputs was highly biased towards Gammaproteobacteria and clades with sequence homology to Gammaproteobacteria (Fig. 2 D). IRcyc-A discovered the greatest number of Fe-cycling genes that were derived largely from the same taxonomic groups (phyla and classes) as the MGX-TIGRFAM algorithm. It is noteworthy that IRcyc-A discovered fewer rpoA reads than MG-RAST in these metagenomes indicating that IRcyc-A does not over-assign. The benchmarking was carried out on the same set of 6 soil metagenomes from temperate agroecosystems (marked with “*” in Supplementary Information File 1 ). How to use IRcyc-A For fast and efficient analysis of Fe cycling genes and microbiome taxonomy, we recommend that the IRcyc-A database file ( Supplementary Information File 3 ) and metagenomics/metatranscriptomics sequencing reads of interest are all uploaded to the Galaxy Europe server 64 ( https://usegalaxy.eu/ ) via a third-party uploading tool (e.g., FileZilla). Galaxy Europe and other international Galaxy servers allow users to create free private accounts with up to 200 Gb storage capacity. Upon successful upload, the IRcyc-A database input available as a FAA file can then be converted into a Diamond database file. The resulting IRcyc-A Diamond database can be used as a query in Diamond blastx searches against the user-provided omics sequencing reads (typically either the R1 or R2 reads from paired-end sequencing libraries would suffice). While setting-up the Diamond running parameters, “ The maximum number of target sequences per query to report alignments for ” is changed from the default 25 to 1, and the “ Format of output file ” parameter is set to BLAST Tabular. In addition to pre-selected fields, the following additional fields are selected for the BLAST Tabular output: “All Subject Seq – id(s)”, “Query sequence length”, “Subject sequence length”, and “All Subject Title(s)”. The output from these Diamond runs will contain matches against the general and specific filter sequences embedded in IRcyc-A to limit false-positive hits. To remove those from the Diamond output and filter only sequences of interest (e.g., Fe cycling gene and rpoA gene reads), the user is advised to utilize the “Select lines that match an expression” tool available in Galaxy. The pattern field within the Select tool can be filled up by removing the existing expression and replacing it with the list of expressions as shown in the IRcyc-A parameter file ( Supplementary Information File 4 ). The resulting output from the select tool should be in a tabular format. This final file can be downloaded and further analyses of it performed in Excel or similar software. The content of the last column, “All Subject Title(s)” can be unfolded using “_” as a delimiter which would provide information on the gene (first column) and taxonomic identity tag (second column). The taxonomic tag containing information for all taxonomic levels from domain to species can be unfolded further using “[“ and “;” as consecutive delimiters. Note that detailed taxonomic information is available only for central Fe(III)-reduction and Fe(II)-oxidation genes and rpoA . Taxonomic tags from siderophore genes and more obscure Fe-cycling genes ( omcS , foxE , etc.) should not be used for further taxonomic analyses. The e-value column in the tabular output file can be used for user-defined cut-offs with hits below certain selected cut-offs removed (here we used a cut-off of e-5). Reads can be counted as they are. Alternatively, gene counts could be normalized for gene length by dividing the query length (for paired-end Illumina reads for instance, that would often be 150 bp) by the subject length multiplied by 3 (since all gene sequences in the IRcyc-A database are present as protein coding sequences). In this study, we preferred the latter approach as to account for the large variation in gene lengths. To normalize for sequencing depth and varying number of sequenced genomes per metagenome, we further normalized the resulting length-normalized gene counts by dividing them by the length-normalized rpoA gene counts. The final rpoA -normalized and length-normalized gene counts per gene per sample can be summarised using the Pivot Table tool in Excel. If analysis on multiple samples/datasets is required, the Select outputs can be pre-merged using the Concatenate tool in Galaxy (“Concatenate multiple datasets tail-to-head by specifying how”) by selecting “Yes” to “Include dataset names?”. The final merged output can then be downloaded, processed and analysed in Excel as described above. We note that within hits matching the iron-reduction genes MtrABC there will be those belonging to known iron-oxidation microorganisms (e.g., representatives of the Gallionellaceae which contain MtoAB homologous to MtrAB ) as well as putative iron-oxidizers (e.g., we here defined these as microorganisms containing cyc2 genes within their genomes). Here, we have opted to separate MtrABC hits matching these organisms and count them towards the group of iron-oxidation genes. A list of these organisms that can be used for filtering MtrABC outputs is included in Supplementary Information File 5 . Additional taxonomic assignment of siderophore biosynthesis genes Unique sequence identifiers of sequences annotated as siderophore biosynthesis genes in the IRcyc-A annotation output for each sample were collected into a list and their FASTA read sequences acquired using the “Filter FASTA” tool against the original metagenome/metatranscriptome samples in the Galaxy Europe platform. Species-level taxonomic assignment for each of these reads was carried out against the RefSeq 2021-v12 database using Diamond blastx (cut-off e-5). Full lineage information was further acquired using the ETE lineage tools in Galaxy with the current (as of January 2023) ETE sqlite DB in NCBI. MAG assembly and genome annotation of Ca. Ferrobasaltibacterium The metagenome assembled genome (MAG) of Ca. Ferrobasaltibacterium australiensis was recovered and functionally annotated from the rock-associated metagenomes of basalt weathered in Australia by using the following pipeline: MEGAHIT (assembly) 65 , MaxBin2.0 (binning) 66 , CheckM (establishing completeness and contamination) 67 , GTDB-Tk (taxonomic annotation based on phylogenomics) 61 , Prodigal (genome-wide protein prediction) 68 , BlastKOALA (genome-wide functional annotation of protein sequences against the KO database) 69 , eggNOG 70 and Diamond searches using Acidithiobacillus ferrooxydans cyc2, cyc1, and cycA1 protein sequences. Declarations Acknowledgements: We thank David Beerling, Fin Ring-Hrubesh, and Simon Cheung for providing useful comments during editing of the manuscript. We also thank the authors of the publicly available data used in this study, without whom this work would not have been possible. 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Supplementary Files SupplementaryInformationFile1.xlsx Dataset 1 SupplementaryInformationFile2.xlsx Dataset 2 SupplementaryInformationFile3IRcycAdatabasefile.txt Dataset 3 SupplementaryInformationFile4IRcycAparameterfile.txt Dataset 4 SupplementaryInformationFile5.xlsx Dataset 5 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4248419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291958347,"identity":"624149ab-6db1-4667-b585-449b8f7b4495","order_by":0,"name":"Dimitar Epihov","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5711-5480","institution":"University of Sheffield","correspondingAuthor":true,"prefix":"","firstName":"Dimitar","middleName":"","lastName":"Epihov","suffix":""},{"id":291958348,"identity":"4973d0a0-a15e-4cf0-8176-2fc140aa0ef7","order_by":1,"name":"Casey Bryce","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Casey","middleName":"","lastName":"Bryce","suffix":""}],"badges":[],"createdAt":"2024-04-10 16:16:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4248419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4248419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55276685,"identity":"c78d5dab-49fa-4af7-aced-7817aa1a9019","added_by":"auto","created_at":"2024-04-25 05:11:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":307650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDissolved iron (dFe) in rivers and iron sulfide draining waters. \u003c/strong\u003ePatterns of dFe (presented here as log10-transformed values of the measured micromolar concentrations) show the lowest levels in rivers draining temperate areas followed by rivers draining forests in the tropics. Much higher are the dissolved iron concentrations of rivers draining temperate and tropical peatlands, flooded grasslands, and swamps. Several orders of magnitude higher yet are the dFe concentrations in waters draining pyrite (iron sulfide, FeS\u003csub\u003e2\u003c/sub\u003e) geologies.\u003c/p\u003e\n\u003cp\u003e(Epihov and Bryce, 2024)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/87dc5505d463a5d1233bee19.png"},{"id":55275966,"identity":"dd004a80-7340-4d94-b6f5-27de645cbc22","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe IRcyc-A database tool – composition and benchmarking. A.\u003c/strong\u003e Composition of the IRcyc-A database; \u003cstrong\u003eB.\u003c/strong\u003e Mean number of reads assigned to iron cycling genes and \u003cem\u003erpoA\u003c/em\u003e in six soil metagenomes by IRcyc-A, MGX-TIGRFAM, and MG-RAST-SEED; \u003cstrong\u003eC.\u003c/strong\u003e Mean number of taxa contributing to the pool of \u003cem\u003eMtrA/DmsE/MtoA\u003c/em\u003e hits assigned by different tools – hits were functionally annotated by the three algorithms and their taxonomy obtained by alignment against the IRcyc-A taxonomic component; \u003cstrong\u003eD.\u003c/strong\u003eBreakdown into phylum- and class-level composition of the \u003cem\u003eMtrA/DmsE/MtoA\u003c/em\u003egene in the six soil metagenomes; \u003cstrong\u003eE.\u003c/strong\u003e Example pipeline for the use of IRcyc-A.\u003c/p\u003e\n\u003cp\u003e(Epihov and Bryce, 2024)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/1d49c26594fc0ed9e63caa0f.png"},{"id":55275971,"identity":"105a94b6-b36b-44a3-bf7c-419f40f7e461","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":470950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIRcyc-A can be used on omics data to assess the dynamics of the microbial iron cycle in response to local ecological gradients. A. \u003c/strong\u003eDepth gradients in\u003cstrong\u003e \u003c/strong\u003eiron sulfide soils at the Risöfladan experimental site in Finland\u003csup\u003e32\u003c/sup\u003e – the actively oxidizing zone exhibits the highest Fe(II) concentrations linked to the greatest expression of Fe(II)-oxidation and Fe(III)-reduction genes in the microbiome, while at the same time showing the lowest expression of catecholate siderophore biosynthesis genes. The expression of catecholate siderophore biosynthesis genes was highest at the greatest depth where pH was highest, and Fe(II) levels were lowest; \u003cstrong\u003eB.\u003c/strong\u003e Intact versus drained peatlands in French Guiana\u003csup\u003e41\u003c/sup\u003e – the drained sites show a marked decline in the abundance of genes involved in energy-generating iron metabolism [anaerobic respiration using Fe(III)-reduction by heterotrophs and autotrophic metabolism using Fe(II)-oxidation]. At the same time however, these drained sites show an increase in the genes encoding the biosynthesis of Fe-scavenging siderophores as available iron concentrations dwindle by changing the waterlogged conditions, soil organic matter and soil pH (not shown) typical to intact peatlands; \u003cstrong\u003eC.\u003c/strong\u003e Weathered iron-containing rocks versus soil in highly weathered basalt-derived systems in Australia\u003csup\u003e45\u003c/sup\u003e – comparison of the relative abundance of Fe(II)-oxidation \u003cem\u003ecyc2\u003c/em\u003e genes (panel 4) show that weathered basalt grains recovered from mesh bags deposited and weathered in soil for ~1 year and surrounding soils exhibit greater abundance than the weathered olivine (92% olivine in dunite rock matrix). This is despite the fact that the highest level of Fe(II) (most of the iron in as olivine and basalt is in ferrous form) is contained in olivine (panel 3), followed by basalt. Note that total Fe, although highest in soil, is mostly in ferric form in these highly weathered tropical soils and therefore soils likely contain the lowest levels of Fe(II). This suggests pH (panel 2) as the main driver of biological versus abiotic iron oxidation in these systems. Furthermore, we recovered a high-quality MAG from weathered basalt. Analysis using the GTDB-Tk tool showed it bore a similarity to the described iron-oxidizing bacterium GJ-E10. We hereby name this previously unnamed genus as Candidatus \u003cem\u003eFerrobasaltibacterium\u003c/em\u003e, gen. nov. and the new species Ca. \u003cem\u003eFerrobasaltibacterium australiensis\u003c/em\u003e, sp. nov. Analysis of the MAG genome annotation confirmed the presence of \u003cem\u003ecyc2 \u003c/em\u003eand \u003cem\u003ecyc1\u003c/em\u003e homologues in addition to a complete set of genes for the Calvin cycle of autotrophic CO\u003csub\u003e2\u003c/sub\u003e fixation, corroborating an autotrophic lifestyle based on Fe(II)-oxidation. Ca. \u003cem\u003eFerrobasaltibacterium\u003c/em\u003e abundance in the three substrates show a clear peak in the weathered-basalt grain samples. Gene counts are based on \u003cem\u003erpoA\u003c/em\u003e-normalized gene counts (for more details refer to the Methods section). The abundance of Ca. Ferrobasaltibacterium is based on percentage of \u003cem\u003erpoA\u003c/em\u003e assigned to it. Error bars all show SEM.\u003c/p\u003e\n\u003cp\u003e(Epihov and Bryce, 2024)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/d1bd36004fcb67b5e5c901e9.png"},{"id":55275974,"identity":"a8a48591-3080-49d7-85f3-b79dc555ce7c","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe IRcyc-A-enabled iron cycle metagenomics of soils reveal across-biome trends consistent with patterns of iron export by rivers. A. \u003c/strong\u003eDistribution of publicly available metagenomes and metatranscriptomes used in this study; \u003cstrong\u003eB.\u003c/strong\u003e Patterns of Fe(III)-reduction genes abundance in soil metagenomes shows greatest levels in microbiomes of peatlands and flooded grasslands. Fe reduction genes include homologues of the decaheme cytochromes \u003cem\u003eMtrA\u003c/em\u003e and \u003cem\u003eMtrC \u003c/em\u003eas well as the decaheme porin \u003cem\u003eMtrB\u003c/em\u003eof \u003cem\u003eShewanella oneidensis\u003c/em\u003e MR-1. In addition, hits matching to the extracellular cytochrome \u003cem\u003eOmcS\u003c/em\u003e of \u003cem\u003eGeobacter sulfurreducens\u003c/em\u003e are also included. \u003cstrong\u003eC.\u003c/strong\u003e Patterns of Fe(II)-oxidation genes abundance in soil metagenomes indicate greatest number in the microbiomes of iron sulfide acidic soils – typical to areas overlain by pyrite lithology. Hits in these group include homologues of the \u003cem\u003ecyc2\u003c/em\u003e of \u003cem\u003eAcidithiobacillus ferrooxydans\u003c/em\u003e, the \u003cem\u003eMtoA,B \u003c/em\u003eof \u003cem\u003eSideroxydans lithotrophicus\u003c/em\u003e, and \u003cem\u003efoxE\u003c/em\u003e of \u003cem\u003eRhodobacter \u003c/em\u003espp. \u003cstrong\u003eD.\u003c/strong\u003e Catecholate siderophore biosynthesis genes abundance across biomes; \u003cstrong\u003eE.\u003c/strong\u003e Hydroxamate siderophore biosynthesis genes abundance across biomes; \u003cstrong\u003eF.\u003c/strong\u003e Mixed ligand siderophore biosynthesis genes abundance across biomes; \u003cstrong\u003eG. \u003c/strong\u003eCorrelation matrix of different classes of Fe cycle genes shows a generally negative association between the abundance of most siderophore biosynthesis genes with the abundance of Fe(III)-reduction and Fe(II)-oxidation genes. Statistical analyses in B-F are ANOVA tests, *** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001. Correlations in G are based on Spearman ranked correlation test, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ** \u0026lt; 0.01, * \u0026lt; 0.05,\u003cem\u003e ns\u003c/em\u003e \u0026gt; 0.05. Error bars indicate SEM.\u003c/p\u003e\n\u003cp\u003e(Epihov and Bryce, 2024)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/d01e8352465754cc60ce851c.png"},{"id":55276262,"identity":"6a51342f-62e3-4c77-b8ef-a8ad90a5f575","added_by":"auto","created_at":"2024-04-25 05:03:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":186039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrder-level taxonomy of the genes implicated in major Fe-cycling processes highlight Acidobacteriota and Actinomycetota as major contributors to different processes within the soil Fe cycle. A. \u003c/strong\u003eTaxonomy of Fe(III)-reducing microorganisms across biomes; \u003cstrong\u003eB.\u003c/strong\u003eTaxonomy of Fe(II)-oxidizing microorganisms across biomes; \u003cstrong\u003eC.\u003c/strong\u003e Taxonomy of hydroxamate siderophore-producing microorganisms across biomes; \u003cstrong\u003eD.\u003c/strong\u003eThe abundances of Acidobacteriota and Actinomycetota bacteria in the microbial community, as major contributors to the pools of Fe-redox and Fe-siderophore cycling, respectively, are negatively correlated and are reliable predictors for the abundance of Fe(III)-reduction, Fe(II)-oxidation, and Hydroxamate siderophore biosynthesis genes. Best-fits for the graphs in D include third order polynomial (first panel) and straight line fit (remaining three panels).\u003c/p\u003e\n\u003cp\u003e(Epihov and Bryce, 2024)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/58277f25825c11e110f320bc.png"},{"id":55277088,"identity":"770595a4-4a93-4f72-9c2f-084888ba9982","added_by":"auto","created_at":"2024-04-25 05:19:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1733008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/71459798-5ef5-4fab-8990-7b8e1982c1ee.pdf"},{"id":55275968,"identity":"75c517d9-82cb-43c1-80b9-927ab7b4cc94","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29735,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 1\u003c/p\u003e","description":"","filename":"SupplementaryInformationFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/b5404a59fe581a152f4d6de7.xlsx"},{"id":55276260,"identity":"38418d7f-8d79-479a-9bb1-0ff93319bd09","added_by":"auto","created_at":"2024-04-25 05:03:46","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13564,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 2\u003c/p\u003e","description":"","filename":"SupplementaryInformationFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/caa65167f58d3c30aa8d2422.xlsx"},{"id":55275976,"identity":"ca03ba2c-364a-4c4f-8157-4e2169e67ac3","added_by":"auto","created_at":"2024-04-25 04:55:54","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":361428279,"visible":true,"origin":"","legend":"Dataset 3","description":"","filename":"SupplementaryInformationFile3IRcycAdatabasefile.txt","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/d5d17621c74c3a68ea5b81b4.txt"},{"id":55275973,"identity":"bfae749c-b2e9-44d5-b210-6ede987e6820","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":945,"visible":true,"origin":"","legend":"Dataset 4","description":"","filename":"SupplementaryInformationFile4IRcycAparameterfile.txt","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/e5a364d87addd96b9b28ca43.txt"},{"id":55275972,"identity":"f53c549d-582e-4aef-ba29-cde5acf63b6e","added_by":"auto","created_at":"2024-04-25 04:55:46","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12760,"visible":true,"origin":"","legend":"Dataset 5","description":"","filename":"SupplementaryInformationFile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4248419/v1/0ea69c9c82b786ab4d9fa26a.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Contrasting microbial communities drive iron cycling across global biomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIron is indispensable to terrestrial and ocean productivity as many important biochemical reactions utilize iron-containing co-factors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Iron is an element essential to chlorophyll biosynthesis, biological nitrogen fixation and its nitrogenase enzymes, cellular respiration and its cytochrome proteins\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite being the most abundant redox-active element in Earth\u0026rsquo;s crust, the modern oxygen-rich atmosphere of our planet renders iron highly unavailable due to oxidation and subsequent precipitation of iron into insoluble iron hydroxide and oxide phases. Although iron metabolism is believed to be among the most ancient and prevalent forms of metabolism among early life on Earth, particularly prior the Great oxidation event, in modern ecosystems the scarcity of soluble iron has had profound impact to the biosphere\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. First, low levels of available iron likely necessitated aerobic organisms to independently evolve the ability to strongly chelate iron by producing high-affinity organic ligands (siderophores of bacteria, siderophores of fungi and phytosiderophores in plants)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, capable of chelating and delivering iron at diminishingly low concentrations. Secondly, it limited iron metabolizing organisms capable of performing Fe(III)-reduction and Fe(II)-oxidation to niches, where low pH and/or low redox condition make the solubility and metabolism of iron thermodynamically favourable\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn land, iron limitation, particularly in systems underlain by calcareous lithologies of high pH limit plant productivity in natural and agricultural ecosystems\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In tropical forests, although iron is rarely limiting due to acid reaction of most tropical soils, phosphate is frequently limiting due to its absorption onto the surface of Fe-oxyhydroxides. Thus the importance of iron cycling to arboreal flora is driven by its ability to unlock phosphate by the process of Fe(III)-reduction that contributes to the availability of limiting phosphate otherwise adsorbed on the surface of Fe-oxyhydroxides\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Fast-growing legume trees in tropical forests, requiring high levels of phosphate to accommodate for their N\u003csub\u003e2\u003c/sub\u003e-fixing ability, may take advantage of that mechanism by containing a specialized belowground microbiome with greater phosphate solubilization capacity via increased Fe(III)-reduction potential\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobally, iron limitation is reported for many parts of the world ocean\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Consequently, the extent of riverine fluxes of dissolved iron are known to positively correlate with enhanced coastal productivity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The global average of riverine exports of dissolved iron (dFe) to the ocean are relatively low, in the range of 0.1 \u0026micro;M to 1.5 \u0026micro;M for temperate rivers\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and 0.6\u0026ndash;5.5 \u0026micro;M dFe for tropical rivers\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, rivers draining peatlands, flooded grasslands and marshes could often contain greater concentrations of dFe with ranges of 3.2\u0026ndash;42.7 \u0026micro;M and 14.8\u0026ndash;43.7 \u0026micro;M for temperate and tropical peatland-draining rivers\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This has been linked to the typically high organic matter, high soil water content, and low redox conditions of peatlands that favour anaerobic metabolism and oxidation of organic matter utilizing iron as the final electron acceptor. The reduced ferrous iron [Fe(II)] is more mobile than its ferric counterpart, [Fe(III)] and can freely leach and be carried away together with counterbalancing negatively charged organic anions (e.g. acetate, phenolates)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This co-leaching, particularly, when coupled with complexation as in the case of phenolics with galloyl and catechol-moieties acts to stabilize the resulting Fe(II)-organic complex, shielding it from Fe(II)-oxidation thus enabling its unconstrained transport to rivers draining such lands\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Areas underlain by iron sulfide (pyrite) lithology frequently exhibit discharge of some of the most acidic and highest in dFe waters on Earth, with ranges between 794 and 2,000,000 \u0026micro;M\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The high solubility of iron sulfide through oxidation of dissolved Fe(II) to Fe(III) and of sulfide to sulfuric acid by iron-oxidizing microorganisms lowers pH to negative values (pH \u0026le; -2) under which conditions Fe(III) is fully soluble even in the most oxic, high redox conditions.\u003c/p\u003e \u003cp\u003eHowever, it remains unclear to what degree the belowground microbial soil communities are coupled to recorded patterns of global Fe cycling derived from geochemical empirical and model data, as well as from incubation studies relying on a few culturable model Fe(III)-reducing and Fe(II)-oxidizing bacteria. As soil represents one of the largest pools of primary and secondary iron minerals, investigation of the processes taking place within its hosted microbial communities is necessary to better understand the role of the biosphere in the modern iron cycle as well as the role of the modern iron cycle in shaping soil microbiomes. To address these and dissect the Fe cycling potential of diverse soil microbiomes, we have created a functional marker database of iron cycling genes aptly termed \u003cem\u003eIR\u003c/em\u003eon \u003cem\u003ecyc\u003c/em\u003ele gene \u003cem\u003eA\u003c/em\u003ennotation (\u003cem\u003eIRcyc-A\u003c/em\u003e). IRcyc-A contains\u0026thinsp;~\u0026thinsp;680,000 gene entries from across the bacterial tree of life (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and is designed to: (i) discover and generate count data for Fe cycling genes in microbiomes (three broad functional categories: Fe(III)-reduction genes, Fe(II)-oxidation genes, siderophore biosynthesis genes involved in Fe(III)-chelation); (ii) limit false positive and double-counting hits that would otherwise affect the calculated abundance of particular iron cycling pathways, and (iii) perform normalization for number of genomes (to derive relative abundance) and provide taxonomic information by using the single copy marker-gene \u003cem\u003erpoA\u003c/em\u003e present in \u0026gt;\u0026thinsp;99% of all bacteria analysed to date. The database can be used as a standalone tool using any search algorithms (BLAST, blat, Diamond) in both user-defined protocols and pre-existing bioinformatics processing platforms. Furthermore, existing tools such as FeGenie\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e require use of python coding and access to external processing power. In contrast, IRcyc-A can be deployed in a user-friendly manner, utilized by end-users without any prior coding experience and/or without access to supercomputing local hubs. For instance, IRcyc-A can be used in the Galaxy platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usegalaxy.eu/\u003c/span\u003e\u003cspan address=\"https://usegalaxy.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that provides free access to a graphical-interface environment with in-built functionality and processing power.\u003c/p\u003e \u003cp\u003eHere, using IRcyc-A we have analysed 222 publicly available metagenomes and metatranscriptomes representing from all the main biomes around the world to (i) directly assess the hypothesis that the abundance of iron cycling microorganisms (derived by functional gene marker counts) in soil microbial communities will follow Fe cycle dynamics as reported in geochemical data and (ii) demonstrate the utility of IRcyc-A to provide novel insights into the function and main players of the iron cycle both globally and locally.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003eBenchmarking IRcyc-A against other tools\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eWe benchmarked IRcyc-A utilized in the Galaxy Europe server environment (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE) against the existing metagenomics platforms MG-RAST\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and MGX\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e that both, similar to IRcyc-A, provide a code-free graphic user interface without the necessity for user-provided access to supercomputing hubs. For the comparative analysis, we used a set of six publicly available soil metagenomes from US agroecosystems. The functional annotation against the SEED database in MG-RAST is capable of assigning reads to Fe(III)-reduction genes based on functional homology to the decaheme cytochrome and porin cluster of genes (\u003cem\u003eMtrA-F\u003c/em\u003e) of \u003cem\u003eShewanella oneidensis\u003c/em\u003e. MGX on the other hand has the capacity to functionally annotate metagenomic reads against the TIGRFAM domain database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e thus also assigning reads to Fe(III)-reduction genes based on homology to the conserved domains TIGR03507 (\u003cem\u003eOmcA/MtrC\u003c/em\u003e family decaheme c-type cytochrome), TIGR03508 (\u003cem\u003eDmsE\u003c/em\u003e family decaheme c-type cytochrome including \u003cem\u003eMtrA\u003c/em\u003e and \u003cem\u003eMtrD\u003c/em\u003e), and TIGR03509 (\u003cem\u003eMtrB/PioB\u003c/em\u003e family decaheme-associated outer membrane protein). Since both the MG-RAST-SEED database and TIGRFAM database used in MGX lack entries to Fe(II)-oxidation genes, we only compared the potential of the three databases to functionally annotate short reads to Fe(III)-reduction genes. Reads annotated by MGX as TIGR03508 (\u003cem\u003eDmsE/MtrA\u003c/em\u003e decaheme) domain resulted in ~ 42% false-positive hits that did not match any of the so-far discovered \u003cem\u003eMtrA\u003c/em\u003e genes provided by the exhaustive list found in the IRcyc-A database. The great number of false-positive hits found in functional annotation of metagenomic reads against the TIGRFAM domain database in MGX most likely results from a significant number of reads that belong to paralogous genes not included in the original TIGRFAM list of domains. Annotation against the SEED database in MG-RAST resulted in a small number of false positive hits – only 2% in line with their smaller number of matches. False-positive hits from the MGX and MG-RAST outputs were excluded for further downstream analyses.\u003c/p\u003e\n \u003cp\u003eOur findings show that IRcyc-A discovered 13-times greater number of reads mapping to the decaheme protein \u003cem\u003eMtrA/DmsE/MtoA\u003c/em\u003e (1650 reads) compared to MG-RAST-SEED (132 reads), and 1.6-times more relative to the MGX-TIGRFAM (1059 reads) algorithm (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). That pattern was also apparent for other iron cycling genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The greater discovery potential of IRcyc-A results from the more exhaustive list of full-length iron cycling protein gene sequences available within the IRcyc-A database. For instance, the IRcyc-A database contains \u003cem\u003eMtrA/DmsE/MtoA\u003c/em\u003e entries from 1435 bacterial genomes compared to just one full length protein sequence, that of \u003cem\u003eShewanella oneidensis\u003c/em\u003e, in the SEED database of MG-RAST and just 13 used in the original seed alignment behind the conserved structure of the TIGR03508 domain.\u003c/p\u003e\n \u003cp\u003eNext, we performed taxonomic analyses of the \u003cem\u003eMtrA\u003c/em\u003e reads reported by the three algorithms and found that in general the greatest number of taxa contributing to the \u003cem\u003eMtrA\u003c/em\u003e pool in these soil metagenomes was found in searches using IRcyc-A followed by TIGRFAM-MGX, with the least found in SEED-MG-RAST searches (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Phylum- and class-level taxonomic profiling revealed similar patterns between outputs of IRcyc-A and MGX-TIGRFAM with those of MG-RAST-SEED being most divergent, with heavy bias towards Proteobacteria, particularly Gammaproteobacteria (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). This bias is consistent with the fact that the SEED database version used in MG-RAST only contains \u003cem\u003eMtrA-F\u003c/em\u003e sequences of the gamma-proteobacterium \u003cem\u003eShewanella oneidensis\u003c/em\u003e. This stands to demonstrate that the size and diversity of the database of iron cycling genes used in alignment searches ultimately determines the reliability and accuracy of downstream analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional validation: local patterns in iron cycling predicted by IRcyc-A\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e• Acid sulfide soils in Finland\u003c/h2\u003e\n \u003cp\u003eAcid sulfide soils are characterised by high amount of pyrite (FeS\u003csub\u003e2\u003c/sub\u003e) minerals found either as primary mineral inclusions in the underlying lithology or formed as a result of diagenesis of the soil substrate during coastal flooding and its subsequent reduced conditions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. They cover an estimated area of 17\u0026nbsp;million hectares, mainly in coastal regions of Australia, North America, Europe, and Asia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The pyrite present in lower horizons (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) is unreactive under anoxic conditions. However, in contact with the oxygen atmosphere permeating upper soil horizons, pyrite interacts with oxygen in an oxidizing weathering process to form sulfuric acid and release ferrous iron (Eq. 1).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"612\" height=\"52\"\u003e\u003c/p\u003e\n \u003cp\u003eThe resulting highly acidic conditions allow for iron to remain in the ferrous Fe(II) form and not become oxidized to Fe(III) despite the more oxic environment of the upper soil horizons\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Under these conditions, biological iron Fe(II)-oxidation by acidophilic bacteria\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and not abiotic oxidation is the dominant process converting Fe(II) to Fe(III) species\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe acid sulfate soils at the Risöfladan experimental site in Finland are the result of a diagenesis process likely resulting from the rise in sea levels during the last 7500 years of the current Holocene era and subsequent rise of iron sulfide rich sediments accumulated in the anoxic sea zone to raise up to 100 m above sea level\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Inspection of the soil profiles at the site\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e indicates the presence of three distinct types of sub-soil. The uppermost layer of the sub-soil contains an already oxidized zone (lowest pH, low ferrous iron) followed by an actively oxidizing zone (acidic pH slightly greater than that of the oxidized zone, highest measured ferrous iron levels) and lastly finishing in an unoxidized zone containing pyrite parent material (highest pH, lowest ferrous iron, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). To check if these recorded patterns in soil chemistry were aligned to changes in microbial iron cycling, we analysed the publicly deposited but as of yet unanalysed metagenomic and metatranscriptomic datasets associated with this experimental site\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Using the IRcyc-A database tool, we find that reads annotated to Fe(II) oxidation genes in the metagenomes predominantly mapped to the cytochrome c gene \u003cem\u003ecyc2\u003c/em\u003e utilized by iron oxidizers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The total summed relative abundance of Fe(II) oxidation genes was greatest in the metagenomes of the oxidized and the actively oxidizing zones, whereas their abundance significantly dropped in the alkaline, unoxidized zone (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Metatranscriptomic data available from this site contains information on the overall pattern of gene expression across the entire microbial community. Its analysis through the IRcyc-A database tool shows that the expression of Fe(II)-oxidation genes peaks in the actively oxidizing zone in line with the greatest levels of ferrous iron necessary for the lithoautotrophic metabolism of iron-oxidizing bacteria. Unlike their abundance in metagenomes, the relative expression of iron oxidation genes was relatively low in the metatranscriptomes of the uppermost oxidized zone. This apparent contrast between the two types of omics data likely results from the history of the oxidized zone. Iron oxidation was high in this zone at some point in the past. The subsequent exhaustion of ferrous iron substrate for oxidation consequently led to the cessation of the process of microbial iron oxidation but nevertheless left a transcriptionally inactive but metagenomically present potential for iron oxidation. The expression of Fe(III)-reduction genes was greatest in the actively oxidizing zone. This apparent synchrony between Fe(II)-oxidation and Fe(III)-reduction may be explained by the increasing concentrations of Fe(III) required by ferric reducers that is derived from the activity of iron oxidizers. This freshly generated Fe(III) will likely form more amorphous hydroxides and thus be more readily available for reductive dissolution by Fe(III)-reducers than the Fe(III) that had been oxidized in the past and that would already have achieved a certain level of crystallinity in its hydroxide minerals. Locally, the microsites enriched in Fe(III) with actively on-going pyrite and ferrous iron oxidation (see Eq.\u0026nbsp;1) would also consume great amounts of available oxygen perhaps creating microniches of anoxic conditions necessary for microbial Fe(III)-reduction. Interestingly, the expression of siderophore biosynthesis genes, in this case exemplified by the most abundant group in these microbiomes – that of the catecholate class of siderophores – was greatest in the metatranscriptomes of the bottommost unoxidized layer. These findings support the view that siderophore production and their iron-chelating activities are most favoured under conditions limiting the supply of iron available for immediate biological uptake by microorganisms – such as those of lowest levels of Fe(II) and alkaline pH as observed in the unoxidized zone.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e• Intact versus drained peatlands in French Guiana\u003c/h2\u003e\n \u003cp\u003ePeatlands cover an estimated 422\u0026nbsp;million hectares of land area worldwide\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and act as important sinks for carbon\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The peat found in peatlands contributes between a third and a half of the global reservoir of soil organic carbon – with temperate peatlands contributing 10–30%\u003csup\u003e36\u003c/sup\u003e and tropical peatlands another ~ 20%\u003csup\u003e37,38\u003c/sup\u003e. Peat is a complex mixture of organic matter in different stages of decomposition formed under the anoxic conditions of these lands. Anoxic conditions develop owing the combined effects of high microbial respiration consuming oxygen and high soil water content limiting oxygen diffusion. Under these conditions, the processes of anaerobic microbial respiration including Fe(III)-reduction are favoured as alternative electron acceptors different to atmospheric oxygen (O\u003csub\u003e2\u003c/sub\u003e) have to be utilized\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The dissimilatory reduction of Fe(III)-containing minerals such as oxides goethite and ferrihydrite, phosphates such as vivianite and strengite, and carbonates such as siderite by microorganisms consumes electrons generated from the oxidation of electron donors – either organic (typically fermentation products such as lactate, acetate)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e or inorganic (hydrogen gas)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"746\" height=\"42\"\u003e\u003c/p\u003e\n \u003cp\u003eDraining is a method employed for converting peatlands for the purposes of agriculture, peat extraction, forestry, and human settlement\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Here we utilized a set of publicly available metagenomes from a study site in French Guiana\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e to investigate the effects of peatland draining on the microbial Fe cycle. In terms of soil chemistry, the effects of draining resulted in predictable decreases in soil water content and soil organic carbon (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The latter was likely caused by the increase of aerobic respiration, which allowed for faster decomposition of materials typically recalcitrant to decomposition under anoxic conditions. Using the IRcyc-A database, we reveal that draining was associated with a decrease in the relative abundance of Fe(II)-oxidation and Fe(III)-reduction genes in the microbial community (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) consistent with a more limited role for biogeochemical Fe cycling in response to the transition from anaerobic to aerobic metabolism. In contrast, transition from intact to drained peatlands was associated with a significant increase in the relative abundance of biosynthesis genes for all three major siderophore classes including the hydroxamate, catecholate, and mixed ligand class (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). These findings likely reflect the increased need of Fe(III)-chelating siderophores under more oxic conditions that would transition iron speciation from the highly mobile and directly available for microbial transport Fe(II) towards the Fe(III) forms intractable to direct biological utilization. Using IRcyc-A, we demonstrate that factors decreasing peatland soil water content such as draining but also probably climate change-driven drought would affect the biogeochemical cycling of iron and decrease the microbiome potential for iron oxidation and reduction while increasing the abundance of iron-chelating siderophore genes. These changes in the microbiome would likely be associated with a decline in the export of dissolved Fe to rivers from drained or dried peatlands.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003e• Biological iron oxidation as a mechanism driving silicate weathering and pedogenesis in tropical forest ecosystems in Australia\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe Atherton Tablelands in Northeast Australia is a plateau developed on deep basaltic soils, the natural climax vegetation of which is luxuriant tropical rainforests. Basalt bedrocks in this system are geologically young, deposited mainly during the Quaternary period\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Mineralogically, these basalts are iron-rich\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, with iron mostly in a ferrous form [Fe(II)] and contributed mainly by olivine and clinopyroxenes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The oxidation of basaltic ferrous iron to ferric iron during weathering could contribute to pedogenesis and the substantial amount of secondary iron oxyhydroxide (mostly hematite\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e) minerals and acidity in these tropical soils. This is exemplified by the dissolution equations of the silicate iron-rich end-member of the olivine group – fayalite:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"504\" height=\"169\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003cp\u003eTo investigate the role of biological iron oxidation of basalt as an acidogenic factor contributing to rock weathering and soil formation (pedogenesis) in this system, we deposited mesh bags containing fresh unweathered basalt grains (basalt bags, \u003cem\u003en\u003c/em\u003e = 16) or unweathered olivine (olivine bags, \u003cem\u003en\u003c/em\u003e = 17) in the top 0–10 cm of the secondary forest soils and left them to weather\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). After a year of \u003cem\u003ein situ\u003c/em\u003e weathering, we recovered the bags and collected soil samples from the immediate surroundings of the deposited bags (\u003cem\u003en\u003c/em\u003e = 2). Next, we extracted total DNA from the collected weathered rock grains and soils and performed shotgun metagenome sequencing\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Here, we run these publicly available metagenomic sequencing data through the IRcyc-A pipeline. Our analyses show that both weathered basalt and soils exhibited significantly greater relative abundance of the Fe(II)-oxidation \u003cem\u003ecyc2\u003c/em\u003e genes than the microbial communities of weathered olivine (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eInterestingly, both weathered basalt and olivine samples exhibited clear evidence for alteration, colour change, and development of iron oxide-rich rind (surface layer – see Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Comparing the pH of the three substrates in our study, it is clear that the weathering of basalt increased pH from 4.2 (surrounding soil) to 4.8 (inside basalt bags) but not to the same extent as olivine at pH 6.6 (inside olivine bags). These findings show that, in agreement with geochemical theory, the pH, but not total ferrous iron content (assuming majority as FeO and not Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e in fresh rock, basalt = 6.8% vs. olivine = 10.3% wt.) determined the extent of \u003cem\u003ebiological\u003c/em\u003e iron oxidation which is favoured by low pH\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn soils, however, both acidity and total Fe are appropriate for biological iron oxidation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), but most of the iron is present in ferric form as hematite. Consequently, the high abundance of \u003cem\u003ecyc2\u003c/em\u003e genes in these soils possibly reflect the redox fluctuations experienced during the wet season\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. After prolonged wet weather, the water content of these tropical soils becomes conducive to microbial iron reduction of Fe(III) to Fe(II)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. During oxic conditions the thus accumulated Fe(II) can become biologically re-oxidized to Fe(III) by \u003cem\u003ecyc2\u003c/em\u003e-carrying bacteria. However, given the large amounts of ferrous iron needed to support autotrophic growth by iron oxidation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, it seems likely that most iron oxidizers in highly weathered tropical forest soils (where most iron is ferric) are mixotrophs or heterotrophs that only partially complement their energy needs by \u003cem\u003ecyc2\u003c/em\u003e-enabled iron oxidation – in an analogy to carboxydotrophic bacteria in soil\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In further support of that, the acidobacterial genus Ca. \u003cem\u003eAcidoferrum\u003c/em\u003e, recovered as a MAG from soil in Panamanian tropical forests\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, does contain \u003cem\u003ecyc2\u003c/em\u003e and \u003cem\u003ecyc1\u003c/em\u003e homologues but no carbon fixation pathway.\u003c/p\u003e\n \u003cp\u003eIn contrast, during the weathering of basalt, there is no necessity for ferrous iron to be supplied by microbial iron reduction since most of the iron released from weathering is ferrous. At the pH of 4.8 (inside basalt rock bags), ferrous iron is relatively stable\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and locally abundant therefore allowing for biological iron oxidation by autotrophs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Our generated data allowed us to examine this hypothesis by isolating several metagenome-assembled genomes (MAGs) from weathered basalt bags recovered from these tropical forest soils. One of these MAGs was identified to belong to a new species within the GJ-E10 lineage – a lineage of unnamed Burkholderiales bacteria previously shown to carry out iron oxidation under acidic conditions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Genomic analysis of our GJ-E10-related MAG revealed the presence of homologues to \u003cem\u003ecyc2\u003c/em\u003e and \u003cem\u003ecyc1\u003c/em\u003e of \u003cem\u003eAcidithiobacillus ferrooxydans\u003c/em\u003e and a full set of genes required for the Calvin cycle of autotrophic carbon dioxide fixation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Here, we propose a new taxonomic nomenclature for this generically named iron-oxidizing autotrophic genus GJ-E10 – Ca. \u003cem\u003eFerrobasaltibacterium\u003c/em\u003e, gen. nov. and for our newly discovered species – Ca. \u003cem\u003eFerrobasaltibacterium australiensis\u003c/em\u003e, sp. nov.\u003c/p\u003e\n \u003cp\u003eTaxonomic analysis of the universal single copy marker gene \u003cem\u003erpoA\u003c/em\u003e from these microbiomes through the IRcyc-A database show that Ca. \u003cem\u003eFerrobasaltibacterium\u003c/em\u003e was 10-fold more abundant in weathered basalt communities relative to those of soils and weathered olivine (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). These results strongly indicate that biological iron oxidation of basalt in these tropical soils requires basalt specialist autotrophic microbial assemblages and that iron oxidizing bacteria are important factor for silicate weathering and soil development under tropical conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGlobal biome patterns in iron cycle genes\u003c/h2\u003e\n \u003cp\u003eWe selected soil microbiomes from a wide range of publicly available datasets with the final collection containing 193 soil shotgun metagenomes and 29 soil metatranscriptomes, representative of all major terrestrial biomes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). A sub-collection of soil metagenomes (\u003cem\u003en\u003c/em\u003e = 15) representing specific treatment conditions were separated and the remaining metagenomes exemplifying untreated soil conditions were used for across-biome comparisons of iron cycling gene abundance.\u003c/p\u003e\n \u003cp\u003eThe abundance of genes involved in the process of Fe(III)-reduction was highest (ANOVA, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, post-hoc multiple comparisons) in soil metagenomes of peatlands and flooded grasslands (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). On average, the abundance of Fe(III)-reduction genes in the soil metagenomes of peatlands and flooded grasslands (mean = 0.79, SE = 0.04) was ~ 3.0-fold greater than the unweighted average across biomes (mean = 0.27, SE = 0.05). To establish the robustness of this observation, we compared the peatland metagenomes of several locations across the globe against the global average. Metagenomes of peatlands located in the United Kingdom, USA, Sweden, and French Guiana all exhibited means greater than the global unweighted average across biomes. Peatlands may thus represent a hotspot for biological Fe(III)-reduction consistent with their role in exporting larger amounts of soluble iron compared to non-peaty catchments (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). From a metabolic perspective, organic matter oxidation coupled to the reduction of Fe(III) is most energetically favourable when alternative electron acceptors (O\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e−\u003c/sup\u003e) are exhausted upon prolonged growth under anaerobic, low-redox conditions created by the high soil water content limiting oxygen diffusion and the high demand for O\u003csub\u003e2\u003c/sub\u003e under the organic-rich peat. In line with these, the abundance of Fe(III)-reduction genes in soil metagenomes were lowest in desert and arctic deserts where available soil water and organic matter are at their lowest.\u003c/p\u003e\n \u003cp\u003eThe abundance of genes involved in Fe(II)-oxidation was greatest (ANOVA, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, post-hoc multiple comparisons) in soil metagenomes of acid iron sulfide soils (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). On average, the metagenomes of acid iron sulfide soils (mean = 0.74, SE = 0.15) exhibited 4.6-fold greater abundance in Fe(II)-oxidizing genes relative to the global across-biome average (unweighted mean = 0.16, SE = 0.06). This observation held for comparisons made between the two sites of acid iron sulfide soils – one in China and one in Finland – against the global average. Both of these acid sulfide soil sites exhibited mean Fe(II)-oxidation gene abundance 2.5-6.6-fold greater than the mean abundance in metagenomes across biomes. Iron(II)-oxidation is most commonly associated with acidic conditions under which Fe(II) remains stable even under highly oxic conditions. Iron sulfide soils thus provide the ideal conditions for biological Fe(II)-oxidation as the dissolution of their contained pyrite produces ferrous iron and the acidity (Eq. 1) necessary to maintain Fe(II) stable in oxic environments. In the absence of pyrite, acidophilic Fe(II)-oxidizers will be dependent upon the external acidity of the substrate (e.g., basalt weathered in tropical soils as in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) and natural fluctuations in soil redox conditions that (e.g., highly weathered oxisols in the tropics \u003cstrong\u003e–\u003c/strong\u003e Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) generate Fe(II) from soil Fe(III)-oxyhydroxides through microbial Fe(III)-reducing activity. These conditions are best exemplified by the soils of tropical rainforests, peatlands, and tundra where high soil acidity and/or fluctuations in soil redox are commonplace. Indeed, our global biome analyses confirm that soil metagenomes from these three biomes exhibited some of the greatest abundance of Fe(II)-oxidizing genes after that hereby reported for acid sulfide soils (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eNext, we analysed the across-biome patterns in the metagenomic gene abundance of different classes of siderophore biosynthesis genes. Genes involved in the biosynthesis of catecholate siderophores (e.g., enterobactin, vibriobactin, bacillibactin etc.) were greatest in the soil microbiomes of tropical grasslands (savannas), acid iron sulfide soils, peatlands, and tropical forests, while agroecosystems and temperate grasslands showed lower abundances (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Lower yet were the catecholate gene abundances of soil microbiomes of tundra, deserts, and arctic deserts. These results may highlight the dual role of catecholates serving as Fe(III)-chelating agents produced in response to iron deficiency\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e as well as anti-oxidant molecules protecting against reactive oxygen species (ROS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. ROS are commonly produced in soil as a result of the respiratory activity of soil microorganisms\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Biotic ROS undergo a catalytic reaction with soil Fe(II) and Fe(III)-(oxyhydr)oxide minerals that generate the highly reactive OH\u003csup\u003e•\u003c/sup\u003e radicals.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"542\" height=\"47\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThese Fenton and Fenton-like reactions are particularly enhanced in soils experiencing high levels of microbial activity, fluctuations in redox state, Fe(II) production via microbial Fe(III)-reduction, and are rich in poorly-crystalline amorphous iron minerals\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. These findings help explain the high abundance of genes involved in the production of ROS-scavenging catecholate siderophores in microbiomes typically associated with high biological availability of iron – e.g., peatlands, acid iron sulfide soils, tropical forest soils etc. or large fluctuations in redox state such savannas and to a lesser extent tropical forests experiencing alternating dry and wet seasons (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). These provide a novel insight between Fe(II)-oxidizers and Fe(III)-reducers on the one hand affecting the levels of reactive iron species and Fenton-based ROS generation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and catecholate siderophore producers on the other hand that can inactivate the produced ROS species\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In addition, the Fenton-based chemistry of iron cycling and the role of catecholates in these cases will ultimately determine the amount of ROS species that can impact soil organic matter stability and decomposition\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. These findings help us gain further insight into the coupling between the global Fe and C cycling.\u003c/p\u003e\n \u003cp\u003eThe metagenomic relative abundance of genes implicated in the production of hydroxamate siderophores (e.g., desferrioxamine B and E, ferrichrome, aerobactin, malleobactin\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e etc.) was highest in agroecosystems and deserts closely followed by temperate forest, temperate grasslands, tropical grasslands, and tropical forests (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). The lowest abundance in hydroxamate biosynthesis genes was observed in the soil metagenomes of acid iron sulfide soils, peatlands, tundra, and arctic deserts. Genes involved in the synthesis of mixed ligand siderophores (e.g., pyoverdine, mycobactin, yersiniabactin etc.) followed a similar pattern to that of hydroxamate siderophores in that agroecosystem soil metagenomes had among the greatest abundance, whereas lowest abundances were recorded for the soil microbiomes of arctic deserts, deserts and tundra. Furthermore, we performed correlational analyses that revealed a cluster of strong positive correlations between the relative abundance of Fe(III)-reducing, Fe(II)-oxidation, and catecholate biosynthesis genes across biomes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). A second cluster of positive correlations was formed between the relative abundance of genes implicated in the biosynthesis of hydroxamate, mixed ligand, and carboxylate siderophores (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). The two clusters were negatively correlating with each other. Our findings can be understood as a result of microbiomes being actively shaped by iron availability and ROS species. For instance, catecholate siderophores, Fe(II)-oxidation genes, and Fe(III) reduction genes are highest in abundance in soil microbiomes subjected to high biological availability of iron in soil allowing for extensive biogeochemical cycling of iron and ROS species evolution. This is consistent with previously suggested views that the processes of iron reduction and oxidation frequently occur simultaneously or cyclically\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In contrast, the genes for the majority of siderophore classes (hydroxamates, mixed ligands, carboxylates) peak in metagenomic abundance where Fe availability is more limited as indicated by their negative association with Fe(II)-oxidation and Fe(III)-reduction genes. These findings are in line with a major role of siderophores in ameliorating Fe deficiency\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eTaxonomic profiling of target Fe cycling gene groups across biomes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) suggest that members of the Acidobacteriota, many of which uncultured\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, dominate the gene pools involved in biogeochemical cycling of iron (oxidation and reduction; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA,B) in topsoils, whereas Actinobacteriota dominate the production of hydroxamate siderophores (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Actinobacteria and Acidobacteriota together with the group of Proteobacteria are among the most important phyla in terms of iron cycling and chelation (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Actinobacteria and Acidobacteria are also among the most abundant bacterial phyla in soil microbiomes across the globe, making up to ~ 60% of all bacteria in soil (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cstrong\u003epanel 1\u003c/strong\u003e). We observed a strong negative correlation between the abundance of these groups in soil microbiomes across biomes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cstrong\u003epanel 1\u003c/strong\u003e). Furthermore, as expected based on their contribution to the pools of Fe cycling genes, the abundance of Acidobacteriota in the metagenome correlated positively with the abundance of Fe(III)-reduction and Fe(II)-oxidation genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cstrong\u003epanels 2 and 3\u003c/strong\u003e). Similarly, the abundance of Actinobacteriota correlated positively with the abundance of hydroxamate siderophore biosynthesis genes in the metagenome (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cstrong\u003epanel 4\u003c/strong\u003e). These findings suggest that the abundance of these two keystone phyla in soil microbiomes is associated with iron availability, chelation and redox cycling, pointing to iron as an overlooked factor in shaping soil microbial communities.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe show that the novel database tool IRcyc-A can be reliably used for functional annotation of iron cycling and chelation genes in soil omics data with results from our benchmarking tests demonstrating its utility compared to existing metagenomics pipelines. We further validate IRcyc-A output by showing its capacity to detect shifts in central Fe cycling genes within the soil community in response to known localized gradients in Fe cycling across soil depths, water regimes, and substrates. Using IRcyc-A to test for patterns in iron cycling potential using metagenomes from all major terrestrial biomes, we show that Fe(III)-reduction potential was greatest in peatland microbiomes in line with greater fluxes of dFe(II) in rivers draining peatlands relative to rivers draining non-peaty areas. Fe(II)-oxidation genes exhibited their greatest abundance in soil microbiomes of acid sulfide soils in line with the greatest concentrations of dFe observed in the highly acidic red streams draining areas of pyrite lithology. These data show that global trends in microbial Fe cycling correlates well with observed fluxes of dFe in rivers. Hydroxamate, mixed ligand, and carboxylate siderophore genes were greatest in abundance in ecosystems with low bioavailability of iron such as agroecosystems in line with major role of siderophores in sequestrating iron under drier, low organic and thus more iron-deficient conditions. In contrast, the abundance of biosynthesis genes for catecholate siderophores in agreement with the dual role of catecholates as iron chelating agents and antioxidants did not follow trends of low Fe availability but instead was highest in areas with high amount of Fe(III)- and Fe(II)-oxidation associated with extensive Fenton-like reactions generating reactive oxygen species. Furthermore, using IRcyc-A, we expand our understanding on the major players in microbial Fe cycling in soils and provide evidence for the major role of iron bioavailability as a force shaping the soil microbiome. Our analyses highlight the important role biology plays in determining the biogeochemical cycling of iron as well as the ability of iron to affect key aspects of soil microbiome composition and function. In addition, improving our understanding of the localized and global patterns in iron cycling in soil may help elucidate the complex interactions and coupling between dynamic processes in iron and soil organic matter transformations that can inform future policies for soil carbon conservation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSelection of publicly available soil metagenomes and metatranscriptomes\u003c/h2\u003e \u003cp\u003ePublicly available metagenomes were selected to include topsoils (0\u0026ndash;15 cm soil depth) and their respective treatments, and any other associated metadata (geographic coordinates, country, biome association, soil chemistry, etc.) were acquired from primary publications, archived manuscripts, and/or public server metadata files. Omics samples recovered from greater soil depths were also obtained and analysed but those were used for localized patterns functional validation and not in the global analyses that only utilized topsoil data. Similarly, omics samples from non-soil substrates found in soil (e.g., mesh bags containing crushed rocks deposited in soil as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and the associated section in the Main Text) were analysed through the IRcyc-A database for functional validation but were not used in the global analyses of the terrestrial iron cycle. Public servers storing omics data used in this study included MG-RAST and NCBI. Biome association was used as indicated by the data uploaded with slight modifications with existing biome classification. List of all of the metagenomic and metatranscriptomic data used in this study and their unique database identifiers is available in \u003cb\u003eSupplementary Information File 1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of IRcyc-A\u003c/h2\u003e \u003cp\u003eThe \u003cb\u003eIR\u003c/b\u003eon \u003cb\u003ecyc\u003c/b\u003eling gene \u003cb\u003eA\u003c/b\u003ennotation (\u003cb\u003eIRcyc-A\u003c/b\u003e) database was developed based on the principle of including pre-annotated functional marker genes exhaustively from across the prokaryotic tree of life. To do so, specific TIGRFAM protein domains\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and Kyoto Encyclopaedia of Genes (KEGG) orthologues (KO) numbers (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/kegg2.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/kegg2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a known role in Fe cycling were searched against the pre-populated genome annotations available as part of the AnnoTree project\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e (GTDB database Release R95, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://annotree.uwaterloo.ca/annotree/app/\u003c/span\u003e\u003cspan address=\"http://annotree.uwaterloo.ca/annotree/app/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All homologues of Fe(III)-reduction genes of \u003cem\u003eShewanella oneidensis\u003c/em\u003e decaheme cytochrome c protein genes (\u003cem\u003eMtrA,C\u003c/em\u003e) and porin protein gene (\u003cem\u003eMtrB\u003c/em\u003e) were downloaded from across all publicly available genomes using matches against the TIGRFAM domains \u003cem\u003eDmsE/MtrA\u003c/em\u003e family decaheme c-type cytochrome (TIGR03508), decaheme c-type cytochrome, \u003cem\u003eOmcA/MtrC\u003c/em\u003e family (TIGR03507), and decaheme-associated outer membrane protein, \u003cem\u003eMtrB/PioB\u003c/em\u003e family (TIGR03509). These included known homologues from other Fe-cycling microorganisms including the \u003cem\u003eMtoAB\u003c/em\u003e genes of \u003cem\u003eSiderooxydans lithotrophicus\u003c/em\u003e and \u003cem\u003ePioAB\u003c/em\u003e from \u003cem\u003eRhodopseudomonas palustris\u003c/em\u003e. The \u003cem\u003ecyc2\u003c/em\u003e gene of \u003cem\u003eAcidithiobacillus ferrooxydans\u003c/em\u003e directly implicated in the process of Fe(II)-oxidation was searched against the pre-populated AnnoTree annotation using the KO number K20150, \u003cem\u003ecyc2\u003c/em\u003e, iron:rusticyanin reductase [EC:1.16.9.1].\u003c/p\u003e \u003cp\u003eThe universal single-copy marker gene \u003cem\u003erpoA\u003c/em\u003e encoding the DNA-directed RNA polymerase alpha subunit\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e is present in most known bacteria, therefore making it a good taxonomic marker. All of the \u003cem\u003erpoA\u003c/em\u003e annotated hits across the prokaryotic tree of life were downloaded from AnnoTree annotated genomes using the KO number K03040. Matches against the \u003cem\u003erpoA\u003c/em\u003e gene when using IRcyc-A on metagenomic and metatranscriptomic data were utilized for studying the taxonomic composition of samples.\u003c/p\u003e \u003cp\u003eNote that the FASTA heading for each of the described iron cycling and \u003cem\u003erpoA\u003c/em\u003e full-length protein coding sequences present in IRcyc-A are formatted to have a taxonomic identity tag containing detailed information on their taxonomic lineage (domain to species as acquired from the GTDB taxonomy\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e - \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtdb.ecogenomic.org/tree\u003c/span\u003e\u003cspan address=\"https://gtdb.ecogenomic.org/tree\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as shown in the example below:\u003c/p\u003e \u003cp\u003e\u0026ldquo; \u0026gt;MtrA/DmsE_NZ_KB892868.1_95[d_Bacteria;p_Proteobacteria;c_Gammaproteobacteria;o_Burkholderiales;f_Rhodocyclaceae;g_\u003cbr\u003eUliginosibacterium;s_Uliginosibacterium gangwonense]_NZ_KB892868.1_95\u003c/p\u003e\n\u003cp\u003eMSIIRKLLVACMLVAGVSGAPLVWAADNIQLPDLSPAKSKAELAQDTLKKDAICTRCHDETEPAPVLSIYQTKHGMRGDARSPS\u0026hellip; \u0026rdquo;\u003c/p\u003e\u003cp\u003eThe structure of the heading is as follows: a. gene name (\u0026ldquo;MtrA/DmsE\u0026rdquo; followed by \u0026ldquo;_\u0026rdquo;), b. unique sequence identifier as acquired from AnnoTree/GTDB database (NZ_KB892868.2_95), c. taxonomic identity tag (from domain to species, divided by \u0026ldquo;;\u0026rdquo; as a delimiter and \u0026ldquo;[\u0026ldquo; and \u0026ldquo;]\u0026rdquo; as delimiters to separate it from the rest of the header), and d. unique sequence identifier repeated at the end. Unfolding taxonomic tags in downstream analyses allows for taxonomic assessment of whole microbiomes (using \u003cem\u003erpoA\u003c/em\u003e since it is present in almost all bacteria) and the iron cycle specifically.\u003c/p\u003e \u003cp\u003eSiderophores are a diverse set of iron-chelating molecules and multiple pathways are involved in their biosynthesis. In addition, their orthologues are not well mapped in the KEGG database (except catecholates and some mixed ligand siderophores). To address that, key siderophore biosynthesis genes from organisms in which the given type of siderophore biosynthesis was first discovered/described were acquired from pathways available in MetaCyc\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metacyc.org/\u003c/span\u003e\u003cspan address=\"https://metacyc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Acquired key genes were then used as queries in pBLAST searches against the UniProt\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Hits matching certain cut-off scores (\u003cb\u003ea. close matches\u003c/b\u003e: positive matches\u0026thinsp;\u0026ge;\u0026thinsp;70%, identical matches\u0026thinsp;\u0026ge;\u0026thinsp;45%, length\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;15% of original query length, \u003cb\u003eb. -like matches\u003c/b\u003e: identical matches\u0026thinsp;\u0026ge;\u0026thinsp;45%, length\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;25% of original query length, and \u003cb\u003ec. similar to siderophores\u003c/b\u003e: three sub-groups \u0026ndash; \u003cem\u003ec.1\u003c/em\u003e: positives\u0026thinsp;\u0026lt;\u0026thinsp;70%, id\u0026thinsp;\u0026ge;\u0026thinsp;45% but length\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;25%, \u003cem\u003ec.2\u003c/em\u003e: positives\u0026thinsp;\u0026ge;\u0026thinsp;70% but length\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;25%, and \u003cem\u003ec.3\u003c/em\u003e: id\u0026thinsp;\u0026lt;\u0026thinsp;45%, with c.1-c.3 included as a filter to sieve out false-positive hits but not counted towards siderophore biosynthesis genes) were all selected and added to the developing IRcyc-A database. It was aimed that only one central gene within each siderophore biosynthetic pathway was included so that over-assignment to particular siderophore types is avoided.\u003c/p\u003e \u003cp\u003eIn order to enable the use of IRcyc-A as a stand-alone database tool for Fe cycle gene annotation and microbiome taxonomy, filter sequences had to be supplied to limit false-positive hits. To do so, two strategies were deployed. First, a general filter set of sequences were added in the form of the SwissProt Uniprot set of protein gene sequences. The latter was added after removal of matching sequences (identical matches\u0026thinsp;\u0026gt;\u0026thinsp;70%) against the so-far assembled IRcyc-A core functional genes. Second, a specific filter set of sequences containing KO paralogs of Fe cycling genes, \u003cem\u003erpoA\u003c/em\u003e, and siderophore biosynthesis genes (including group c. similar to siderophores hits from the performed pBLASTs as described above) were added to the IRcyc-A database. All genes present in the resulting IRcyc-A database were included as amino acid FASTA (FAA) protein sequences with a summarised breakdown shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and a more detailed breakdown available in \u003cb\u003eSupplementary Information File 2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBenchmarking IRcyc-A\u003c/h2\u003e \u003cp\u003eA central driver in our motivation to develop IRcyc-A was the premise of easy non-programmatic access and use. Consequently, we compared IRcyc-A against other existing tools that do not require coding experience and contain a user-friendly graphical interface. Those included MGX\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and MG-RAST\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The comparison was based upon the TIGRFAM domain annotation in MGX (particularly, domains Fe(III)-reduction gene-specific domains TIGR03507, TIGR03508, TIGR03509) and the Fe(III)-respiration pathway of \u003cem\u003eShewanella\u003c/em\u003e available in the Subsystem-database annotation output from MG-RAST (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB,C). Taxonomy was assigned against the IRcyc-A database using the provided taxonomic identity tags. Since the \u003cem\u003eMtr\u003c/em\u003e-genes available in the MG-RAST Subsystem database are only derived from the gamma-proteobacterium Shewanella oneidensis, the taxonomy of Fe(III)-reduction genes in MG-RAST outputs was highly biased towards Gammaproteobacteria and clades with sequence homology to Gammaproteobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). IRcyc-A discovered the greatest number of Fe-cycling genes that were derived largely from the same taxonomic groups (phyla and classes) as the MGX-TIGRFAM algorithm. It is noteworthy that IRcyc-A discovered fewer \u003cem\u003erpoA\u003c/em\u003e reads than MG-RAST in these metagenomes indicating that IRcyc-A does not over-assign. The benchmarking was carried out on the same set of 6 soil metagenomes from temperate agroecosystems (marked with \u0026ldquo;*\u0026rdquo; in \u003cb\u003eSupplementary Information File 1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHow to use IRcyc-A\u003c/h2\u003e \u003cp\u003eFor fast and efficient analysis of Fe cycling genes and microbiome taxonomy, we recommend that the IRcyc-A database file (\u003cb\u003eSupplementary Information File 3\u003c/b\u003e) and metagenomics/metatranscriptomics sequencing reads of interest are all uploaded to the Galaxy Europe server\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usegalaxy.eu/\u003c/span\u003e\u003cspan address=\"https://usegalaxy.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via a third-party uploading tool (e.g., FileZilla). Galaxy Europe and other international Galaxy servers allow users to create free private accounts with up to 200 Gb storage capacity. Upon successful upload, the IRcyc-A database input available as a FAA file can then be converted into a Diamond database file. The resulting IRcyc-A Diamond database can be used as a query in Diamond blastx searches against the user-provided omics sequencing reads (typically either the R1 or R2 reads from paired-end sequencing libraries would suffice). While setting-up the Diamond running parameters, \u0026ldquo;\u003cem\u003eThe maximum number of target sequences per query to report alignments for\u003c/em\u003e\u0026rdquo; is changed from the default 25 to 1, and the \u0026ldquo;\u003cem\u003eFormat of output file\u003c/em\u003e\u0026rdquo; parameter is set to BLAST Tabular. In addition to pre-selected fields, the following additional fields are selected for the BLAST Tabular output: \u0026ldquo;All Subject Seq \u0026ndash; id(s)\u0026rdquo;, \u0026ldquo;Query sequence length\u0026rdquo;, \u0026ldquo;Subject sequence length\u0026rdquo;, and \u0026ldquo;All Subject Title(s)\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe output from these Diamond runs will contain matches against the general and specific filter sequences embedded in IRcyc-A to limit false-positive hits. To remove those from the Diamond output and filter only sequences of interest (e.g., Fe cycling gene and \u003cem\u003erpoA\u003c/em\u003e gene reads), the user is advised to utilize the \u0026ldquo;Select lines that match an expression\u0026rdquo; tool available in Galaxy. The pattern field within the Select tool can be filled up by removing the existing expression and replacing it with the list of expressions as shown in the IRcyc-A parameter file (\u003cb\u003eSupplementary Information File 4\u003c/b\u003e). The resulting output from the select tool should be in a tabular format. This final file can be downloaded and further analyses of it performed in Excel or similar software. The content of the last column, \u0026ldquo;All Subject Title(s)\u0026rdquo; can be unfolded using \u0026ldquo;_\u0026rdquo; as a delimiter which would provide information on the gene (first column) and taxonomic identity tag (second column). The taxonomic tag containing information for all taxonomic levels from domain to species can be unfolded further using \u0026ldquo;[\u0026ldquo; and \u0026ldquo;;\u0026rdquo; as consecutive delimiters. Note that detailed taxonomic information is available only for central Fe(III)-reduction and Fe(II)-oxidation genes and \u003cem\u003erpoA\u003c/em\u003e. Taxonomic tags from siderophore genes and more obscure Fe-cycling genes (\u003cem\u003eomcS\u003c/em\u003e, \u003cem\u003efoxE\u003c/em\u003e, etc.) should not be used for further taxonomic analyses. The e-value column in the tabular output file can be used for user-defined cut-offs with hits below certain selected cut-offs removed (here we used a cut-off of e-5). Reads can be counted as they are. Alternatively, gene counts could be normalized for gene length by dividing the query length (for paired-end Illumina reads for instance, that would often be 150 bp) by the subject length multiplied by 3 (since all gene sequences in the IRcyc-A database are present as protein coding sequences). In this study, we preferred the latter approach as to account for the large variation in gene lengths. To normalize for sequencing depth and varying number of sequenced genomes per metagenome, we further normalized the resulting length-normalized gene counts by dividing them by the length-normalized \u003cem\u003erpoA\u003c/em\u003e gene counts. The final \u003cem\u003erpoA\u003c/em\u003e-normalized and length-normalized gene counts per gene per sample can be summarised using the Pivot Table tool in Excel. If analysis on multiple samples/datasets is required, the Select outputs can be pre-merged using the Concatenate tool in Galaxy (\u0026ldquo;Concatenate multiple datasets tail-to-head by specifying how\u0026rdquo;) by selecting \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Include dataset names?\u0026rdquo;. The final merged output can then be downloaded, processed and analysed in Excel as described above.\u003c/p\u003e \u003cp\u003eWe note that within hits matching the iron-reduction genes \u003cem\u003eMtrABC\u003c/em\u003e there will be those belonging to known iron-oxidation microorganisms (e.g., representatives of the Gallionellaceae which contain \u003cem\u003eMtoAB\u003c/em\u003e homologous to \u003cem\u003eMtrAB\u003c/em\u003e) as well as putative iron-oxidizers (e.g., we here defined these as microorganisms containing \u003cem\u003ecyc2\u003c/em\u003e genes within their genomes). Here, we have opted to separate \u003cem\u003eMtrABC\u003c/em\u003e hits matching these organisms and count them towards the group of iron-oxidation genes. A list of these organisms that can be used for filtering \u003cem\u003eMtrABC\u003c/em\u003e outputs is included in \u003cb\u003eSupplementary Information File 5\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAdditional taxonomic assignment of siderophore biosynthesis genes\u003c/h2\u003e \u003cp\u003eUnique sequence identifiers of sequences annotated as siderophore biosynthesis genes in the IRcyc-A annotation output for each sample were collected into a list and their FASTA read sequences acquired using the \u0026ldquo;Filter FASTA\u0026rdquo; tool against the original metagenome/metatranscriptome samples in the Galaxy Europe platform. Species-level taxonomic assignment for each of these reads was carried out against the RefSeq 2021-v12 database using Diamond blastx (cut-off e-5). Full lineage information was further acquired using the ETE lineage tools in Galaxy with the current (as of January 2023) ETE sqlite DB in NCBI.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMAG assembly and genome annotation of Ca.\u003c/em\u003e Ferrobasaltibacterium\u003c/p\u003e \u003cp\u003eThe metagenome assembled genome (MAG) of Ca. \u003cem\u003eFerrobasaltibacterium australiensis\u003c/em\u003e was recovered and functionally annotated from the rock-associated metagenomes of basalt weathered in Australia by using the following pipeline: MEGAHIT (assembly)\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, MaxBin2.0 (binning)\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, CheckM (establishing completeness and contamination)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, GTDB-Tk (taxonomic annotation based on phylogenomics)\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, Prodigal (genome-wide protein prediction)\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, BlastKOALA (genome-wide functional annotation of protein sequences against the KO database)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, eggNOG\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and Diamond searches using \u003cem\u003eAcidithiobacillus ferrooxydans\u003c/em\u003e cyc2, cyc1, and cycA1 protein sequences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eWe thank David Beerling, Fin Ring-Hrubesh, and Simon Cheung for providing useful comments during editing of the manuscript. We also thank the authors of the publicly available data used in this study, without whom this work would not have been possible. D.Z.E acknowledges funding from the European Research Council (ERC) through the ERC Advanced grant (CDREG, 2993) and the Leverhulme Trust through the Leverhulme Research Centre award (RC- 2015-019) both awarded to David Beerling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKendall B, Anbar AD, Kappler A, Konhauser KO (2012) The Global Iron Cycle. In: Knoll A, Canfield D, Konhauser K (eds) Fundamentals of Geobiology, 1st edn. Blackwell Publishing, pp 65\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHell R, Stephan UW (2003) Iron uptake, trafficking and homeostasis in plants. Planta 216:541\u0026ndash;551\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruns H et al (2018) Function-related replacement of bacterial siderophore pathways. 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Mol Biol Evol 38:5825\u0026ndash;5829\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"global iron cycle, Fe cycle, soil microbiome, iron oxidation, iron reduction, Acidobacteria, siderophores, Actinomycetes, biomes","lastPublishedDoi":"10.21203/rs.3.rs-4248419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4248419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global iron (Fe) cycle governs important aspects of biosphere function by defining Fe availability thus supporting productivity of terrestrial and ocean ecosystems. However, the link between soil microbiome function to global patterns in terrestrial iron cycling remains poorly investigated. Here, we developed a novel database termed \u003cem\u003eIR\u003c/em\u003eon \u003cem\u003ecyc\u003c/em\u003ele \u003cem\u003eA\u003c/em\u003ennotation (IRcyc-A) targeted at discovering and annotating Fe cycle genes within omics data that we validated against known localized patterns of iron cycling. We leveraged this new tool to analyse the Fe cycle of over 220 publicly available soil metagenomes and metatranscriptomes encompassing a wide range of biomes on Earth. We show that the greatest abundance of Fe(III)-reduction and Fe(II)-oxidation genes were attributed to Acidobacteriota and were most abundant in the microbiomes of peatlands and iron sulfide soils, respectively. This is consistent with the high levels of dissolved Fe recorded in rivers draining such areas. In contrast, genes encoding the biosynthesis of siderophores deployed in iron sequestration in response to Fe deficiency peaked in agroecosystems with the majority assigned to Actinomycetota. Siderophore synthesis genes were negatively correlated with Fe(III)-reduction and Fe(II)-oxidation genes, supporting the view of divergent communities under low and high iron availability. Our findings highlight how iron availability shapes terrestrial microbial communities and how microbial processes can in turn contribute to global patterns in terrestrial Fe and C cycling.\u003c/p\u003e","manuscriptTitle":"Contrasting microbial communities drive iron cycling across global biomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 04:55:41","doi":"10.21203/rs.3.rs-4248419/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0da1358a-f6c7-4ae0-872c-e7da62b52a09","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":30758692,"name":"Earth and environmental sciences/Biogeochemistry"},{"id":30758693,"name":"Biological sciences/Microbiology/Environmental microbiology/Soil microbiology"}],"tags":[],"updatedAt":"2024-04-25T04:55:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 04:55:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4248419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4248419","identity":"rs-4248419","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}