Hydrocarbons have been shown to both inhibit and enhance soil bacterial ureolysis. This suggests that indigenous ureolytic communities may perform better in the presence of contaminants than in their absence, pointing to an unresolved adaptation mechanism. In this study we conducted a molecular investigation on ureC gene expression of model Microbial-Induced Calcite Precipitation (MICP) bacterium Sporosarcina pasteurii during growth under increasing concentrations of hydrocarbon water extracts obtained from a coal tar polluted soil to ensure environmental relevance. Results indicated S. pasteurii ability to carry out ureolysis from low initial cell concentration (OD600, initial = 0.01). Flow cytometry showed insignificant differences in growth despite undergoing significant cell membrane damage at the highest hydrocarbon concentration. Quantification of ureC gene revealed a significant increase in urease transcription per unit cell with increasing hydrocarbon concentration, indicating higher urease synthesis to maintain similar ureolysis rates. We postulate this adaptation mechanism was in response to the nitrogen deficiency caused by intracellular urease inhibition by hydrocarbons. The temporal dynamics in ureC gene expression indicated urease transcription in S. pasteurii was inductive rather than constitutive. This gene up-regulation mechanism resulted in earlier ureolysis and achievement of environmental conditions favourable for calcium carbonate precipitation compared to the absence of hydrocarbons. The results of this study could reveal the underlying mechanism leading to enhanced soil ureolysis observed under certain hydrocarbon pollution scenarios.

Antibiotic resistance has become a serious threat to global health. It frequently emerges in real-world environments such as biofilms, where antibiotic gradients impose selective pressures that drive bacterial adaptation. However, the mechanisms underlying resistance development remain poorly understood, largely due to challenges in experimentally controlling confounding parameters. Here, we report a 3D microfluidic platform that enables real-time monitoring of bacterial dynamics under well-defined antibiotic gradients, along with programmable cell retrieval to identify key factors in resistance development. We show that motile Escherichia coli migrated randomly within the gradients regardless of antibiotic class (i.e., gentamicin, ciprofloxacin, and ampicillin). Resistance emerged only when a critical mass of bacteria sustained migration through the gradient for a sufficiently duration, revealing a previously unrecognized migration-duration threshold for adaptive evolution. Whole-genome resequencing revealed gene mutations in the resistant cells, including mutations in ATP synthase-related genes (i.e., atpG, atpD, and atpF). A trade-off between resistance and cell growth was also observed. Similar phenomena were observed in other motile bacteria species, including Serratia marcescens. Overall, we demonstrate that cell motility is a critical determinant of resistance development in heterogeneous antibiotic landscapes. Targeting cell motility and growth could offer effective strategies to combat antibiotic resistance in motile bacteria.

Determining which members of a microbial community are metabolically active remains a central challenge in microbial ecology. Although the 16S rRNA gene is the dominant marker for bacterial community profiling, it cannot reliably distinguish active cells from dormant or dead populations. As a result, complementary phylogenetic markers whose transcript abundance more closely reflects cellular activity are needed. Here, we systematically evaluated 80 Bacterial protein-coding marker genes and identified rpoB, encoding the beta subunit of bacterial RNA polymerase, as the optimal candidate. We designed a new primer pair (1528F 2041R) from a curated database of 305,274 unique rpoB sequences and validated it for quantitative PCR and amplicon sequencing of DNA and RNA templates. The rpoB qPCR assay achieved a limit of quantification two orders of magnitude lower than the benchmark 16S rRNA assay, for which a limit of detection could not be determined because of no-template-control amplification. In soil and sediment communities, rpoB recovered community composition comparable to 16S rRNA while providing a quantitative activity signal: rpoB cDNA:DNA ratios correlated significantly with taxon-level transcript abundance (R squared between 0.22 and 0.29, p < 0.001), whereas 16S rRNA ratios did not (p > 0.5). In a biological activated carbon biofilter experiment, rpoB transcript abundance tracked the decline in dissolved organic carbon removal rates across a 72 hour time series (correlation coefficients between 0.84 and 0.99), whereas 16S rRNA transcripts were uninformative (correlation coefficients between -0.4 and 0.98). These results establish rpoB as a quantitatively robust, activity-responsive complement to 16S rRNA for linking community composition to ecosystem processes.

Microbial-Induced Calcite Precipitation (MICP) is an effective bioremediation method for heavy metals, which often co-exist with organic pollutants in soils. Organic pollutants such as hydrocarbons inhibit soil urea hydrolysis critical in MICP whilst its feasibility in such enviroments is poorly understood. This study presents an investigation on the potential of biostimulation and bioaugmentation of MICP in soils polluted by polycyclic aromatic hydrocarbons (PAH) and their effect on ureolyisis at cell and enzyme level. Biostimulation of urea hydrolysis by soil autochthonous ureolytic bacteria was not detected over 62 days. Flow cytometry revealed Sproposarcina pasteurii at initial OD600 = 0.01 was able to grow in soil water extracts of increasing hydrocarbon concentration (TOC = 0.035-35 mg L-1), showing no negative effects on cell membrane stability. Urease activity assays in soil water extracts inoculated with S. pasteurii (OD600 = 0.01 and 1) and soybean Glycine Max urease enzyme (1 and 100 g L-1) indicated hydrocarbons negative effect on cell and enzyme urease activity was dependant on hydrocarbon and cell/enzyme concentrations, indicating the mechanism of inhibition was competitive. Glycine Max urease activity was unaffected at 100 g L-1 but at 1 g L-1 decreased with increasing hydrocarbon concentration up to 61%, whilst S. pasteurii urease activity (OD600 = 1) readily decreased at the lowest hydrocarbon concentration (TOC = 0.35 mg L-1) to an overall reduction of 31% at the highest TOC concentration. Bioaugmentation of S. pasteurii (OD600 = 1) inoculated in the soil matrix successfully hydrolysed urea within 24 h. These results evidence for the first time the ability of model MICP bacteria S. pasteurii to grow and maintain relevant metabolic ureolytic activity in soils significantly polluted by PAH.

Drinking water biofilters rely on complex microbial communities to remove contaminants, yet studies have predominantly focused on bacteria, limiting understanding of the eukaryotes and their roles. Eukaryotes may influence biofilter performance through trophic interactions and cross-domain associations. This study aimed to characterise eukaryotic diversity and examine associations with bacterial communities during the start-up of laboratory-scale granular activated carbon (GAC) biofilters treating surface water using 18S rRNA and 16S rRNA amplicon sequencing. Eukaryotic biofilter communities displayed relatively low richness (60–80 ASVs), that spanned over 25 phyla, accounting for 98.9

Shallow groundwater in semi-arid regions provides a critical year-round water source, naturally filtered and protected from evaporation. However, many communities continue to suffer from preventable diseases caused by waterborne pathogens. This study applies ecological theory to investigate the microbial ecology of shallow aquifers in southest Kenya. We used quantitative PCR and 16S rRNA gene sequencing to characterize pathogenic indicator species and bacterial communities in three isolated sand dam aquifers across seasons and between various abstraction methods used by local communities, including sealed hand pumps (classified by the WHO as an improved source), open wells, scoop holes, and downstream surface water.Both faecal and opportunistic environmental pathogens were detected in all samples. However, our models indicate that contamination largely originates from unsealed abstraction points such as open wells and cracked hand pumps which expose water to the environment. In contrast, natural sand filtration effectively limited the dispersal of species between sample points.Abstraction method was the dominant factor shaping microbial community structure, exceeding seasonal or locational effects. Unimproved sources exhibited unstable, stressed ecosystems with reduced diversity and resilience, consistent with strong environmental selection pressures. Across all abstraction points, community composition reflected adaptation to local environmental niches that remained consistent between sites and seasons. Groundwater contained significantly fewer total microbes (16S rRNA copies) than surrounding surface water, and hand pump samples had the lowest abundance of pathogen indicators, suggesting growth-limiting conditions and effective in-dam filtration.Our findings reinforce the value of sealed, sanitary hand pumps for water abstraction from shallow sand aquifers. Ensuring robust construction and regular maintenance of such hand pumps is important for low-income regions relying on shallow groundwater for water with minimal secondary treatment. Potential contamination routes identified at all sites indicate the need for targeted infrastructure improvements and continual maintenance.

Understanding beta diversity is fundamental to assessing how ecological communities respond to environmental change. However, traditional linear methods often fail to capture the high-dimensional, non-linear complexities inherent in multi-source microbial data. Here, we introduce DeepBeta, an algorithmic framework for non-linear information fusionleveraging deep undercomplete autoencoders. By integrating and compressing ecological dissimilarity matrices through a constrained neural bottleneck, DeepBeta filters stochastic noise and fuses latent features that conventional methods overlook. We evaluate the fusion performance across bacterial, eukaryotic, and integrated datasets. Results demonstrate that DeepBeta consistently extracts a higher density of information, identifying stronger community separation across temporal and depth gradients compared to standard approaches. By providing superior algorithmic resolution for detecting subtle shifts, DeepBeta offers a robust, integrative fusion solution for analyzing complex high-dimensional systems. This framework establishes a new benchmark for representation learning in microbial biogeography and the analysis of community assembly.

Biofiltration offers a sustainable, low-energy solution for drinking water treatment but suffers from inconsistent performance due to complex microbial dynamics. Current studies lack insight into early biofilter microbial community assembly. Here we perform a high-resolution spatial and temporal investigation of biomass accumulation and community development within biological activated carbon (BAC) filters over the first 6 months of operation. We found that initial biomass accumulation is not linear, instead characterised by periods of growth and decay. Mass balance identified an estimated + 6.54 × 108 new cells daily during the growth phase (days 34–62), falling to a loss of 1.69 × 109 by the decay phase (days 83–162). There was no significant increase in richness until the decay phase (ANOVA p values > 0.05 between days 34, 62 and 83). Significant stratification (ANOVA p values < 0.05) was observed with bed depth with 79

Despite the critical role of biofilters in water quality and sustainability, predicting their performance remains challenging due to the complexity of microbial interactions and limitations of sparse, high-dimensional datasets. Here, we introduce EnviroPiNet, a novel physics-guided AI framework designed to predict biofilter performance by accurately modeling carbon concentration dynamics. EnviroPiNet incorporates a physics-inspired backbone that enables the model to learn the physical properties of complex environments, ensuring predictions are grounded in system behavior. Additionally, we implement an ensemble hybrid approach to identify and extract key parameters essential for accurate carbon concentration predictions. We benchmark EnviroPiNet against conventional methods that lack physics-guided variable selection, demonstrating its superiority in identifying variables critical to biofilter performance evaluation. Trained on biofilter datasets, EnviroPiNet achieves a high coefficient of determination ( $$\text {R}^{2}$$ = 0.9) on test sets, highlighting its predictive accuracy and robustness.

AIMS:This study aimed to evaluate existing and de novo lacZ primers using in silico and experimental validation to develop a quantitative polymerase chain reaction (qPCR) assay capable of reliably quantifying coliforms and differentiating them from non-coliform Enterobacteriaceae as currently defined. METHODS AND RESULTS:A comprehensive lacZ sequence database was compiled to define coliform and non-coliform targets. Both published and de novo primers were assessed for specificity and coverage. The de novo primer set LZ1 (F: CCGWGYRTKATCATCTGGTC, R: TSATCSACGCGSGCGTACAT; 173 bp amplicon) showed 87.5% coverage of the test panel and was optimal for qPCR. Compared with culture-based methods and flow cytometry, LZ1 quantified Escherichia coli in drinking water at 1 × 10³ cfu 100 ml-1. The limit of quantification indicated an 80% probability of detecting 100 copies with > 3 replicates. Existing primer LZ3 best distinguished coliforms from non-coliforms and is a promising target for identification. CONCLUSIONS:We present a validated qPCR assay targeting the lacZ gene, supported by in silico and experimental validation, and a phylogenetic analysis of lacZ and 16S rRNA sequences, highlighting the challenges associated with coliform detection.

Biofiltration, a sustainable water treatment technology relying on microbial processes to remove contaminants, offers a promising approach to achieving the United Nations Sustainable Goal 6 of universal access to clean water and sanitation by 2030. However, a key barrier to optimising biofiltration is the incomplete understanding of the biological mechanisms governing its performance. Despite numerous studies examining how engineering decisions impact biofilter performance and the associated microbiome, the significant influence of geographical location on microbial communities raises the question of whether these findings are universally applicable or location-specific. To address this, we conducted a meta-analysis of 15 biofilter microbiomes using 16S rRNA high-throughput sequencing (HTS) data, mainly originating from rapid gravity and/or granular activated carbon (GAC) filters. Despite different types and scales, results highlight geographical location as the major contributor to microbiome dissimilarity in biofilter samples (Top and Bottom) (R2∼ 0.5; p-value<0.001). The same was observed for influent waters (PERMANOVA R2= 0.76; p-value<0.001), indicating location-specific microbiomes as opposed to differences driven by different biofilter operating parameters. Irrespective of location, the higher percentage of the microbiome was assembled through deterministic processes (∼55 %) compared to stochastic processes (∼45 %). Finally, our findings suggest that the depth stratification of biofilter microbiomes may be associated with the enrichment of taxa capable of metabolising more complex organic carbon in deeper filter layers (10 enriched pathways in biofilter Bottom layers compared to 3 at the Top). These insights provide a broader understanding of biofiltration microbiomes and offer possible research avenues for targeted and effective biofilter design strategies.

The accumulation, growth, and re-mobilization of pathogens on the pipe walls in drinking water distribution systems are processes that affect the risk of exposure at the tap. We present a model that uses the Buckingham Pi theory to embody the physics of Pseudomonas aeruginosa accumulation and move within the system. We apply it to model experimental data from a biofilm annular reactor operated in conditions that are commensurate with the flow in DWDS. By calibrating the model for this benchtop system, we intend to identify the most important physical parameters for use in a simpler, more prudent model, for application in large-scale DWDS.

Biofiltration is a low-cost, low-energy technology that employs a biologically activated bed of porous medium to reduce the biodegradable fraction of the dissolved organic matter (DOM) pool in source water, resulting in the production of drinking water. Microbial communities at different bed depths within the biofilter play crucial roles in the degradation and removal of dissolved organic carbon (DOC), ultimately impacting its performance. However, the relationships between the composition of microbial communities inhabiting different biofilter depths and their utilisation of various DOC fractions remain poorly understood. To address this knowledge gap, we conducted an experimental study where microbial communities from the upper (i.e., top 10 cm) and lower (i.e., bottom 10 cm) sections of a 30-cm long laboratory-scale biofilter were recovered. These communities were then individually incubated for 10 days using the same source water as the biofilter influent. Our study revealed that the bottom microbial community exhibited lower diversity yet had a co-occurrence network with a higher degree of interconnections among its members compared to the top microbial community. Moreover, we established a direct correlation between the composition and network structure of the microbial communities and their ability to utilise various DOM compounds within a DOM pool. Interestingly, although the bottom microbial community had only 20% of the total cell abundance compared to the top community at the beginning of the incubation, it utilised and hence removed approximately 60% more total DOC from the DOM pool than the top community. While both communities rapidly utilised labile carbon fractions, such as low-molecular-weight neutrals, the utilisation of more refractory carbon fractions, like high-molecular-weight humic substances with an average molecular weight of more than ca. 1451 g/mol, was exclusive to the bottom microbial community. By employing techniques that capture microbial diversity (i.e., flow cytometry and 16S rRNA amplicon sequencing) and considering the complexities of DOM (i.e., LC-OCD), our study provides novel insights into how microbial community structure could influence the microbial-mediated processes of engineering significance in drinking water production. Finally, our findings could offer the opportunity to improve biofilter performances via engineering interventions that shape the compositions of biofilter microbial communities and enhance their utilisation and removal of DOM, most notably the more classically humified and refractory DOM compound groups.

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTScaling-up Engineering Biology for Enhanced Environmental SolutionsFrancis HassardFrancis HassardCranfield University, Bedford MK43 0AL, U.K.More by Francis Hassardhttps://orcid.org/0000-0003-4803-6523, Thomas P. CurtisThomas P. CurtisNewcastle University, Newcastle upon Tyne NE4 5TG, U.K.More by Thomas P. Curtishttps://orcid.org/0000-0002-9009-1748, Gabriela C. DotroGabriela C. DotroCranfield University, Bedford MK43 0AL, U.K.More by Gabriela C. Dotro, Peter GolyshinPeter GolyshinBangor University, Gwynedd LL57 2UW, U.K.More by Peter Golyshinhttps://orcid.org/0000-0002-5433-0350, Tony GutierrezTony GutierrezHeriot-Watt University, Edinburgh, EH14 4AS, U.K.More by Tony Gutierrez, Sonia HeavenSonia HeavenUniversity of Southampton, Southampton SO16 7QF, U.K.More by Sonia Heaven, Louise HorsfallLouise HorsfallUniversity of Edinburgh, Edinburgh EH9 3FF, U.K.More by Louise Horsfallhttps://orcid.org/0000-0003-1594-2992, Bruce JeffersonBruce JeffersonCranfield University, Bedford MK43 0AL, U.K.More by Bruce Jefferson, Davey L. JonesDavey L. JonesBangor University, Gwynedd LL57 2UW, U.K.More by Davey L. Jones, Natalio KrasnogorNatalio KrasnogorNewcastle University, Newcastle upon Tyne NE4 5TG, U.K.More by Natalio Krasnogorhttps://orcid.org/0000-0002-2651-4320, Vinod KumarVinod KumarCranfield University, Bedford MK43 0AL, U.K.More by Vinod Kumarhttps://orcid.org/0000-0001-8967-6119, David J. Lea-SmithDavid J. Lea-SmithUniversity of East Anglia, Norwich NR4 7TJ, U.K.More by David J. Lea-Smith, Kristell Le Corre PidouKristell Le Corre PidouCranfield University, Bedford MK43 0AL, U.K.More by Kristell Le Corre Pidou, Yongqiang LiuYongqiang LiuUniversity of Southampton, Southampton SO16 7QF, U.K.More by Yongqiang Liuhttps://orcid.org/0000-0001-9688-1786, Tao LyuTao LyuCranfield University, Bedford MK43 0AL, U.K.More by Tao Lyuhttps://orcid.org/0000-0001-5162-8103, Ronan R. McCarthyRonan R. McCarthyBrunel University London, Uxbridge UB8 3PH, U.K.More by Ronan R. McCarthy, Boyd McKewBoyd McKewUniversity of Essex, Colchester, Essex CO4 3SQ, U.K.More by Boyd McKew, Cindy SmithCindy SmithUniversity of Glasgow, Glasgow G12 8LT, U.K.More by Cindy Smith, Alexander YakuninAlexander YakuninBangor University, Gwynedd LL57 2UW, U.K.More by Alexander Yakuninhttps://orcid.org/0000-0003-0813-6490, Zhugen YangZhugen YangCranfield University, Bedford MK43 0AL, U.K.More by Zhugen Yanghttps://orcid.org/0000-0003-4183-8160, Yue ZhangYue ZhangUniversity of Southampton, Southampton SO16 7QF, U.K.More by Yue Zhang, and Frederic Coulon*Frederic CoulonCranfield University, Bedford MK43 0AL, U.K.*Email: [email protected]More by Frederic Coulonhttps://orcid.org/0000-0002-4384-3222Cite this: ACS Synth. Biol. 2024, 13, 6, 1586–1588Publication Date (Web):June 21, 2024Publication History Received25 April 2024Published online21 June 2024Published inissue 21 June 2024https://pubs.acs.org/doi/10.1021/acssynbio.4c00292https://doi.org/10.1021/acssynbio.4c00292article-commentaryACS PublicationsCopyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-AlertscloseSUBJECTS:Bioengineering and biotechnology,Biology,Biotechnology,Genomics,Synthetic biology Get e-Alerts

Abstract Background Salmon aquaculture involves freshwater and seawater phases. Recently there has been an increase in multifactorial gill health challenges during the seawater phase which has led to an urgent need to understand the gill microbiome. There is a lack of understanding on what drives the composition of the gill microbiome, and the influence the freshwater stage has on its long-term composition. We characterise the gill microbiome from seven cohorts of Atlantic salmon raised in six different freshwater operational systems—recirculating aquaculture system (RAS), flowthrough (FT) and loch-based system, prior to and after transfer to seven seawater farms, over two different input seasons, S0 (2018) and S1 (2019). Results Using the V1-V2 region of the 16S rRNA gene, we produced amplicon libraries absent of host contamination. We showed that hatchery system influenced the gill microbiome (PERMAOVA R2 = 0.226, p < 0.001). Loch and FT systems were more similar to each other than the three RAS systems, which clustered together. On transfer to sea, the gill microbiomes of all fish changed and became more similar irrespective of the initial hatchery system, seawater farm location or season of input. Even though the gill microbiome among seawater farm locations were different between locations (PERMAOVA R2 = 0.528, p < 0.001), a clustering of the gill microbiomes by hatchery system of origin was still observed 7–25 days after transfer (PERMAOVA R = 0.164, p < 0.001). Core microbiomes at genera level were observed among all fish in addition to freshwater only, and seawater only. At ASV level core microbiomes were observed among FT and loch freshwater systems only and among all seawater salmon. The gill microbiome and surrounding water at each hatchery had more shared ASVs than seawater farms. Conclusion We showed hatchery system, loch, FT or RAS, significantly impacted the gill microbiome. On transfer to sea, the microbiomes changed and became more similar. After transfer, the individual sites to which the fish were transferred has a significant influence on microbiome composition, but interesting some clustering by hatchery system remained. Future gill disease mitigation methods that target enhancing the gill microbiome may be most effective in the freshwater stage, as there were more shared ASVs between water and gill at hatchery, compared to at sea.

The potential of DWDS pipewall biofilms to shelter and propagate opportunistic pathogens is currently poorly understood. Here, we use an annular biofilm reactor approach to quantify the fate of the opportunistic pathogen Pseudomonas aeruginosa when introduced to a simulated DWDS environment. We found that P. aeruginosa was capable of swift attachment to surfaces and able to persist for up to 14 days under shear stress conditions. Further, we demonstrate that P. aeruginosa is capable of detachment/reattachment and mobilisation through the bulk water, potentially acting as a source of inoculum to drinking water.

Pesticide pollution of surface water is a global threat to drinking water safety. The need for improved drinking water treatment methods is discussed by using Brazil as a case study. Brazil's agriculture is intensive, and pesticide consumption is high, while current drinking water treatment methods are inadequate for effectively removing pesticides. Available data on surface water contamination in Brazil show widespread occurrence of pesticides in natural waters, thereby putting pressure on the water treatment system and threatening the quality and safety of drinking water. Pesticide concentrations in drinking water frequently exceeded the maximum permissible concentrations if EU regulations (0.1 μg/L) were applied, highlighting the need for improvements in drinking water treatment. (Advanced) drinking water treatment for the removal of pesticides has been intensely researched over the past decade. However, challenges such as high cost and energy intensity, as well as the production of hazardous byproducts, must be assessed critically. Safely managed drinking water is crucial to the sustainable development of low- and middle-income countries and can be achieved only through appropriate technology. Engineered biofiltration has been put forward as a sustainable alternative to conventional and advanced drinking water treatment. This review highlights the promising potential of engineered biofiltration and its associated challenges.

Exploring differences in nitrification within adjacent sedimentary structures of ridges and runnels on the Brouage mudflat, France, we quantified Potential Nitrification Rates (PNR) alongside amoA genes and transcripts. PNR was lower in ridges (≈1.7 fold-lower) than runnels, despite higher (≈1.8 fold-higher) ammonia-oxidizing bacteria (AOB) abundance. However, AOB were more transcriptionally active in runnels (≈1.9 fold-higher). Sequencing of amoA genes and transcripts revealed starkly contrasting profiles with transcripts from ridges and runnels dominated (≈91 % in ridges and ≈98 % in runnels) by low abundant (≈4.6 % of the DNA community in runnels and ≈0.8 % in ridges) but highly active phylotypes. The higher PNR in runnels was explained by higher abundance of this group, an uncharacterised Nitrosomonas sp. cluster. This cluster is phylogenetically similar to other active ammonia-oxidizers with worldwide distribution in coastal environments indicating its potential, but previously overlooked, contribution to ammonia oxidation globally. In contrast DNA profiles were dominated by highly abundant but low-activity clusters phylogenetically distinct from known Nitrosomonas (Nm) and Nitrosospira (Ns). This cluster is also globally distributed in coastal sediments, primarily detected as DNA, and often classified as Nitrosospira or Nitrosomonas. We therefore propose to classify this cluster as Ns/Nm. Our work indicates that low abundant but highly active AOB could be responsible for the nitrification globally, while the abundant AOB Ns/Nm may not be transcriptionally active, and as such account for the lack of correlation between rate processes and gene abundances often reported in the literature. It also raises the question as to what this seemingly inactive group is doing?