Effects of different exogenous signal molecules on the reactor performance, sludge properties and microbial community structures of mixotrophic nitrogen removal process

Effects of different exogenous signal molecules on the reactor performance, sludge properties and microbial community structures of mixotrophic nitrogen removal process | 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 Effects of different exogenous signal molecules on the reactor performance, sludge properties and microbial community structures of mixotrophic nitrogen removal process Nan Zhang, Xiaojing Zhang, Han Zhang, Denghui Wei, Bingbing Ma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4668330/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 As a form of microbial interaction, quorum-sensing signal molecules can control the expression and functionality of related genes within microorganisms. The current study looked into the effects of various signal molecules on the process of nitrogen removal. The findings illustrated that 2µM signal molecules, namely C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL diminished the overall nitrogen removal efficiency (TRE) from 37.8% in the control, down to 26.8%, 26.0%, 28.1%, 27.6%, and 27.7%, respectively. Nevertheless, these molecules only slightly affected ammonia removal efficiency, reducing it from 67.9–63.7%, 62.8%, 62.6%, 63.7%, and 62.9%, respectively. C8-HSL, C10-HSL, and 3-oxo-C8-HSL significantly enhanced the relative abundance of denitrifying bacteria from an initial value of 36.3–37.00%, 35.76%, and 36.86%, in contrast to C6-HSL and C12-HSL, which caused a reduction to 24.39% and 26.56% respectively. The signal molecules were suspended in methanol, resulting in an elevation of the relative abundance of denitrifying bacteria from an initial 14.31–30.09%, paralleled by an increased TRE value of 27.6–37.8%. Environmental alterations, together with methanol provision, both constrained the Anammox activity. Furthermore, the incorporation of C6-HSL led to a decrease in the secretion of extracellular polymeric substance while a corresponding increase in soluble microbial products was noted. This research implies that 2 µM signal molecules could considerably influence reactor performance and microbial components of the mixotrophic nitrogen removal operation. The information presented will contribute additional insights into the impact of signal molecules on both the Anammox and mixotrophic nitrogen removal procedures. Nitrogen removal Signal molecules Extracellular polymer Denitrification Quorum sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Over recent years, the expulsion of nitrogenous wastewater into water bodies - a result of human activities - has significantly increased. This excessive discharge of nitrogen pollutants has sparked severe eutrophication, leading to considerable environmental damage( 2 , 27 , 33 ). Hence, developing efficient strategies for nitrogen removal is currently a pivotal topic in the field of wastewater treatment. An emerging method is autotrophic nitrogen removal, a biological nitrogen removal process which has various advantages, such as minimal energy consumption, no necessity for external organic carbon sources, and superior nitrogen removal efficiency( 4 , 20 ). It has been extensively utilized in wastewater treatment, particularly in environments with high temperatures (over 30°C) and lower carbon to nitrogen ratios( 3 , 10 ). The process involves an initial oxidation of some ammonia to nitrite by aerobic ammonia-oxidizing bacteria (AOB). Next, anaerobic ammonia-oxidizing bacteria (AnAOB) convert the leftover ammonia and the nitrite previously produced into N 2 gas with minimal nitrate generation( 20 ). However, implementing this process effectively and sustainably poses a significant challenge due to the slow growth rate of the functional microorganisms ( 15 ). Recent research has suggested that the biomass and activity of AnAOB may be affected by their quorum sensing capabilities ( 30 ). Quorum sensing refers to a form of intracellular communication that coordinates microbial behavior through the regulation of specific gene expression ( 16 ). Bacteria are equipped with a quorum sensing regulatory system which is capable of releasing and recognizing chemical signaling molecules, thereby allowing them to monitor their population density ( 9 ). The feasibility of this density sensing stems from the fact that the concentration of quorum sensing signaling molecules increases proportionally with population density. Furthermore, the activation of certain gene expression only takes place when the population density reaches a sufficiently high level ( 26 , 28 ). Exogenous signal molecules enhance quorum sensing, promoting biofilm formation and affecting both microbial composition and growth rate, leading to changes in the microbial community structure. Research shows that these signal molecules, at a concentration of 2 µM, can accelerate methanol decomposition, and modify the microbial diversity, community function, and composition in methanol wastewater treatment ( 32 ). In the context of aquaculture wastewater treatment, the signal molecules C6-HSL and 3-oxo-C8-HSL boost the biofilm biomass through quorum sensing promotion ( 34 ). The addition of 50 nM signal molecules triggered a shift in activated sludge from suspended growth mode to adhered growth, with minimal changes in extracellular polymeric substances (EPS)( 11 ). The inclusion of C6-HSL and C8-HSL also improved the nitrification and denitrification efficiency in the activated sludge system, promoting the relative abundance of AOB and denitrifying bacteria ( 35 ). Interestingly, the signaling molecule 3-oxo-C8-HSL at a concentration of 0.1 ng L − 1 , was found to increase cell density, boost EPS production, and possibly activate the Lux I/Lux R quorum sensing system owing to increased cell density ( 34 ). In a related study, C8-HSL only activated an increase of AnAOB activity, while C6-HSL simultaneously elevated the AnAOB growth rate and the percentage of anammox from 81–88%, relative to the control experiment ( 31 ). Conversely, the signal molecule C12-HSL was shown to stimulate the growth of heterotrophic bacteria, while at the same time reducing the activity of AnAOB( 37 ). In another report, the introduction of 3-oxo-C6-HSL shortened the start-up period of Anammox from 64 to 50 days ( 30 ). Furthermore, the exogenous addition of C8-HSL and C10-HSL respectively uplifted the EPS yield by 3.5% and 7.8%( 22 ). Current research suggests that acyl homoserine lactones (AHLs)-based quorum sensing signals may regulate microbial behavior. However, comprehensive understanding of how these signal molecules influence the growth of Anammox and denitrification bacteria within mixotrophic nitrogen removal systems is absent. Identifying an appropriate type and concentration of signal molecule could potentially augment microbial activity, thereby enhancing nitrogen removal. The aim of this study is to thoroughly investigate the effects of signal molecules on the autotrophic nitrogen removal process. Initially, we will examine the influence of signal molecules on reactor performance and microbial community dynamics. Subsequently, we will monitor the evolution of EPS in reaction to perturbations induced by these molecules. Finally, we will analyze the correlation between reactor performance, microbial composition, and EPS. The results of this research will augment our understanding and present a deeper insight into the impact of signal molecules on biological nitrogenous wastewater treatment. 2. Material and methods 2.1 Sewage and sludge Simulated wastewater was utilized in the experiment, the primary components of which were 0.942 g L − 1 (NH 4 ) 2 SO 4 , 2.014 g L − 1 NaHCO 3 , 0.068g L − 1 CaCl 2 , 0.15g L − 1 MgSO 4 , 0.068 g L − 1 KH 2 PO 3 , and trace elements ( 38 ). (NH 4 ) 2 SO 4 provided the ammonia nitrogen, while NaHCO 3 supplied the alkalinity. The seed sludge employed for the experiment was procured from a membrane bioreactor operating under an autotrophic nitrogen removal process. 2.2 Experimental setup and operating conditions The seeding sludge was distributed across seven serum bottles (R0 through R6), each with a 0.25 L volume, and settled using natural aeration on a shaker. Signal molecules, procured from Cayman Company, were dissolved in methanol at a concentration of 0.1g L − 1 . Methanol also functioned as an organic carbon source for denitrifying bacteria. Two control reactors were employed for the experiment, as detailed in Table 1 . The experiment spanned a total of 30 days, with two operational cycles taking place each day. Prior to each cycle, the dissolved oxygen in the wastewater was eliminated through nitrogen stripping. Each cycle, lasting for 10 hours, consisted of 5 minutes of inflow, 10 hours of stirring, 30 minutes of settling, and 5 minutes of drainage. R0 acted as the control reactor, devoid of added methanol or signal molecules. During the initial cycle, 1 mL of methanol was incorporated into R1, while R2 through R6 were each issued 2 µM of C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. During the second cycle of each reactor, no signal molecules or methanol were introduced. Table 1 Operational conditions of the reactor Reactor Signal molecules The concentration of signal molecules (µM) T (℃) pH DO Inf.NH 4 + -N (mg L − 1 ) R0 0 0 22.1 ± 0.48 7.82 ± 0.02 0.03 ± 0.01 173.4 ± 6.9 R1 0 0 22.0 ± 1.6 7.75 ± 0.03 0.04 ± 0.01 171.6 ± 6.8 R2 C6-HSL 2 20.9 ± 1.6 7.77 ± 0.03 0.04 ± 0.01 173.6 ± 6.3 R3 C8-HSL 2 22.1 ± 1.7 7.76 ± 0.02 0.03 ± 0.01 175.2 ± 6.9 R4 C10-HSL 2 22.1 ± 1.7 7.77 ± 0.03 0.03 ± 0.01 175.0 ± 5.3 R5 C12-HSL 2 22.2 ± 1.7 7.77 ± 0.03 0.03 ± 0.01 173.6 ± 5.9 R6 3-oxo-C8-HSL 2 22.1 ± 1.7 7.79 ± 0.04 0.03 ± 0.01 174.4 ± 6.6 2.3 Analytical methods The multi-parameter portable detector was utilized to monitor temperature and pH. The concentrations of ammonia nitrogen, nitrite, and nitrate nitrogen were determined using ultraviolet spectrophotometry consistent with the method reported by Chen et al.( 5 ). The nitrogen components in the influent and effluent were monitored daily, and the results for each reactor were presented as mean ± standard deviation values. An analysis of variance conducted tested the significance of the treatment effects. The Total Nitrogen Removal Efficiency (TRE) was calculated using Eq. (1), whereas the Ammonia Removal Efficiency (ARE) was computed using Eq. (2). The analyses of EPS and Soluble Microbial Products (SMP), which included polysaccharides (PS) and proteins (PRO), were conducted in accordance with the method reported by Tang et al( 30 ). 2.4 High-throughput sequencing Sludge specimens were collected from each reactor upon the conclusion of the experiment. The Qubit2.0 DNA detection kit (Sangon Shanghai) was utilized to analyze the DNA of these sludge specimens. As per past reports, high-throughput pyrosequencing was implemented using the V3-V4 universal PCR primer 341F/805R. High-throughput sequencing was conducted on the MiSeq sequencing platform provided by Illumina, Inc., San Diego. Over 50,000 sequences each measuring 410 bp were obtained from each specimen, these sequences were subsequently compared with the microorganism sequences contained in the Silva database. The Usearch (Usearch v5.2.236) was used to examine the Operational taxonomic units (OTUs) using a similarity threshold of 0.97. 3. Results and discussion 3.1 Nitrogen removal performance Figure 1 depicts the nitrogen components. The control reactor, R0, was neither exposed to any signaling molecules nor methanol. The mean influent ammonia nitrogen was quantified at a concentration of 173.4 ± 6.9 mg L − 1 . As time progressed, both the levels of ammonia nitrogen and nitrite in the effluent gradually escalated, peaking at 54.3 ± 8.9 and 65.0 ± 2.6 mg L − 1 , respectively. This led to a sustained reduction in TRE down to 27.6 ± 6.1%. The absence of mechanical aeration, coupled with limited oxygen transfer, might have restricted the microbial activity in contrast to the parent reactor. Figure 2 presents the nitrogen removal efficiencies of the six reactors, each fed with different signaling molecules. Reactor R1, to which only methanol was added, resulted in an average effluent ammonia nitrogen and nitrite nitrogen concentration of 53.0 ± 6.4 and 47.4 ± 4.2 mg L − 1 , respectively, after 30 days. Furthermore, the average TRE and ARE reached 37.8 ± 4.1% and 67.9 ± 3.4% respectively, in the final days. It is plausible that this outcome is due to methanol acting as a substrate, leading to the induction of denitrifying bacteria. Section 3.3 provides further evidence supporting this hypothesis. Consequently, the introduction of methanol appears to induce denitrifying bacteria and enhance nitrogen removal. The addition of 2 µM C6-HSL to R2 resulted in a gradual rise in the concentration of ammonia and nitrite nitrogen, reaching 65.5 ± 4.9 and 64.7 ± 3.3 mg L − 1 , respectively, after 30 days. This escalation could be ascribed to the inhibitory effect of C6-HSL, which consequently delayed nitrite consumption and led to a decrease in the TRE. Consequently, both ARE and TRE were observed to decrement to 63.7 ± 3.7% and 26.8 ± 3.9%, respectively. Observations of R3, R4, R5, and R6 revealed parallel outcomes, where both effluent ammonia nitrogen and nitrite concentrations escalated whilst Total Removal Efficiency (TRE) gradually reduced to 26.0 ± 3.4%, 28.1 ± 5.2%, 27.6 ± 4.5%, and 27.7 ± 5.5%, respectively. In conjunction, the ARE witnessed a decrease to 62.8 ± 4.7%, 62.6 ± 4.8±%, 63.7 ± 7.1%, and 62.9 ± 5.8%, respectively. This data suggests that the integration of signal molecules curtailed the activity of both AOB and AnAOB. Despite potentially augmenting denitrifying bacteria, these changes impaired the nitrogen removal process. Research conducted by De Clippeleir et al. suggests that the activity of AnAOB in autotrophic nitrification-denitrification biofilms can be augmented by the addition of C12-HSL, although it exhibits limited impact on AOB( 6 ). Another study posits that the activity of AnAOB is positively influenced by the presence of C8-HSL, while C6-HSL elevates both the activity of AnAOB and its growth rate. However, C12-HSL has been shown to encourage the proliferation of heterotrophic bacteria, which, in turn, depresses the activity of AnAOB ( 31 ). The inconsistencies observed in various studies surrounding the effect of exogenously added C12-HSL on AnAOB may be attributed to variations in operating conditions and the structure of the bacterial flora( 21 , 30 ). The nitrogen removal capabilities of microorganisms in a wastewater treatment reactor can be shaped by the kind and amount of exogenous AHLs ( 13 ). Nonetheless, in this study, inhibition was seen regardless of the signal molecules introduced, potentially attributable to the high concentrations of the added signal molecules. 3.2 Sludge properties Figure 3 (a) showcases the outcomes of the EPS determination. In the Control Reactor R0, the PRO and PS contents were recorded as 26.6 and 6.4 mg g − 1 SS, respectively. Strikingly, Reactor R1 exhibits notably augmented quantities of both PRO and PS, at 45.2 and 10.2 mg g − 1 SS, respectively. This observed increment is attributable to the amplifying effect of methanol on denitrifying bacteria, resulting in a marked increase in EPS production. The levels of PRO demonstrated an increase after the addition of 2 µM signal molecules. However, in comparison to R1, the PRO levels exhibited a decrease to 30.7, 37.9, 39.0, 37.3, 29.7 mg g − 1 SS under the application of 2 µM C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. This decrease is likely the result of the inhibitory effects these signal molecules have on denitrifying bacteria, ultimately leading to a reduction in PRO levels. High concentrations of these signal molecules have been demonstrated in studies to lead to excessive PRO secretion and impaired nitrogen removal( 39 ). This decrease in nitrogen removal performance was also observed in the present study. The application of these signal molecules also reduced PS levels, which may be due to denitrifying bacteria growth. PS is utilized as a substrate during endogenous denitrification, hence, a pronounced reduction in its levels was observed along with a decrease in nitrate concentration in the effluent. C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL have been established to aid in microorganism growth ( 12 , 29 ). These microorganisms may metabolize PS during growth, thereby decreasing its concentration. Consistent with the conclusion drawn in this study, a slight reduction in biofilm weight, from 835 to 827 mg, was observed when the concentration of the signal molecule was 1 µM ( 11 ). The PS content in EPS was reduced from 6.4 to 10.2, 4.1, 5.9, 5.4, 6.4, 5.3 mg g − 1 SS by signal molecules C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. Thus, the introduction of 2 µM signal molecules could potentially reduce PS secretion on EPS. Figure 3 b presents the results of SMP. In R0, the concentrations of PRO and PS in SMP were 54.2 mg L − 1 and 10.5 mg L − 1 , respectively. In R1, PRO concentration decreased to 44.7 mg L − 1 as PS concentration climbed to 22.5 mg L − 1 . This increase in PS is likely the outcome of methanol inducing heterotrophic microorganisms' growth. As these organisms proliferated, they used SMP as a nutrient, causing its decline. The addition of C6-HSL escalated the concentrations of PRO and PS to 77.7 and 19.9 mg L − 1 , respectively. This increase might be attributed to the role of C6-HSL in boosting microorganisms' growth. As a result, there is an amplified secretion of SMP. However, in R3, R4, R5, and R6, the concentrations of PRO declined to 49.8, 43.0, 48.1, and 44.7 mg L − 1 respectively, while PS concentrations escalated to 22.7, 10.3, 21.0, and 18.1 mg L − 1 respectively. This implies that compounds C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL exerted more minor influences on SMP secretion compared to C6-HSL. Hydroxylamine oxidoreductase (HAO) is a widely scrutinized enzyme with a significant role in Anammox metabolism. It catalyzes hydrazine oxidation ( 1 ), and its relationship to not only Aerobic Oxidizing Bacteria (AOB), but also Anaerobic Ammonia-Oxidizing Bacteria (AnAOB) and denitrifying bacteria has been determined. As a potent catalyst, HAO can facilitate both the oxidation of hydroxylamine to nitrite and the reduction of nitrite to hydroxylamine. Therefore, the efficiency of nitrogen removal in autotrophic nitrogen removal systems directly corresponds to HAO activity ( 8 , 24 ). Figure 3 (c) indicates that the HAO enzyme activity in R0 was 0.4449 EU g − 1 SS, increasing to 0.7983 EU g − 1 SS in R1. This notable increase is predominantly attributable to methanol-enhanced denitrifying bacteria. Furthermore, the HAO enzyme activity in R3 and R5 rose to 1.137 EU g − 1 SS and 1.111 EU g − 1 SS, respectively. However, there was variation in R2, R4, and R6. Analysis of denitrification performance revealed that adding 2 µM of signal molecules amplified denitrifying bacteria activity, triggering an increase in HAO enzyme activity. Additionally, C8-HSL and C12-HSL can bolster heterotrophic bacteria growth, further contributing to the HAO enzyme activity boost. Heme-c, a critical coenzyme in the Anammox process, has a concentration directly associated with AnAOB activity. Research has established the presence of Heme-c in cells as a significant contributing factor to the characteristic red color of AnAOB ( 18 ). As illustrated in Fig. 3 (d), the reactor's Heme-c concentration was examined and studied. The minimal Heme-c content in R0 reflects the relatively scarce abundance of AnAOB bacteria in the reactor, a conclusion corroborated in section 3.3 . This low AnAOB abundance might result from inhibition due to environmental variation. The Heme-c concentration in R1 dipped to 0.00834 µmol g − 1 SS, attributed to an overgrowth of heterotrophic bacteria triggered by methanol. Conversely, in R2, R5, and R6, Heme-c rose to 0.01753, 0.01605, and 0.02121 µmol g − 1 SS, respectively. This outcome suggests that C6-HSL, C12-HSL, and 3-oxo-C8-HSL might positively impact AnAOB growth, though further investigation is required to substantiate this claim. 3.3 Microbial community structures The taxonomic findings at the genus level, as obtained by leveraging the Silva database, are demonstrated in Fig. 4 . The relative frequencies of AOB and denitrifying bacteria are summarized in Table 2 . Nitrosomonas and Arenimonas emerged as the primary nitrogen-eliminating microorganisms in the seed sludge, possessing relative abundances of 4.78% and 16.61% respectively. In Reactor 0, the relative frequency of Nitrosomonas elevated from 4.78–7.93%, concurrent with a corresponding Areal Removal Efficiency (ARE) of 70% in the reactor. The relative frequency of Arenimonas reduced from 16.61–8.45%, with a corresponding TRE of 27.6 ± 6.1%. Following the transfer of sludge into experimental reactors from the seeding reactor, the increase in Nitrosomonas and decrease in Arenimonas could possibly be attributed to environmental changes. Such changes seem to inhibit AnAOB, leading to an inability to consume nitrite timely, which promoted the growth of denitrifying bacteria by utilizing nitrite. In Reactor 1, Hyphomicrobium, the denitrifying bacterium, emerged as the dominant microorganism, with the relative abundance escalating from 3.60–20.55%. With a TRE of 37.8 ± 4.1% in Reactor 1, it can be hypothesized that nitrogen removal in the reactor was primarily driven by the denitrification pathway. The escalation of denitrifying bacteria could have been facilitated by the addition of methanol, used as the organic carbon source for denitrification. As a result, the abundance of denitrifying bacteria surged as SMP were utilized as substrate, leading to a decline in SMP. In Reactor 2, the relative frequency of denitrifying bacteria experienced a drop, perhaps because C6-Homoserine Lactone (C6-HSL) exerted a certain inhibitory influence on the denitrification bacteria. Table 2 The relative abundance of the nitrogen removal-related bacteria (%). Reactor AOB Denitrifying bacteria Total Denitrifying bacteria Nitrosomonas Hyphomicrobium Arenimonas Denitratisoma Thermomonas Seed 4.87 3.83 16.61 2.06 0.15 22.65 R0 7.93 3.60 8.45 2.00 0.26 14.31 R1 5.83 20.55 7.36 1.91 0.27 30.09 R2 6.11 16.88 5.77 1.49 0.25 24.39 R3 5.51 28.55 6.50 1.81 0.19 37.00 R4 4.80 26.92 7.23 1.40 0.21 35.76 R5 7.57 19.45 5.76 1.16 0.19 26.56 R6 5.36 29.42 6.00 1.27 0.17 36.86 Upon the addition of C8-HSL, C10-HSL, and 3-oxo-C8-HSL, the relative abundance of denitrifying bacteria rose to 37.00%, 35.76% and 36.86%, respectively. The enhancement in nitrification and denitrification efficiency of the activated sludge system due to these signal molecules potentially caused this trend, leading to a rise in the denitrifying bacteria's relative abundance ( 35 ). Moreover, a promotion of heterotrophic bacteria growth in the community is linked to C8-HSL, C10-HSL, and 3-oxo-C8-HSL ( 13 , 19 ). Despite the similar situation with C6-HSL, feeding R5 with C12-HSL resulted in a decrease in the relative abundance of denitrifying bacteria. From the findings, it can be inferred that the introduction of methanol promoted the growth of denitrifying bacteria. However, the presence of signal molecules such as C6-HSL and C12-HSL reduced the relative abundance of these denitrifying bacteria while simultaneously increasing the relative abundance of AOB. Conversely, molecules such as C8-HSL, C10-HSL, and 3-oxo-C8-HSL had the opposite effect, leading to an increase in the abundance of denitrifying bacteria and a decrease in AOB. 3.4 Mechanism and the prospect Existing literature demonstrates that nitrogen removal can be promoted by low concentrations of signal molecules ( 31 , 35 ), while high concentrations may impede this process ( 30 ). The current study suggests that the amount of signal molecules added may have exceeded an optimal level, causing inhibition of microbial activity, suppression of associated gene expression, and thus a decline in nitrogen removal performance. Research consistently underscores the pivotal role of AHLs in enabling bacterial aggregation through EPS concentration regulation. More specifically, a positive correlation has been identified between the EPS content and AHLs such as C8-HSL and C10-HSL, which are typically present in granular sludge ( 14 , 23 ). This promotion of sludge granulation by AHLs can predominantly be attributed to their capacity to control EPS content, with the standout being C4-HSL that significantly enhances PRO formation ( 7 ). Comparative observations indicate that granular sludge fortified with AHLs like C6-HSL and 3-oxo-C6-HSL possesses a higher EPS content relative to sludge devoid of AHLs ( 19 , 25 ). Given that EPS constitutes an organic substrate that stimulates denitrifying bacterial proliferation, this discovery has substantial implications for the efficiency of microbial processes in wastewater systems. The incorporation of methanol into the system could adversely affect nitrogen removal and promote the growth of heterotrophic denitrifying bacteria. Consequently, it is advisable to refrain from using organic solvents such as methanol when introducing signal molecules. Instead, the union of signal molecules with carriers should be considered. For example, magnetic enzyme carriers have been effectively generated through immobilizing acylase on magnetic carriers, which have exhibited high recyclability and resilience, even after repetitive trials ( 36 ). Another alternative method involves immobilizing acylase on magnetic circular mesoporous silica via adsorption and cross-linking, a method that has proven to successfully foster and delay biofilm formation over extended periods ( 17 ). This synergy of signal molecules with carriers can extend their duration of action while renouncing the use of organic solvents. While the AHLs did not enhance nitrogen removal in this study, the findings remain significant for future research. It was discovered that higher concentrations might inflict inhibition, thus future studies should investigate the impact of reduced AHLs concentration. Additionally, it is imperative to devise new strategies for integrating AHLs into the nitrogen removal system, necessitating thorough exploration, with a particular focus on mitigating methanol toxicity. Finally, the value of in situ experimental systems was highlighted in this study since observed environmental changes led to the inhibition of AnAOB. 4. Conclusion The introduction of C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL at a concentration of 2 µM negative impacted the removal of TN, while its effect on ammonia removal was minimal. C8-HSL, C10-HSL, and 3-oxo-C8-HSL notably stimulated the proliferation of denitrifying bacteria, unlike C6-HSL and C12-HSL which reduced their growth. The growth of Anammox bacteria in all the experimental reactors, operated under mixotrophic nitrogen removal processes, was consistently inhibited. Adding 2 µM signal molecules led to alterations in the secretion of EPS and SMP within the reactors. Consequently, these 2 µM signal molecules may detrimentally influence the mixotrophic nitrogen removal process, indicating a need for further study into their regulatory effects. Declarations Ethics approval This study did not involve studies of human or animal subjects. Consent to Participate This study did not involve studies of human subjects. Consent to Publish All the authors agreed that the experimental data involved in the manuscript should be published. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Nan Zhang, Xiaojing Zhang, Han Zhang, Denghui Wei, Bingbing Ma, Hongli Zhang. The first draft of the manuscript was written by Nan Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Program for Innovative Research Team (in Science and Technology) in University of Henan Province (24IRTSTHN016), and the Natural Science Foundation of Henan Province (232300420171). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Consent to Participate This study did not involve studies of human subjects. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author, [Xiaojing Zhang]，upon reasonable request. References AHN, Y. (2006) Sustainable nitrogen elimination biotechnologies: A review. Process Biochemistry , 41, 1709-1721. Andreas, S., Tanja, G., Andreas, B., Filippo, B., Thomas, D., Jim, J., Josep, P., Martina, P., Anne-Katrin, P., Jordi, S., Arne, V. and Wim de, V. (2019) Responses of forest ecosystems in Europe to decreasing nitrogen deposition. Environmental Pollution , 244, 980-994. 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(2016) Quorum sensing signal–response systems in Gram-negative bacteria. Nature Reviews Microbiology , 14, 576-588. Kim, J., Choi, D., Yeon, K., Kim, S. and Lee, C. (2011) Enzyme-immobilized nanofiltration membrane to mitigate biofouling based on quorum quenching. Environmental Science & Technology , 45, 1601-1607. Kuenen, J. (2008) Anammox bacteria: from discovery to application. Nature Reviews Microbiology , 6, 320-326. Li, A., Hou, B. and Li, M. (2015) Cell adhesion, ammonia removal and granulation of autotrophic nitrifying sludge facilitated by N-acyl-homoserine lactones. Bioresource Technology , 196, 550-558. Li, B., Wang, Y., Li, J., Yang, L., Li, X., Zhou, Z., Li, Y., Chen, X. and Wu, L. (2019) The symbiosis of anaerobic ammonium oxidation bacteria and heterotrophic denitrification bacteria in a size-fractioned single-stage partial nitrification/anammox reactor. Biochemical Engineering Journal , 151, 107353. Liu, Y., Guo, J., Lian, J., Chen, Z., Li, Y., Xing, Y. and Wang, T. (2018) Effects of extracellular polymeric substances (EPS) and N-acyl-L-homoserine lactones (AHLs) on the activity of anammox biomass. International Biodeterioration & Biodegradation , 129, 141-147. Ma, H., Ma, S., Hu, H., Ding, L. and Ren, H. (2018) The biological role of N-acyl-homoserine lactone-based quorum sensing (QS) in EPS production and microbial community assembly during anaerobic granulation process. Scientific Reports , 8, 15793. Ma, H., Wang, X., Zhang, Y., Hu, H., Ren, H., Geng, J. and Ding, L. (2018) The diversity, distribution and function of N-acyl-homoserine lactone (AHL) in industrial anaerobic granular sludge. Bioresource Technology , 247, 116-124. Ni, S. and Zhang, J. (2013) Anaerobic Ammonium Oxidation: From Laboratory to Full-Scale Application. Biomed Research International , 469360. Olafsdottir, L., Whelan, J. and Snyder, G. (2018) A systematic review of adenosine triphosphate as a surrogate for bacterial contamination of duodenoscopes used for endoscopic retrograde cholangiopancreatography. American Journal of Infection Control , 46, 697-705. Schuster, M., Sexton, D., Diggle, S. and Greenberg, E. (2013) Acyl-homoserine lactone quorum sensing: from evolution to application. Annual Review of Microbiology , 67, 43-63. Semedo, M. and Song, B. (2020) From genes to nitrogen removal: determining the impacts of poultry industry wastewater on tidal creek denitrification. Environmental Science & Technology , 54, 146-157. Solano, C., Echeverz, M. and Lasa, I. (2014) Biofilm dispersion and quorum sensing. Current Opinion in Microbiology , 18, 96-104. Tan, C., Koh, K., Xie, C., Tay, M., Zhou, Y., Williams, R., Ng, W., Rice, S. and Kjelleberg, S. (2014) The role of quorum sensing signalling in EPS production and the assembly of a sludge community into aerobic granules. Isme Journal , 8, 1186-1197. Tang, X., Guo, Y., Chen, S., Tao, L. and Liu, H. (2018) Metabolomics uncovers the regulatory pathway of acyl-homoserine lactones based quorum sensing in anammox consortia. Environmental Science & Technology , 52, 2206-2216. Tang, X., Liu, S. and Zhang, Z. (2015) Identification of the release and effects of AHLs in anammox culture for bacteria communication. Chemical Engineering Journal , 273, 184-191. Valle, A., Bailey, M., Whiteley, A. and Manefield, M. (2010) N‐acyl‐L‐homoserine lactones (AHLs) affect microbial community composition and function in activated sludge. Environmental Microbiology , 6, 424-433. Wang, M., Tang, T., Burek, P., Havlik, P., Krisztin, T., Kroeze, C., Leclere, D., Strokal, M., Wada, Y. and Wang, Y. (2019) Increasing nitrogen export to sea: A scenario analysis for the Indus River. The Science of the Total Environment , 694, 133629. Xia, S., Zhou, L. and Zhang, Z. (2012) Influence and mechanism of N-(3-oxo-oxtanoyl)-L-homoserine lactone (3-oxo-C8-HSL) on biofilm behaviors at early stage. Journal of Environmental Sciences, , 24, 2035-2040. Xu, Y., Zhang, S. and Hou, Z. (2020) Effects of exogenous N-acyl-homoserine lactones on nutrient removal, sludge properties and microbial community structures during activated sludge process. Chemosphere , 255, 126945. Yeon, K., Lee, C. and Kim, J. (2009) Magnetic Enzyme Carrier for Effective Biofouling Control in the Membrane Bioreactor Based on Enzymatic Quorum Quenching. Environmental Science and Technology , 43, 7403-7409. Zhang, J., Li, J., Zhao, B.-h., Zhang, Y.-c., Wang, X.-j. and Chen, G.-h. (2019) Long-term effects of N-acyl-homoserine lactone-based quorum sensing on the characteristics of ANAMMOX granules in high-loaded reactors. Chemosphere , 218, 632-642. Zhang, X., Zhou, Y., Zhang, N., Zhao, S., H, Z. and Zhai, H. (2017) Effect of CuO nanoparticles on ammonia removal and EPS secretion of CANON sludge in the presence of nitrite suppression. Environmental Technology , 39, 2551-2558. Zhao, R., Zhang, H., Zou, X. and Yang, F. (2016) Effects of inhibiting acylated homoserine lactones (AHLs) on anammox activity and stability of granules. Current Microbiology , 73, 108-114. 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. 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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-4668330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323062357,"identity":"2325d228-c632-468c-b7c2-499b8295e0e0","order_by":0,"name":"Nan Zhang","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Zhang","suffix":""},{"id":323062358,"identity":"84ef15ef-b65c-446f-8242-ef038323e4e9","order_by":1,"name":"Xiaojing Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":true,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Zhang","suffix":""},{"id":323062359,"identity":"606c3223-3f43-4ba4-af1d-777135b698ff","order_by":2,"name":"Han Zhang","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""},{"id":323062360,"identity":"e93be75b-731f-4074-98b6-bcc92b3cff74","order_by":3,"name":"Denghui Wei","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Denghui","middleName":"","lastName":"Wei","suffix":""},{"id":323062361,"identity":"fa66fc17-6b90-4fb3-adcc-f4334903e7b7","order_by":4,"name":"Bingbing Ma","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Bingbing","middleName":"","lastName":"Ma","suffix":""},{"id":323062362,"identity":"3eeb3c95-d514-4d8b-8b1c-18cc31621d02","order_by":5,"name":"Hongli Zhang","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Hongli","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-01 13:17:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4668330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4668330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61435708,"identity":"65d51cea-e93e-4154-b837-b52e5f622424","added_by":"auto","created_at":"2024-07-30 17:29:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28257,"visible":true,"origin":"","legend":"\u003cp\u003eNitrogen components and the nitrogen removal of the control reactor (R0).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4668330/v1/7fd6e0880c7d46b722503a7f.png"},{"id":61435709,"identity":"85d581db-48da-4885-b38c-0d3b29bf22dd","added_by":"auto","created_at":"2024-07-30 17:29:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424084,"visible":true,"origin":"","legend":"\u003cp\u003eReactor performance in each reactor with different signaling molecules addition. (a. R1 with methanol b. R2 with 2 μM C6-HSL, c. R3 with 2 μM C8-HSL, d. R4 with 2 μM C10-HSL, e. R5 with 2 μM C12-HSL,f. R6 with 2 μM 3-oxo-C8-HSL).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4668330/v1/5518bd8c7a409822b7e914b9.png"},{"id":61435707,"identity":"cb7bb655-9c7d-4816-87c7-0c9c36c77363","added_by":"auto","created_at":"2024-07-30 17:29:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60462,"visible":true,"origin":"","legend":"\u003cp\u003eSludge properties in the sludge of each reactor (a. EPS; b. SMP; c. HAO; d. Heme-c).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4668330/v1/89df558bbe032b54541b5aaf.png"},{"id":61435710,"identity":"e1c5660a-59a0-4f68-ada6-dc20b862bfd9","added_by":"auto","created_at":"2024-07-30 17:29:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84734,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of the dominate genus in each reactors\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4668330/v1/ed6ec3599acb16624d5c4441.png"},{"id":61763782,"identity":"7d799519-a8ff-4092-95fb-29e94dc7bd74","added_by":"auto","created_at":"2024-08-05 09:57:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1058961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4668330/v1/1c1b97eb-33b0-4151-aeb5-b6219eb02ad5.pdf"}],"financialInterests":"","formattedTitle":"Effects of different exogenous signal molecules on the reactor performance, sludge properties and microbial community structures of mixotrophic nitrogen removal process","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver recent years, the expulsion of nitrogenous wastewater into water bodies - a result of human activities - has significantly increased. This excessive discharge of nitrogen pollutants has sparked severe eutrophication, leading to considerable environmental damage(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Hence, developing efficient strategies for nitrogen removal is currently a pivotal topic in the field of wastewater treatment. An emerging method is autotrophic nitrogen removal, a biological nitrogen removal process which has various advantages, such as minimal energy consumption, no necessity for external organic carbon sources, and superior nitrogen removal efficiency(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). It has been extensively utilized in wastewater treatment, particularly in environments with high temperatures (over 30\u0026deg;C) and lower carbon to nitrogen ratios(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The process involves an initial oxidation of some ammonia to nitrite by aerobic ammonia-oxidizing bacteria (AOB). Next, anaerobic ammonia-oxidizing bacteria (AnAOB) convert the leftover ammonia and the nitrite previously produced into N\u003csub\u003e2\u003c/sub\u003e gas with minimal nitrate generation(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, implementing this process effectively and sustainably poses a significant challenge due to the slow growth rate of the functional microorganisms (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research has suggested that the biomass and activity of AnAOB may be affected by their quorum sensing capabilities (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Quorum sensing refers to a form of intracellular communication that coordinates microbial behavior through the regulation of specific gene expression (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Bacteria are equipped with a quorum sensing regulatory system which is capable of releasing and recognizing chemical signaling molecules, thereby allowing them to monitor their population density (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The feasibility of this density sensing stems from the fact that the concentration of quorum sensing signaling molecules increases proportionally with population density. Furthermore, the activation of certain gene expression only takes place when the population density reaches a sufficiently high level (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExogenous signal molecules enhance quorum sensing, promoting biofilm formation and affecting both microbial composition and growth rate, leading to changes in the microbial community structure. Research shows that these signal molecules, at a concentration of 2 \u0026micro;M, can accelerate methanol decomposition, and modify the microbial diversity, community function, and composition in methanol wastewater treatment (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In the context of aquaculture wastewater treatment, the signal molecules C6-HSL and 3-oxo-C8-HSL boost the biofilm biomass through quorum sensing promotion (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The addition of 50 nM signal molecules triggered a shift in activated sludge from suspended growth mode to adhered growth, with minimal changes in extracellular polymeric substances (EPS)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The inclusion of C6-HSL and C8-HSL also improved the nitrification and denitrification efficiency in the activated sludge system, promoting the relative abundance of AOB and denitrifying bacteria (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Interestingly, the signaling molecule 3-oxo-C8-HSL at a concentration of 0.1 ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, was found to increase cell density, boost EPS production, and possibly activate the Lux I/Lux R quorum sensing system owing to increased cell density (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In a related study, C8-HSL only activated an increase of AnAOB activity, while C6-HSL simultaneously elevated the AnAOB growth rate and the percentage of anammox from 81\u0026ndash;88%, relative to the control experiment (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Conversely, the signal molecule C12-HSL was shown to stimulate the growth of heterotrophic bacteria, while at the same time reducing the activity of AnAOB(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In another report, the introduction of 3-oxo-C6-HSL shortened the start-up period of Anammox from 64 to 50 days (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Furthermore, the exogenous addition of C8-HSL and C10-HSL respectively uplifted the EPS yield by 3.5% and 7.8%(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent research suggests that acyl homoserine lactones (AHLs)-based quorum sensing signals may regulate microbial behavior. However, comprehensive understanding of how these signal molecules influence the growth of Anammox and denitrification bacteria within mixotrophic nitrogen removal systems is absent. Identifying an appropriate type and concentration of signal molecule could potentially augment microbial activity, thereby enhancing nitrogen removal.\u003c/p\u003e \u003cp\u003eThe aim of this study is to thoroughly investigate the effects of signal molecules on the autotrophic nitrogen removal process. Initially, we will examine the influence of signal molecules on reactor performance and microbial community dynamics. Subsequently, we will monitor the evolution of EPS in reaction to perturbations induced by these molecules. Finally, we will analyze the correlation between reactor performance, microbial composition, and EPS. The results of this research will augment our understanding and present a deeper insight into the impact of signal molecules on biological nitrogenous wastewater treatment.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Sewage and sludge\u003c/h2\u003e\n \u003cp\u003eSimulated wastewater was utilized in the experiment, the primary components of which were 0.942 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 2.014 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e, 0.068g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e CaCl\u003csub\u003e2\u003c/sub\u003e, 0.15g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e MgSO\u003csub\u003e4\u003c/sub\u003e, 0.068 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e3\u003c/sub\u003e, and trace elements (\u003cspan\u003e38\u003c/span\u003e). (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e provided the ammonia nitrogen, while NaHCO\u003csub\u003e3\u003c/sub\u003e supplied the alkalinity. The seed sludge employed for the experiment was procured from a membrane bioreactor operating under an autotrophic nitrogen removal process.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Experimental setup and operating conditions\u003c/h2\u003e\n \u003cp\u003eThe seeding sludge was distributed across seven serum bottles (R0 through R6), each with a 0.25 L volume, and settled using natural aeration on a shaker. Signal molecules, procured from Cayman Company, were dissolved in methanol at a concentration of 0.1g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Methanol also functioned as an organic carbon source for denitrifying bacteria. Two control reactors were employed for the experiment, as detailed in Table \u003cspan\u003e1\u003c/span\u003e. The experiment spanned a total of 30 days, with two operational cycles taking place each day. Prior to each cycle, the dissolved oxygen in the wastewater was eliminated through nitrogen stripping. Each cycle, lasting for 10 hours, consisted of 5 minutes of inflow, 10 hours of stirring, 30 minutes of settling, and 5 minutes of drainage. R0 acted as the control reactor, devoid of added methanol or signal molecules. During the initial cycle, 1 mL of methanol was incorporated into R1, while R2 through R6 were each issued 2 \u0026micro;M of C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. During the second cycle of each reactor, no signal molecules or methanol were introduced.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOperational conditions of the reactor\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignal molecules\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThe concentration of signal molecules (\u0026micro;M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT (℃)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInf.NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC6-HSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC8-HSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC10-HSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC12-HSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-oxo-C8-HSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Analytical methods\u003c/h2\u003e\n \u003cp\u003eThe multi-parameter portable detector was utilized to monitor temperature and pH. The concentrations of ammonia nitrogen, nitrite, and nitrate nitrogen were determined using ultraviolet spectrophotometry consistent with the method reported by Chen et al.(\u003cspan\u003e5\u003c/span\u003e). The nitrogen components in the influent and effluent were monitored daily, and the results for each reactor were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation values. An analysis of variance conducted tested the significance of the treatment effects.\u003c/p\u003e\n \u003cp\u003eThe Total Nitrogen Removal Efficiency (TRE) was calculated using Eq.\u0026nbsp;(1), whereas the Ammonia Removal Efficiency (ARE) was computed using Eq.\u0026nbsp;(2). The analyses of EPS and Soluble Microbial Products (SMP), which included polysaccharides (PS) and proteins (PRO), were conducted in accordance with the method reported by Tang et al(\u003cspan\u003e30\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1722360458.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 High-throughput sequencing\u003c/h2\u003e\n \u003cp\u003eSludge specimens were collected from each reactor upon the conclusion of the experiment. The Qubit2.0 DNA detection kit (Sangon Shanghai) was utilized to analyze the DNA of these sludge specimens. As per past reports, high-throughput pyrosequencing was implemented using the V3-V4 universal PCR primer 341F/805R. High-throughput sequencing was conducted on the MiSeq sequencing platform provided by Illumina, Inc., San Diego. Over 50,000 sequences each measuring 410 bp were obtained from each specimen, these sequences were subsequently compared with the microorganism sequences contained in the Silva database. The Usearch (Usearch v5.2.236) was used to examine the Operational taxonomic units (OTUs) using a similarity threshold of 0.97.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Nitrogen removal performance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the nitrogen components. The control reactor, R0, was neither exposed to any signaling molecules nor methanol. The mean influent ammonia nitrogen was quantified at a concentration of 173.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. As time progressed, both the levels of ammonia nitrogen and nitrite in the effluent gradually escalated, peaking at 54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 and 65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. This led to a sustained reduction in TRE down to 27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1%. The absence of mechanical aeration, coupled with limited oxygen transfer, might have restricted the microbial activity in contrast to the parent reactor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the nitrogen removal efficiencies of the six reactors, each fed with different signaling molecules. Reactor R1, to which only methanol was added, resulted in an average effluent ammonia nitrogen and nitrite nitrogen concentration of 53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 and 47.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, after 30 days. Furthermore, the average TRE and ARE reached 37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1% and 67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4% respectively, in the final days. It is plausible that this outcome is due to methanol acting as a substrate, leading to the induction of denitrifying bacteria. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e provides further evidence supporting this hypothesis. Consequently, the introduction of methanol appears to induce denitrifying bacteria and enhance nitrogen removal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe addition of 2 \u0026micro;M C6-HSL to R2 resulted in a gradual rise in the concentration of ammonia and nitrite nitrogen, reaching 65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 and 64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, after 30 days. This escalation could be ascribed to the inhibitory effect of C6-HSL, which consequently delayed nitrite consumption and led to a decrease in the TRE. Consequently, both ARE and TRE were observed to decrement to 63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7% and 26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9%, respectively.\u003c/p\u003e \u003cp\u003eObservations of R3, R4, R5, and R6 revealed parallel outcomes, where both effluent ammonia nitrogen and nitrite concentrations escalated whilst Total Removal Efficiency (TRE) gradually reduced to 26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4%, 28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2%, 27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5%, and 27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5%, respectively. In conjunction, the ARE witnessed a decrease to 62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7%, 62.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u0026plusmn;%, 63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1%, and 62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8%, respectively. This data suggests that the integration of signal molecules curtailed the activity of both AOB and AnAOB. Despite potentially augmenting denitrifying bacteria, these changes impaired the nitrogen removal process.\u003c/p\u003e \u003cp\u003eResearch conducted by De Clippeleir et al. suggests that the activity of AnAOB in autotrophic nitrification-denitrification biofilms can be augmented by the addition of C12-HSL, although it exhibits limited impact on AOB(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Another study posits that the activity of AnAOB is positively influenced by the presence of C8-HSL, while C6-HSL elevates both the activity of AnAOB and its growth rate. However, C12-HSL has been shown to encourage the proliferation of heterotrophic bacteria, which, in turn, depresses the activity of AnAOB (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The inconsistencies observed in various studies surrounding the effect of exogenously added C12-HSL on AnAOB may be attributed to variations in operating conditions and the structure of the bacterial flora(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The nitrogen removal capabilities of microorganisms in a wastewater treatment reactor can be shaped by the kind and amount of exogenous AHLs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Nonetheless, in this study, inhibition was seen regardless of the signal molecules introduced, potentially attributable to the high concentrations of the added signal molecules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sludge properties\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a) showcases the outcomes of the EPS determination. In the Control Reactor R0, the PRO and PS contents were recorded as 26.6 and 6.4 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, respectively. Strikingly, Reactor R1 exhibits notably augmented quantities of both PRO and PS, at 45.2 and 10.2 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, respectively. This observed increment is attributable to the amplifying effect of methanol on denitrifying bacteria, resulting in a marked increase in EPS production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe levels of PRO demonstrated an increase after the addition of 2 \u0026micro;M signal molecules. However, in comparison to R1, the PRO levels exhibited a decrease to 30.7, 37.9, 39.0, 37.3, 29.7 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS under the application of 2 \u0026micro;M C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. This decrease is likely the result of the inhibitory effects these signal molecules have on denitrifying bacteria, ultimately leading to a reduction in PRO levels. High concentrations of these signal molecules have been demonstrated in studies to lead to excessive PRO secretion and impaired nitrogen removal(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This decrease in nitrogen removal performance was also observed in the present study. The application of these signal molecules also reduced PS levels, which may be due to denitrifying bacteria growth. PS is utilized as a substrate during endogenous denitrification, hence, a pronounced reduction in its levels was observed along with a decrease in nitrate concentration in the effluent. C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL have been established to aid in microorganism growth (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). These microorganisms may metabolize PS during growth, thereby decreasing its concentration. Consistent with the conclusion drawn in this study, a slight reduction in biofilm weight, from 835 to 827 mg, was observed when the concentration of the signal molecule was 1 \u0026micro;M (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The PS content in EPS was reduced from 6.4 to 10.2, 4.1, 5.9, 5.4, 6.4, 5.3 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS by signal molecules C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL. Thus, the introduction of 2 \u0026micro;M signal molecules could potentially reduce PS secretion on EPS.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb presents the results of SMP. In R0, the concentrations of PRO and PS in SMP were 54.2 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 10.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. In R1, PRO concentration decreased to 44.7 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as PS concentration climbed to 22.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This increase in PS is likely the outcome of methanol inducing heterotrophic microorganisms' growth. As these organisms proliferated, they used SMP as a nutrient, causing its decline. The addition of C6-HSL escalated the concentrations of PRO and PS to 77.7 and 19.9 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. This increase might be attributed to the role of C6-HSL in boosting microorganisms' growth. As a result, there is an amplified secretion of SMP. However, in R3, R4, R5, and R6, the concentrations of PRO declined to 49.8, 43.0, 48.1, and 44.7 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively, while PS concentrations escalated to 22.7, 10.3, 21.0, and 18.1 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively. This implies that compounds C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL exerted more minor influences on SMP secretion compared to C6-HSL.\u003c/p\u003e \u003cp\u003eHydroxylamine oxidoreductase (HAO) is a widely scrutinized enzyme with a significant role in Anammox metabolism. It catalyzes hydrazine oxidation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and its relationship to not only Aerobic Oxidizing Bacteria (AOB), but also Anaerobic Ammonia-Oxidizing Bacteria (AnAOB) and denitrifying bacteria has been determined. As a potent catalyst, HAO can facilitate both the oxidation of hydroxylamine to nitrite and the reduction of nitrite to hydroxylamine. Therefore, the efficiency of nitrogen removal in autotrophic nitrogen removal systems directly corresponds to HAO activity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c) indicates that the HAO enzyme activity in R0 was 0.4449 EU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, increasing to 0.7983 EU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS in R1. This notable increase is predominantly attributable to methanol-enhanced denitrifying bacteria. Furthermore, the HAO enzyme activity in R3 and R5 rose to 1.137 EU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS and 1.111 EU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, respectively. However, there was variation in R2, R4, and R6. Analysis of denitrification performance revealed that adding 2 \u0026micro;M of signal molecules amplified denitrifying bacteria activity, triggering an increase in HAO enzyme activity. Additionally, C8-HSL and C12-HSL can bolster heterotrophic bacteria growth, further contributing to the HAO enzyme activity boost.\u003c/p\u003e \u003cp\u003eHeme-c, a critical coenzyme in the Anammox process, has a concentration directly associated with AnAOB activity. Research has established the presence of Heme-c in cells as a significant contributing factor to the characteristic red color of AnAOB (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (d), the reactor's Heme-c concentration was examined and studied. The minimal Heme-c content in R0 reflects the relatively scarce abundance of AnAOB bacteria in the reactor, a conclusion corroborated in section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e. This low AnAOB abundance might result from inhibition due to environmental variation. The Heme-c concentration in R1 dipped to 0.00834 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, attributed to an overgrowth of heterotrophic bacteria triggered by methanol. Conversely, in R2, R5, and R6, Heme-c rose to 0.01753, 0.01605, and 0.02121 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SS, respectively. This outcome suggests that C6-HSL, C12-HSL, and 3-oxo-C8-HSL might positively impact AnAOB growth, though further investigation is required to substantiate this claim.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Microbial community structures\u003c/h2\u003e \u003cp\u003eThe taxonomic findings at the genus level, as obtained by leveraging the Silva database, are demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The relative frequencies of AOB and denitrifying bacteria are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Nitrosomonas and Arenimonas emerged as the primary nitrogen-eliminating microorganisms in the seed sludge, possessing relative abundances of 4.78% and 16.61% respectively. In Reactor 0, the relative frequency of Nitrosomonas elevated from 4.78\u0026ndash;7.93%, concurrent with a corresponding Areal Removal Efficiency (ARE) of 70% in the reactor. The relative frequency of Arenimonas reduced from 16.61\u0026ndash;8.45%, with a corresponding TRE of 27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1%. Following the transfer of sludge into experimental reactors from the seeding reactor, the increase in Nitrosomonas and decrease in Arenimonas could possibly be attributed to environmental changes. Such changes seem to inhibit AnAOB, leading to an inability to consume nitrite timely, which promoted the growth of denitrifying bacteria by utilizing nitrite. In Reactor 1, Hyphomicrobium, the denitrifying bacterium, emerged as the dominant microorganism, with the relative abundance escalating from 3.60\u0026ndash;20.55%. With a TRE of 37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1% in Reactor 1, it can be hypothesized that nitrogen removal in the reactor was primarily driven by the denitrification pathway. The escalation of denitrifying bacteria could have been facilitated by the addition of methanol, used as the organic carbon source for denitrification. As a result, the abundance of denitrifying bacteria surged as SMP were utilized as substrate, leading to a decline in SMP. In Reactor 2, the relative frequency of denitrifying bacteria experienced a drop, perhaps because C6-Homoserine Lactone (C6-HSL) exerted a certain inhibitory influence on the denitrification bacteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relative abundance of the nitrogen removal-related bacteria (%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eDenitrifying bacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Denitrifying bacteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNitrosomonas\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHyphomicrobium\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eArenimonas\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDenitratisoma\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eThermomonas\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUpon the addition of C8-HSL, C10-HSL, and 3-oxo-C8-HSL, the relative abundance of denitrifying bacteria rose to 37.00%, 35.76% and 36.86%, respectively. The enhancement in nitrification and denitrification efficiency of the activated sludge system due to these signal molecules potentially caused this trend, leading to a rise in the denitrifying bacteria's relative abundance (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Moreover, a promotion of heterotrophic bacteria growth in the community is linked to C8-HSL, C10-HSL, and 3-oxo-C8-HSL (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Despite the similar situation with C6-HSL, feeding R5 with C12-HSL resulted in a decrease in the relative abundance of denitrifying bacteria.\u003c/p\u003e \u003cp\u003eFrom the findings, it can be inferred that the introduction of methanol promoted the growth of denitrifying bacteria. However, the presence of signal molecules such as C6-HSL and C12-HSL reduced the relative abundance of these denitrifying bacteria while simultaneously increasing the relative abundance of AOB. Conversely, molecules such as C8-HSL, C10-HSL, and 3-oxo-C8-HSL had the opposite effect, leading to an increase in the abundance of denitrifying bacteria and a decrease in AOB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mechanism and the prospect\u003c/h2\u003e \u003cp\u003eExisting literature demonstrates that nitrogen removal can be promoted by low concentrations of signal molecules (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), while high concentrations may impede this process (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The current study suggests that the amount of signal molecules added may have exceeded an optimal level, causing inhibition of microbial activity, suppression of associated gene expression, and thus a decline in nitrogen removal performance.\u003c/p\u003e \u003cp\u003eResearch consistently underscores the pivotal role of AHLs in enabling bacterial aggregation through EPS concentration regulation. More specifically, a positive correlation has been identified between the EPS content and AHLs such as C8-HSL and C10-HSL, which are typically present in granular sludge (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This promotion of sludge granulation by AHLs can predominantly be attributed to their capacity to control EPS content, with the standout being C4-HSL that significantly enhances PRO formation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Comparative observations indicate that granular sludge fortified with AHLs like C6-HSL and 3-oxo-C6-HSL possesses a higher EPS content relative to sludge devoid of AHLs (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Given that EPS constitutes an organic substrate that stimulates denitrifying bacterial proliferation, this discovery has substantial implications for the efficiency of microbial processes in wastewater systems.\u003c/p\u003e \u003cp\u003eThe incorporation of methanol into the system could adversely affect nitrogen removal and promote the growth of heterotrophic denitrifying bacteria. Consequently, it is advisable to refrain from using organic solvents such as methanol when introducing signal molecules. Instead, the union of signal molecules with carriers should be considered. For example, magnetic enzyme carriers have been effectively generated through immobilizing acylase on magnetic carriers, which have exhibited high recyclability and resilience, even after repetitive trials (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Another alternative method involves immobilizing acylase on magnetic circular mesoporous silica via adsorption and cross-linking, a method that has proven to successfully foster and delay biofilm formation over extended periods (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This synergy of signal molecules with carriers can extend their duration of action while renouncing the use of organic solvents.\u003c/p\u003e \u003cp\u003eWhile the AHLs did not enhance nitrogen removal in this study, the findings remain significant for future research. It was discovered that higher concentrations might inflict inhibition, thus future studies should investigate the impact of reduced AHLs concentration. Additionally, it is imperative to devise new strategies for integrating AHLs into the nitrogen removal system, necessitating thorough exploration, with a particular focus on mitigating methanol toxicity. Finally, the value of in situ experimental systems was highlighted in this study since observed environmental changes led to the inhibition of AnAOB.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe introduction of C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL at a concentration of 2 µM negative impacted the removal of TN, while its effect on ammonia removal was minimal. C8-HSL, C10-HSL, and 3-oxo-C8-HSL notably stimulated the proliferation of denitrifying bacteria, unlike C6-HSL and C12-HSL which reduced their growth. The growth of Anammox bacteria in all the experimental reactors, operated under mixotrophic nitrogen removal processes, was consistently inhibited. Adding 2 µM signal molecules led to alterations in the secretion of EPS and SMP within the reactors. Consequently, these 2 µM signal molecules may detrimentally influence the mixotrophic nitrogen removal process, indicating a need for further study into their regulatory effects.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eThis study did not involve studies of human or animal subjects.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eThis study did not involve studies of human subjects.\u003c/p\u003e\n\u003cp\u003eConsent to Publish\u003c/p\u003e\n\u003cp\u003eAll the authors agreed that the experimental data involved in the manuscript should be published.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by\u0026nbsp;Nan Zhang, Xiaojing Zhang, Han Zhang, Denghui Wei, Bingbing Ma, Hongli Zhang. The first draft of the manuscript was written by Nan Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Program for Innovative Research Team (in Science and Technology) in University of Henan Province (24IRTSTHN016), and the Natural Science Foundation of Henan Province (232300420171).\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eThis study did not involve studies of human subjects.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, [Xiaojing Zhang]，upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAHN, Y. 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Current Microbiology\u003cem\u003e,\u003c/em\u003e \u003cstrong\u003e73,\u003c/strong\u003e 108-114.\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":"Nitrogen removal, Signal molecules, Extracellular polymer, Denitrification, Quorum sensing","lastPublishedDoi":"10.21203/rs.3.rs-4668330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4668330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a form of microbial interaction, quorum-sensing signal molecules can control the expression and functionality of related genes within microorganisms. The current study looked into the effects of various signal molecules on the process of nitrogen removal. The findings illustrated that 2\u0026micro;M signal molecules, namely C6-HSL, C8-HSL, C10-HSL, C12-HSL, and 3-oxo-C8-HSL diminished the overall nitrogen removal efficiency (TRE) from 37.8% in the control, down to 26.8%, 26.0%, 28.1%, 27.6%, and 27.7%, respectively. Nevertheless, these molecules only slightly affected ammonia removal efficiency, reducing it from 67.9\u0026ndash;63.7%, 62.8%, 62.6%, 63.7%, and 62.9%, respectively. C8-HSL, C10-HSL, and 3-oxo-C8-HSL significantly enhanced the relative abundance of denitrifying bacteria from an initial value of 36.3\u0026ndash;37.00%, 35.76%, and 36.86%, in contrast to C6-HSL and C12-HSL, which caused a reduction to 24.39% and 26.56% respectively. The signal molecules were suspended in methanol, resulting in an elevation of the relative abundance of denitrifying bacteria from an initial 14.31\u0026ndash;30.09%, paralleled by an increased TRE value of 27.6\u0026ndash;37.8%. Environmental alterations, together with methanol provision, both constrained the Anammox activity. Furthermore, the incorporation of C6-HSL led to a decrease in the secretion of extracellular polymeric substance while a corresponding increase in soluble microbial products was noted. This research implies that 2 \u0026micro;M signal molecules could considerably influence reactor performance and microbial components of the mixotrophic nitrogen removal operation. The information presented will contribute additional insights into the impact of signal molecules on both the Anammox and mixotrophic nitrogen removal procedures.\u003c/p\u003e","manuscriptTitle":"Effects of different exogenous signal molecules on the reactor performance, sludge properties and microbial community structures of mixotrophic nitrogen removal process","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-30 17:29:12","doi":"10.21203/rs.3.rs-4668330/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":"55966a6b-d182-4db5-90c1-cf621a0585ec","owner":[],"postedDate":"July 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T09:49:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-30 17:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4668330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4668330","identity":"rs-4668330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}