Copper effects on net N2O production and associated gene expression by the deep-sea isolate Shewanella loihica PV-4

Copper effects on net N2O production and associated gene expression by the deep-sea isolate Shewanella loihica PV-4 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Copper effects on net N 2 O production and associated gene expression by the deep-sea isolate Shewanella loihica PV-4 Laurine Mathé, Leonor Pizarro, C. Marisa R. Almeida, Catarina Magalhães, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7261601/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract After seabed disturbance, increased concentrations of dissolved copper (Cu) may occur and impact deep-sea bacterial metabolism. In this study, we investigated the effects of Cu on the net production of nitrous oxide (N 2 O), a potent greenhouse gas, in a model deep-sea strain, Shewanella loihica PV-4. We tested these effects in a series of exposure incubations, monitoring PV-4 growth, headspace N 2 O concentrations, and gene expression of nitrite reductase ( nirK ) and nitrous oxide reductase ( nosZ ). Despite no impact on growth, net N 2 O production was increased when 1 µM of Cu was added to the medium. Patterns of nirK and nosZ gene expression only partially explained the observed increase. This study shows that Cu plays an important role in mediating net N 2 O production by S. loihica PV-4, with potential consequences to local greenhouse gas emissions. The larger ecosystem implications of this finding, however, require further studies with other bacterial models and complex communities. copper deep-sea nitrous oxide metal impact gene expression nitrous oxide reductase Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Shewanella loihica is a facultative anaerobic bacterium isolated from an iron-rich microbial mat at the active deep-sea hydrothermal Naha vent, which is part of the Fe-rich deposits of the Loihi seamount located in the Pacific Ocean at 1325 m depth (Gao et al. 2006 ; Rouxel et al. 2018 ). This species is one of 32 other species of the Shewanella genus present in aquatic environments and sediments. These bacteria are known to be able to use a wide range of electron acceptors (oxygen, nitrate, metals, and sulfur compounds) and able to grow under extreme conditions making them ubiquitous. This species can grow at temperatures ranging from 0 to 42°C with an optimum of 18°C. It can tolerate pH from 4.5 to 10 with an optimum around 6 to 8. Na⁺ is required for its growth with a minimum of 0.5% up to 5% and an optimum of 2% (Gao et al. 2006 ). The deep sea is considered the largest ecosystem on Earth, representing around 50% of the planet. The seafloor contains a wide variety of metals, such as Cu, which represents 0.02 to 13.4 wt % (percentage by weight) of the total metals found in sediments, varying from locations and type of crust (Edgcomb et al. 2004a ; Paul et al. 2021 ). In the case of the Naha vent deposit, it contains 10 to 28 wt % of iron (Fe), where 12 to 75 µg of Cu per g of Fe deposit can be found, indicating that S. loihica PV-4 can naturally occur in Cu-rich environments (Rouxel et al. 2018 ). Dissolved Cu concentration is modulated by its binding potential with other compounds and by environmental factors, making it potentially non-soluble and so non-bioavailable for deep-sea organisms (Edgcomb et al. 2004b ; Paul et al. 2021 ). The average bioavailable concentration of dissolved Cu (dCu) in bottom waters ranges from 4 nM to 40 µM (Edgcomb et al. 2004b ). In the case of the Naha vent, a concentration of 6.9 nM dCu was found in the bottom seawater and up to 544.4 nM in the hydrothermal fluid of other active vents nearby (Rouxel et al. 2018 ). Despite being an essential micronutrient, Cu can be toxic for some microorganisms. It can lead to the formation of reactive oxygen and nitrogen species due to its redox-active potential, as well as lipid peroxidation and subsequent damage to cell membranes and tissues. Copper can also impact other processes mediated by bacteria, such as chemosynthesis, carbon cycling, and metal cycling, as well as community structure and function (Magalhães et al. 2011 ). The known toxicity range of Cu for microorganisms is 1 µM to 250 µM, and an inhibition of deep-sea bacterial growth usually appears between 50 µM and 150 µM (Edgcomb et al. 2004b ; Bird et al. 2013 ). Other studies, however, have shown higher minimum inhibitory concentrations (MIC), from 2.8 to 5.7 mM, in deep-sea isolated strains (Gillard et al. 2019 ). Even though Cu can be detoxified by resistant bacteria, this process is energy-consuming, which may reduce the free energy available for other processes. This “fitness-cost” may represent the potential loss of important ecosystem functions, which we are exploring here regarding the production and reduction of nitrous oxide (N 2 O), a potent greenhouse gas and important intermediate of the N cycle. Nitrogen (N) is one of the most abundant elements found on earth (Stein and Klotz 2016 ). Its biogeochemical cycle, consisting of the reduction and oxidation of nitrogen species leading to a change in N oxidation state, is mainly mediated by microorganisms. During denitrification, the respiratory stepwise reduction of nitrate (NO 3 − ) and nitrite (NO 2 − ) to nitric oxide (NO), nitrous oxide (N 2 O), and dinitrogen gas (N 2 ), significant amounts of N 2 O can dissipate if bacteria are unable to reduce it. The release of N 2 O in the environment is a major concern due to its sustained-flux global warming potential, which is around 300 times greater than CO 2 , and its ozone-depleting properties (Ravishankara et al. 2009 ; Neubauer and Megonigal 2015 ). The production of N 2 O gas during denitrification is preceded by NO 2 − reduction, performed by nitrite reductase enzymes encoded by nirK/S genes, and NO reduction, performed by nitric oxide reductase enzymes encoded by norB/C . Nitrous oxide reduction is driven by N 2 O reductase enzymes, encoded by the nosZ gene, present in denitrifying and non-denitrifying organisms (Hallin et al. 2018 ). The balance between N 2 O producing and N 2 O reducing steps ultimately drives net N 2 O production in a particular strain or environment. Concerning S. loihica PV-4, nirK , norB , and nosZ can be found in its genome (Graf et al. 2014 ). Thus, this species is able to produce and reduce N 2 O (Gao et al. 2006 ). Due to its multi-Cu sulfide centre, Cu plays a crucial role in N 2 O reductase catalysis (Felgate et al. 2012 ). Additionally, Cu can regulate the expression of the nos operon and other pathways that control the expression of nosZ and nirK at the gene expression level (Gaimster et al. 2018 ). Since Cu is a redox-active metal, the generation of reactive oxygen species (ROS) can also play a role in controlling nosZ gene expression and/or subsequent net N 2 O production (Chen et al. 2023 ). However, the exact impact of Cu on nosZ expression varies depending on the Cu concentration, environmental factors, and the studied strain. Research has shown that nosZ can be either upregulated or downregulated by Cu in different conditions, with varying impacts on N 2 O production rates (Magalhães et al. 2011 ; Felgate et al. 2012 ; Black et al. 2016 ; Gaimster et al. 2018 ). To our knowledge, however, no previous studies have investigated Cu impacts on model deep-sea isolated strains, which is a significant knowledge gap considering the magnitude and specificity of this ecosystem. Emerging deep-sea industries, such as seabed mining, marine carbon dioxide removal, or genetic resources extraction, may lead to sediment disturbance and consequent increase in dissolved metal concentrations in the surrounding seawater (Hauton et al. 2017 ; Washburn et al. 2023 ), which may lead to negative environmental impacts. Understanding the physiological consequences of metal exposure on deep-sea N 2 O metabolism may be relevant for future environmental impact assessments of such emerging activities, as it provides a mechanistic understanding of potential ecosystem impacts and enables the testing of gene expression indicators. In this study, we aimed to investigate how an increased concentration of Cu may impact S. loihica PV-4 net N 2 O production. For that, a series of Cu exposure experiments was conducted to determine the N 2 O fluxes under anoxic conditions, as well as the relative expression of the nirK and nosZ genes. 2. Material and methods 2.1. Bacterial culture Shewanella loihica PV-4 T (DSM 17748) was acquired from the German Collection of Microorganisms and Cell Cultures (DSMZ). After culture activation according to the provided instructions, cells were stored in cryogenic tubes at -80°C. As previously described (Pizarro et al. 2023 ), the frozen strains were reactivated before each experiment and pre-grown in Luria Bertani (LB) agar plates at 28 ºC for 9 days to obtain enough biomass for the metal exposure experiments, which were performed in liquid marine basal media (MBM). Media composition is described in Table S1 (Supplementary Material). 2.2. Growth monitoring in preliminary trials To first assess the impact of different concentrations of Cu on S. loihica PV-4 growth, seven dissolved concentrations were tested in triplicate culture flasks (1, 4, 7, 10, 40, 80, and 160 µM). The Cu solution was prepared by dissolving a suitable amount of CuCl 2 .2H 2 O in sterile deionized water. The 75 mL culture flasks were filled with 50 mL of MBM media, 10 mM of glucose as a carbon source, and the different concentrations of Cu. Control flasks were incubated under the same conditions without the addition of Cu. A volume of the pre-grown inoculum, representing 20% of the total volume, was added. The flasks were then incubated under oxic conditions, in the dark, at 28°C with agitation (250 rpm) for 3 to 6 days (until it reached the stationary phase). Bacterial growth was monitored over time through changes in optical density at 600 nm (OD 600 ). A linear regression on the exponential phase was used to determine the growth rate, with the maximum growth rate corresponding to the slope of the regression (Castilleja et al. 2017 ). 2.3. Metal exposure experiments in semi-closed bioreactors After the growth trials, a Cu concentration that did not inhibit PV-4 growth and that was reasonable to be found in a deep-sea environment (Edgcomb et al. 2004a ), 1 µM, was selected to assess the impacts on N 2 O metabolism. These experiments were performed in semi-closed 400 mL bioreactors Bio-Xplorer 400P, similarly to those previously described for Cd exposure experiments (Pizarro et al. 2023 ). A total of five replicate reactors were incubated with 1 µM of Cu and compared to four control reactors (without Cu addition) during three replicate experiments (A, B, and C). Each bioreactor was filled with 280 mL of MBM media (120 mL of headspace gas), 10 mM of glucose as a carbon and electron source, 1 mM of potassium nitrate (KNO 3 ) as an electron acceptor under anoxic conditions, 1 µM of Cu (from CuCl 2 .2H 2 O solution), and an inoculum representing 20% of the total volume. Control reactors were incubated under the same conditions without the addition of Cu. After inoculum addition, bacteria were initially grown under oxic conditions with a synthetic air rate of 10 mL/min until reaching the mid-exponential phase ( ca . 0.500 OD 600 ). Then, anoxia was induced by replacing the synthetic air with gaseous N 2 to stimulate denitrification with the NO 3 − present in the media. Once approximate anoxia was reached (< 2% dissolved O 2 ), the inflow gas was stopped and the reactors sealed to retain headspace gas and monitor N 2 O concentrations over time. The impact of Cu on net N 2 O production was then assessed by measuring the following parameters over a two-hour period (120 min): 1) headspace N 2 O concentration; 2) liquid NO 3 − and NO 2 − concentration; and 3) the relative expression of the nosZ and nirK genes. Following the period of induced anoxia and sample collection, the synthetic air supply was restored, allowing cell growth until reaching the stationary phase. 2.4. Nitrous oxide quantification The N 2 O concentration was measured in the headspace of each bioreactor at 0, 10, 20, 30, 60, 90, and 120 minutes after anoxia induction. For each sample, 10 mL of headspace gas was extracted with a glass syringe and stored in pre-evacuated vials. The N 2 O concentration was then measured by gas chromatography coupled with electron-capture detection (GC-ECD ) . Three standards of 100 ppm N 2 O (in 99.99% N 2 ) were used as calibration controls, and the N 2 O concentration was determined as previously described (Pizarro et al. 2023 ). A net production or consumption rate of N 2 O (N 2 O flux) was calculated from the slope of the linear change (positive or negative, respectively) in the N 2 O concentration over the 120-min sampling period for each reactor. For R 2 values of the linear regression lower than 0.80, we considered that there was no linear relationship, so the flux was considered null (zero). 2.5. Nitrite and nitrate quantification For NOx quantification (NO 3 − and NO 2 − ), liquid media samples (3 mL) were taken from the bioreactors just before anoxia induction (Ti) and then at 0 (when anoxia was reached), 30, 60, 90, and 120 minutes during anoxic conditions. The samples were immediately filtered with a 0.20 µm disk filter to obtain cell-free samples and stored at -20 ºC until analysis. The NOx concentrations were quantified using LCK cuvette kits with a DR3900 spectrophotometer (Hach LANGE), according to the manufacturer’s instructions. 2.6. Expression of nirK and nosZ genes To measure the expression of nirK and nosZ genes, 10 mL of liquid media samples were collected from the bioreactors just before anoxia induction (Ti) and then at 10, 60, and 120 minutes during anoxic conditions. Samples were centrifuged for 10 minutes at 3000 g and 4°C to precipitate the cells. The cell pellets were washed with cold PBS 1X and centrifuged for 10 minutes at 3000 g once again at 4°C. After PBS washing, the cell pellets were instantly frozen with liquid nitrogen and stored at -80°C until analysis. The RNA was extracted using the RNeasy Plus Mini Kit (Qiagen) according to the manufacturer’s instructions. The extracted RNA concentration and quality (A 260/230 and A 260/280 ratios) were assessed with the DeNovix DS-11 FX spectrophotometer. Afterwards, the RNA was cleaned from genomic DNA with the RapidOut DNA Removal Kit (Thermo Scientific). DNA removal was confirmed by negative PCR amplification of the recA gene. The treated RNA was then used for cDNA synthesis using the QuantiTect Reverse Transcription Kit (Qiagen) according to manufacturer instructions. Real-time quantitative PCRs were performed on the collected cDNA for the quantification of nirK transcripts, preceding N 2 O production, and of nosZ transcripts, responsible for N 2 O reduction. Two reference genes ( recA and rpoB ) were used to obtain normalized and comparable relative expression results (Rocha et al. 2015 ). These qPCRs were prepared with the reaction mix described in Table S2 and ran in a StepOne Plus real-time PCR system (Applied Biosystems). The primers used to target the four genes are described in Table S3. 2.7. Statistical analysis Growth rates in the different Cu treatments were compared with a one-way ANOVA followed by a Dunnett post-hoc test, after the assumptions of normality and homogeneity of variance were verified with Q-Q plots and histograms. Since the N 2 O fluxes and concentrations displayed large departures from normality, a non-parametric test (unpaired two-sample Mann-Whitney-Wilcoxon) was used to identify significant differences between treatments (Control and Cu). A two-way ANOVA was performed to compare the relative gene expression of nirK and nosZ between control and Cu reactors over time after ANOVA assumptions were verified. The NO 2 − / NO 3 − ratios did not follow a normal distribution and an unpaired two-sample Mann-Whitney-Wilcoxon test was also used to compare significant differences between treatments. 3. Results 3.1. Impact of copper on the grow of S. loihica PV-4 To determine the impact of Cu on the grow of S. loihica PV-4, we incubated the strain in culture flasks with two ranges of Cu concentrations known to potentially inhibit bacterial growth. A short range, from 1 to 10 µM, was used to assess the impact of low Cu concentrations, and a high range, from 1 to 160 µM, to determine the effects of high Cu concentrations. In both concentration ranges, the growth rate of S. loihica PV-4 was not affected by Cu, even at the highest concentrations tested ( Figure S1 ). Since this study focuses on environmentally realistic concentrations, we selected the concentration of 1 µM for subsequent Cu exposure experiments in semi-closed bioreactors to determine Cu impacts on net N 2 O production and associated gene expression. Similar to the preliminary growth trials, exposure to 1 µM of Cu in the bioreactors did not impact S. loihica PV-4 growth (Fig. 1 ), with similar growth rates observed in control (0.020 ± 0.003 OD.h − 1 , mean ± SEM) and Cu treatments (0.018 ± 0.006 OD.h − 1 , mean ± SEM) ( Table S4 ). 3.2. Impact of copper on net N 2 O production in S. loihica PV-4 S. loihica PV-4 produced a higher concentration of N 2 O in Cu-treated reactors than in the control treatment, with a net N 2 O flux of 0.10 ± 0.04 µmol N 2 O-N.min − 1 (mean ± SEM) and 0.04 ± 0.02 µmol N 2 O-N.min − 1 (mean ± SEM), respectively (Fig. 2 ). However, due to the high variability observed and some reactors without linear slopes ( Figure S2 ), the Mann–Whitney–Wilcoxon test returned a p-value of 0.387, greater than the alpha threshold of 0.05, indicating that the differences were not significant. Nevertheless, the highest levels of headspace N 2 O were consistently found in Cu-treated reactors (Fig. 3 ) and were significantly higher than in control (p = 0.021, Mann-Whitney-Wilcoxon). Among 63 measurements of headspace N 2 O, nine of the 10 highest levels were found in reactors treated with Cu and only one in control reactors. Besides the higher mean and peak values of headspace N 2 O, it is also important to note the higher variances in Cu-treated reactors, with N 2 O concentrations of 6.32 ± 8.02 µmol N 2 O-N per reactor (mean ± SEM) when compared to control reactors with 1.66 ± 2.69 µmol N 2 O-N per reactor (mean ± SEM). The concentration of NO 2 − and NO 3 − in control and Cu-treated reactors showed no significant difference between the two treatments ( Figure S3 ). During the 2-hour anoxic period, NO 3 − concentration continuously decreased, while the concentration of NO 2 − , initially absent, increased in the first 30 minutes and then decreased to below detection levels, indicating that all NO 2 − produced during anoxia was consumed. Regarding the NO 2 − :NO 3 − ratio, known to play a role in net N 2 O production, no significant differences were detected between control and Cu-treated reactors (p-value = 0.796, Mann-Whitney-Wilcoxon test), with average values of 3.64 and 3.55, respectively, during the anoxic period ( Table S6 ). The results show that the overproduction of N 2 O observed in Cu-treated reactors was not associated to an overproduction of NO 2 − , as no significant differences were detected between the control and the Cu treatment. Overall, the consumption of NO 3 − , NO 2 − , and the NO 2 − :NO 3 − ratio remained unaffected by Cu. 3.3. Impact of copper on S. loihica PV-4 gene expression The relative expression of nirK and nosZ genes was determined to understand if changes in net N 2 O production were associated to gene transcription regulation. The relative expression of both genes showed a strong upregulation by the Cu treatment after 10 minutes of anoxia, followed by a slight downregulation after 60 and 120 minutes in nirK expression and after 120 min in nosZ expression (Fig. 4 , Table S5 ). Cu-treated reactors showed higher nirK relative expression than control reactors even before the anoxic phase, suggesting that Cu may upregulate nirK expression in the presence of oxygen. In contrast to nirK , no significant increase in nosZ relative expression was observed prior to anoxia, which caused a higher nirK / nosZ ratio in Cu reactors, when compared to control, at this timepoint ( Table S5 ). Besides the treatment differences on average values, it is also worth noticing the substantially higher variability observed for nirK and nosZ expression over time in Cu-treated reactors when compared to control. While the expression of both genes steadily increased over time in control reactors (expected due to anoxic induction), their expression fluctuated in Cu-treated reactors. NirK relative expression variance was 64.48 in control reactors and 82.97 in Cu-treated reactors, while nosZ relative expression variance was 26.05 in control reactors and 87.42 in Cu-treated reactors. The nirK/nosZ relative expression ratio can be used as an indicator of changes in N 2 O regulation, with ratios > 1 indicating potential for N 2 O production and ratios < 1 indicating potential for N 2 O reduction (Pizarro et al. 2023 ). Despite the higher nirK / nosZ ratio observed in Cu-treated reactors while O 2 was still present (before anoxia), there were no significant differences in the nirK/nosZ ratios detected between the two treatments over time (2-way ANOVA p-value time*treatment = 0.582). Both treatments indicate a stronger potential for N 2 O reduction, with values below 1 most of the time in ( Table S5 ). 4. Discussion The impact of Cu on S. loihica PV-4 growth was initially investigated to find a suitable concentration to test Cu impacts on net N 2 O production. The target concentration would have to be non-toxic, i.e. without effects on growth, and potentially expected after seabed disturbance, i.e. slightly above background environmental levels. The obtained results showed that S. loihica PV-4 was able to tolerate all tested Cu concentrations added to the medium, from 1 to 160 µM, which is in accordance with previous studies that have shown that this strain is particularly resistant to Cu, even when compared to other Shewanella strains (Lv et al. 2018 ). This Cu tolerance contrasts with sensitivity of this strain to cadmium (Cd), with growth inhibition at levels around 25 µM (Pizarro et al. 2023 ), underscoring the differential impacts that different metals may have on bacterial growth (Brown et al. 2017 ; Gillard et al. 2019 ). Previous studies with other isolated strains and bacterial communities suggest that Cu plays a crucial role in regulating the expression of nirK and nosZ genes and the activity of their respective enzymes (Magalhães et al. 2011 ; Felgate et al. 2012 ; Black et al. 2016 ). Since both nirK and nosZ genes are present in the genome of S. loihica PV-4 (Yoon et al. 2015a ), Cu was expected to affect its net N 2 O production. The results obtained in this study support this hypothesis, by showing an overproduction of N 2 O by S. loihica PV-4 when exposed to 1 µM of Cu. A similar increase in net N 2 O production was also observed in a nirS -carrying strain of the denitrifying soil bacterium Pseudomonas stutzeri when exposed to 150, 1000 and 5000 µM of Cu (Black et al. 2016 ). In other nirK -carrying microorganisms ( Achromobacter xylosoxidans ) and nirS -carrying microorganisms ( Paracoccus denitrificans PD 1222), N 2 O production was enhanced under Cu exposure to low (1 µM) and high (13 µM) concentrations (Felgate et al. 2012 ). However, the nirK -carrying species was shown to produce more N 2 O when exposed to higher Cu concentrations compared to lower concentrations, while the nirS -carrying species produced more N 2 O when exposed to low Cu concentrations (Felgate et al. 2012 ). If the nitrite reductase co-factor plays a role in the response of net N 2 O production to Cu exposure, one can expect even higher N 2 O production in S. loihica PV-4 under exposure to higher Cu concentrations since it is a nirK -carrying organism. However, this requires further research and testing at higher Cu concentrations and with other nirK and nirS carrying strains. Even though Cu is expected to upregulate nosZ gene transcription (Gaimster et al. 2018 ), the observed increase in net N 2 O production in our study suggests that this regulation may not be continuous or that it may be insufficient to assimilate all N 2 O produced. It is interesting to note that, while nirK upregulation by Cu started even before anoxia, nosZ upregulation only occurred 10 minutes after anoxia was reached (Fig. 4 ). It is possible that nosZ transcription did not immediately respond to the N 2 O generated in the oxic/anoxic transition due to the generation of reactive oxygen species (ROS), which have been shown to inhibit nosZ transcription in oxic conditions (Chen et al. 2023 ). Regardless of the mechanism, this differential response of nirK (potential N 2 O source) and nosZ (potential N 2 O sink) in oxic conditions can be important in deep seawater, where oxic conditions are frequently found. As S. loihica is a facultative anaerobic, this species could be exposed to oxic conditions in the deep-sea floor, which would affect its denitrification pathway. In the case of this study, the early upregulation of nirK in Cu-treated reactors may have supported the immediate reduction of NO 2 − to NO and N 2 O right after entering anoxia, when nosZ upregulation was also initiated (Fig. 4 , Table S5 ). This sequential onset of denitrifying gene transcription has been shown before in other denitrifying species (Liu et al. 2013 ). However, despite the strong nosZ upregulation observed in Cu-treated reactors 10 min into anoxia, the net N 2 O production was still higher in these reactors (Figs. 2 and 3 ), suggesting that the upregulation of nosZ expression was not sufficient to reduce the N 2 O produced by NO 2 − reductase. This observation indicates that Cu also has a potential impact on the enzymatic activity. The low nirK / nosZ expression ratio observed in Cu-treated reactors also supports a post-transcriptional inhibition of the N 2 O reductase activity that would explain the higher net N 2 O production. This inhibition, however, was not expected since multiple studies have shown that Cu is necessary and, in fact, stimulates N 2 O reductase activity in other denitrifying bacteria (Granger and Ward 2003 ; Felgate et al. 2012 ). The post-transcriptional inhibition observed in our study may be a result of differences in the Cu concentrations tested (1 µM in this study versus lower concentrations in cited studies) or the tested strain. The higher variability observed for gene expression in Cu-treated reactors suggests that stochastic processes may have a stronger influence when microorganisms are under metal stress, while more deterministic processes may play a stronger role in control conditions. Stochastic control of denitrifying gene expression, i.e., random initiation of gene transcription, has been shown before in denitrifying bacteria, associated with a bet hedging strategy under oxic-anoxic transitions (Lycus et al. 2018 ). Since metal resistance and detoxification are also under the control of multiple gene expressions (Das et al. 2016 ), it is possible that added controls of gene expression in Cu-treated cells increased the variability of denitrifying gene expression. This observation may be worth investigating further to understand the microbial response to disturbance and to estimate potential metabolic trade-offs. The NO 2 − :NO 3 − ratio is known to play an important role in the net production of N 2 O and transcription of nirK in S. loihica PV-4. A low ratio promotes N 2 O production and nirK transcription, while a high ratio (above 3) inhibits both due to NO 2 − toxicity (Yoon et al. 2015b ). In our study, the concentrations of NO 2 − and NO 3 − remained similar between Cu-treated and control reactors, with no significant change in the ratios, most of them being above 3. Thus, the NO 2 − :NO 3 − ratio likely did not play a crucial role in explaining gene expression and net N 2 O production variation under Cu exposure in this study. This study reveals that Cu at 1 µM significantly affects the denitrification pathway in S. loihica PV-4 and increases net production of N 2 O in anoxic conditions. When compared to Cd, the two metals show opposite effects (Pizarro et al. 2023 ). Cadmium appears to inhibit net N 2 O production, whereas Cu increases it. Regarding gene expression, the two metals also have different effects on nirK and nosZ relative gene expression. While Cd inhibits the relative expression of both genes (Pizarro et al. 2023 ), Cu upregulates nirK and nosZ relative gene expression at the beginning of the anoxic period and slightly downregulates it at the end, with substantially higher variability over time when compared to control. Although both metals have the same ionic form in solution and similar toxicity modes (Brown et al. 2017 ), their impact on S. loihica PV-4 differs significantly. This observation has environmental implications since the two metals may have different availability in different deep-sea environments. 5. Conclusions In summary, this study demonstrates that a relatively low concentration of Cu can increase the net production of N 2 O by S. loihica PV-4. The average and peak values of N 2 O production were higher in Cu-treated reactors when compared to control. This higher net N 2 O production may lead to local increases in greenhouse gas emissions, due to the high global warming potential of N 2 O. It is important to say, however, that the larger ecosystem implications of this finding require further studies with other bacterial models as well as with complex microbial communities. Regarding Cu-driven impacts on gene expression, we observed that the timing of the oxic-to-anoxic transition may play an important role. While nirK is upregulated by Cu in oxic conditions, nosZ was unaffected, increasing the risk of net N 2 O production. In anoxic conditions, both genes were mostly upregulated by Cu, and despite the stronger upregulation of nosZ transcription at the beginning of the anoxic stage, the N 2 O reductase activity was not sufficient to reduce all produced N 2 O. It seems that the overproduction of N 2 O may be linked to gene expression controls in oxic conditions and in early anoxia, but post-transcriptional or post-translational changes in N 2 O reductase activity may be more important in later anoxia. Further investigation on the whole transcriptome of this deep-sea isolate as well as other N 2 O producing and reducing strains is needed to unveil what regulatory mechanisms may explain the observed results and to estimate potential effects of metal exposure on important N cycling processes more widely. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was performed within the scope of the DeepResist project (2022.06475.PTDC), funded by the Portuguese Foundation for Science and Technology (FCT), as well as the MIDFun project, funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 101038095. This work was also supported by the ATLANTIDA project (NORTE-01- 0145-FEDER-000040), supported by the North Portugal Regional Operational Program (NORTE2020), under the PORTUGAL 2020 Partnership Agreement and through the European Regional Development Fund (ERDF) and national funds through FCT within the scope of UIDB/04423/2020, UIDP/04423/2020 and LA/P/0101/2020. Author MS also acknowledges the work contract through the Scientific Employment Stimulus Individual Call (CEEC), funded by the FCT ( https://doi.org/10.54499/2023.08554.CEECIND/CP2848/CT0004 ). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LM, LP, and MS. The first draft of the manuscript was written by LM and all authors commented on previous versions. All authors read and approved the final manuscript. References Bird LJ, Coleman ML, Newman DK (2013) Iron and Copper Act Synergistically To Delay Anaerobic Growth of Bacteria. Appl Environ Microbiol 79:3619–3627. https://doi.org/10.1128/AEM.03944-12 Black A, Hsu P-CL, Hamonts KE, et al (2016) Influence of copper on expression of nirS, norB and nosZ and the transcription and activity of NIR, NOR and N2OR in the denitrifying soil bacteria Pseudomonas stutzeri. Microb Biotechnol 9:381–388. https://doi.org/https://doi.org/10.1111/1751-7915.12352 Brown A, Thatje S, Hauton C (2017) The Effects of Temperature and Hydrostatic Pressure on Metal Toxicity: Insights into Toxicity in the Deep Sea. Environ Sci Technol 51:10222–10231. https://doi.org/10.1021/acs.est.7b02988 Castilleja DEM, Tapia JAA, Arvizu Medrano SM, et al (2017) Growth Kinetics for the Selection of Yeast Strains for Fermented Beverages. In: Morata A, Loira I (eds) Yeast. IntechOpen, Rijeka, p Ch. 3 Chen X, Yang J, Zeng RJ, et al (2023) Reactive Oxygen Species Promote Nitrous Oxide (N2O) Emissions from Soil/Sediment during the Anoxic–Oxic Transition. Environ Sci Technol 57:801–809. https://doi.org/10.1021/acs.est.2c07081 Das S, Dash HR, Chakraborty J (2016) Genetic basis and importance of metal resistant genes in bacteria for bioremediation of contaminated environments with toxic metal pollutants. Appl Microbiol Biotechnol 100:2967–2984. https://doi.org/10.1007/s00253-016-7364-4 Edgcomb VP, Molyneaux SJ, Saito MA, et al (2004a) Sulfide Ameliorates Metal Toxicity for Deep-Sea Hydrothermal Vent Archaea. Appl Environ Microbiol 70:2551–2555. https://doi.org/10.1128/AEM.70.4.2551-2555.2004 Edgcomb VP, Molyneaux SJ, Saito MA, et al (2004b) Sulfide Ameliorates Metal Toxicity for Deep-Sea Hydrothermal Vent Archaea. Appl Environ Microbiol 70:2551–2555. https://doi.org/10.1128/AEM.70.4.2551-2555.2004 Felgate H, Giannopoulos G, Sullivan MJ, et al (2012) The impact of copper, nitrate and carbon status on the emission of nitrous oxide by two species of bacteria with biochemically distinct denitrification pathways. Environ Microbiol 14:1788–1800. https://doi.org/https://doi.org/10.1111/j.1462-2920.2012.02789.x Gaimster H, Alston M, Richardson DJ, et al (2018) Transcriptional and environmental control of bacterial denitrification and N2O emissions. FEMS Microbiol Lett 365:. https://doi.org/10.1093/femsle/fnx277 Gao H, Obraztova A, Stewart N, et al (2006) Shewanella loihica sp. nov., isolated from iron-rich microbial mats in the Pacific Ocean. Int J Syst Evol Microbiol 56:1911–1916. https://doi.org/https://doi.org/10.1099/ijs.0.64354-0 Gillard B, Chatzievangelou D, Thomsen L, Ullrich MS (2019) Heavy-Metal-Resistant Microorganisms in Deep-Sea Sediments Disturbed by Mining Activity: An Application Toward the Development of Experimental in vitro Systems. Front Mar Sci 6: Graf DRH, Jones CM, Hallin S (2014) Intergenomic Comparisons Highlight Modularity of the Denitrification Pathway and Underpin the Importance of Community Structure for N2O Emissions. PLoS One 1–20. https://doi.org/10.1371/journal.pone.0114118 Granger J, Ward BB (2003) Accumulation of nitrogen oxides in copper-limited cultures of denitrifying bacteria. Limnol Oceanogr 48:313–318. https://doi.org/https://doi.org/10.4319/lo.2003.48.1.0313 Hallin S, Philippot L, Löffler FE, et al (2018) Genomics and Ecology of Novel N2O-Reducing Microorganisms. Trends Microbiol 26:43–55. https://doi.org/10.1016/j.tim.2017.07.003 Hauton C, Brown A, Thatje S, et al (2017) Identifying toxic impacts of metals potentially released during deep-sea mining-A synthesis of the challenges to quantifying risk. Front Mar Sci 4 Liu B, Mao Y, Bergaust L, et al (2013) Strains in the genus Thauera exhibit remarkably different denitrification regulatory phenotypes. Environ Microbiol 15:2816–2828. https://doi.org/10.1111/1462-2920.12142 Lv Q, Zhang B, Xing X, et al (2018) Biosynthesis of copper nanoparticles using Shewanella loihica PV-4 with antibacterial activity: Novel approach and mechanisms investigation. J Hazard Mater 347:141–149. https://doi.org/https://doi.org/10.1016/j.jhazmat.2017.12.070 Lycus P, Soriano-Laguna MJ, Kjos M, et al (2018) A bet-hedging strategy for denitrifying bacteria curtails their release of N2O. Proceedings of the National Academy of Sciences 115:11820–11825. https://doi.org/10.1073/pnas.1805000115 Magalhães CM, Machado A, Matos P, Bordalo AA (2011) Impact of copper on the diversity, abundance and transcription of nitrite and nitrous oxide reductase genes in an urban European estuary. FEMS Microbiol Ecol 77:274–284. https://doi.org/10.1111/j.1574-6941.2011.01107.x Neubauer SC, Megonigal JP (2015) Moving Beyond Global Warming Potentials to Quantify the Climatic Role of Ecosystems. Ecosystems 18:1000–1013. https://doi.org/10.1007/s10021-015-9879-4 Paul SAL, Zitoun R, Noowong A, et al (2021) Copper-binding ligands in deep-sea pore waters of the Pacific Ocean and potential impacts of polymetallic nodule mining on the copper cycle. Sci Rep 11:18425. https://doi.org/10.1038/s41598-021-97813-3 Pizarro L, Magalhães C, Almeida CMR, et al (2023) Cadmium effects on net N2O production by the deep-sea isolate Shewanella loihica PV-4. FEMS Microbiol Lett fnad047. https://doi.org/10.1093/femsle/fnad047 Ravishankara AR, Daniel JS, Portmann RW (2009) Nitrous Oxide (N2O): The Dominant Ozone-Depleting Substance Emitted in the 21st Century. Science (1979) 326:123–125 Rocha DJP, Santos CS, Pacheco LGC (2015) Bacterial reference genes for gene expression studies by RT-qPCR: survey and analysis. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology 108:685–693. https://doi.org/10.1007/s10482-015-0524-1 Rouxel O, Toner B, Germain Y, Glazer B (2018) Geochemical and iron isotopic insights into hydrothermal iron oxyhydroxide deposit formation at Loihi Seamount. Geochim Cosmochim Acta 220:449–482. https://doi.org/https://doi.org/10.1016/j.gca.2017.09.050 Stein LY, Klotz MG (2016) The nitrogen cycle. Current Biology 26:R94–R98. https://doi.org/10.1016/j.cub.2015.12.021 Washburn TW, Iguchi A, Yamaoka K, et al (2023) Impacts of the first deep-sea seafloor massive sulfide mining excavation tests on benthic communities. Mar Ecol Prog Ser 712:1–19 Yoon S, Cruz-García C, Sanford R, et al (2015a) Denitrification versus respiratory ammonification: environmental controls of two competing dissimilatory NO3(-)/NO2(-) reduction pathways in Shewanella loihica strain PV-4. ISME J 9:1093–1104. https://doi.org/10.1038/ismej.2014.201 Yoon S, Sanford RA, Loffler FE (2015b) Nitrite Control over Dissimilatory Nitrate/Nitrite Reduction Pathways in Shewanella loihica Strain PV-4. Appl Environ Microbiol 81:3510–3517. https://doi.org/10.1128/AEM.00688-15 Additional Declarations No competing interests reported. Supplementary Files PV4copperN2OpapersupplementaryMaterialAoMMS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7261601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495499669,"identity":"4f31b652-9b10-4c53-ae0c-4437210f2383","order_by":0,"name":"Laurine Mathé","email":"","orcid":"","institution":"CIIMAR/CIMAR LA, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Laurine","middleName":"","lastName":"Mathé","suffix":""},{"id":495499670,"identity":"62ffb3a6-738c-4405-8171-2767d7ca9cbc","order_by":1,"name":"Leonor Pizarro","email":"","orcid":"","institution":"CIIMAR/CIMAR LA, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Leonor","middleName":"","lastName":"Pizarro","suffix":""},{"id":495499671,"identity":"5ffe900a-e692-4b0d-b47d-596849013ff9","order_by":2,"name":"C. Marisa R. Almeida","email":"","orcid":"","institution":"CIIMAR/CIMAR LA, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"Marisa R.","lastName":"Almeida","suffix":""},{"id":495499672,"identity":"17118571-8ba3-4205-be66-e3e05fc425bf","order_by":3,"name":"Catarina Magalhães","email":"","orcid":"","institution":"CIIMAR/CIMAR LA, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Catarina","middleName":"","lastName":"Magalhães","suffix":""},{"id":495499673,"identity":"ad8075a7-0888-4a63-9b7e-1c5bb085515e","order_by":4,"name":"Miguel Semedo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYNACNgY5BmbGBtK0GCNrIUYzG0Misir8WnT7Dx/d8KHMJr2/nbl1w889DInbGXiPP8CnxexGWtrNGefScmccZmy72fOMIXFnAx+KlVi08Jjd5m07nLuBmbHtBs8BhsQNB3gM8Ws5f/7b7b9th9MNgFpu/iFKy4EcttuMbYcTQFpuE2fLjTSzmz3n0gxBfrktc0DCeGczX+IM/A47/OzGjzIbef7+489uvjlgI7udvffAB3xa0IEEgwEzDykaQMCAgWQto2AUjIJRMMwBACzCUcMNbERTAAAAAElFTkSuQmCC","orcid":"","institution":"CIIMAR/CIMAR LA, University of Porto","correspondingAuthor":true,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Semedo","suffix":""}],"badges":[],"createdAt":"2025-07-31 11:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7261601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7261601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88444839,"identity":"f2e64997-2e33-4cf7-903e-7ff289aa1c4c","added_by":"auto","created_at":"2025-08-06 13:30:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eS. loihica\u003c/em\u003e PV-4 growth during Cu exposure experiments. Each point represents the OD600 measurement from a single reactor at a given time point. Control treatment (without Cu spike) is represented in blue and Cu treatment (at 1 µM) is represented in orange. The N\u003csub\u003e2\u003c/sub\u003eO and gene expression sampling phase (anoxic period) is indicated by the grey doted area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/475173a3839ef87a2e649fb1.png"},{"id":88443812,"identity":"78ee3627-8f33-4635-98e0-98c7b0f5fe38","added_by":"auto","created_at":"2025-08-06 13:22:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44729,"visible":true,"origin":"","legend":"\u003cp\u003eNitrous oxide fluxes during anoxia in control (blue) and Cu-treated (orange) reactors. Each bar represents the N2O flux in a single reactor. The fluxes were calculated from the slope of the linear change in the N2O concentration over the 120-min sampling period. For linear regressions with R² \u0026lt; 0.80 the flux was considered null (= 0).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/ec16a32559f4b6752b1538d7.png"},{"id":88444844,"identity":"f06628d2-5c8d-4808-8f1d-5ca2e2bf9414","added_by":"auto","created_at":"2025-08-06 13:30:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31778,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal N\u003csub\u003e2\u003c/sub\u003eO concentrations in control and Cu-treated reactors. Each sample is represented by one point while the boxes represent the first and third quartiles, with median value bisecting each box. The whiskers extend to the largest/smallest value, excluding outliers (data beyond 1.5 inter-quartile range).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/781057d0bc94087435184c53.png"},{"id":88443814,"identity":"b7307a88-1730-4383-9d31-cfd1f039b182","added_by":"auto","created_at":"2025-08-06 13:22:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e gene expression in \u003cem\u003eS. loihic\u003c/em\u003ea PV-4 in control (blue) and Cu-treated (orange) reactors. Each bar represents the mean ± SEM of 4 replicates for control treatment and 5 replicates for Cu treatment from experiments A, B, and C. Dissimilar letters (a or b) indicate significant differences between treatments (two-way ANOVA with interaction between time and treatment, p \u0026lt; 0.05). Relative expression was obtained by normalising \u003cem\u003enirK\u003c/em\u003eand \u003cem\u003enosZ\u003c/em\u003e transcript copy numbers by the average of \u003cem\u003erecA\u003c/em\u003e and \u003cem\u003erpoB\u003c/em\u003etranscript copy numbers in each sample.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/3c83ec7c63d1914d8507c597.png"},{"id":91553784,"identity":"c3a995fc-5b48-4c67-98ec-302441e36e98","added_by":"auto","created_at":"2025-09-17 16:38:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":994388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/76c7e3f1-7370-402e-b6dd-88525738d60c.pdf"},{"id":88443820,"identity":"17ced852-2817-429b-8758-1fb7600e23dc","added_by":"auto","created_at":"2025-08-06 13:22:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2171742,"visible":true,"origin":"","legend":"","description":"","filename":"PV4copperN2OpapersupplementaryMaterialAoMMS.docx","url":"https://assets-eu.researchsquare.com/files/rs-7261601/v1/b9887481043006f5b1cbf786.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCopper effects on net N\u003csub\u003e2\u003c/sub\u003eO production and associated gene expression by the deep-sea isolate Shewanella loihica PV-4\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cem\u003eShewanella loihica\u003c/em\u003e is a facultative anaerobic bacterium isolated from an iron-rich microbial mat at the active deep-sea hydrothermal Naha vent, which is part of the Fe-rich deposits of the Loihi seamount located in the Pacific Ocean at 1325 m depth (Gao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rouxel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This species is one of 32 other species of the \u003cem\u003eShewanella\u003c/em\u003e genus present in aquatic environments and sediments. These bacteria are known to be able to use a wide range of electron acceptors (oxygen, nitrate, metals, and sulfur compounds) and able to grow under extreme conditions making them ubiquitous. This species can grow at temperatures ranging from 0 to 42\u0026deg;C with an optimum of 18\u0026deg;C. It can tolerate pH from 4.5 to 10 with an optimum around 6 to 8. Na⁺ is required for its growth with a minimum of 0.5% up to 5% and an optimum of 2% (Gao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe deep sea is considered the largest ecosystem on Earth, representing around 50% of the planet. The seafloor contains a wide variety of metals, such as Cu, which represents 0.02 to 13.4 wt % (percentage by weight) of the total metals found in sediments, varying from locations and type of crust (Edgcomb et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the case of the Naha vent deposit, it contains 10 to 28 wt % of iron (Fe), where 12 to 75 \u0026micro;g of Cu per g of Fe deposit can be found, indicating that \u003cem\u003eS. loihica\u003c/em\u003e PV-4 can naturally occur in Cu-rich environments (Rouxel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Dissolved Cu concentration is modulated by its binding potential with other compounds and by environmental factors, making it potentially non-soluble and so non-bioavailable for deep-sea organisms (Edgcomb et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The average bioavailable concentration of dissolved Cu (dCu) in bottom waters ranges from 4 nM to 40 \u0026micro;M (Edgcomb et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e). In the case of the Naha vent, a concentration of 6.9 nM dCu was found in the bottom seawater and up to 544.4 nM in the hydrothermal fluid of other active vents nearby (Rouxel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite being an essential micronutrient, Cu can be toxic for some microorganisms. It can lead to the formation of reactive oxygen and nitrogen species due to its redox-active potential, as well as lipid peroxidation and subsequent damage to cell membranes and tissues. Copper can also impact other processes mediated by bacteria, such as chemosynthesis, carbon cycling, and metal cycling, as well as community structure and function (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The known toxicity range of Cu for microorganisms is 1 \u0026micro;M to 250 \u0026micro;M, and an inhibition of deep-sea bacterial growth usually appears between 50 \u0026micro;M and 150 \u0026micro;M (Edgcomb et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e; Bird et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Other studies, however, have shown higher minimum inhibitory concentrations (MIC), from 2.8 to 5.7 mM, in deep-sea isolated strains (Gillard et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Even though Cu can be detoxified by resistant bacteria, this process is energy-consuming, which may reduce the free energy available for other processes. This \u0026ldquo;fitness-cost\u0026rdquo; may represent the potential loss of important ecosystem functions, which we are exploring here regarding the production and reduction of nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), a potent greenhouse gas and important intermediate of the N cycle.\u003c/p\u003e\u003cp\u003eNitrogen (N) is one of the most abundant elements found on earth (Stein and Klotz \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Its biogeochemical cycle, consisting of the reduction and oxidation of nitrogen species leading to a change in N oxidation state, is mainly mediated by microorganisms. During denitrification, the respiratory stepwise reduction of nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) to nitric oxide (NO), nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), and dinitrogen gas (N\u003csub\u003e2\u003c/sub\u003e), significant amounts of N\u003csub\u003e2\u003c/sub\u003eO can dissipate if bacteria are unable to reduce it. The release of N\u003csub\u003e2\u003c/sub\u003eO in the environment is a major concern due to its sustained-flux global warming potential, which is around 300 times greater than CO\u003csub\u003e2\u003c/sub\u003e, and its ozone-depleting properties (Ravishankara et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Neubauer and Megonigal \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The production of N\u003csub\u003e2\u003c/sub\u003eO gas during denitrification is preceded by NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e reduction, performed by nitrite reductase enzymes encoded by \u003cem\u003enirK/S\u003c/em\u003e genes, and NO reduction, performed by nitric oxide reductase enzymes encoded by \u003cem\u003enorB/C\u003c/em\u003e. Nitrous oxide reduction is driven by N\u003csub\u003e2\u003c/sub\u003eO reductase enzymes, encoded by the \u003cem\u003enosZ\u003c/em\u003e gene, present in denitrifying and non-denitrifying organisms (Hallin et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The balance between N\u003csub\u003e2\u003c/sub\u003eO producing and N\u003csub\u003e2\u003c/sub\u003eO reducing steps ultimately drives net N\u003csub\u003e2\u003c/sub\u003eO production in a particular strain or environment. Concerning \u003cem\u003eS. loihica\u003c/em\u003e PV-4, \u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enorB\u003c/em\u003e, and \u003cem\u003enosZ\u003c/em\u003e can be found in its genome (Graf et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, this species is able to produce and reduce N\u003csub\u003e2\u003c/sub\u003eO (Gao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to its multi-Cu sulfide centre, Cu plays a crucial role in N\u003csub\u003e2\u003c/sub\u003eO reductase catalysis (Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, Cu can regulate the expression of the nos operon and other pathways that control the expression of \u003cem\u003enosZ\u003c/em\u003e and \u003cem\u003enirK\u003c/em\u003e at the gene expression level (Gaimster et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Since Cu is a redox-active metal, the generation of reactive oxygen species (ROS) can also play a role in controlling \u003cem\u003enosZ\u003c/em\u003e gene expression and/or subsequent net N\u003csub\u003e2\u003c/sub\u003eO production (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the exact impact of Cu on \u003cem\u003enosZ\u003c/em\u003e expression varies depending on the Cu concentration, environmental factors, and the studied strain. Research has shown that \u003cem\u003enosZ\u003c/em\u003e can be either upregulated or downregulated by Cu in different conditions, with varying impacts on N\u003csub\u003e2\u003c/sub\u003eO production rates (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Black et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gaimster et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To our knowledge, however, no previous studies have investigated Cu impacts on model deep-sea isolated strains, which is a significant knowledge gap considering the magnitude and specificity of this ecosystem. Emerging deep-sea industries, such as seabed mining, marine carbon dioxide removal, or genetic resources extraction, may lead to sediment disturbance and consequent increase in dissolved metal concentrations in the surrounding seawater (Hauton et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Washburn et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which may lead to negative environmental impacts. Understanding the physiological consequences of metal exposure on deep-sea N\u003csub\u003e2\u003c/sub\u003eO metabolism may be relevant for future environmental impact assessments of such emerging activities, as it provides a mechanistic understanding of potential ecosystem impacts and enables the testing of gene expression indicators. In this study, we aimed to investigate how an increased concentration of Cu may impact \u003cem\u003eS. loihica\u003c/em\u003e PV-4 net N\u003csub\u003e2\u003c/sub\u003eO production. For that, a series of Cu exposure experiments was conducted to determine the N\u003csub\u003e2\u003c/sub\u003eO fluxes under anoxic conditions, as well as the relative expression of the \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Bacterial culture\u003c/h2\u003e\u003cp\u003e\u003cem\u003eShewanella loihica\u003c/em\u003e PV-4\u003csup\u003eT\u003c/sup\u003e (DSM 17748) was acquired from the German Collection of Microorganisms and Cell Cultures (DSMZ). After culture activation according to the provided instructions, cells were stored in cryogenic tubes at -80\u0026deg;C. As previously described (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the frozen strains were reactivated before each experiment and pre-grown in Luria Bertani (LB) agar plates at 28 \u0026ordm;C for 9 days to obtain enough biomass for the metal exposure experiments, which were performed in liquid marine basal media (MBM). Media composition is described in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e (Supplementary Material).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Growth monitoring in preliminary trials\u003c/h2\u003e\u003cp\u003eTo first assess the impact of different concentrations of Cu on \u003cem\u003eS. loihica\u003c/em\u003e PV-4 growth, seven dissolved concentrations were tested in triplicate culture flasks (1, 4, 7, 10, 40, 80, and 160 \u0026micro;M). The Cu solution was prepared by dissolving a suitable amount of CuCl\u003csub\u003e2\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO in sterile deionized water. The 75 mL culture flasks were filled with 50 mL of MBM media, 10 mM of glucose as a carbon source, and the different concentrations of Cu. Control flasks were incubated under the same conditions without the addition of Cu. A volume of the pre-grown inoculum, representing 20% of the total volume, was added. The flasks were then incubated under oxic conditions, in the dark, at 28\u0026deg;C with agitation (250 rpm) for 3 to 6 days (until it reached the stationary phase). Bacterial growth was monitored over time through changes in optical density at 600 nm (OD\u003csub\u003e600\u003c/sub\u003e). A linear regression on the exponential phase was used to determine the growth rate, with the maximum growth rate corresponding to the slope of the regression (Castilleja et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Metal exposure experiments in semi-closed bioreactors\u003c/h2\u003e\u003cp\u003eAfter the growth trials, a Cu concentration that did not inhibit PV-4 growth and that was reasonable to be found in a deep-sea environment (Edgcomb et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e), 1 \u0026micro;M, was selected to assess the impacts on N\u003csub\u003e2\u003c/sub\u003eO metabolism. These experiments were performed in semi-closed 400 mL bioreactors Bio-Xplorer 400P, similarly to those previously described for Cd exposure experiments (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A total of five replicate reactors were incubated with 1 \u0026micro;M of Cu and compared to four control reactors (without Cu addition) during three replicate experiments (A, B, and C). Each bioreactor was filled with 280 mL of MBM media (120 mL of headspace gas), 10 mM of glucose as a carbon and electron source, 1 mM of potassium nitrate (KNO\u003csub\u003e3\u003c/sub\u003e) as an electron acceptor under anoxic conditions, 1 \u0026micro;M of Cu (from CuCl\u003csub\u003e2\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO solution), and an inoculum representing 20% of the total volume. Control reactors were incubated under the same conditions without the addition of Cu. After inoculum addition, bacteria were initially grown under oxic conditions with a synthetic air rate of 10 mL/min until reaching the mid-exponential phase (\u003cem\u003eca\u003c/em\u003e. 0.500 OD\u003csub\u003e600\u003c/sub\u003e). Then, anoxia was induced by replacing the synthetic air with gaseous N\u003csub\u003e2\u003c/sub\u003e to stimulate denitrification with the NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e present in the media.\u003c/p\u003e\u003cp\u003eOnce approximate anoxia was reached (\u0026lt;\u0026thinsp;2% dissolved O\u003csub\u003e2\u003c/sub\u003e), the inflow gas was stopped and the reactors sealed to retain headspace gas and monitor N\u003csub\u003e2\u003c/sub\u003eO concentrations over time. The impact of Cu on net N\u003csub\u003e2\u003c/sub\u003eO production was then assessed by measuring the following parameters over a two-hour period (120 min): 1) headspace N\u003csub\u003e2\u003c/sub\u003eO concentration; 2) liquid NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentration; and 3) the relative expression of the \u003cem\u003enosZ\u003c/em\u003e and \u003cem\u003enirK\u003c/em\u003e genes. Following the period of induced anoxia and sample collection, the synthetic air supply was restored, allowing cell growth until reaching the stationary phase.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Nitrous oxide quantification\u003c/h2\u003e\u003cp\u003eThe N\u003csub\u003e2\u003c/sub\u003eO concentration was measured in the headspace of each bioreactor at 0, 10, 20, 30, 60, 90, and 120 minutes after anoxia induction. For each sample, 10 mL of headspace gas was extracted with a glass syringe and stored in pre-evacuated vials. The N\u003csub\u003e2\u003c/sub\u003eO concentration was then measured by gas chromatography coupled with electron-capture detection (GC-ECD\u003cem\u003e)\u003c/em\u003e. Three standards of 100 ppm N\u003csub\u003e2\u003c/sub\u003eO (in 99.99% N\u003csub\u003e2\u003c/sub\u003e) were used as calibration controls, and the N\u003csub\u003e2\u003c/sub\u003eO concentration was determined as previously described (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A net production or consumption rate of N\u003csub\u003e2\u003c/sub\u003eO (N\u003csub\u003e2\u003c/sub\u003eO flux) was calculated from the slope of the linear change (positive or negative, respectively) in the N\u003csub\u003e2\u003c/sub\u003eO concentration over the 120-min sampling period for each reactor. For R\u003csup\u003e2\u003c/sup\u003e values of the linear regression lower than 0.80, we considered that there was no linear relationship, so the flux was considered null (zero).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Nitrite and nitrate quantification\u003c/h2\u003e\u003cp\u003eFor NOx quantification (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), liquid media samples (3 mL) were taken from the bioreactors just before anoxia induction (Ti) and then at 0 (when anoxia was reached), 30, 60, 90, and 120 minutes during anoxic conditions. The samples were immediately filtered with a 0.20 \u0026micro;m disk filter to obtain cell-free samples and stored at -20 \u0026ordm;C until analysis. The NOx concentrations were quantified using LCK cuvette kits with a DR3900 spectrophotometer (Hach LANGE), according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Expression of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes\u003c/h2\u003e\u003cp\u003eTo measure the expression of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes, 10 mL of liquid media samples were collected from the bioreactors just before anoxia induction (Ti) and then at 10, 60, and 120 minutes during anoxic conditions. Samples were centrifuged for 10 minutes at 3000 g and 4\u0026deg;C to precipitate the cells. The cell pellets were washed with cold PBS 1X and centrifuged for 10 minutes at 3000 g once again at 4\u0026deg;C. After PBS washing, the cell pellets were instantly frozen with liquid nitrogen and stored at -80\u0026deg;C until analysis. The RNA was extracted using the RNeasy Plus Mini Kit (Qiagen) according to the manufacturer\u0026rsquo;s instructions. The extracted RNA concentration and quality (A\u003csub\u003e260/230\u003c/sub\u003e and A\u003csub\u003e260/280\u003c/sub\u003e ratios) were assessed with the DeNovix DS-11 FX spectrophotometer. Afterwards, the RNA was cleaned from genomic DNA with the RapidOut DNA Removal Kit (Thermo Scientific). DNA removal was confirmed by negative PCR amplification of the \u003cem\u003erecA\u003c/em\u003e gene. The treated RNA was then used for cDNA synthesis using the QuantiTect Reverse Transcription Kit (Qiagen) according to manufacturer instructions. Real-time quantitative PCRs were performed on the collected cDNA for the quantification of \u003cem\u003enirK\u003c/em\u003e transcripts, preceding N\u003csub\u003e2\u003c/sub\u003eO production, and of \u003cem\u003enosZ\u003c/em\u003e transcripts, responsible for N\u003csub\u003e2\u003c/sub\u003eO reduction. Two reference genes (\u003cem\u003erecA\u003c/em\u003e and \u003cem\u003erpoB\u003c/em\u003e) were used to obtain normalized and comparable relative expression results (Rocha et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These qPCRs were prepared with the reaction mix described in \u003cb\u003eTable S2\u003c/b\u003e and ran in a StepOne Plus real-time PCR system (Applied Biosystems). The primers used to target the four genes are described in \u003cb\u003eTable S3.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e\u003cp\u003eGrowth rates in the different Cu treatments were compared with a one-way ANOVA followed by a Dunnett post-hoc test, after the assumptions of normality and homogeneity of variance were verified with Q-Q plots and histograms. Since the N\u003csub\u003e2\u003c/sub\u003eO fluxes and concentrations displayed large departures from normality, a non-parametric test (unpaired two-sample Mann-Whitney-Wilcoxon) was used to identify significant differences between treatments (Control and Cu). A two-way ANOVA was performed to compare the relative gene expression of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e between control and Cu reactors over time after ANOVA assumptions were verified. The NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e / NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e ratios did not follow a normal distribution and an unpaired two-sample Mann-Whitney-Wilcoxon test was also used to compare significant differences between treatments.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Impact of copper on the grow of \u003cem\u003eS. loihica\u003c/em\u003e PV-4\u003c/h2\u003e\u003cp\u003eTo determine the impact of Cu on the grow of \u003cem\u003eS. loihica\u003c/em\u003e PV-4, we incubated the strain in culture flasks with two ranges of Cu concentrations known to potentially inhibit bacterial growth. A short range, from 1 to 10 \u0026micro;M, was used to assess the impact of low Cu concentrations, and a high range, from 1 to 160 \u0026micro;M, to determine the effects of high Cu concentrations. In both concentration ranges, the growth rate of \u003cem\u003eS. loihica\u003c/em\u003e PV-4 was not affected by Cu, even at the highest concentrations tested (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eSince this study focuses on environmentally realistic concentrations, we selected the concentration of 1 \u0026micro;M for subsequent Cu exposure experiments in semi-closed bioreactors to determine Cu impacts on net N\u003csub\u003e2\u003c/sub\u003eO production and associated gene expression. Similar to the preliminary growth trials, exposure to 1 \u0026micro;M of Cu in the bioreactors did not impact \u003cem\u003eS. loihica\u003c/em\u003e PV-4 growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with similar growth rates observed in control (0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003 OD.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and Cu treatments (0.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 OD.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Impact of copper on net N\u003csub\u003e2\u003c/sub\u003eO production in \u003cem\u003eS. loihica\u003c/em\u003e PV-4\u003c/h2\u003e\u003cp\u003e\u003cem\u003eS. loihica\u003c/em\u003e PV-4 produced a higher concentration of N\u003csub\u003e2\u003c/sub\u003eO in Cu-treated reactors than in the control treatment, with a net N\u003csub\u003e2\u003c/sub\u003eO flux of 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 \u0026micro;mol N\u003csub\u003e2\u003c/sub\u003eO-N.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;mol N\u003csub\u003e2\u003c/sub\u003eO-N.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, due to the high variability observed and some reactors without linear slopes (\u003cb\u003eFigure S2\u003c/b\u003e), the Mann\u0026ndash;Whitney\u0026ndash;Wilcoxon test returned a p-value of 0.387, greater than the alpha threshold of 0.05, indicating that the differences were not significant. Nevertheless, the highest levels of headspace N\u003csub\u003e2\u003c/sub\u003eO were consistently found in Cu-treated reactors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and were significantly higher than in control (p\u0026thinsp;=\u0026thinsp;0.021, Mann-Whitney-Wilcoxon). Among 63 measurements of headspace N\u003csub\u003e2\u003c/sub\u003eO, nine of the 10 highest levels were found in reactors treated with Cu and only one in control reactors. Besides the higher mean and peak values of headspace N\u003csub\u003e2\u003c/sub\u003eO, it is also important to note the higher variances in Cu-treated reactors, with N\u003csub\u003e2\u003c/sub\u003eO concentrations of 6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02 \u0026micro;mol N\u003csub\u003e2\u003c/sub\u003eO-N per reactor (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) when compared to control reactors with 1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69 \u0026micro;mol N\u003csub\u003e2\u003c/sub\u003eO-N per reactor (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe concentration of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in control and Cu-treated reactors showed no significant difference between the two treatments (\u003cb\u003eFigure S3\u003c/b\u003e). During the 2-hour anoxic period, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentration continuously decreased, while the concentration of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, initially absent, increased in the first 30 minutes and then decreased to below detection levels, indicating that all NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e produced during anoxia was consumed. Regarding the NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e ratio, known to play a role in net N\u003csub\u003e2\u003c/sub\u003eO production, no significant differences were detected between control and Cu-treated reactors (p-value\u0026thinsp;=\u0026thinsp;0.796, Mann-Whitney-Wilcoxon test), with average values of 3.64 and 3.55, respectively, during the anoxic period (\u003cb\u003eTable S6\u003c/b\u003e). The results show that the overproduction of N\u003csub\u003e2\u003c/sub\u003eO observed in Cu-treated reactors was not associated to an overproduction of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, as no significant differences were detected between the control and the Cu treatment. Overall, the consumption of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and the NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e ratio remained unaffected by Cu.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Impact of copper on \u003cem\u003eS. loihica\u003c/em\u003e PV-4 gene expression\u003c/h2\u003e\u003cp\u003eThe relative expression of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes was determined to understand if changes in net N\u003csub\u003e2\u003c/sub\u003eO production were associated to gene transcription regulation. The relative expression of both genes showed a strong upregulation by the Cu treatment after 10 minutes of anoxia, followed by a slight downregulation after 60 and 120 minutes in \u003cem\u003enirK\u003c/em\u003e expression and after 120 min in \u003cem\u003enosZ\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eTable S5\u003c/b\u003e). Cu-treated reactors showed higher \u003cem\u003enirK\u003c/em\u003e relative expression than control reactors even before the anoxic phase, suggesting that Cu may upregulate \u003cem\u003enirK\u003c/em\u003e expression in the presence of oxygen. In contrast to \u003cem\u003enirK\u003c/em\u003e, no significant increase in \u003cem\u003enosZ\u003c/em\u003e relative expression was observed prior to anoxia, which caused a higher \u003cem\u003enirK\u003c/em\u003e/\u003cem\u003enosZ\u003c/em\u003e ratio in Cu reactors, when compared to control, at this timepoint (\u003cb\u003eTable S5\u003c/b\u003e). Besides the treatment differences on average values, it is also worth noticing the substantially higher variability observed for \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e expression over time in Cu-treated reactors when compared to control. While the expression of both genes steadily increased over time in control reactors (expected due to anoxic induction), their expression fluctuated in Cu-treated reactors. \u003cem\u003eNirK\u003c/em\u003e relative expression variance was 64.48 in control reactors and 82.97 in Cu-treated reactors, while \u003cem\u003enosZ\u003c/em\u003e relative expression variance was 26.05 in control reactors and 87.42 in Cu-treated reactors.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003enirK/nosZ\u003c/em\u003e relative expression ratio can be used as an indicator of changes in N\u003csub\u003e2\u003c/sub\u003eO regulation, with ratios\u0026thinsp;\u0026gt;\u0026thinsp;1 indicating potential for N\u003csub\u003e2\u003c/sub\u003eO production and ratios\u0026thinsp;\u0026lt;\u0026thinsp;1 indicating potential for N\u003csub\u003e2\u003c/sub\u003eO reduction (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the higher \u003cem\u003enirK\u003c/em\u003e/\u003cem\u003enosZ\u003c/em\u003e ratio observed in Cu-treated reactors while O\u003csub\u003e2\u003c/sub\u003e was still present (before anoxia), there were no significant differences in the \u003cem\u003enirK/nosZ\u003c/em\u003e ratios detected between the two treatments over time (2-way ANOVA p-value time*treatment\u0026thinsp;=\u0026thinsp;0.582). Both treatments indicate a stronger potential for N\u003csub\u003e2\u003c/sub\u003eO reduction, with values below 1 most of the time in (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe impact of Cu on \u003cem\u003eS. loihica\u003c/em\u003e PV-4 growth was initially investigated to find a suitable concentration to test Cu impacts on net N\u003csub\u003e2\u003c/sub\u003eO production. The target concentration would have to be non-toxic, i.e. without effects on growth, and potentially expected after seabed disturbance, i.e. slightly above background environmental levels. The obtained results showed that \u003cem\u003eS. loihica\u003c/em\u003e PV-4 was able to tolerate all tested Cu concentrations added to the medium, from 1 to 160 \u0026micro;M, which is in accordance with previous studies that have shown that this strain is particularly resistant to Cu, even when compared to other \u003cem\u003eShewanella\u003c/em\u003e strains (Lv et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This Cu tolerance contrasts with sensitivity of this strain to cadmium (Cd), with growth inhibition at levels around 25 \u0026micro;M (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), underscoring the differential impacts that different metals may have on bacterial growth (Brown et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gillard et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies with other isolated strains and bacterial communities suggest that Cu plays a crucial role in regulating the expression of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes and the activity of their respective enzymes (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Black et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since both \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e genes are present in the genome of \u003cem\u003eS. loihica\u003c/em\u003e PV-4 (Yoon et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), Cu was expected to affect its net N\u003csub\u003e2\u003c/sub\u003eO production. The results obtained in this study support this hypothesis, by showing an overproduction of N\u003csub\u003e2\u003c/sub\u003eO by \u003cem\u003eS. loihica\u003c/em\u003e PV-4 when exposed to 1 \u0026micro;M of Cu. A similar increase in net N\u003csub\u003e2\u003c/sub\u003eO production was also observed in a \u003cem\u003enirS\u003c/em\u003e-carrying strain of the denitrifying soil bacterium \u003cem\u003ePseudomonas stutzeri\u003c/em\u003e when exposed to 150, 1000 and 5000 \u0026micro;M of Cu (Black et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In other \u003cem\u003enirK\u003c/em\u003e-carrying microorganisms (\u003cem\u003eAchromobacter xylosoxidans\u003c/em\u003e) and \u003cem\u003enirS\u003c/em\u003e-carrying microorganisms (\u003cem\u003eParacoccus denitrificans\u003c/em\u003e PD 1222), N\u003csub\u003e2\u003c/sub\u003eO production was enhanced under Cu exposure to low (1 \u0026micro;M) and high (13 \u0026micro;M) concentrations (Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the \u003cem\u003enirK\u003c/em\u003e-carrying species was shown to produce more N\u003csub\u003e2\u003c/sub\u003eO when exposed to higher Cu concentrations compared to lower concentrations, while the \u003cem\u003enirS\u003c/em\u003e-carrying species produced more N\u003csub\u003e2\u003c/sub\u003eO when exposed to low Cu concentrations (Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). If the nitrite reductase co-factor plays a role in the response of net N\u003csub\u003e2\u003c/sub\u003eO production to Cu exposure, one can expect even higher N\u003csub\u003e2\u003c/sub\u003eO production in \u003cem\u003eS. loihica\u003c/em\u003e PV-4 under exposure to higher Cu concentrations since it is a \u003cem\u003enirK\u003c/em\u003e-carrying organism. However, this requires further research and testing at higher Cu concentrations and with other \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enirS\u003c/em\u003e carrying strains.\u003c/p\u003e\u003cp\u003eEven though Cu is expected to upregulate \u003cem\u003enosZ\u003c/em\u003e gene transcription (Gaimster et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the observed increase in net N\u003csub\u003e2\u003c/sub\u003eO production in our study suggests that this regulation may not be continuous or that it may be insufficient to assimilate all N\u003csub\u003e2\u003c/sub\u003eO produced. It is interesting to note that, while \u003cem\u003enirK\u003c/em\u003e upregulation by Cu started even before anoxia, \u003cem\u003enosZ\u003c/em\u003e upregulation only occurred 10 minutes after anoxia was reached (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is possible that \u003cem\u003enosZ\u003c/em\u003e transcription did not immediately respond to the N\u003csub\u003e2\u003c/sub\u003eO generated in the oxic/anoxic transition due to the generation of reactive oxygen species (ROS), which have been shown to inhibit \u003cem\u003enosZ\u003c/em\u003e transcription in oxic conditions (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Regardless of the mechanism, this differential response of \u003cem\u003enirK\u003c/em\u003e (potential N\u003csub\u003e2\u003c/sub\u003eO source) and \u003cem\u003enosZ\u003c/em\u003e (potential N\u003csub\u003e2\u003c/sub\u003eO sink) in oxic conditions can be important in deep seawater, where oxic conditions are frequently found. As \u003cem\u003eS. loihica\u003c/em\u003e is a facultative anaerobic, this species could be exposed to oxic conditions in the deep-sea floor, which would affect its denitrification pathway. In the case of this study, the early upregulation of \u003cem\u003enirK\u003c/em\u003e in Cu-treated reactors may have supported the immediate reduction of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e to NO and N\u003csub\u003e2\u003c/sub\u003eO right after entering anoxia, when \u003cem\u003enosZ\u003c/em\u003e upregulation was also initiated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eTable S5\u003c/b\u003e). This sequential onset of denitrifying gene transcription has been shown before in other denitrifying species (Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, despite the strong \u003cem\u003enosZ\u003c/em\u003e upregulation observed in Cu-treated reactors 10 min into anoxia, the net N\u003csub\u003e2\u003c/sub\u003eO production was still higher in these reactors (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that the upregulation of \u003cem\u003enosZ\u003c/em\u003e expression was not sufficient to reduce the N\u003csub\u003e2\u003c/sub\u003eO produced by NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e reductase. This observation indicates that Cu also has a potential impact on the enzymatic activity. The low \u003cem\u003enirK\u003c/em\u003e/\u003cem\u003enosZ\u003c/em\u003e expression ratio observed in Cu-treated reactors also supports a post-transcriptional inhibition of the N\u003csub\u003e2\u003c/sub\u003eO reductase activity that would explain the higher net N\u003csub\u003e2\u003c/sub\u003eO production. This inhibition, however, was not expected since multiple studies have shown that Cu is necessary and, in fact, stimulates N\u003csub\u003e2\u003c/sub\u003eO reductase activity in other denitrifying bacteria (Granger and Ward \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Felgate et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The post-transcriptional inhibition observed in our study may be a result of differences in the Cu concentrations tested (1 \u0026micro;M in this study versus lower concentrations in cited studies) or the tested strain.\u003c/p\u003e\u003cp\u003eThe higher variability observed for gene expression in Cu-treated reactors suggests that stochastic processes may have a stronger influence when microorganisms are under metal stress, while more deterministic processes may play a stronger role in control conditions. Stochastic control of denitrifying gene expression, i.e., random initiation of gene transcription, has been shown before in denitrifying bacteria, associated with a bet hedging strategy under oxic-anoxic transitions (Lycus et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Since metal resistance and detoxification are also under the control of multiple gene expressions (Das et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), it is possible that added controls of gene expression in Cu-treated cells increased the variability of denitrifying gene expression. This observation may be worth investigating further to understand the microbial response to disturbance and to estimate potential metabolic trade-offs.\u003c/p\u003e\u003cp\u003eThe NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e ratio is known to play an important role in the net production of N\u003csub\u003e2\u003c/sub\u003eO and transcription of \u003cem\u003enirK\u003c/em\u003e in \u003cem\u003eS. loihica\u003c/em\u003e PV-4. A low ratio promotes N\u003csub\u003e2\u003c/sub\u003eO production and \u003cem\u003enirK\u003c/em\u003e transcription, while a high ratio (above 3) inhibits both due to NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e toxicity (Yoon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). In our study, the concentrations of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e remained similar between Cu-treated and control reactors, with no significant change in the ratios, most of them being above 3. Thus, the NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e ratio likely did not play a crucial role in explaining gene expression and net N\u003csub\u003e2\u003c/sub\u003eO production variation under Cu exposure in this study.\u003c/p\u003e\u003cp\u003eThis study reveals that Cu at 1 \u0026micro;M significantly affects the denitrification pathway in \u003cem\u003eS. loihica\u003c/em\u003e PV-4 and increases net production of N\u003csub\u003e2\u003c/sub\u003eO in anoxic conditions. When compared to Cd, the two metals show opposite effects (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cadmium appears to inhibit net N\u003csub\u003e2\u003c/sub\u003eO production, whereas Cu increases it. Regarding gene expression, the two metals also have different effects on \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e relative gene expression. While Cd inhibits the relative expression of both genes (Pizarro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Cu upregulates \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e relative gene expression at the beginning of the anoxic period and slightly downregulates it at the end, with substantially higher variability over time when compared to control. Although both metals have the same ionic form in solution and similar toxicity modes (Brown et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), their impact on \u003cem\u003eS. loihica\u003c/em\u003e PV-4 differs significantly. This observation has environmental implications since the two metals may have different availability in different deep-sea environments.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this study demonstrates that a relatively low concentration of Cu can increase the net production of N\u003csub\u003e2\u003c/sub\u003eO by \u003cem\u003eS. loihica\u003c/em\u003e PV-4. The average and peak values of N\u003csub\u003e2\u003c/sub\u003eO production were higher in Cu-treated reactors when compared to control. This higher net N\u003csub\u003e2\u003c/sub\u003eO production may lead to local increases in greenhouse gas emissions, due to the high global warming potential of N\u003csub\u003e2\u003c/sub\u003eO. It is important to say, however, that the larger ecosystem implications of this finding require further studies with other bacterial models as well as with complex microbial communities. Regarding Cu-driven impacts on gene expression, we observed that the timing of the oxic-to-anoxic transition may play an important role. While \u003cem\u003enirK\u003c/em\u003e is upregulated by Cu in oxic conditions, \u003cem\u003enosZ\u003c/em\u003e was unaffected, increasing the risk of net N\u003csub\u003e2\u003c/sub\u003eO production. In anoxic conditions, both genes were mostly upregulated by Cu, and despite the stronger upregulation of \u003cem\u003enosZ\u003c/em\u003e transcription at the beginning of the anoxic stage, the N\u003csub\u003e2\u003c/sub\u003eO reductase activity was not sufficient to reduce all produced N\u003csub\u003e2\u003c/sub\u003eO. It seems that the overproduction of N\u003csub\u003e2\u003c/sub\u003eO may be linked to gene expression controls in oxic conditions and in early anoxia, but post-transcriptional or post-translational changes in N\u003csub\u003e2\u003c/sub\u003eO reductase activity may be more important in later anoxia. Further investigation on the whole transcriptome of this deep-sea isolate as well as other N\u003csub\u003e2\u003c/sub\u003eO producing and reducing strains is needed to unveil what regulatory mechanisms may explain the observed results and to estimate potential effects of metal exposure on important N cycling processes more widely.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was performed within the scope of the DeepResist project (2022.06475.PTDC), funded by the Portuguese Foundation for Science and Technology (FCT), as well as the MIDFun project, funded by the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N\u0026deg; 101038095. This work was also supported by the ATLANTIDA project (NORTE-01- 0145-FEDER-000040), supported by the North Portugal Regional Operational Program (NORTE2020), under the PORTUGAL 2020 Partnership Agreement and through the European Regional Development Fund (ERDF) and national funds through FCT within the scope of UIDB/04423/2020, UIDP/04423/2020 and LA/P/0101/2020. Author MS also acknowledges the work contract through the Scientific Employment Stimulus Individual Call (CEEC), funded by the FCT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54499/2023.08554.CEECIND/CP2848/CT0004\u003c/span\u003e\u003cspan address=\"10.54499/2023.08554.CEECIND/CP2848/CT0004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LM, LP, and MS. The first draft of the manuscript was written by LM and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBird LJ, Coleman ML, Newman DK (2013) Iron and Copper Act Synergistically To Delay Anaerobic Growth of Bacteria. Appl Environ Microbiol 79:3619\u0026ndash;3627. https://doi.org/10.1128/AEM.03944-12\u003c/li\u003e\n \u003cli\u003eBlack A, Hsu P-CL, Hamonts KE, et al (2016) Influence of copper on expression of nirS, norB and nosZ and the transcription and activity of NIR, NOR and N2OR in the denitrifying soil bacteria Pseudomonas stutzeri. Microb Biotechnol 9:381\u0026ndash;388. https://doi.org/https://doi.org/10.1111/1751-7915.12352\u003c/li\u003e\n \u003cli\u003eBrown A, Thatje S, Hauton C (2017) The Effects of Temperature and Hydrostatic Pressure on Metal Toxicity: Insights into Toxicity in the Deep Sea. Environ Sci Technol 51:10222\u0026ndash;10231. https://doi.org/10.1021/acs.est.7b02988\u003c/li\u003e\n \u003cli\u003eCastilleja DEM, Tapia JAA, Arvizu Medrano SM, et al (2017) Growth Kinetics for the Selection of Yeast Strains for Fermented Beverages. In: Morata A, Loira I (eds) Yeast. IntechOpen, Rijeka, p Ch. 3\u003c/li\u003e\n \u003cli\u003eChen X, Yang J, Zeng RJ, et al (2023) Reactive Oxygen Species Promote Nitrous Oxide (N2O) Emissions from Soil/Sediment during the Anoxic\u0026ndash;Oxic Transition. Environ Sci Technol 57:801\u0026ndash;809. https://doi.org/10.1021/acs.est.2c07081\u003c/li\u003e\n \u003cli\u003eDas S, Dash HR, Chakraborty J (2016) Genetic basis and importance of metal resistant genes in bacteria for bioremediation of contaminated environments with toxic metal pollutants. Appl Microbiol Biotechnol 100:2967\u0026ndash;2984. https://doi.org/10.1007/s00253-016-7364-4\u003c/li\u003e\n \u003cli\u003eEdgcomb VP, Molyneaux SJ, Saito MA, et al (2004a) Sulfide Ameliorates Metal Toxicity for Deep-Sea Hydrothermal Vent Archaea. Appl Environ Microbiol 70:2551\u0026ndash;2555. https://doi.org/10.1128/AEM.70.4.2551-2555.2004\u003c/li\u003e\n \u003cli\u003eEdgcomb VP, Molyneaux SJ, Saito MA, et al (2004b) Sulfide Ameliorates Metal Toxicity for Deep-Sea Hydrothermal Vent Archaea. Appl Environ Microbiol 70:2551\u0026ndash;2555. https://doi.org/10.1128/AEM.70.4.2551-2555.2004\u003c/li\u003e\n \u003cli\u003eFelgate H, Giannopoulos G, Sullivan MJ, et al (2012) The impact of copper, nitrate and carbon status on the emission of nitrous oxide by two species of bacteria with biochemically distinct denitrification pathways. Environ Microbiol 14:1788\u0026ndash;1800. https://doi.org/https://doi.org/10.1111/j.1462-2920.2012.02789.x\u003c/li\u003e\n \u003cli\u003eGaimster H, Alston M, Richardson DJ, et al (2018) Transcriptional and environmental control of bacterial denitrification and N2O emissions. FEMS Microbiol Lett 365:. https://doi.org/10.1093/femsle/fnx277\u003c/li\u003e\n \u003cli\u003eGao H, Obraztova A, Stewart N, et al (2006) Shewanella loihica sp. nov., isolated from iron-rich microbial mats in the Pacific Ocean. Int J Syst Evol Microbiol 56:1911\u0026ndash;1916. https://doi.org/https://doi.org/10.1099/ijs.0.64354-0\u003c/li\u003e\n \u003cli\u003eGillard B, Chatzievangelou D, Thomsen L, Ullrich MS (2019) Heavy-Metal-Resistant Microorganisms in Deep-Sea Sediments Disturbed by Mining Activity: An Application Toward the Development of Experimental in vitro Systems. Front Mar Sci 6:\u003c/li\u003e\n \u003cli\u003eGraf DRH, Jones CM, Hallin S (2014) Intergenomic Comparisons Highlight Modularity of the Denitrification Pathway and Underpin the Importance of Community Structure for N2O Emissions. PLoS One 1\u0026ndash;20. https://doi.org/10.1371/journal.pone.0114118\u003c/li\u003e\n \u003cli\u003eGranger J, Ward BB (2003) Accumulation of nitrogen oxides in copper-limited cultures of denitrifying bacteria. Limnol Oceanogr 48:313\u0026ndash;318. https://doi.org/https://doi.org/10.4319/lo.2003.48.1.0313\u003c/li\u003e\n \u003cli\u003eHallin S, Philippot L, L\u0026ouml;ffler FE, et al (2018) Genomics and Ecology of Novel N2O-Reducing Microorganisms. Trends Microbiol 26:43\u0026ndash;55. https://doi.org/10.1016/j.tim.2017.07.003\u003c/li\u003e\n \u003cli\u003eHauton C, Brown A, Thatje S, et al (2017) Identifying toxic impacts of metals potentially released during deep-sea mining-A synthesis of the challenges to quantifying risk. Front Mar Sci 4\u003c/li\u003e\n \u003cli\u003eLiu B, Mao Y, Bergaust L, et al (2013) Strains in the genus Thauera exhibit remarkably different denitrification regulatory phenotypes. Environ Microbiol 15:2816\u0026ndash;2828. https://doi.org/10.1111/1462-2920.12142\u003c/li\u003e\n \u003cli\u003eLv Q, Zhang B, Xing X, et al (2018) Biosynthesis of copper nanoparticles using Shewanella loihica PV-4 with antibacterial activity: Novel approach and mechanisms investigation. J Hazard Mater 347:141\u0026ndash;149. https://doi.org/https://doi.org/10.1016/j.jhazmat.2017.12.070\u003c/li\u003e\n \u003cli\u003eLycus P, Soriano-Laguna MJ, Kjos M, et al (2018) A bet-hedging strategy for denitrifying bacteria curtails their release of N2O. Proceedings of the National Academy of Sciences 115:11820\u0026ndash;11825. https://doi.org/10.1073/pnas.1805000115\u003c/li\u003e\n \u003cli\u003eMagalh\u0026atilde;es CM, Machado A, Matos P, Bordalo AA (2011) Impact of copper on the diversity, abundance and transcription of nitrite and nitrous oxide reductase genes in an urban European estuary. FEMS Microbiol Ecol 77:274\u0026ndash;284. https://doi.org/10.1111/j.1574-6941.2011.01107.x\u003c/li\u003e\n \u003cli\u003eNeubauer SC, Megonigal JP (2015) Moving Beyond Global Warming Potentials to Quantify the Climatic Role of Ecosystems. Ecosystems 18:1000\u0026ndash;1013. https://doi.org/10.1007/s10021-015-9879-4\u003c/li\u003e\n \u003cli\u003ePaul SAL, Zitoun R, Noowong A, et al (2021) Copper-binding ligands in deep-sea pore waters of the Pacific Ocean and potential impacts of polymetallic nodule mining on the copper cycle. Sci Rep 11:18425. https://doi.org/10.1038/s41598-021-97813-3\u003c/li\u003e\n \u003cli\u003ePizarro L, Magalh\u0026atilde;es C, Almeida CMR, et al (2023) Cadmium effects on net N2O production by the deep-sea isolate Shewanella loihica PV-4. FEMS Microbiol Lett fnad047. https://doi.org/10.1093/femsle/fnad047\u003c/li\u003e\n \u003cli\u003eRavishankara AR, Daniel JS, Portmann RW (2009) Nitrous Oxide (N2O): The Dominant Ozone-Depleting Substance Emitted in the 21st Century. Science (1979) 326:123\u0026ndash;125\u003c/li\u003e\n \u003cli\u003eRocha DJP, Santos CS, Pacheco LGC (2015) Bacterial reference genes for gene expression studies by RT-qPCR: survey and analysis. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology 108:685\u0026ndash;693. https://doi.org/10.1007/s10482-015-0524-1\u003c/li\u003e\n \u003cli\u003eRouxel O, Toner B, Germain Y, Glazer B (2018) Geochemical and iron isotopic insights into hydrothermal iron oxyhydroxide deposit formation at Loihi Seamount. Geochim Cosmochim Acta 220:449\u0026ndash;482. https://doi.org/https://doi.org/10.1016/j.gca.2017.09.050\u003c/li\u003e\n \u003cli\u003eStein LY, Klotz MG (2016) The nitrogen cycle. Current Biology 26:R94\u0026ndash;R98. https://doi.org/10.1016/j.cub.2015.12.021\u003c/li\u003e\n \u003cli\u003eWashburn TW, Iguchi A, Yamaoka K, et al (2023) Impacts of the first deep-sea seafloor massive sulfide mining excavation tests on benthic communities. Mar Ecol Prog Ser 712:1\u0026ndash;19\u003c/li\u003e\n \u003cli\u003eYoon S, Cruz-Garc\u0026iacute;a C, Sanford R, et al (2015a) Denitrification versus respiratory ammonification: environmental controls of two competing dissimilatory NO3(-)/NO2(-) reduction pathways in Shewanella loihica strain PV-4. ISME J 9:1093\u0026ndash;1104. https://doi.org/10.1038/ismej.2014.201\u003c/li\u003e\n \u003cli\u003eYoon S, Sanford RA, Loffler FE (2015b) Nitrite Control over Dissimilatory Nitrate/Nitrite Reduction Pathways in Shewanella loihica Strain PV-4. Appl Environ Microbiol 81:3510\u0026ndash;3517. https://doi.org/10.1128/AEM.00688-15\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"copper, deep-sea, nitrous oxide, metal impact, gene expression, nitrous oxide reductase","lastPublishedDoi":"10.21203/rs.3.rs-7261601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7261601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAfter seabed disturbance, increased concentrations of dissolved copper (Cu) may occur and impact deep-sea bacterial metabolism. In this study, we investigated the effects of Cu on the net production of nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), a potent greenhouse gas, in a model deep-sea strain, \u003cem\u003eShewanella loihica\u003c/em\u003e PV-4. We tested these effects in a series of exposure incubations, monitoring PV-4 growth, headspace N\u003csub\u003e2\u003c/sub\u003eO concentrations, and gene expression of nitrite reductase (\u003cem\u003enirK\u003c/em\u003e) and nitrous oxide reductase (\u003cem\u003enosZ\u003c/em\u003e). Despite no impact on growth, net N\u003csub\u003e2\u003c/sub\u003eO production was increased when 1 \u0026micro;M of Cu was added to the medium. Patterns of \u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e gene expression only partially explained the observed increase. This study shows that Cu plays an important role in mediating net N\u003csub\u003e2\u003c/sub\u003eO production by \u003cem\u003eS. loihica\u003c/em\u003e PV-4, with potential consequences to local greenhouse gas emissions. The larger ecosystem implications of this finding, however, require further studies with other bacterial models and complex communities.\u003c/p\u003e","manuscriptTitle":"Copper effects on net N2O production and associated gene expression by the deep-sea isolate Shewanella loihica PV-4","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 13:22:41","doi":"10.21203/rs.3.rs-7261601/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6337a48-a327-436a-80e0-954f957c3e22","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T16:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 13:22:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7261601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7261601","identity":"rs-7261601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}