Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes

Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes Grace Pold , Aurélien Saghaï , Christopher M Jones , Sara Hallin doi: https://doi.org/10.1101/2025.01.07.631734 Grace Pold 1 Department of Soil and Environment, Swedish University of Agricultural Sciences , Uppsala Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aurélien Saghaï 2 Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences , Uppsala Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher M Jones 2 Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences , Uppsala Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara Hallin 2 Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences , Uppsala Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: sara.hallin{at}slu.se Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Diverse microorgani sms can execute one or more steps in denitrification, during which nitrate or nitrite is successively reduced into nitric oxide, nitrous oxide, and ultimately dinitrogen. Many of the best-characterized denitrifiers are “complete” denitrifiers capable of executing all steps in the pathway, but whether they dominate in natural communities and what metabolic traits and environmental factors drive the global distribution of complete vs. partial denitrifiers remains to be determined. To address this, we conducted a comparative analysis of denitrification genes in 61,293 genomes, 3,991 metagenomes covering all major biomes, and 413 terrestrial and aquatic metatranscriptomes. We show that partial denitrifiers outnumber complete denitrifiers and the potential to initiate denitrification is more common than the potential to terminate it, both among genomes and at the community level across most biomes, particularly in nutrient rich environments. These patterns were also reflected in the metatranscriptomes. Our results further indicate that complete denitrifiers are more likely to be fast-growing organisms, favoring organic acid over sugar metabolism, and encoding the ability to oxidize and reduce a broader range of organic and inorganic compounds compared to partial denitrifiers. This suggests complete denitrifiers are metabolically flexible opportunists. Together, our results indicate an environmental footprint on the presence of denitrification genes which favors the genomic potential for partial over complete denitrification in most biomes and highlight that completion of the denitrification pathway is a community effort. Main The potent greenhouse gas and ozone depleting agent nitrous oxide (N 2 O) is produced in several microbial N-cycle processes, of which denitrification plays a dual role in both producing and consuming N 2 O. In this facultative anaerobic microbial respiratory pathway, nitrate (NO - ) or nitrite (NO - ) are used as electron acceptors and successively reduced into nitric oxide (NO), N 2 O, and dinitrogen (N 2 ) by a diverse range of predominantly bacterial species. As the genes encoding the enzymes involved in these reductive steps can be independently gained and lost 1 – 3 , they are found in different combinations among genomes 4 , such that denitrification can be executed by complete denitrifiers with genes encoding enzymes for all steps or, alternatively, split between multiple partial denitrifier community members. Recent studies suggest that genetically complete denitrifiers are scarce among genomes assembled from environmental data 5 – 10 . However, much of our understanding of the physiology and regulation of denitrification is based on studies of a very phylogenetically restricted set of complete denitrifiers 11 – 16 . Thus, there is pressing need for a broader look at the prevalence, diversity and ecology of organisms genetically capable of complete and partial denitrification, particularly considering their differential roles in serving as sources and sinks of N 2 O 17 . Split pathways divide the protein production costs for a pathway between multiple cells and may enable higher ATP flux 18 , 19 , increasing the specific growth rate. Although this could be a fitness advantage under nitrite-rich conditions, ATP yields per molecule nitrate or nitrite would be lower for partial compared to complete denitrification. This is expected to lead to partial denitrification being favored when electron acceptor supply is not limiting, but complete denitrification being favored when the flux of electron donors such as carbon (C) compounds is high relative to electron acceptor availability. Splitting the denitrification pathway also increases dependencies between community members, not only because the denitrification product of one organism serves as the electron acceptor for another 20 , 21 , but also because the regulation of specific reactions may rely on intermediates that those executing the reaction themselves cannot use 22 . Further, there is less buildup of toxic intermediate compounds, which may allow microorganisms only capable of carrying out the initial steps of denitrification avoid the cytotoxic effects of NO 2 - , particularly under low pH environments 11 , 23 . This jointly indicates that split pathways may open more niches, thereby supporting a greater diversity of denitrifiers 21 . If organisms encoding partial vs. complete denitrification fulfill different niches, this should also be reflected in additional traits, such as growth rate, abiotic optima, and the ability to metabolize different substrates and use electron acceptors beyond those in the denitrification pathway. For instance, if complete denitrification is indeed associated with C influx, complete denitrifiers would be expected to be capable of higher growth and broader substrate catabolism compared to partial denitrifiers. Addressing the linkages between the complete or partial denitrification and the capacity for other traits can help identify the broader roles and conditions that favor each denitrifier type. Here we combined comparative genomics ( Figure 1a ) with a global survey of denitrification gene ratios in environmental metagenomes and metatranscriptomes to evaluate the diversity of bacterial denitrifier types, their metabolic traits, and their distribution across engineered, environmental and host-associated biomes. We posited that environmental factors favoring or disfavoring execution of the different steps leave a genetic footprint in the form of presence and absence of genes encoding the enzymes responsible for them, as well as via differential associations between denitrification enzyme genes and redox traits. Furthermore, considering organisms encoding a complete denitrification pathway are able to use a broader range of nitrogenous terminal electron acceptors than those encoding fewer, we hypothesized that the former would have broader metabolic flexibility. We used a framework in which partial denitrifiers include genetically defined “initiators,” which encode genes involved in NO 2 - but not N 2 O reduction, and “terminators,” which encode genes for N 2 O but not NO 2 - reduction ( Figure 1b ). We used NO 2 - reduction, the decisive step of denitrification 24 , as the first point of our analysis. We combined comparative genomics of 61,293 non-redundant bacterial isolate, metagenome assembled (MAGs), and single cell genomes with 3,991 metagenomes derived from all major biomes and 413 terrestrial and aquatic metatranscriptomes to determine the frequency of a complete vs. partial denitrification pathway and the genetic potential to initiate vs. terminate denitrification at the genome and community level. We interpret our results in the context of resource availability and complementary genomic traits associated with the denitrifier types in our framework. Download figure Open in new tab Fig. 1: Comparative genomics indicates denitrifier types are unequally common and dominated by distinct phyla. a Overview of experimental approach for comparative genomics. Metagenome-assembled, single cell and isolate genomes covering a broad range of environmental, host-associated, and engineered biomes were extracted from GTDB v.214.1 99 ( Supplementary Data 2 ). After quality filtering the genomes, denitrification genes were searched using a combination of HMM-based searches, phylogeny construction, and manual inspection of alignments. Denitrifier genomes were then searched for additional functional traits. Maximum growth rate was inferred based on codon use bias in highly expressed ribosomal proteins, while transcription factors were detected using DeepTFactor, and potential to use various organic and inorganic electron donors and acceptors with a range of tools. b Venn diagram of prevalence of denitrifier types. The number inside each circle denotes the percentage of denitrifier genomes with that genotype. Letters in italics denote whether genotype is considered an “initiator” ( i ), “terminator” ( t ) or both ( it ). Reactions completed by each enzyme and corresponding denitrifier type name are listed below. Genomes encoding nor without accompanying nir or nosZ genes are marked with an asterisk and classified as belonging to non-denitrifiers. c Stacked bar charts denoting relative proportion of genomes belonging to phyla within each denitrifier type, with non-denitrifiers defined as containing neither nir nor nosZ , or being archaeal nitrifiers or anammox bacteria (see methods). The number of genomes corresponding to each denitrifier type is indicated above the corresponding bar. d Proportion of genomes within dominant phyla encoding each denitrification enzyme gene, among denitrifiers. Numbers to the right in panel d indicate the number of denitrifier genomes belonging to each phylum denoted in graph. Phyla represented by fewer than 100 denitrifier genomes are aggregated under “other” and can be found in Supplementary Data 11. Results Partial denitrification dominates microbial genomes We assessed the prevalence of partial and complete denitrification pathways among 61,293 bacterial genomes represented in GTDB release 214.1 ( Fig. 1a ). Genes for the denitrification enzymes NirK ( nirK ) and NirS ( nirS ) catalyzing NO 2 - reduction to NO, all proposed NO-reducing heme copper oxidases (i.e. Nor ( nor ) 25 ), and NosZ ( nosZ I, nosZ II) catalyzing N 2 O reduction were annotated using hidden Markov models trained on manually curated databases of these genes 25 . Genomes carrying nor but not nir or nosZ accounted for 5% of the total number of assemblies and were excluded in downstream analyses because Nor in nor -only genomes are likely involved in detoxification rather than respiration. With this conservative approach, 18% of the genomes (n=11,126) were considered denitrifiers and included nir and/or nosZ with or without nor . Among these, 23% encoded genes for complete NO 2 - reduction to N 2 (“complete denitrifiers”; Fig. 1b ), with the complete denitrification trait more prevalent among isolates compared to MAGs (29% vs. 17%, respectively, Supplementary Table 1 ). However, there was considerable variation between phyla ( Fig. 1c , Supplementary Fig. 1 ). Twenty-eight different bacterial phyla encoded the complete denitrification trait, with an overwhelming dominance of Pseudomonadota in our dataset ( Fig. 1c ). Among phyla represented by at least 100 denitrifier genomes, complete denitrifiers were most prevalent within Campylobacterota (38% of denitrifiers), Pseudomonadota (37%), and Myxococcota (26%, Fig. 1d ). Using our framework of initiators and terminators, we found that initiators encoding nir with or without nor but not nosZ were broadly more prevalent than terminators having nosZ with or without nor but not nir (49% vs. 24% of genomes with denitrification genes, respectively). This may represent the potentially divergent functions for NO - versus N O reduction as a means of growth or detoxification vs. maintenance 26 . Partial denitrifiers occurred in 68 bacterial phyla, with initiators being overrepresented within Actinomycetota (98% initiators, 1% terminators) and Nitrospirota (88%, 8%) ( Fig. 1d ). However, Nitrospirota accounted for just a small fraction of initiator genomes (2.7%), while the majority were Pseudomonadota (47%) and Actinomycetota (22%) ( Fig 1c ). We found a higher proportion of terminators than initiators within Planctomycetota (76% terminators, 19% initiators) and Bacteroidota (60%, 18%) ( Fig. 1d ), with the latter phylum accounting for the majority of terminators in the dataset (53%; Fig. 1c ). We also found genomes carrying nir and nosZ but not nor, suggesting a third category: the “initiator-terminators”. They accounted for just 4% of genomes with denitrification genes, including 13% of Gemmatimonadota, 13% of Chloroflexota, 9% of Bacteroidota and 5% of Verrucomicrobiota ( Fig. 1c ). It is possible that these organisms either depend on NO reduction by other organisms or produce N 2 O via pathways that do not depend on Nor 27 – 30 . Among all genomes carrying nosZ (i.e. terminators, initiator-terminators, and complete denitrifiers), those encoding clade I nosZ were more likely to encode the complete denitrification trait compared to genomes encoding clade II (74% in clade I vs. 26% in clade II; Supplementary Fig. 2 ), as previously observed 4 . Within clade II, complete denitrification was most prevalent in Pseudomonadota and Aquificota, with 80% of Pseudomonadota and 94% of Aquificota encoding complete denitrification compared to an average of 20% in the remaining phyla. This suggests that the difference in clade I vs. clade II prevalence can be used as a proxy for complete vs. partial denitrifier prevalence within the N 2 O reducing community, but not if clade II is dominated by Pseudomonadota or Aquificota. Inferred ecological preferences of complete vs. partial denitrifiers We next assessed whether the genetic potential for complete denitrification or for initiating rather than terminating denitrification was associated with the predicted growth rate and resource use patterns among bacteria. Using the codon bias-based maximum growth rate prediction tool gRodon 31 , we categorized genomes into fast- (growth rate faster than 0.2 h -1 ) and slow-growing (growth rate lower than 0.2 h -1 ) 31 . Rapid growth was more common among complete denitrifiers (83%) compared to initiators (70%), terminators (65%) and initiator-terminators (62%; Fig. 2a upper panel ) and for genomes encoding clade I (85%) compared to clade II NosZ (65%; Fig. 2a lower panel ). Similarly, estimated median growth rates of complete denitrifiers (0.46 h -1 ) were greater than that of initiators (0.28 h - 1 ), terminators (0.23 h -1 ) and initiator-terminators (0.20 h -1 ; overall Kruskall-Wallis χ 2 = 41, Dunn P < 0.001 in all cases). This faster potential growth rate was associated with a lower sugar to acid preference of complete denitrifiers compared to all three of the categories of partial denitrifiers, based on the total sum of genes involved in each pathway 32 ( Fig. 2b ). This was accompanied by a higher density of transcription factors ( Fig. 2c ) and transporters (68 Mbp -1 in complete denitrifiers vs. 65 Mbp -1 in initiators, 40 Mbp -1 in terminators, and 42 Mbp -1 in initiator-terminators, P < 2 x 10 -16 ). Furthermore, complete denitrifiers were inferred to grow on more of the 56 organic substrates examined (median of 20 corresponding to 4.75 Mbp -1 ), compared to initiators (13; 3.32 Mbp -1 ), terminators (11; 2.90 Mbp -1 ), and initiator-terminators (9; 2.55 Mbp -1 ; Fig. 2d ). This was driven by complete denitrifiers using a broader range of organic and amino acids compared to partial denitrifiers (median of 15 vs. 6-7), rather than sugars (median of 2 vs. 2-3). Download figure Open in new tab Fig. 2: Genome-inferred traits of complete and partial denitrifier bacteria. a Estimated maximum growth rate of organisms based on codon usage bias. Stacked bar charts show the maximum predicted growth rate for genomes separated by denitrifier type (upper panel) and NosZ clade (lower panel). Slow-growing taxa (maximum growth rate < 0.2 h - 1 ) are depicted to the left on the horizontal axis and fast-growing taxa to the right. Numbers to the right denote number of genomes used in maximum growth rate predictions and exclude genomes with fewer than 10 ribosomal proteins annotated. b Boxplots of sugar and organic acid preference of denitrifiers, based on KEGG annotation ratios 32 . c Genomic density of transcription factors inferred using DeepTFactor. d Boxplot of substrate use counts by denitrifier type and substrate category based on GapMind, with colors following panels b-c. e Heatmap of terminal electron acceptors and donors, with intensity of color and number denoting proportion of genomes within denitrifier type encoding redox ability. In panels b-d, box boundaries represent first and third quartiles, with midline denoting median. Whiskers denote the 1.5 IQR, and outliers are shown as individual points. Asterisks above boxes indicate a difference between complete denitrifiers and the partial denitrifier type and no comparisons between partial denitrifier types were made. Numbers to the right in panel e denote number of genomes used in panels b-e. We subsequently examined the potential for partial and complete denitrifiers to use a range of inorganic compounds as electron donors and acceptors ( Fig. 2e ). While the ability to oxidize inorganic compounds such as hydrogen may be associated with an ability to tolerate energy-limited conditions 33 , the ability to reduce inorganic compounds may indicate that one type or another of denitrifiers is better able to tolerate lower redox conditions. Although most genomes lacked the genetic potential to oxidize inorganic substances or reduce non-nitrogenous terminal electron acceptors (median = 0), complete denitrifiers had the genetic potential to both oxidize and reduce more compounds than partial denitrifiers (Kolmogorov-Smirnov statistic P < 0.0001 in all cases). The inferred abiotic niche breadths of complete denitrifiers were not uniformly broader than that of partial denitrifiers. Complete denitrifiers had slightly broader inferred temperature ranges compared to initiators (26.84 vs. 25.12 °C; t = -24.583, P < 2 x 10 - 16 ), terminators (25.54 °C, t = -18.569, P < 2 x 10 -16 ) and initiator-terminators (25.93 °C, t = -8.038, P = 1.02 x 10 -15 ). pH ranges were narrower in complete denitrifiers compared to initiators (3.75 vs. 3.83; t = 8.136, P = 4.44 x 10 -16 ) and broader compared to terminators (3.58; t = -6.756, P = 1.50 x 10 -11 ). Salinity ranges were broader in complete denitrifiers than initiators (5.64 vs. 5.31; t = -3.775, P = 0.000162) but narrower than in initiator-terminators (5.92; t = -2.441, P = 0.014665). We subsequently assessed the degree to which complete and partial denitrifiers were associated with high or low resource availability using CoverM 34 to map reads from the TARA Oceans 35 project to MAGs derived from the same data 36 . No subset of the metagenome data we collected or other databases we screened had a sufficient number of complete denitrifier MAGs and environmental data to allow for this analysis, highlighting the rarity of complete denitrifiers in global ecosystems. Nitrate was used as a proxy for denitrification electron acceptor availability and chlorophyll concentration as a proxy for electron donor supply because of its positive association with gross primary productivity 37 . The proportion of reads mapping to the collection of 170 MAGs in the dataset differed by sample and ranged from 5 to 30%. We calculated a standardized environmental response by correlating the proportion of reads mapping the denitrifier types among the MAGs (7 complete, 13 initiators, 14 terminators, 134 non-denitrifiers and 2 initiator-terminators) with chlorophyll and NO - concentrations in different samples and standardizing this to the maximum observed relative abundance of each MAG. We found that terminators and complete denitrifiers tended to have a greater increase in relative abundance with increasing NO - than non-denitrifiers, but there was no difference between complete denitrifiers and partial denitrifiers ( Supplementary Fig. 3a ). Mean standardized chlorophyll and chlorophyll: NO 3 - ratio responses were similar across all groups of organisms, independent of denitrifier type ( Supplementary Figs. 3b,c ). One possible reason for the apparent lack of correlation between denitrifier type and resource availability is that these bulk measurements likely do not represent the microhabitat that denitrifiers experience. For instance, pre-filtering of the samples depleted much of the particulate organic matter, where denitrification activity is concentrated 38 , and where modeling indicates that complete denitrifiers are favored 21 . Nonetheless, the results from this pelagic marine dataset do not indicate that measured resource availability affects the prevalence of complete or partial denitrifier genomes. Prevalence of denitrification initiators and terminators varies across global biomes Next, we assessed variation in denitrification initiator and terminator prevalence and complete and partial denitrifiers among terminators ( i.e. N 2 O reducers) at the community level in a broad range of environments. We screened 3,991 metagenomes derived from soil, aquatic, engineered and host-associated biomes for nirK , nirS , and nosZ clades I and II using an HMM-based search and phylogenetic placement approach 39 . The difference in the copy number of nosZ and nir genes normalized to Gbp sequenced in each metagenome (δ nos-nir ) was used as a proxy for the genetic potential for termination compared to initiation of denitrification, and nosZ clade I vs. clade II (δ nosZ I -nosZ II) as a proxy for complete denitrifier dominance within the N 2 O reducing community. Delta values were chosen for their ability to account for differences in overall denitrifier prevalence between biomes, including cases where one or both of the denitrification genes being compared were not detected. Across the majority of biomes, nir gene fragments were typically more abundant than nosZ , indicating a greater or similar potential to initiate than terminate denitrification within the denitrifier communities ( Fig. 3a ). Marine mats served as an exception, and nosZ prevalence exceeded that of nir by 60%. This would occur if there were higher rates of NO - assimilation or low inputs of NO 2 - from nitrification 40 , as suggested by the overall low prevalence of nir and lack of ammonifier associated nirK clades observed previously in these samples 25 . nosZ Clade II was more prevalent than nosZ Clade I in nearly all biomes considered, ( Fig. 3b ). This indicates that partial denitrifiers generally dominate terminator communities because partial denitrification is more prevalent among Clade II nosZ . This is further supported by our observation that phyla depleted in complete denitrifiers, such as Bacteroidetes, Chloroflexota and Gemmatimonadota, dominated nosZ clade II across biomes ( Figs. 3c,d ). Therefore, we can conclude that the majority of organisms capable of terminating denitrification in global biomes do not also initiate this process. This is particularly the case in croplands, marshes, and activated sludge from municipal wastewater treatment plants, which were the biomes where δ nosZ I -nosZ II is lowest for terrestrial, marine, and engineered environments ( Fig. 3b ). The dominance of nosZ II coincides with the highest total nosZ and nir gene abundances ( Supplementary Data 1 ), indicating conditions that are overall more favorable for denitrification also promote N 2 O reducers that are partial rather than complete denitrifiers. A notable exception to this pattern is sewage communities, which were dominated by clade I nosZ despite having high overall nir gene fragment prevalence, and seems to largely reflect the preponderance of Acidovorax in these samples 41 . Download figure Open in new tab Fig. 3: Balance in prevalence of denitrification genes shows dominance of initiators at the community level across global biomes. a, b Boxplots showing difference in nos versus nir counts (( nosZ I + nosZ II) – ( nirK + nirS )) ( a ); and clade I and clade II nosZ counts ( b ) per Gb sequenced. Biomes were compared using Benjamini-Hochberg FDR-corrected pairwise ranked comparisons following Kruskall-Wallis, and common letters to the right in each boxplot denotes biomes with similar median gene prevalences. Numbers to the right denote the number of metagenomes included for each biome. Box boundaries represent first and third quartiles, with midline denoting the median and whiskers the 1.5 IQR. c, d Mean phylum level composition of clade I ( c ) and clade II nosZ reads ( d ), organized by biome. Biomes represented by fewer than 20 metagenomes are excluded from the figure. The percentage of genomes having clade II nosZ and a complete denitrification pathway are indicated for each phylum in the legend and can be seen for both clades in Supplementary Fig. 2. Standard errors corresponding for each phylum are available in Supplementary Data 6. Host assoc. = host-associated; Engineer. = engineered. We also assessed the correlation between the diversity of denitrifiers based on phylogenetic diversity of the dominant nitrite reductase gene, nirK and potential initiator-terminator or clade I and II differential abundance 25 . We found that nirK phylogenetic diversity was positively correlated with δ nos-nir in soils (Spearman’s ρ = 0.46, P < 2.2 x 10 -16 ) and marine samples (ρ = 0.19, P = 1.18 x 10 - 11 ), while nirK diversity was positively correlated with δ nosZ I -nosZ II in soils (ρ = 0.29, P < 2.2 x 10 -16 ) and uncorrelated in marine samples (ρ = 0.03, P = 0.35). These results indicate that more diverse denitrifier communities occur where capacities for initiating and terminating denitrification are relatively more balanced, such that complete denitrification at the community level is associated with greater niche partitioning. Environmental drivers of nir vs. nosZ and nosZ clade prevalence were assessed using random forest modeling on the largest soil and marine datasets with complete metadata represented in our analysis 42 . We generated accumulated local effect plots to show the main effect of the target variable on predicted δ nos-nir ( Fig. 4 ) and δ nosZ I -nosZ II ( Fig. 5 ), while accounting for the other predictors. Models explained less than half of the variance in gene differences, except for δ nosZ I -nosZ II in soils. Increases in soil organic carbon content up to ∼1% positively affected δ nos-nir, while the effect of soil NO - content was always negative ( Fig. 4a ). nosZ was also predicted to become less prevalent compared to nir as available phosphorus increased, indicating that high nutrient conditions in soils favor NO 2 - reducers over N 2 O reducers. Within the marine dataset, predicted δ nos-nir was lower at the highest concentrations of the sum of NO 3 - and NO 2 - compared to the lowest concentrations, and showed a dramatic shift in prediction from a decreasing to increasing relationship with temperature at 22 °C ( Fig. 4b ). Download figure Open in new tab Fig. 4: Environmental predictors of balance between nos and nir counts using random forest models. a, b Abiotic predictors of the difference in nos and nir gene counts, calculated as ( nosZ I + nosZ II) – ( nirK + nirS ) and normalized per Gb sequenced in soil ( a ) and marine metagenomes ( b ). Accumulated local effects plots show the differences in prediction of the δ nos - nir (y-axis) compared to the mean prediction along the range of each predictor (x-axis), while accounting for potential correlations among predictor values. Values above zero indicate the model predicts higher than average dominance of nosZ over nir at a given value of the predictor variable. The analyses were performed on a subset of the soil (n = 298) and marine (n = 93) metagenomes for which relevant environmental metadata was available (see methods). Root mean square error and variance explained were 37 and 29% in the soil ( a ) and 20% and 20% in marine ( b ) models, respectively. Vertical marks on the x axis in each panel denote data density. Five hundred trees were run, and mtry, nodesize, and sampsize parameters were 3, 10, and 176 for soil and 3,10, 88 for the marine models, respectively. Download figure Open in new tab Fig. 5: Environmental predictors of balance between nosZ clade I and clade II using random forest models. a, b Abiotic predictors of the difference in nosZ clade I and II gene counts normalized per Gb sequenced in soil ( a ) and marine metagenomes ( b ). Accumulated local effects plots show the differences in prediction of the δ nosZ I- nosZ II (y-axis) compared to the mean prediction along the range of each predictor (x-axis), while accounting for potential correlations among predictor values. Values above zero indicate the model predicts higher than average dominance of nosZ clade I over nosZ clade II at a given value of the predictor variable. The analyses were performed on a subset of the soil (n = 298) and marine (n = 93) metagenomes for which relevant environmental metadata was available (see methods). Root mean squared error and variance explained were 40 and 59% in the soil ( a ) and 14 and 33% in marine ( b ) models, respectively. Vertical marks on the x axis in each panel denote data density. Five hundred trees were run, and mtry, nodesize, and sampsize parameters were 4, 6 and 209 for soil and 3,6, and 88 for the marine models, respectively. Available NO - , phosphorus, potassium, and zinc had a negative effect on predicted δ nosZ I -nosZ II ( Fig. 5a ), which suggest that partial N 2 O reducers increase with increasing nutrient levels. pH and clay content also had a negative effect on predicted δ nosZ I -nosZ II, though only pH had a negative effect over the entirety of measured values. Increases in elevation at low altitude were associated with increased predicted δ nosZ I -nosZ II, but additional increases in elevation were not associated with further increases. In the marine study, temperature and ammonium concentrations had opposing unimodal effects on predicted values over their observed ranges ( Fig. 5b ). Expression of denitrification termination and initiation in the environment We subsequently quantified the presence of nosZ and nir transcripts in 413 metatranscriptomes from soils and aquatic environments, distributed among five biomes, to assess whether the biome-level differences and associations between environmental variables and δ nosZ I -nosZ II and δ nos-nir gene abundance were also apparent in gene expression. nirK transcripts were strongly dominated by archaeal nitrifier reads (median 50%, range 0-100%), which we excluded in our analysis (see methods). Consistent with the metagenome analysis, nir was more prevalent than nosZ and clade II nosZ was more prevalent than clade I nosZ ( Fig. 6a ). The soil studies either directly or indirectly manipulated carbon availability, and soils associated with higher carbon availability had higher δ nos-nir expression than their paired lower-carbon samples (SMD 0.75, p=0.015) but did not have higher δ nosZ I -nosZ II (SMD 0.06, p = 0.73). Among aquatic studies, which were all observational, correlation coefficients were negative between δ nos-nir and the NO - + NO - (mean - 0.27, CI: - 0.53 - 0.00) and positive between δ nos-nir and chlorophyll concentrations (0.22 CI 0.04, 0.41; Fig. 6b ). δ nos-nir was not correlated with dissolved oxygen or phosphate concentrations, or with bacterial production, another proxy for resource availability. δ nosZ I -nosZ II was positively correlated with dissolved oxygen content, which would occur if the dominant organisms encoding clade I NosZ lowered oxygen concentrations around their N 2 O reductase better than those encoding clade II 43 . Download figure Open in new tab Fig. 6: Balance in prevalence of denitrification gene transcripts shows dominance of initiators at the community level in terrestrial and aquatic biomes. a δ nos-nir and δ nos ZI- nosZ II normalized per Gbp mRNA sequenced across different biomes. Numbers to the right denote number of metatranscriptomes included in each category. b Mean and 95 % confidence intervals of spearman correlation coefficients between δ nos-nir or δ nos ZI- nosZ II and environmental factors in aquatic metatranscriptomes. Coloring follows panel a . Circle size and numbers to the right denote the number of studies the effect size is calculated from. Soil samples with root exudate addition are categorized under rhizosphere in a . Box boundaries represent first and third quartiles, with midline denoting median and whiskers the 1.5 IQR. Values for all biomes can be found in Supplementary Data 12 . Discussion By combining a comparative analysis of genomes with broad metagenomic and metatranscriptomic surveys, we provide unprecedented insight into the prevalence of the complete and partial denitrification trait among global microbial communities. In contrast to a previous comparative genomics study based on a small set of isolated microbes 4 but consistent with more recent studies of MAGs from different environments 5 , 9 , 44 – 47 , we found that the genetic potential for complete denitrification is less common than that for partial denitrification, among both genome-sequenced microorganisms and environmental communities from all major biomes. Our results show denitrification is primarily a community trait based on division of labour, thereby relying cross feeding between microorganisms producing and consuming NO 2 - , NO and N 2 O to complete the pathway. Furthermore, other genes and genomic traits implicate complete denitrifiers as metabolically flexible generalists compared to any partial denitrifier type. Various explanations exist for the dominance of partial denitrifiers. We show that partial denitrification enables greater overall niche partitioning, which was supported by narrower substrate ranges, lower capacity for oxidizing and reducing various inorganic electron donors and acceptors, and a lower genomic density of transcription factors and transporters among partial denitrifiers compared to complete denitrifiers. In the environment, higher nirK phylogenetic diversity, an indication of greater functional diversity among nitrite reducers 25 , was associated with an increasing balance between capacity to initiate and terminate denitrification at the community level. This would further support niche partitioning if the pathways were split among partial denitrifiers, which is likely considering that that we mainly observed negative δ nos-nir values in the environment and the fact that MAGs are predominantly (84%) partial denitrifiers. Partial denitrifiers may also dominate communities due to the presence of a rate-efficiency tradeoff, i.e. that energy flux is slower through long pathways but potentially enables more complete usage of the terminal electron acceptor when it is limiting 19 . However, the dominance of partial denitrifiers among N 2 O reducers in the environment was not coherently explained by a rate-efficiency tradeoff. If this were the case, we would expect complete denitrification to decline as NO - increases or as C:NO - and C:NO 2 - ratios decrease, assuming denitrifiers are predominantly heterotrophs in the environment. This agrees with predictions based on modelling and observations in oxygen minimum zones in the oceans, proposing that the prevalence of a complete pathway increases as the limiting substrate shifts from C to N 21 . Accordingly, our random forest modeling inferred that the N 2 O reducing community in higher NO 3 - soils became more dominated by partial denitrifiers, i.e. those carrying nosZ clade II, and fertilized croplands had more negative δ nosZ I -nosZ II than other soils in our cross-biome study. However, a similar reduction in δ nosZ I -nosZ II was not associated with increasing NO 3 - + NO - in aquatic metagenomes and metatranscriptomes or in the analysis mapping reads to marine MAGs. In addition, there was no relationship between increasing carbon content in soil or higher chlorophyll concentrations in aquatic samples and higher prevalence of complete denitrifiers in either metagenomes or metatranscriptomes. This is consistent with a recent qualitative analysis of ∼1600 MAGs 46 , which concluded that a rate-efficiency tradeoff cannot explain observed partial denitrifier dominance. Based on our findings of broader substrate ranges and greater genomic allocation to substrate uptake and transcriptional regulation, we instead propose that complete denitrifiers are adapted to flexibly take advantage of resources varying in time and space, while partial denitrifiers are restricted to slower and therefore less variable growth rates. This may explain the over-representation of complete denitrifiers among model denitrifiers and isolates that grow readily under standard resource-rich lab conditions. Under carbon-rich conditions supporting rapid growth in the environment, complete denitrifiers may benefit from using available NO 3 - /NO 2 - more efficiently 21 , while under rapidly fluctuating conditions many have the flexibility to use whichever electron donor and terminal electron acceptors are most readily available. This indicates that partial denitrifiers dominate because they have cheaper metabolisms to run, that do not require such complex regulatory mechanisms. This is consistent with the overall dominance of slow-growing organisms in natural environments 31 , 48 , 49 . The community level genetic balance between initiation and termination of denitrification indicates a higher capacity for N 2 O production than reduction in nearly all biomes. The variations between biomes and among the specific ecosystems within biome categories were readily explained by environmental variables. We found that high NO 3 - levels were associated with lower δ nos - nir in both soils and aquatic systems, indicating greater imbalance between the two genes and a decrease in the relative importance of N 2 O reducers. In soils, the same pattern was observed for phosphorous. High concentrations of NO 3 - may also allow denitrifiers to outcompete ammonifiers, which both theoretical 50 , 51 and empirical evidence indicate prevail under high C:NO 3 - 52 , 53 , including in the soil metagenomes used in this study 54 . Overall, this shows that nutrient rich environments promote denitrifier communities dominated by initiators and similar patterns were observed at the transcriptional level in aquatic environments (no data for soils). The observed imbalance in the denitrifier community could explain why nutrient loaded environments like agricultural soils are major sources of N 2 O. However, complete denitrifiers have been observed to express only enzymes involved in denitrification initiation under high NO 3 - and/or NO - 55 , 56 , which also lead to accumulation of N 2 O. Furthermore, high fertilization rates commonly favor nitrifier-denitrification 57 , and hydroxylamine from nitrification and high NO 2 - concentrations can both induce N 2 O production from chemodenitrification 57 , 58 . Finally, N 2 O may also be produced during the detoxification of NO from host immune responses 59 , 60 . Therefore, there are a wealth of pathways that are distinct from denitrification and can reduce NO 2 - and/or produce N 2 O, and it is plausible that inter-pathway competition for NO 2 - favors the presence of initiators ready to consume NO 2 - in denitrification rather than letting it enter other pathways. Terminators could conceivably take advantage of the accumulated N 2 O from all these possible sources, in particular obligate terminators (i.e. predominantly nosZ clade II) which use N 2 O as terminal electron acceptor and thereby serve as important N 2 O sinks 17 , 61 . This reasoning agrees with the observed low δ nosZ I -nosZ II in metagenomes from agricultural soils and wastewater treatment plants. We conclude that high levels of NO - allow both initiation and termination specialists to prevail, but with a dominance of initiators which altogether increase the risk of N 2 O emissions. Fixed carbon availability was associated with reduced dominance of nir over nosZ in aquatic transcriptomes and soil metagenomes, in particular in soils with less than 1% SOC. The soil metatranscriptomes also indicated increased carbon availability could help Nos Z overcome a low competitiveness for electrons 62 – 64 . However, the chemical composition of the available carbon is expected to affect the balance between initiation and termination of denitrification and complete vs. partial denitrifiers. Organic acids have short catabolic pathways and fewer opportunities for enzyme bottlenecks to slow the flow of electrons into the electron transport chain compared to sugars and should favor complete denitrifiers able to use an array of terminal electron acceptors, in agreement with our comparative genome analysis. At the community level however, organisms metabolize and co-metabolize 65 substrates from among tens of thousands of molecularly-distinct potential electron donors of varying bioavailability 66 , 67 , leading to conflicting effects of sugar and organic acids addition on N 2 O to N 2 production from soils 68 – 70 . High dissolved oxygen concentrations were also positively associated with dominance of clade I over clade II nosZ I expression but not gene abundance in aquatic samples. There is no clear consensus on clade I being less sensitive to high oxygen than clade II NosZ 71 , 72 , and oxygen niche of N 2 O reducers may be a function of organism oxygen consumption 43 or even indirectly their competition for NO 2 - with nitrite oxidizing bacteria 21 . Thus, while the mechanism for oxygen preference cannot be inferred from our data, our results nonetheless indicate that terminators and complete denitrifiers make distinct contributions to N 2 O reduction as a function of environmental conditions. Our results indicate that the prevalence of denitrification functional types within and between environments should be considered in light of not only the potential organism level advantages to having a particular denitrification genotype under given environmental conditions, but also through biotic interactions. Denitrification does not occur in isolation, and its steps may occur in response to its intermediates being produced by other pathways. Further, spatiotemporal variation in electron donor and acceptor ratios can enable coexistence of organisms completing different steps of denitrification. Future work addressing phenotypic plasticity of denitrifiers under realistic environmental conditions is necessary to resolve this uncertainty and disentangle biotic and abiotic drivers of denitrifier community assembly. This will be particularly enhanced by improved generation of high-quality assemblies of representative environmental genomes, and by the ability to sample microbial communities on the scale at which they interact with one another and the environment. Methods Denitrification enzyme databases Alignments and phylogenies for NirK and NirS were obtained from Pold et al. 25 . The Nor reference database was derived from Murali et al. 73 and consisted of 67 Nor sequences (cNOR, qNOR, bNOR, eNOR, gNOR and sNOR, and nNOR) and 865 other heme copper oxidases. A hidden Markov model for NosZ was generated by updating an alignment from Graf et al. 4 with NosZ sequences present in RefSeq release 95, following the procedure described in Pold et al. 25 . After manually checking the alignment for conserved residues 74 and trimming the alignment to its conserved core, we built a phylogeny using IQ-TREE v. 2.1.3 with the best model identified by modelfinder (LG+R10) 75 . Nitrate reductases, catalyzing the first step in the denitrification pathway, were excluded from the analyses because they are found in a wide variety of microorganisms and their activity often occurs in isolation from the characteristic gas-producing denitrification process itself 24 , 47 , 76 . Genome annotation We used the representative set of genomes from GTDB v214.1 for our analysis ( Supplementary Data 2 ).Genomes less than 80% complete, more than 5% contaminated, or which failed taxonomic consistency of contigs based on GUNC v.1.0.6 77 were excluded from our analysis, resulting in 61,293 genomes in our final dataset. We included both isolate (49% of assemblies) and culture-independent genome assemblies (metagenome assembled and single cell genomes; 51%) in our analysis to balance genome quality with inclusion of genetic diversity representative of environmental samples. We identified genes for NirK, NirS, Nor, and NosZ in each genome using an HMMsearch 78 against the corresponding amino acid databases as described above. After aligning potential proteins to the reference, we imported them into ARB v.7.0 79 and used a combination of phylogeny building with FastTreeMP v.2.1 80 and manual curation to exclude reads lacking conserved ligand binding domains. Nor and NosZ were also classified according to protein class (i.e. bNOR, cNOR, eNOR, gNOR, nNOR, qNOR and sNOR for Nor and halophiles, clade I and clade II for NosZ). We then classified each genome into non-denitrifiers (lacking nir and nosZ ), complete denitrifiers ( nir , nor , and nosZ ), and partial denitrifiers (lacking nir or nosZ ), with partial denitrifiers further divided into “initiators” ( nir with or without nor ), terminators ( nosZ with or without nor ) and initiator-terminators ( nir and nosZ ; Fig. 1b ). Anammox genomes carrying nir (n = 47) were classified as non-denitrifiers because their nitrite reductase is not thought to be involved in denitrification 81 . Similarly, genomes encoding just nor were denoted as non-denitrifiers (n = 3,064) as a conservative measure since they are likely involved in detoxification rather than respiration. Since we only used bacterial genomes, our analysis includes ammonia oxidizing bacteria potentially capable of nitrifier-denitrification, but not ammonia oxidizing archaea for which a respiratory function for nitrite reductase has not yet been established 82 , 83 . A range of tools were used to identify and validate genes associated with potential electron donors and acceptors used by microorganisms. Genes for proteins involved in arsenite oxidation (AioBA; validated with alignment from Quemeneur et al. 84 ), anammox (Hzs, Hdh) and iron redox were identified in genomes using MagicLamp v.1.0 with final annotations curated to match minimum subunit composition proposed for each enzyme 85 . We used AmoA as a marker for ammonia oxidation in bacteria using the alignment of AmoA, HmoA and PmoA from Diamond et al. 5 . Respiratory reductive dehalogenases were identified using the RDaseDB alignment (v.2020 86 ) combined with a literature search to exclude catabolic enzymes; we note that the TAT motif proposed to be indicative of respiratory enzymes 87 was absent from the majority of sequences and could not be used. Sulfur redox genes were identified using HMSS2 and categorized into oxidation or reduction-associated enzymes following Tanabe and Dahl 88 . Sulfide: quinone reductase (SQR) enzymes were excluded from our analysis, both because their physiological role is variable, and because we found the HMMs were unspecific. Hydrogenases were identified using MagicLamp, refined using the residues in Greening et al. 33 , and verified as to their clade and probable physiological function using the HydDB web server 89 . Clades 1a-d, 1g,h and j and clade 2 were considered involved in hydrogen oxidation, and clades 4 a,b,c and h were considered involved in reduction. ArrA and ArxA involved in arsenate reduction and arsenite oxidation, respectively, were identified using an HMM built from sequences from Wells et al. 90 , and validated using the residues from Ospino et al. 91 . All redox gene annotations are reported in Supplementary Data 3 . Sugar-acid preference of organisms was inferred using the sum of KEGG orthologues identified by enrichM (v0.6.3, default settings) that were annotated to sugar vs. acid metabolism 32 , noting that the majority of the genomes fall outside the originally-calibrated range of GC contents ( Supplementary Data 4 ). GapMind carbon 92 (March 22nd, 2021 database version) was run to infer ability to grow on 62 compounds. We used custom pathway completeness cutoff scores for each pathway based on annotations for organisms for which growth had been reported on that substrate in BacDive ( Supplementary Data 5 ). Our positive controls dataset did not have data for 6 substrates, leaving 56 for analysis. Transporters were annotated using DIAMOND against the TCDB database, with hits showing at least 40% identity to the reference and 70% coverage of both reference and query sequence, with no more than 10% difference in alignment length kept for analysis 93 . Transcription factors were annotated using DeepTFactor 94 (v. 2020-11-09) with default settings ( Supplementary Data 6 ). We used GenomeSPOT to infer temperature, pH, and salinity ranges 95 , and used the difference between the maximum and minimum values for each genome in our analysis ( Supplementary Data 7 ). Unless otherwise noted, all subsequent analyses were completed in R v.4.1 96 . The maximum growth rate of each taxon was estimated using gRodon 31 , which used codon usage bias of ribosomal proteins annotated in the genomes using hmmearch against the ribosomal proteins extracted from Uniprot PFAM v. 35.0 with profile-specific cutoffs ( Supplementary Data 8 ). Differences in growth rates between denitrifier types were assessed using Kruskall-Wallis tests (stats::kruskal.test) followed by Dunn tests with Benjamini-Hochberg correction for multiple testing (FSA::dunnTest), where all maximum growth rates less than 0.2 doublings h -1 were given the same rank. This cutoff was needed because codon use bias saturates with growth rates slower than this 31 . To determine whether complete and partial denitrifiers differ in transcription factor, transporter, carbohydrate-active enzyme, or catabolized substrate count or sugar acid preference, we fit linear models, logging or taking the exponent of variables where needed. However, we analyzed GapMind data with a generalized linear mixed model with a Tweedie distribution in glmmTMB 97 (v.1.1.10) due to the poor fit of OLS and high proportion (10-37%) of assemblies where no substrates within a chemical class were predicted to be used. We subsequently ascertained that the model residuals were not phylogenetically clustered using phytools::phylosig (v.2.3.0 98 ) and an organism tree derived from the GTDB reference tree 99 . This tree was rerooted in Fusobacteriota 100 and forced to be ultrametric using the castor::date_tree_red function 101 (v.1.8.2). Blomberg’s K 102 of model residuals was less than one in all instances (P < 0.05), so we did not include phylogenetic relatedness as a random effect in our analysis. We evaluated differences between complete and partial denitrifier genome content using post-hoc multiple comparisons with the sandwich::vcovHC function (v.3.1.1 103 ) to account for heteroskedasticity. Due to the large number of zeroes, we assessed the hypothesis that partial denitrifiers encode the potential to oxidize and reduce a smaller number of inorganic compounds than complete denitrifiers by comparing the cumulative density distributions using a one-sided Kolmogorov-Smirnov test using stats::ks.test. Metagenome and metadata collection To assess the dominance of complete vs. partial denitrifier communities across biomes, we selected metagenomes with at least 100,000 reads ≥150 nt, primarily from larger sampling campaigns where uniform metadata were available ( Supplementary Data 9 ). The majority of soil metagenomes came from the Australian Microbiome Project/BASE 104 , the National Ecological Observatory Network 105 , Topsoils Microbiome Project 106 , Long-Term Soil Productivity experiment 107 , and the Stordalen Mire 108 . The majority of aquatic metagenomes came from the Australian Microbiome Project 109 , Linnaeus Microbial Observatory 110 , the Amazon Continuum Project 111 and BATS, GEOTRACE and HOTS 112 . Engineered metagenomes included those from drinking water 113 sewage 41 , and wastewater treatment plants 114 . The host-associated metagenomes dataset consists primarily of plant-associated sequences, such as from leafy greens 115 , beans 116 , citrus rhizosphere 117 , switchgrass 118 and Arabidopsis 119 , though we also included a handful of sequences from invertebrates such as sponges and gutless marine worms. For unpublished metagenomes where contact details were provided, we emailed authors to request permission to use them. We completed a similar search for metatranscriptomes, focusing on soil (including rhizosphere) and marine studies where carbon and/or nutrient availability had either been manipulated or logic or metadata indicated they differed between samples ( Supplemental Data S10 ). These metatranscriptomes were identified based on searching the American Society for Microbiology journals website for metatranscriptomes and google databases for the terms “soil” or “marine” and “carbon” or “nutrient” and “metatranscript*”. Where multiple sequencing runs or files existed for the same sample, we combined the fastq files prior to searching. We used only the forward read in the case of paired end reads, and only the first 150 nt of any reads longer than that. In total, our dataset comprised 3,991 metagenomes, with 1,489 from aquatic habitats, 1,642 from soil, 658 host-associated, and 202 from engineered habitats. Our final set of metatranscriptomes only shares samples with a handful of samples from a single study in our metagenome dataset and contains 413 metatranscriptomes (64 rhizosphere or soil with root exudates added, 92 other soil, 185 primarily coastal and estuarine marine, 24 lake sediment, and 48 river metatranscriptomes). Biomes were assigned to metagenomes based on The Nature Conservancy Terrestrial Ecoregions for terrestrial samples 120 and latitude for marine samples (polar: latitude > 60°; westerlies: latitude 30-60°; trades: latitude 0-30°). Terrestrial biome assignment required use of the packages sp v.1.4-5 121 , rgdal v.1.523 122 , and rgeos v0.5-5 123 . Soils under cultivation were excluded from the biome-based approach and instead categorized as croplands. Dominance of complete vs. partial denitrifier communities based on proxy gene searches across biomes We used GraftM 39 to identify denitrification genes ( nirK , nirS and nosZ ) in the metagenomes and metatranscriptomes, as previously described 25 . Briefly, GraftM uses a two-step process in which a HMM search identifies candidate reads, followed by phylogenetic placement on a reference tree using pplacer (v.1.1.19; 124 ). We used the accumulate function in gappa 125 to find the position on the tree where at least 95% of the mass for each read descended from, and excluded all reads with any mass placed in the outgroup. We further excluded sequences placed in the non-denitrifying anammox NirS clade 1h (median 0% of nirS reads in metagenomes and 0% in metatranscriptomes), archaeal nitrifier NirK clades 2 and 4 (1.4% of nirK reads in metagenomes and 50% in metatranscriptomes), as well as the eukaryotic NirK clades 1b and 1e (0% and 0%) 25 . Our analysis also excluded the 0.4% of reads placed in the bioinformatically inferred “clade III” nosZ 10 , which forms the outgroup in our reference tree. This metagenome and metatranscriptome search depended on: hmmer (v.3.2.1; 78 ), OrfM (v.0.7.1; 126 ), bbtools (v.38.90; “ https://sourceforge.net/projects/bbmap/ ”), lbzip2 (v.2.5; “ https://lbzip2.org/ ”), and fxtract (v.2.3; “ https://github.com/ctSkennerton/fxtract ”). We validated these search and placement and post-processing parameters by generating 150 nt fragments of full-length sequences that were picked up by the NirK, NirS and NosZ HMMs, which in addition to the target proteins also included homologous multicopper oxidase proteins from Cyanobacteriota and Thermoprotetoa, NirN and NirF, and various yet to be characterized NosZ-like proteins, respectively. Sensitivity is the fraction of total ingroup fragments searched which were placed in the ingroup, and specificity is one minus the fraction of outgroup fragments incorrectly placed in the ingroup (or, in the case of nosZ , in a clade other than the target clade). Sensitivity was 76% for nirK, 93% for nirS, 91% for clade I nosZ and 94% for clade II nosZ . Specificity was 97% for nirK , 100% for nirS , 100% for clade I nosZ and 100% for clade II nosZ . We compared gene prevalence across biomes using the absolute difference in counts of clade I and II nosZ or nir and nosZ genes standardized by the number of Gbp sequenced to account for differences in sequencing depth. For metatranscriptomes we standardized based on Gbp mRNA sequenced, which was determined by excluding rRNA reads from total RNA using SortMeRNA v.4.3.7 with the smr_v4.3_default_db database and settings “--other --fastx --num-alignments 1 --no-best”. For nir vs. nosZ abundance we used Eq. 1 such that positive values denote nosZ is more abundant than nir : For nosZ , we used Eq. 2 such that positive values denote clade I is more abundant than clade II: This method of calculating gene prevalence enables the abundance of both genes to be considered concurrently. This is particularly relevant for the 14% of metagenomes and 26% of metatranscriptomes where nosZ I was not detected but nos ZII was found in 1-13,041 (1-10,926 in metatranscriptomes) copies, and the 0.8% of metagenomes where nosZ was not found but 3-28 copies of nir were identified. Environmental correlates with biome gene counts and denitrifier MAGs We used the TARA Oceans 35 MAGs present in the OceanDNA MAG database to test the hypothesis that complete denitrifiers are more abundant under conditions with high electron donor to acceptor ratios. These MAGs were chosen because the database is sufficiently large that multiple MAGs from each denitrifier type category were present, and where multiple conspecific MAGs were present, those with inconsistent denitrification gene repertoires could be excluded. After quality-controlling the raw paired-end reads with cutadapt (-m 100 -q 20 –max-n 0 –trim-n), we mapped them to all MAGs using CoverM (v. 0.6.1; 34 ; -m relative abundance –min-read-aligned-percent 0.75 –min-read-percent-identity 0.95 –min-covered-fraction 0) then determined the total relative abundance of each MAG-OTU in each sample (95% ANI). Only the reads for the 3 µm filter fraction were used in this analysis to allow for bigger cells or cell aggregates than using the 0.22 µm fraction. We subsequently determined the standardized relative abundance by dividing the observed relative abundance within each sample to the maximum relative abundance observed across all samples. We then fit a linear model predicting the standardized relative abundance of each MAG OTU based on the combination of NO 3 - and chlorophyll concentrations (i.e. NO 3 - after accounting for chlorophyll, chlorophyll after accounting for NO 3 - , and the logarithm of chlorophyll: NO 3 - ). The mean slope and standard error for each MAG OTU were extracted and used to calculate the weighted mean response and 95% confidence intervals for each denitrifier type. Differences in mean response to NO 3 - , chlorophyll and their ratios between denitrifier types were established based on non-overlapping 95% confidence intervals. We evaluated the relationship between differences in normalized gene counts and resource availability using random forests. This analysis was restricted to metagenomes from the BASE and Australian marine projects 104 , 112 as examples of studies with a large number of metagenomes with uniformly-collected metadata for variables associated with denitrification, including NO 3 - /NO 2 - levels, pH, carbon, and micronutrients. A pre-selection of variables for inclusion in the model was completed to exclude collinear variables (Spearman’s ρ >0.7 or variance inflation factor > 4); in the collinear groups, the variable hypothesized to be most strongly and directly explicable of gene ratio was retained (Supplementary Fig. 3; Supplementary Tables 2 and 3). We subsequently ran VSURF v. 1.1.0 127 100 times to identify the best predictors for each ratio and biome, keeping only those variables retained in at least 95 of the 100 iterations. We then used the randomForest package v. 4.7.1-1 128 to model the relationship between gene ratios and the final set of retained variables. A grid search was used to find the combination of tuning parameters yielding lowest out-of-bag root-mean-square error. Accumulated local effects plots were generated to visualize results using iml v. 0.11 129 . Finally, we verified whether high carbon and/or low nutrient availability favored the transcription of nosZ over nir or clade I nosZ over clade II nosZ by calculating δ nos− nir and δ nosZ I- nosZ II in the metatranscriptomes, respectively. We calculated Hedges G in a meta-analysis of effect sizes for soil studies (meta::metacont), taking the lower carbon soil as the reference and carbon-enriched soil as the treatment (e.g. bulk soil or unamended soil compared to rhizosphere, glucose, glycine, or root exudate amended soils). Aquatic studies were observational, so we fit a Spearman correlation coefficient between δ nos-nir or δ nosZ I- nosZ II and environmental variables (NO - + NO - , phosphate, bacterial production and chlorophyll A concentrations as proxies of resource availability, and oxygen). Where communities were captured and analyzed on two different filter sizes from the same sample, we determined the correlation separately for the two size fractions but counted them as a single study in our overall effect size calculations. We used psychmeta::ma_r to calculate overall correlations in the aquatic data. Data availability All genomes, metagenomes, and metatranscriptomes used in this paper are publicly available in NCBI or other sources under the identifiers denoted in Supplementary Data 2, 9 and 10 . Hidden Markov Models, and reference databases used for denitrification gene searches are available in FigShare (10.6084/m9.figshare.23913078 for NirK and NirS and 10.6084/m9.figshare.24922776 for NosZ). Source Data are provided with this manuscript. Code availability Scripts used to generate figures are available in FigShare (10.6084/m9.figshare.24922776). Author contributions Conceptualization: S.H. Data curation: G.P., A.S. and C.J. Formal analysis: G.P. Funding acquisition: S.H and G.P. Investigation: G.P. Methodology: G.P., A.S., C.J. and S.H. Project administration: G.P. and S.H. Resources: S.H. Software: C.J., G.P and A.S. Visualization: GP, AS. Writing – original draft preparation: G.P., A.S. and S.H. Writing – review and editing: G.P., A.S., C.J. and SH. Competing interests The authors declare no competing interests. Acknowledgments This work was supported by the Swedish University of Agricultural Sciences Senior Career Grant 2019-2025 to SH and the Swedish Research Council Grant Agreements No. 2016-03551 to SH and 2023-03627 to GP. Computing resources were provided by the Department of Forest Mycology and Plant Pathology, Swedish National Infrastructure for Computing project SNIC 2022/22-1119 and NAISS 2023/22-1278, funded by the Swedish Research Council through Grant Agreement No. 2022-06725, and the Swedish National Infrastructure for Computing (SNIC) at the PDC Center for High Performance Computing, KTH Royal Institute of Technology, partially funded by the Swedish Research Council through grant agreement no. 2016-07213 (NAISS 2024/22-133). Funder Information Declared Swedish Research Council , 2019-2025 , 2023-03627 Swedish University of Agricultural Sciences , Senior Career Grant 2019-2025 Footnotes Inluded an updated dataset go genomes Additional analyses of genomes Included a and analysed dataset of environmental metatranscriptomes. Revised Figure 1, New Figures 2 and 6. References 1. ↵ Alvarez , L. , Bricio , C. , Gómez , M. J. & Berenguer , J . Lateral transfer of the denitrification pathway genes among Thermus thermophilus strains . Appl. Environ. Microbiol . 77 , 1352 – 1358 ( 2011 ). OpenUrl Abstract / FREE Full Text 2. Rees , E. , Siddiqui , R. A. , Köster , F. , Schneider , B. & Friedrich , B . Structural gene ( nirS ) for the cytochrome cd1 nitrite reductase of Alcaligenes eutrophus H16 . Appl. Environ. Microbiol . 63 , 800 – 802 ( 1997 ). OpenUrl Abstract / FREE Full Text 3. ↵ Schwintner , C. , Sabaty , M. , Berna , B. , Cahors , S. & Richaud , P . 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Share Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes Grace Pold , Aurélien Saghaï , Christopher M Jones , Sara Hallin bioRxiv 2025.01.07.631734; doi: https://doi.org/10.1101/2025.01.07.631734 Share This Article: Copy Citation Tools Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes Grace Pold , Aurélien Saghaï , Christopher M Jones , Sara Hallin bioRxiv 2025.01.07.631734; doi: https://doi.org/10.1101/2025.01.07.631734 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7617) Biochemistry (17633) Bioengineering (13856) Bioinformatics (41841) Biophysics (21399) Cancer Biology (18529) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24281) Genetics (15582) Genomics (22461) Immunology (17700) Microbiology (40295) Molecular Biology (17140) Neuroscience (88413) Paleontology (666) Pathology (2823) Pharmacology and Toxicology (4813) Physiology (7632) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)