Kosakonia cowanii Cp1 strain was isolated from seeds of Capsicum pubescens R. & P. cultivated in Michoacan, Mexico. Genetic and ecological role analyses were conducted for better characterization. The results show that genome has a length of 4.7 Mbp with 56.22% G + C and an IncF plasmid of 128 Kbp with 52.51% G + C. Furthermore, pathogenicity test revealed nonpathogenic traits confirmed by the absence of specific virulence-related genes. Interestingly, when fungal inhibitory essays were carried out, the bacterial synthesis of volatile organic compounds (VOCs) with antifungal activity showed that Sclerotinia sp. and Rhizoctonia solani were inhibited by 87.45% and 77.24%, respectively. Meanwhile, Sclerotium rolfsii, Alternaria alternata, and Colletotrichum gloeosporioides demonstrated a mean radial growth inhibition of 52.79%, 40.82%, and 55.40%, respectively. The lowest inhibition was by Fusarium oxysporum, with 10.64%. The VOCs’ characterization by headspace solid–phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC–MS) revealed 65 potential compounds. Some of the compounds identified with high relative abundance were ketones (22.47%), represented by 2-butanone, 3-hydroxy (13.52%), and alcohols (23.5%), represented by ethanol (5.56%) and 1-butanol-3-methyl (4.83%). Our findings revealed, for the first time, that K. cowanii Cp1 associated with C. pubescens seeds possesses potential traits indicating that it could serve as an effective biocontrol.The  Capsicum spp.) has an exciting history of domestication and has seen more than 6000 years of use in diverse foodstuffs in Mexico, such as in fresh, dried, or processed products. It is believed that this crop originated in South America  × 100, where dc and dt represent the mycelial growth diameters (in mm) of the control and treatment groups, respectively. The experiments were conducted in triplicate for a statistical analysis of variance and for Duncan’s multiple range test (p = 0.05), which were performed using DPS V12.01 software. Alternatively, bacterial antagonism toward S. rolfsii was tested by dual-culture assay on PDA to prove additional mechanism of inhibition. Bacterial strain  was inoculated close to and around fungal disk. The essay was kept at 28 °C over a 2- to 3-day period.The inhibitory assays were evaluated by radial mycelial growth according to the methodology described previously in . In geneSclerotium rolfsii), a cell-free filtrate was obtained of K. cowanii Cp1 grown on Tryptic soy broth (TSB) medium, with continuous shaking (100 rpm) at 37 °C, which was sampled every 6 h up to a total of 48 h. TSB medium without K. cowanii Cp1 strain was used as the control. The cell-free filtrate was obtained via centrifugation at 14,000 rpm for 5 min and after being filtered through a 0.2 μm membrane (Sigma-Aldrich). Then, 500 μL of the sample was placed on PDA medium , and a 7 mm mycelial disk of Sclerotium rolfsii was inoculated. The radial mycelial that was grown was evaluated according to the equation mentioned above. Furthermore, the sample that was obtained after 18 h of bacterial growth displayed the highest inhibitory effect; thus, it was used for VOC characterization. To recover the VOCs from the bacterial culture that was grown over 18 h on TSB medium, the sample was processed according to the methodology, and using the device, that was previously reported in [As the first step in obtaining a bacterial culture with volatile organic compounds ; 50, 100, and 150 μL/plate of benzyl alcohol; and 50, 100, and 200 μL/plate of ethanol. Sterile distilled water was used as control. Each device was incubated at 28 °C for a 3-day period to obtain the radial mycelial growth, according to equation previously described. The experiments were conducted in triplicate.Among the bacterial volatiles obtained, and based on the commercial availability , seven standard compounds were evaluated individually on the inhibition of radial mycelial growth. Using the previous two-compartment plastic plate device, different amounts of individual VOCs were added to sterile filter papers on one side and on the upper side of the device, and an C. annuum and C. pubescens were obtained and processed for bacterial isolation on TSA medium. In a previous work [K. cowanii strain that was resistant to β-lactam antibiotics; as such, in this case, we decided to supplement the TSA medium with 100 μg/mL of ampicillin in order to determine if this could be a similar case. From diverse seeds that were obtained from the fruits of Capsicum spp., a bacterial growth was noted on the seeds of manzano peppers (C. pubescens) that were obtained from Michoacan, Mexico. In 100% of the seeds of one fruit, there was a similar phenotype to K. cowanii  of 128,063 bp with 52.51% of G + C, one of them was selected randomly for genomic characterization. The genome of  of G + C. The annAdditionally, according to the reference database for bacterial virulence factors (VDFB), virulence-related genes are principally classified as flagellar, iron uptake, and siderophore and vir/avr genes were not identified .K. cowanii Cp1 has a resistance to the penicillin antibiotic class, such as ampicillin, penicillin G, and carbenicillin, probably due to the TEM β-lactamase detected. In addition, the bacterial strain showed resistance to erythromycin, clindamycin, and vancomycin. We suspect that this is most likely due to the genes being classified as efflux pumps  genes predicted in the genome were classified into eight categories . Howeverux pumps . Intereshibition , which dKosakonia genus isolates from diverse sources and geographical regions, we decided to perform a phylogenomic analysis that was based on 100 core genes for K. cowanii Cp1 and on the related strains of the Kosakonia genus : alcohoK. cowanii Cp1 on TSA medium, there was a fermentation process that produced a mixture of VOCs—some of them, most likely, exerted effects on mycelial growth inhibition. However, further analysis is necessary to understand the potential mechanisms leading to the production of each VOC and its effects on cellular processes.As is indicated in S. rolfsii was benzyl alcohol, with 82.5% using 50 μL/plate and 100% using 100 and 150 μL/plate. Then, acetic acid (unidentified compound as VOC component in K. cowanii Cp1 and used as control positive) showed 85 and 100% inhibition using 10 and 20 μL/plate, respectively, and only 21% inhibition using 5 μL/plate. For 2-nonanone, a low inhibition of 25% was observed at 10 μL/plate, while an inhibition of 72.5 and 90% was observed with 20 and 40 μL/plate, respectively. With 2-butanone, 3-hydroxy, an inhibition of 70 and 82.5% was observed with 100 and 150 μL/plate, respectively, while the inhibition observed with 50 μL/plate was only 37.5%. With 2-butanone, inhibition was 15, 56.2, and 72.5% using 50, 100, and 150 μL/plate, respectively. Acetone only showed 25% inhibition using the highest volume (150 μL/plate). Ethanol was the compound that showed the lowest inhibition rate; only at the highest volume of 200 μL/plate was 21.25% inhibition observed  [Mabea fistulifera Mart. (Euphorbiaceae), necrotic spots can occur on leaves [Capsicum genus in either fresh, dried, or processed products could be relevant in terms of avoiding the bacterial spread that comes with a potential risk of causing infection in susceptible Capsicum plants.The genome sequencing results of the motility . Interes disease . Based od fruits , we can anii Ch1 , which sx Willd) ,26. In Ex Willd)  are presn leaves . TherefoK. cowanii Cp1, such as TEM β-lactamase  is a pressing concern. In addition, the antibiotic efflux pump system that was detected and represented by acrAB-tolC and acrAD-tolC, respectively, is an intrinsic mechanism of multidrug resistance in the diverse members of Gram-negative bacteria. The antibiotic profile of the pump system includes tetracycline, novobiocin, chloramphenicol, fluoroquinolone, fusidic acid, nalidixic acid, lipophilic antibiotics, and β-lactam antibiotics [emrAB-tolC, which has been demonstrated to induce overexpression in Escherichia coli, which causes a resistance to nalidixic acid, thiolactomycin, and nitroxoline [macAB-tolC has been identified as the most likely cause of erythromycin resistance [mdfA/cmr that has also been identified contributes to multidrug resistance in Gram-negative bacteria [marAB, transcriptional repressor marR, target modification, and the protein-altering cell wall charge conferring antibiotic resistance—are shown in K. cowanii Cp1 make it necessary to conduct further work so as to evaluate any potential environmental risks. This is required because of its presence in Capsicum seeds and chili powder that are used in human consumption.On the other hand, it is recognized that the antibiotic resistance in bacteria is part of their natural genetic characteristics and evolution processes, which play important roles during environmental competition . Howeveribiotics . Anotherroxoline . Also, tsistance . The trabacteria . AdditioK. cowanii Cp1 during a dual experiment with six tested phytopatogenic fungi suggested that the volatile organic compounds produced during the growth of this bacterial strain on TSA medium has inhibitory effects on the mycelial rate growth of Sclerotinia sp., R. solani, S. rolfsii, A. alternata, and C. gloeosporioides, and this occurs by different mechanisms when compared with F. oxysporum (which showed a lower degree of effect). Our evidence, obtained by HS-SPME-GC–MS, demonstrates that the diverse chemical classes of VOCs were produced by K. cowanii Cp1 during its growth fermentation on TSA medium , as well as ethanol (at 5.40%), and 1-butanol-3-methyl (4.88%) [K. cowanii Cp1, it is too complicated to understand because it is part of a heterogeneous mix of VOCs that have fluctuating concentrations during the fermentation processes that were tested. This interpretation was confirmed using standard VOCs, where 2-butanone, 3-hydroxy showed an inhibition rate of 82.5% when the highest volume was used (150 μL/plate) compared with acetic acid, benzyl alcohol, or 2-nonanone, which demonstrated high inhibition activities when using a lower volume. In addition, relative peak areas identified were 0.68% for benzyl alcohol, 1.32% for 2-nonanone, and 0.90% for 2-butanone compared with 13.52% for 2-butanone, 3-hydroxy or 5.56% for ethanol  . Accordi (4.88%) . Althoug ethanol . TherefoK. cowanii Cp1, metabolic pathways were present that produce 2-butanone, 3-hydroxy through the enzymatic activities of acetolactate decarboxylase and 2,3-butanediol dehydrogenase via the diverse carbon sources that are used in fermentation [K. cowanii Cp1). These alcohol classes are components of the VOCs that are present in a diverse array of microorganisms with antifungal activity [Magnaporthe oryzae, Phytophthora capsici, and A. solani [According to the genome sequencing analysis of entation . Evidencentation , droughtentation , and defentation . On the activity . Another. solani . Althoug. solani . TherefoK. cowanii Cp1 as a microorganism that is associated with Capsicum pubescens seeds and which has antifungal properties through VOC production opens up the possibility of further exploration of important ecological roles as well as of the plant–bacteria interactions with diverse molecules such as capsaicinoids and phenolic compounds. Additionally, the Capsicum genus, cultivated throughout all Mexican territory under different environmental conditions, remains to be explored in order to understand the microbiomes associated with the seeds that are of particular interest in terms of finding potential bacterial strains, such as Kosakonia cowanii, for the development of biocontrol agents.In conclusion, the finding of


R)-enantiomer inhibited OCT2 4-fold more thanthe (S)-enantiomer. OCT3 showed the greatest stereoselectivityin its inhibition. (R)-Tolterodine and (S)-zolmitriptan inhibited OCT3 11-fold and 25-fold more than theirrespective counterparts. Interestingly, in most cases, the pharmacodynamicallyactive enantiomer was also the stronger OCT inhibitor. In addition,stereoselectivity in the OCT inhibition appeared not to depend onthe transported substrate. For high-affinity MATs, our data confirmedthe stereoselective inhibition of NET and SERT by several antidepressants.However, the stereoselectivity measured here was generally lower thanthat reported in the literature. Unexpectedly, the high-affinity MATswere not significantly more stereoselectively inhibited than the polyspecificOCTs. Combining our in vitro OCT inhibition data with available stereoselectivepharmacokinetic analyses revealed different risks of drug–druginteractions, especially at OCT2. For the tricyclic antidepressantdoxepine, only the (E)-isomer showed an increasedrisk of drug–drug interactions according to guidelines fromregulatory authorities for renal transporters. However, most chiraldrugs show only minor stereoselectivity in the inhibition of OCTsin vitro, which is unlikely to translate into clinical consequences.Many drugs have chiralcenters and are therapeutically appliedas racemates. Thus, the stereoselectivity in their interactions withmembrane transporters needs to be addressed. Here, we studied stereoselectivityin inhibiting organic cation transporters (OCTs) 1, 2, and 3 and thehigh-affinity monoamine transporters (MATs) NET and SERT. Selectivityby the inhibition of 35 pairs of enantiomers significantly variedamong the three closely related OCTs. OCT1 inhibition was nonselectivein almost all cases, whereas OCT2 was stereoselectively inhibitedby 45% of the analyzed drugs. However, the stereoselectivity of theOCT2 was only moderate with the highest selectivity observed for pramipexole.The ( SLC6A4). The (S)-enantiomers of citalopram,1 duloxetine,2 and venlafaxine3 inhibit SERT significantly more potent than the(R)-enantiomers, and duloxetine was from the beginningapproved as an enantiopure drug. For citalopram, a chiral switch wasperformed, and the (S)-enantiomer with the genericname escitalopram may have a better risk–benefit ratio thanracemic citalopram.4 On the other hand,chiral switching was not always successful. Exemplarily, (R)-fluoxetine, (S)-fenfluramine, and (S)-sotalol had a worse outcome in clinical studies.7 The underlying mechanisms, not in all instances well understood,may rely on different off-target effects of the enantiomers; for instance,(S)-sotalol has none of the possibly beneficial beta-receptorblocking activity.Relevantstereoselectivity is well known for several inhibitorsof the high-affinity serotonin transporter are highly expressed in the liver and kidney,10 respectively,whereas OCT3 (SLC22A3) has no dominant primary tissueexpression and can be found in cardiac tissue,11 in the brain,12 and at the blood–brainbarrier.13 All three mediate the cellularuptake of numerous drugs but also of endogenous and environmentalcompounds.15The polyspecific organic cation transporters(OCTs) of the SLC22Agene family are important for the absorption, distribution, and eliminationof many drugs and other substances.18 A higher molecular weight, higher number of rings,and increased lipophilicity have been identified as the most relevantfeatures of the OCT inhibitors compared to non- or only weakly inhibitingsubstances. However, many studies neglected the aspect of ligand stereochemistryand simply studied racemates as inhibitors or only one enantiomer.This appears as a gap because most small-molecule drugs are chiralproducts clinically used as racemates.20 The enantiomersof many drugs differ significantly in their biological activity.21 Accordingly, stereospecific differences in the inhibition of membranetransporters could be another reason to prefer enantiopure substancesover their racemic mixtures, assuming that enantiopure substancescause fewer drug–drug interactions (DDIs) in membrane transport.OCT inhibition has beenstudied in detail. Data for several hundredinhibitors of OCT1, 2, and 3 have been published.22 However,the underlying transporters were not identified. Another study addressedthe binding of propranolol and atenolol enantiomers to OCT1 usingimmobilized OCT1 as the liquid chromatography stationary phase,23 but only minor effects of stereoselectivitywere observed. A consensus variant of OCT1 was stereoselectively inhibitedby the enantiomer of the antiarrhythmic drug verapamil but this variantwas characterized by a sequence identity of 87% toward the wild-typesequence.24 Another study of the inhibitionof OCTs by central nervous system drugs revealed a twofold differencein the inhibition of OCT1 by the α-pyrrolidinovalerophenoneenantiomers but no difference in OCT2.25 For OCT3, no comprehensive data on stereoselective inhibition areavailable so far.An early study on membrane transport on the brush border membraneof opossum kidney (OK) cells identified stereoselective inhibitionof TEA uptake by verapamil.17 We then studied selectedinhibitors, where both enantiomers were commercially available. Intotal, we analyzed 71 stereoisomers of 35 chiral substances beingknown or presumed inhibitors of OCTs , Roche Pharma , Santa Cruz Biotechnology , Sigma-Aldrich , and Toronto Research Chemicals  with puritiesof at least 95%. A complete list of all substances, including therespective manufacturer and their catalog number, is provided in 28 whereas the OCT3-overexpressing HEK293 cells were generous giftof from Drs. Koepsell and Gorbulev . Amino acid sequences of overexpressed transporters are givenin Figure S1. All cells were cultivated in Dulbecco’s modifiedEagle’s medium (DMEM) supplemented with 10% (v/v) FCS, penicillin(100 U/mL), and streptomycin (100 μg/mL) and kept in culturefor no longer than 30 passages.All experimentswere carried out in transporter-overexpressing HEK293 cells. OCT1-,OCT2-, NET-, DAT-, and SERT-overexpressing cells as well as the empty-vectortransfected control cell line were generated as described earlier,Table S2 and Figures S2 and S3). Thereby, we ensured that the model substrate concentrationused in the subsequent inhibition experiments was sufficiently belowthe Km value of its uptake kinetics. Forthe uptake experiments, 300,000 HEK293 cells were plated in poly-d-lysine-coated 24-well plates 48 h ahead of the experiments.On the day of the experiment, the cells were washed once with prewarmedto 37 °C HBSS supplemented with 10 mM HEPES and pH adjusted to 7.4 (Sigma-Aldrich)—hereaftertermed HBSS+. For concentration-dependent experiments, the cells wereincubated with increasing substrate concentrations for 2 min. Uptakewas terminated by adding ice-cold HBSS+. Afterward, the cells werewashed twice with ice-cold HBSS+, and cell lysis was done with 80%(v/v) acetonitrile  to which an internalstandard for eventual LC–MS/MS analysis had been added. Intracellularlyaccumulated substrates were then quantified by LC–MS/MS analysis,and calibration was performed with known concentrations of the respectivesubstances. All uptake data were normalized to the number of seededcells by measuring the total protein content in radioimmunoprecipitationassay buffer-lysed cells in a bicinchoninic acid assay.29Prior to inhibition studies,the used model substrates were validated in concentration-dependentuptake experiments if no literature data were available -transfected cells were used as a controlto account for the passive influx of the model substrate, and twowells of transporter-overexpressing cells were used to determine thenoninhibited uptake of the model substrate. Cells were initially washedonce with 37 °C HBSS+ and then incubated with 2 μM modelsubstrate (0.2 μM for MPP+ uptake by MATs) with andwithout 20 μM inhibitor for 5 min. For concentration-dependentinhibition experiments, the cells were incubated with increasing inhibitorconcentrations. The incubation was terminated by aspiration of thebuffer before cells were washed twice with ice-cold HBSS+ and lysedin 80% acetonitrile. Cell lysates were transferred into a black 96-wellplate for the fluorescence measurement of ASP+ or usedfor sample preparation for LC–MS/MS analysis. ASP+ was quantified using a Tecan Ultra multiplate reader using an excitation wavelength of 482 nm and anemission wavelength of 612 nm. Fluorescence quantification was performedin technical duplicates.As for the transportexperiments, 300,000 HEK293 cells were plated in poly-Table S3.Intracellulardrug concentrations of nonfluorescent substrates were quantified byliquid chromatography coupled to mass spectrometry (LS–MS/MS).The HPLC was a Shimadzu Nexera HPLC system . Chromatography was carriedout on a Brownlee SPP RP-Amide column  with aC18 precolumn. The aqueous mobile phase contained 0.1% (v/v) formicacid and organic additive ranging from 3 to 20% to adjust the retention times of the analytes.Separation was done at a flow rate of 0.3 or 0.4 mL/min with a columnoven temperature of 40 °C. Compound detection was performed withan API 4000 tandem mass spectrometer operating in multiple reaction monitoring mode. Analyte peaks wereintegrated using Analyst software . A completelist of MS detection parameters and the mobile phase compositionsis provided in v = vmax ×[S]/(Km + [S])) using GraphPad Prism . Vmax is the maximumtransport velocity, while Km is definedas the substance concentration that is required to reach half of vmax. The ratio of vmax over Km is termed intrinsic clearanceClint. For inhibition experiments, the transport activitywas calculated by using the following equationTransporter-mediated net uptake was calculatedas the difference between uptake in transporter-overexpressing cellsand that in the empty-vector-transfected control. Transport kineticparameters were determined by nonlinear regression following the Michaelis–Mentenequation .15 Except for ibuprofen, all substances were positivelycharged at physiological pH and had at least one chiral center.The investigated inhibitorsincluded known OCT substrates suchas salbutamol, sparteine, and zolmitriptan as well as substances previouslyidentified as not-transported inhibitors like tolterodine and verapamil.+ as a model substrate -Tolterodine showed a 94% inhibition of OCT3, whereas the corresponding(S)-enantiomer inhibited OCT3 only to 54% at ourscreening concentration . This revealed additionally relevant stereoselectivity in the inhibitionof the OCT2 by duloxetine, fenfluramine, and palonosetron. In contrastto OCT2, OCT3-mediated ASP+ uptake was generally most weaklyinhibited by the investigated substances -ethambutol for all three OCTsand sumatriptan, N-ethyllidocaine, and (S)-zolmitriptan as rather specific substrates for OCT1, −2,and −3, respectively (Table S4).Generally, there was reasonable consistency with the results obtainedfor ASP+ . ASP+ was usedas a model substrate where possible. However, when the screening resultsshowed low inhibition of ASP+ uptake and thus indicatedthat high inhibitor concentrations might be required to depict thefull inhibition curve, ethambutol was used as a model substrate instead.The enantiomers that showed relevant differences in their inhibitoryeffects were further characterized by concentration-dependent analyses(Table S5). In line with the screeningresults, stereoselectivity in OCT1 inhibition was very low. Most pairsof enantiomers were tested for stereoselective OCT2 inhibition. Theselectivity ratios ranged from 1.5-fold for tolterodine enantiomersup to 4.0-fold for the enantiomers of pramipexole. The highest selectivitieswere observed for OCT3. (R)-tolterodine was 11-foldmore potent in the inhibition of OCT3 and (S)-zolmitriptaneven 25-fold more potent than their respective counterparts.The observed stereoselectivity ratios ranged from1.2-fold forbupivacaine enantiomers in the inhibition of the OCT1 up to 25-foldfor zolmitriptan enantiomers and the inhibition of the OCT3, respectively.A largeclass of the investigated substances were drugs used as monoaminereuptake inhibitors. For a comparative analysis, we tested these substancesfor their inhibition of MATs, as well. Since all monoamine reuptakeinhibitors available here as pairs of the two enantiomers were eitherclassified as selective serotonin reuptake inhibitors (SSRIs) or asserotonin-norepinephrine reuptake inhibitors (SNRI), we tested thesedrugs only for SERT or for NET and SERT inhibition (S)-citalopram showed a sixfoldmore potent SERT inhibition compared to the corresponding (R)-enantiomer. In contrast, fluoxetine enantiomers showedidentical inhibitory potencies. NET and SERT were inhibited morestrongly by the (E)-isomer of doxepine, with selectivityratios of 3.8- and 3.3-fold, respectively. Also a similar stereoselectivitywas also observed for the fenfluramine isomers. (R)-Fenfluramine was more potent in inhibiting NET and SERT, with selectivityratios of 4.8- and 6.1-fold. However, both fenfluramine enantiomerswere approximately 20-fold more potent in SERT inhibition than inNET inhibition. Interestingly, venlafaxine enantiomers showed theopposite stereoselectivity in NET and SERT inhibition. (S)-Venlafaxine had an 8.8-fold higher IC50 value for NETinhibition than the (R)-enantiomer, whereas (R)-venlafaxine was only slightly (selectivity ratio of 1.9)more potent in SERT inhibition. Similar to fenfluramine, the venlafaxineenantiomers were also more potent as SERT inhibitors than as NET inhibitors.Among the SSRIs, -enantiomer of tolterodine than the corresponding(S)-enantiomer. Additionally, zolmitriptan enantiomersdiffered in their level of inhibition of OCT3 by 25-fold. However,tolterodine is marked as l-tartrate salt of the single (R)-enantiomer, and zolmitriptan is also used in the enantiopureform as (S)-enantiomer. Accordingly, high stereoselectivityin transporter inhibition has no direct clinical consequences withthese two drugs. Among the stereoselective OCT2 inhibitors were manydrugs used as monoamine reuptake inhibitors such as citalopram, doxepine,and tomoxetine. Interestingly, the enantiomers more active at MATsalso inhibited OCT2 more strongly than their respective counterparts.Overall,stereoselectivity did not appear as a general featureof OCT inhibition, although certain drug–transporter pairsshowed relevant differences in the inhibition between their enantiomers.However, this cannot be generalized since we observed remarkable differencesbetween the three closely related transporters. Especially, OCT1 showedalmost no relevant stereoselective inhibition. In contrast, OCT2 wasinhibited stereoselectively by 45% of the tested drugs, although onlyto a moderate extent. For OCT3, we observed the strongest effectsof stereoselectivity in this study. OCT3 was inhibited 11-fold strongerby the  have confirmed a fundamentallysimilar substrate binding cavities of all three OCTs.35 Nevertheless, there were also more than 10 amino acids that differbetween the three transporters and may therefore be responsible forsubstrate- and stereoselectivity. The long-known polyspecificity ofOCTs36 was reflected for OCT1 by an orthostericand opportunistic ligand binding site.24 However, without extensive site-directed mutagenesis driven by thenewly available structures, any explanation of the differences instereoselective OCT inhibition remains speculative. Additional cryo-EMstructures with bound zolmitriptan or tolterodine enantiomers mightbe interesting. However, the moderate differences in inhibitory potenciesfor most drugs do not guarantee success in the identification of aminoacids responsible for the stereoselectivity.The trend of higher stereoselectivity in OCT2 and −3 inhibitioncompared to OCT1 aligns with previous studies on stereoselective OCTtransport demonstrating that OCT2 and OCT3 act more stereoselectivelythan OCT1.50 or Ki values . The U.S. Food andDrug Administration (FDA) applies a more liberal cutoff of 0.1 forthe risk of renal interactions at OCT2.To estimate whetherthe observed effects of stereoselectivity couldlead to stereoselective DDIs in vivo, we compared our data to availabledata on stereoselective pharmacokinetics. For this, we combined enantiospecificplasma concentrations with protein binding whenever this was available1. HoweveImax,u/IC50 ratios showed considerabledifferences between the enantiomers of several drugs. Not reachingthe thresholds is partially caused by the fact that stereoselectivityis not always considered in pharmacokinetic studies, and therefore,the stereospecific pharmacokinetic parameters are not available. Foramisulpride, several pharmacokinetic studies are available with higherdosing up to 1200 mg and accordingly also higher plasma concentrationsbut without stereoselective analyses.48 Using these plasma concentrations for drug–drug risk estimationwould result in scores above the EMA or FDA threshold. Another considerationis the impact of the model substrate used for transporter inhibition.As shown exemplarily for the tolterodine enantiomers -stereoisomer showed a higher inhibitionof OCT2 and a strongly increased risk of DDI compared to the (Z)-stereoisomer. Apparently, (E)-doxepineat OCT2 was the only drug that would raise concerns regarding DDIsaccording to the EMA or FDA guidelines in our study. In contrast tothat of OCT2, OCT3 was inhibited more strongly by the (Z)-stereoisomer of doxepine. Due to an 85:15 formulation of (E)- versus (Z)-doxepine, the higher plasmalevels resulted also in a higher risk of DDI at the OCT3 by (E)-doxepine compared to (Z)-doxepine. Nevertheless,based on the available pharmacokinetic data, the risk score did notreach the EMA threshold for both stereoisomers. Significant stereoselectivitywas also described for the metabolism of doxepine, and (E)-doxepine biotransformation was shown to be highly dependent onthe highly polymorphic CYP2D6.42 Accordingly,individuals with intermediate or poor metabolizer genotypes mightbe at a significantly increased risk of (E)-doxepine-mediatedDDIs.Interestingly, in most cases where we observed stereoselectivityin the OCT inhibition, the pharmacodynamically active stereoisomerinhibited the transporters most strongly. With doxepine, the pharmacodynamicallyactive -fenfluramine over that of its counterpartcould be confirmed in our system.Stereoselectivity in the inhibitionof MATs has been studied alreadyin detail.52 in the context of OCT inhibition. Therefore,using enantiopure formulations might result in a higher selectivityas a result of a reduced potential of DDIs at OCTs.Altogether,the extent of stereoselectivity in OCT inhibition variedgreatly, although all three transporters are closely related. Thisfurther substantiates that stereoselectivity is a highly transporter-specificproperty. Consequently, all transporters potentially exposed to DDIsby chiral drugs should be characterized individually, regarding possibleeffects of stereoselectivity in their inhibition. The highly similarinhibitory potencies of most chiral substances in particular at OCT1could still be seen as a further reason to prefer enantiopure formulationsover their racemic mixtures in drug therapy. Since for most chiraldrugs, the intended bioactivity mainly relies on one enantiomer, theother enantiomer might be indeed considered as “isomeric ballast”


The resulting strain is homozygous viable and extends the survival of L1-arrested larvae. This survival-extending phenotype suggests that the phosphomimetic mutations might promote activation of JKK-1 during the arrest. Thisjkk-1potential gain-of-function allele might be useful for studying the regulation and functions of JKK-1.Phosphomimetic substitutions have been instrumental in understanding the role of phosphorylation in protein function. Here we describe the design and construction of a predicted phosphomimetic allele of the JUN kinase kinase gene Many proteins are regulated by phosphorylation at serine, tyrosine and threonine amino acids. Phosphorylation introduces both negative charges as well as steric alterations to the protein, thus changing its function. Interfering with the phosphorylation system of a protein can be instrumental in understanding its function. An example for manipulation of phosphorylation-based protein function is the introduction of phosphomimetic substitutions, by which amino acids that are phosphorylated as part of the physiological control of this protein are substituted for negatively charged amino acids. For example, serine can be replaced by aspartic acid or glutamic acid, thus constitutively introducing a negative charge at this residue. While phosphorylated serine is similar to aspartic acid and glutamic acid, the degree of similarity is limited, as these substitutions do not fully recapitulate the formal charges and the hydration layer caused by serine phosphorylation .C. elegansis part of the JUN kinase phosphorylation signaling pathway that is involved in multiple physiological process including stress responses and locomotion.The JNK Kinase JKK-1 injkk-1that causes three phosphomimetic substitutions by copying a design that had been previously established in mammals. The mammalian homolog of JKK-1 is MKK7. A constitutively active MKK7 mutant has been constructed by replacing S271, T275, and S277 with glutamic acid (MKK73E) . We used Protein BLAST to identify the homologous amino acids in JKK-1. The BLAST alignment suggested that the homologous amino acids in JKK-1 are S274, S278, and S280E .Here we designed and created an allele ofWe in silico introduced DNA substitutions for all three of these serine codons to replace them with glutamic acid codons. For this editing, GAG was chosen as the codon, as this codon is optimal for translation . These substitutions should result in a phosphomimetic version of JKK-1, JKK-1, or JKK-1(3E). These substitutions are labelled in red in Figure 1B-C).jkk-1was edited using Crispr-Cas9 by a commercial service provider (Sunybiotech) to introduce these gene edits, resulting in the allelejkk-1(syb3225). Synonymous mutations were also introduced for the process of gene editing, to prevent recutting by the nuclease during the editing procedure . The final sequence of the gene can be seen in Figure 1B. The allele was Sanger sequenced, which confirmed that the edits had been introduced as designed. The strain carryingjkk-1(syb3225)(PHX3225) appears superficially normal and is homozygous viable.The endogenous locus ofjkk-1reduces adult lifespan . FoxO activation is also crucial for survival of L1 larvae during starvation-induced developmental arrest . We hence tested survival of JKK-1(3E) worms during L1 arrest by hatching the worms in the absence of food. JKK-1(3E) worms had an increased survival in L1 arrest  and also had a higher chance to recover from the arrest upon addition of food and to develop to fertile adults compared to wild type . These results suggest that JKK-1(3E) might confer a gain-of-function phenotype during L1 arrest that is likely caused by increased JKK-1 activity. Additional experiments should be carried out to further characterize the effects of JKK-1(3E) and to more directly test the gain-of-function phenotype during different stages and conditions. The most direct assessment of JKK-1 activity would be to examine the phosphorylation of its target JNK-1, using available phospho-JNK antibodies . The next most direct method would be to examine DAF-16 phosphorylation and/or nuclear localization, using commonly available reagents.JUN kinase signaling and JKK-1 are required for the activation of the FoxO transcription factor DAF-16, and loss-of-function mutation ofThe strain PHX3225 carrying JKK-1(3E) is available at the CGC and it should be useful for further studying the functions of JKK-1. et al., 2022; Wu et al., 2018). Briefly, worms were synchronized via hypochlorite treatment and kept in 2ml Eppendorf tubes in 1ml M9 buffer without any food in a rotator at 20°C. L1 arrested worms were tested for survival and recovery every 2ndday for the first 9 days and then every day. For this test, approximately 100 larvae were transferred with a pipette onto a NGM plate seeded with OP50 bacteria and it was counted manually how many worms were alive and dead. Three to five days later, the plate was scored again and we counted how many worms had reached the adult stage. The experiment was performed as three technical replicates. Data from all three replicates were combined by adding all worms together. For statistical testing, Fisher’s Exact Test was conducted on day 19, when approximately 50% of wild-type worms were still alive.The survival assay was carried out as described previously (KoutsoumparisN2: wild typejkk-1(syb3225)PHX3225:


Planolites, Scolicia, and burrows produced by deposit feeders increase during episodes of seasonal sea-ice cover. In contrast, intervals with more severe sea-ice conditions are characterized by chemosymbiotic traces such as Chondrites and Trichichnus/Mycellia, suggesting lower food delivery and poorly ventilated bottom water conditions. The study thus confirms previous reconstructions of sea-ice conditions on the Yermak Plateau during interglacials, demonstrating that bioturbation variation provides insights into bentho-pelagic coupling under variable sea ice regimes in the Arctic Ocean.Central Arctic, interglacial intervals have traditionally been associated with diverse and intense bioturbation, and abundant foraminifera, interpreted as indicating relatively low sea-ice concentrations and productive surface waters, while glacial intervals, typically barren, support the inverse. In this respect, the Yermak Plateau is anomalous. Biomarker studies suggest that glacial intervals were characterized by comparatively open water, while interglacials are marked by severe sea-ice conditions. Here we study downcore Ethological Ichno Quotient (EIQ) variations in trace fossils and bioturbation to test the hypothesis that different ethological classes vary in accordance with late Pleistocene changes in sea-ice extent, with deposit feeders increasing during reduced sea-ice cover and chemosymbiotic traces increasing during periods of thick perennial sea-ice conditions. Our results generally demonstrate that the abundance of traces like  Since 1978, the areal extent of the arctic summer sea-ice cover has decreased by around 3% per decade, according to data from satellite remote sensing8. Although, during summer the sea ice extent has declined more dramatically than winter sea ice extent. Particularly, the Barents Sea, has exhibited notable reductions9. Sea ice plays an important role in the Earth’s energy budget, where melting of Arctic sea ice lowers the Earth’s albedo resulting in a positive feedback for global warming13. Despite its central role in the climate system and its alarming disappearance, we have only a fragmentary view of how Arctic sea ice evolved and changed over geological timescales using proxies like biomarkers, foraminifera and dinocyst assemblages, as well as sedimentary ancient DNA17. This is in part due to the limited set of proxy tools that are available to reconstruct sea ice from marine sediment cores and challenges with their interpretation.A prominent consequence of Earth’s recent warming is the melting of Arctic sea ice21. The organic biomarker most commonly used to reconstruct sea-ice variability is IP25. This lipid is derived from sea-ice diatoms that occupy the interstitial channels at the base of the sea ice24 and tend to be more prolific (higher IP25 concentrations) in situations of seasonal sea-ice cover. Consequently, enhanced values of IP25 are interpreted as conditions proximal to the sea-ice margin. In contrast, production of IP25 in open water or under perennial sea-ice is very low or absent, and therefore lower IP25 concentration depicts such conditions, with the caveat that zero IP25 signal could equally be explained by thick perennial sea ice cover26. The use of these organic biomarkers, especially IP25, has become common recently29 compared to traditional proxies for sea ice, such as planktonic and benthic microfossil abundance31, and studies on ice rafted debris32. Foraminiferal assemblages and abundance are also useful proxies for reconstructing sea-ice variability, as they are generally abundant under open-water conditions, but scarce during periods of perennial sea-ice cover34. However, compared to other ocean basins, nanno- and microfossils, including calcareous and siliceous forms, are generally scarce in Arctic Ocean sediment records and show large spatial and downcore differences in preservation35. There are longstanding debates on whether this absence is a primary depositional signal , or results from dissolution at or below the seafloor as a function of organic matter supply and subsequent remineralization38. For example, in the central Arctic Ocean, there is seemingly better preservation of calcareous microfossils below perennial sea-ice cover where organic supply and microbial sedimentary CO2 is lower compared to the more productive ice-margin environments39, suggesting that there is a significant preservational bias.Over the past 10–15 years tremendous progress has been made in reconstructing sea-ice extent using biogeochemical proxies such as organic biomarkers40, presumably allowing for increased surface primary production, organic carbon export to the seafloor and thus bioturbation in the sediment column. However, on the Yermak Plateau, which is located next to Fram Strait, the major gateway for Atlantic waters entering the Arctic Ocean, the opposite seems to be true. Glacial periods, which are well-dated in marine sediment cores from this region, are characterized by seemingly open waters while interglacials are characterized by perennial sea-ice conditions42. The presence of seasonal sea-ice conditions on the Yermak Plateau and the Barents-Kara margin during glacials is argued to arise from the formation of a coastal polynya system in front of the Barents ice sheet43. Here we investigate downcore variations of bioturbation in combination with trace fossil diversity on the Yermak Plateau. Importantly, a basic assumption is that the diversity of bioturbation and trace fossil types is directly influenced by the flux of particulate food falling from surface waters45 (i.e. export production and bentho-pelagic coupling). Consequently, we typically would expect higher bioturbation intensity under less severe sea ice or more open water/ice marginal environments, where photosynthetic primary productivity is higher46. Since sea-ice conditions on the Yermak Plateau during the past two glacial cycles are relatively well documented and the age models on glacial-interglacial timescale are robust48, it provides an excellent opportunity to test the use of bioturbation and trace fossils as a sea-ice proxy.The central Arctic Ocean is typically conceived as being covered with thick sea ice during glacial periods, with more open water conditions during the interglacial periods44, which in the Arctic Ocean are all strongly influenced by sea-ice conditions. The findings from the trace fossil analysis are compared to published sea-ice biomarker proxy data. Bioturbation and trace fossils have the immediate advantage of being largely resistant to diagenetic processes and are typically preserved in-situ in undisturbed sediment cores.The aim of our study is to explore the quantitative analysis of bioturbation and trace fossils in sediment cores as an additional proxy-approach for exploring Pleistocene sea-ice history in the Arctic Ocean. The occurrence and activity of benthic fauna responsible for producing traces in deep-sea sediments depends on food availability, sediment substrate, bottom-water currents and oxygenation49. This warm AW is the primary source of heat for the deeper Arctic Ocean basin50. After entering Fram Strait, the warm AW in the West Spitsbergen Current (WSC) branches out reaching the Yermak Plateau51, where the flow of AW is confined between 200–500 m depth52. At about 80°N WSC branches up into the Svalbard Branch and the Yermak Branch. The Svalbard Branch follows the continental shelf northeastward, and eventually sinks into the deeper Arctic53. The Yermak Branch flows northwest until about 81°N, and then east around the north western corner of the YP and eventually southward in the return Atlantic current54. The seafloor below about 700 m is bathed by Arctic Deep Water comprising some combination of northward flowing Nordic Seas Deep Water and ‘modified’ Nordic Seas Deep Water, which exits the Arctic after circling the Eurasian Basin55.The studied cores are obtained from the Yermak Plateau (YP), a submarine high situated northwest of Svalbard Fig. . The Yer59, is not observed in the lower part of these cores  provide a definitive stratigraphic correlation between these two records Fig.  and high61. At the water depths of the coring sites, these layers most likely reflect unsorted melt out from icebergs or sea ice61. Moreover, in PS92/039, at the depths of around 685, 647, 629, and 300 cm, erosional or diagenetic layers are identified , where these LDB are characterized by less-dense material filling the interior while the outer boundary of the burrow is not well defined, and Lined Burrow (LB)  Fig. , as wellOverall, PS92/039 shows higher abundance of trace fossils along with a more diverse trace fossil fauna, containing ~ 8 different ichnotaxa, compared to core PS92/045, which contains ~ 6 different trace types  index was generated for both deposit feeding and chemosymbiotic ethological class traces  generated by Kremer et al.21  should also have covered the Yermak Plateau during MIS 671. Many studies of MIS 6 have indicated the recurrent waxing and waning of an unstable SBIS onto the outer shelf due to episodically intensified advection of warm AW76. The biomarker record from Kremer et al.21, showed higher IP25 flux and increased brassicasterol biomarker concentrations during most of the MIS 6  Ice Sheet was one of the Arctic's largest Quaternary glaciations21, biomarker and organic matter concentrations remain low throughout MIS 5  and Kasten Corer (PS92/039)92 .Both cores were logged shipboard on a Multi-Sensor core logger to acquire high-resolution downcore profiles of bulk density and magnetic susceptibility. They were later scanned using the Aavatech XRF Core Scanner at the Department of Geosciences. Basic sediment parameters were assessed onboard48. The age models primarily rely on a strong correlation between the rock magnetic parameters  and the global benthic δ18O curve48. This correlation is supported by AMS14C dating, the occurrence of the benthic foraminifera Pullenia bulloides, used as a stratigraphic marker for Marine Isotope Stage (MIS) 5.1 (~ 81 ka) in the polar North Atlantic93, δ18O of planktonic foraminifera (PS92/045)48 and rates of amino-acid racemization in planktonic and benthic foraminifera47 , where EIQ is defined as a the sum of identified bioturbation intensities in the respective ethological classes, thus quantifying the degree or intensity of bioturbation visible in the X-ray radiographs. EIQ calculation for both the ethological classes is shown in Eqs. (Supplementary Figure 1.Supplementary Table 1.


The frontal position of an ice shelf is an important parameter for ice dynamic modelling, the computation of mass fluxes, mapping glacier area change, calculating iceberg production rates and the estimation of ice discharge to the ocean. Until now, continuous and up-to-date information on Antarctic calving front locations is scarce due to the time-consuming manual delineation of fronts and the previously limited amount of suitable earth observation data. Here, we present IceLines, a novel data set on Antarctic ice shelf front positions to assess calving front change at an unprecedented temporal and spatial resolution. More than 19,400 calving front positions were automatically extracted creating dense inter- and intra-annual time series of calving front change for the era of Sentinel-1 (2014-today). The calving front time series can be accessed via the EOC GeoService hosted by DLR and is updated on a monthly basis. For the first time, the presented IceLines data set provides the possibility to easily include calving front dynamics in scientific studies and modelling to improve our understanding about ice sheet dynamics. Retreating or even disintegrating ice shelves can reduce buttressing effects resulting in enhanced mass loss of the AIS2. Within the last decades, disintegration events along the Antarctic Peninsula (AP) and the West Antarctic Ice Sheet (WAIS) demonstrated the dynamic behavior and vulnerable state of Antarctic ice shelves5. In total since 1997, Antarctic ice shelf area loss due to disintegration and calving events has been dominant compared to ice shelf area gain by advancing fronts7. The growth and decay of an ice shelf is controlled by several factors such as internal ice dynamics, ice shelf geometry, pinning points, bed topography and external mechanical and climatic forces. There is growing evidence that long-term atmospheric and ocean forcing cause ice shelf front retreat along the AP and WAIS10. For the East Antarctic Ice Sheet (EAIS), the body of evidence is not as clear as calving front time series are shorter and less frequent13. The calving front position is of significant value to Antarctic studies focusing on oceanography, sea ice, glaciology and terrestrial or ocean ecology. Especially in glaciology, the front position is an important parameter for ice dynamic modelling14, computing mass fluxes15, mapping glacier area change8, calculating the iceberg production rate16 and estimating the export of ice mass to the ocean17. Therefore, knowledge about sub-annual calving front dynamics and their short-term controlling mechanisms are essential for a better understanding of ice sheet dynamics determining the mass loss or gain of the AIS15. But so far, such data does not exist for the entire AIS due to time-consuming manual front delineations and the previously limited availability of satellite imagery12. To overcome the tedious manual work of calving front delineation, traditional imaging techniques have been used to develop automated approaches not suitable for intra-annual calving front extraction20 due to seasonal variations in sea ice and surface melt. To date, only deep learning based approaches are able to provide accurate and dense time series of calving front location change23.Three-quarters of the Antarctic coastline consist of floating ice shelves regulating the ice discharge of the Antarctic Ice Sheet (AIS)24, a data set of Antarctic ice shelf front positions providing continuous and up-to-date calving front time series. IceLines is a novel deep learning-based framework providing calving front locations (CFL) on different temporal scales  for Antarctic ice shelves automatically extracted from Sentinel-1 radar imagery. The data set includes all intact Antarctic ice shelves listed by the SCAR Composite Gazetter25 with a width of at least 30 km. Additionally, six dynamic glacier fronts being of key interest (e.g. Pine Island Glacier) have been selected for monitoring. Figure 24 is freely available via DLR’s GeoService (https://geoservice.dlr.de/web/maps/eoc:icelines).We take advantage of these innovative techniques and introduce IceLines24 was developed to automatically monitor Antarctic ice shelf front positions and to deliver dense time series on calving front change. This requires a high degree of automatization as time-consuming manual front delineations cannot keep up with rapidly growing satellite archives12. Figure IceLines26. We selected EW data as it provides more spatial context to the deep learning model at a defined tiles size (in pixels) and the data coverage for dual polarized EW scenes over the Antarctic coastline is better than for IW data. To avoid unnecessary high data amounts and the processing of scenes only partly covering selected fronts, the download is limited by filtering for suitable satellite orbits fully covering each front. Hence, the availability of daily front positions depends on Sentinel-1 acquisitions covering the front and can vary approximately from one to eight observations per month. Sentinel-1 data availability in Antarctica is limited before the adjustment of the acquisition scheme in May 2017 due to the launch of Sentinel-B and after its failure in December 2021. In cases where a front is never fully covered by an entire scene, adjacent scenes acquired on the same day are merged after pre-processing. The pre-processing is performed with the Graph Processing Tool of the ESA SNAP Toolbox27 on a Hadoop Cluster with 63 nodes (32/64 GB RAM) within a Docker container. For single polarized scenes, the pre-processing includes subsetting to the shelf extent, thermal noise removal, radiometric calibration, geometric terrain correction with the TanDEM-X PolarDEM 90 m28 and masking the imagery by a 100 km wide coastline buffer. Additionally, for dual polarized imagery, the ratio between the HH and HV polarization is calculated and added to the scene stack as described in Baumhoer et al. 2019 (ref. 23).On a monthly basis, available Sentinel-1 GRD EW scenes (40 m resolution) are downloaded for each monitored ice shelf defined by an area of interest (AOI) from the Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC)et al.29 and adapted for a circum-Antarctic utilisation in IceLines. The adaptation included a more diverse training data set with 81 Sentinel-1 scenes (instead of 11 scenes) covering 17 coastal regions (instead of only 2) and the ability of the network to automatically recognize and predict dual and single polarized imagery. This is necessary as Sentinel-1 imagery acquired before 2017 only includes one polarization. Moreover, for some coastal areas only single polarized imagery is available (e.g. Bellingshausen Sea) during the entire acquisition period of Sentinel-1.The calving front is extracted with a HED-UNet which was initially developed by Heidler The HED-UNet is a neural network architecture designed specifically for the extraction of calving fronts that builds on the two principles of segmentation and edge detection. While segmentation aims to differentiate glacier pixels from ocean pixels, edge detection directly classifies the pixels on the calving front. HED-UNet incorporates both approaches to encourage the model to learn visual patterns that are characteristic of the ice sheet, the ocean, and the calving zone. By working on multiple resolution levels, the model can consider a larger spatial context than previous models, which has been found important for the task of calving front detection. The training data covers the time period May 2017 until August 2019.Figure 28 referenced to the WGS84 ellipsoid) to the class ice sheet. This threshold is also applicable for future front positions except entire ice shelves disintegrate in combination with drastic grounding line retreat. This removes erroneous classifications over the dry snow zone in high altitude ice sheet areas (sometimes mistaken with low backscatter areas over the ocean) but does not influence predictions over lower ice shelf areas. The elevation supported prediction probability is binarized by a threshold of 50% . Afterwards, morphological filtering is applied to ensure that both classes are contiguous areas. After obtaining the final classification result, temporal aggregation is applied by merging all available segmentation results per month, season and year. For monthly products the daily prediction probability is merged by averaging. For seasonal and annual products, the monthly and seasonal classifications are merged by averaging the binary masks, respectively.The post-processing step uses the prediction probability of the HED-UNet segmentation merging head for the classes “ice sheet” and “ocean” where the border of both classes marks the coastline. An elevation threshold is applied on the prediction probability to exclude areas above the grounding line by assigning all areas above an empirically set elevation threshold of 110 m  before a line is extracted from the polygon. The line is clipped to remove the scene boundaries and retrieve the coastline. The clipping extent equals the extent of the initial satellite image eroded by 15 pixels. In case of monthly averaged products scene boundaries might still occur if scene extents varied within one month. Lastly, the coastline is clipped by the AOI for each monitored ice shelf or glacier to obtain the final calving front position.31. Nevertheless, occasional failures occur where incorrect front positions are extracted due to icebergs, mélange, surface melt or cloud cover. These erroneous fronts can easily be detected visually but the automatization of this process is still challenging. Either, unsuitable optical imagery  can be eliminated in beforehand21 or the front itself can be verified by polygon complexity measures22. In our case, both approaches did not provide satisfying results which led us to the creation of a reliability measure based on the calving front time series. The calving front time series is calculated along perpendicular centerlines comparable to the centre-line method described in Lea et al.32. Instead of one single centerline we used several centerlines to better cover the ice shelf front and measure the sea-ward intersection if several intersections (e.g. due to crevasses) exist. The number (3 to 15 centerlines per shelf) and spacing (3 to 65 km) of centerlines varies depending on ice shelf size and complexity of the front. In rare cases, where ice shelf front areas are known to be repeatedly extracted ambiguously  centerlines are either skipped or moved to obtain the actual movement pattern of the front not biased by these areas. Potentially incorrect front positions can be detected within the time series as they are either significantly land-inwards in case of surface melt or sea-wards due to mélange or sea ice cover. In order to detect theses outliers, it is presumed that a calving front position cannot deviate more than the mean plus/minus the standard deviation (minimum 2 pixels (80 meters) to account for certain degree of variation) of eight  previous and successive front positions. The number of eight (four) front positions ensures to cover consecutive erroneous fronts e.g. during periods of surface melt. Most recent front positions at the end of the time series are considered again in the outlier detection during monthly processing to ensure that front positions of large calving events are finally not classified as outliers. In practice this means when searching for a front position of a recent calving event it is more likely to find it in the folder ‘fronts-eliminated’ within the first three months. Front positions classified as outliers are stored in a separate folder indicating the user to check these fronts visually. This approach works reliable for fronts with clear movement patterns of advance and retreat but often excludes too many confident front positions when frequent and smaller calving events happen especially for the daily product. The share between confident and potentially unreliable front detections varies between <1% and 99% depending strongly on ice shelf front geometry, periods of surface melt, fast ice areas and calving pattern. On average, 47.4% of the daily and 38.8% of the monthly front positions require further visual inspection.Deep neural networks demonstrated their superior performance for calving front detection in several studies in means of speed and still achieve accuracies better or comparable to manual delineations24 is available at DLR’s GeoService and updated on a monthly basis. Front positions are provided as line shapefiles for each ice shelf. In case of ice shelves being larger than the acquired Sentinel-1 scenes, the front positions are divided in sub-fronts and numbered as indicated in Fig. The IceLines data setpolarization]_[YYYYMMDD]_[S1 unique product ID]-[ice shelf name].gpkgDaily:[polarization]_[YYYYMM]-[ice shelf name].gpkgMonthly:[YYYY][Quartal]_mean-[ice shelf name].gpkgSeasonal:[YYYY]noQ1_mean-[ice shelf name].gpkgAnnual:[24 products can be downloaded from the DLR GeoService (https://download.geoservice.dlr.de/icelines/files/) as single GeoPackage or combined ZIP files for each temporal product and ice shelf separated in the folders ‘fronts’ and ‘fronts-eliminated. Moreover, visual exploration of annual and monthly calving front positions is possible via the web browser of the DLR GeoService (https://geoservice.dlr.de/web/maps/eoc:icelines).“1SDH” indicates that the front was extracted from a dual polarized input scene, “1SSH” from a single polarized one. The quarters are defined as austral seasons with Q1 summer , Q2 autumn , Q3 winter  and Q4 spring . The annual average excludes the summer season where mostly surface melt occurs (indicated by “noQ1” in the file name). The attribute table of each file includes the date of the front position (‘DATE_’), ice shelf name (‘name’), date of last update  and version number (‘version’). For daily and monthly products, the name of the corresponding Sentinel-1 scene is included (‘s1name’). The shapefiles are in Antarctic polar stereographic projection (EPSG:3031). All IceLines31. Nevertheless, manual front delineation is a very challenging task and underlies a high degree of subjectivity. Having several experts delineating the same front will always generate different results34. Hence, the automated front position is not necessarily wrong if it does not align with the manual reference. To perform a comprehensive validation including all kinds of different calving front morphologies and acquisition dates, 92 fronts were selected for an accuracy assessment. The selection criteria required two fronts for each ice shelf, one derived from single polarized imagery between October 2014 and April 2017 and one derived from dual polarized imagery between August 2018 and October 2021. If only single polarized imagery were available, two single polarized samples were taken. Both time periods were selected to lie outside the time span of the training data. The exact distribution of training and validation data is shown in Fig. IceLines is validated by calculating the distance difference between the automatically extracted daily fronts and manual front delineations. This is a commonly applied method to assess the accuracy of the derived front positions34, 86.76 ± 1.43 m (2.25 pixel)21, 38 m (~6 pixel)30 and 86 m22 for Greenland and 108 m (2.7 pixel)23, 222 ± 23 (Wilkes Land)29, 345 ± 24 (Antarctic Peninsula)29, 237.12 m35 and 330.63 m (2.35 pixel)21 for Antarctica. It should be noted that these accuracies are not directly comparable due to temporal and spatial variations in the validation data sets spanning from one34 to 6221 glaciers during different time periods. For further comparison, manual delineations from different experts can deviate between 92.5 m34, 33 m (5.5 pixel)30 and 183 m (4.6 pixel)23 depending on image resolution and difficulty of the calving front delineation.The distance difference between manual and automated fronts was tested by measuring the distance from the manual to the automated front along pixel spaced 40 m) points. This measure gives an impression how good the extracted front matches the manual delineation. Figure 0 m point24, the distance accuracy was calculated along perpendicular centerlines already utilized for the time series generation. The frontal movement accuracy is determined as the mean distance between the manual and automated front along the defined centerlines. Figure To assess how accurately the frontal movement of a calving front can be determined with the IceLines data set24 exist due to SAR specific backscatter properties. Calving front detections during the occurrence of surface melt or within the dry snow zone  remain challenging especially with single polarized SAR imagery. But the benefits of illumination- and cloud-independent SAR imagery outweigh these limitations and do not significantly affect the unprecedented value of the IceLines data set24 represented by regularly updated, circum-Antarctic time series of calving front change.The comprehensive validation of IceLines demonstrates that CNN-derived calving fronts offer a good and accurate alternative to laborious manual front delineations. The front position can deviate from manual delineated fronts due to fast ice, mélange and icebergs close to the front by 209 ± 12 m (5.2 pixel) on dual polarized imagery and 432 ± 21 m (8.8 pixel) for single polarized imagery whereas the frontal movement can be determined with higher accuracies of 63 ± 68 m (1.6 pixel) for dual and 107 ± 126 m (2.7 pixel) for single polarized imagery. Limitations of the IceLines data set24. Figure 2 = 0.89) shows a slight tendency to higher advance rates in 2015 compared to 2021 for stronger advancing (>0.65 km /yr) shelves. It has to be mentioned that this trend is strongly dominated by the disintegrating glacier tongue of Thwaites not having a clear advancing front in 2021 compared to 2015. In contrast to this overall tendency, the frontal advance of Pine Island, Totten, Ross West, Ronne, Larsen C and Brunt1 ice shelves accelerated in 2021 compared to 2015. Furthermore, it is noteworthy that shelves with a calving event between 2015 and 2021 more likely changed their frontal advance rate compared to shelves without calving . Areas of failed front extraction can easily be identified by winding lines not located between previous and consecutive front positions. If in doubt, the front positions can be compared with the original Sentinel-1 imagery by the unique Sentinel-1 ID and acquisition date or the Sentinel-1 scene name in the attribute table of each file. Sentinel-1 imagery can be downloaded from ASF  and ESA (scihub.copernicus.eu). Pre-processed Sentinel-1 imagery can be accessed via Google Earth Engine (earthengine.google.com). Moreover, the probability of a failed front extraction decreases with higher temporal aggregation. This means that temporal aggregated products  should be preferred over daily products. The IceLines data set is registered under the DOI 10.15489/btc4qu75gr92.The IceLines data setSupplementary


B, a plastoquinone molecule bound to the D1 subunit of PSII. Many artificial electron acceptors (AEAs) with molecular structures similar to that of plastoquinone can accept electrons from PSII. However, the molecular mechanism by which AEAs act on PSII is unclear. Here, we solved the crystal structure of PSII treated with three different AEAs, 2,5-dibromo-1,4-benzoquinone, 2,6-dichloro-1,4-benzoquinone, and 2-phenyl-1,4-benzoquinone, at 1.95 to 2.10 Å resolution. Our results show that all AEAs substitute for QB and are bound to the QB-binding site (QB site) to receive electrons, but their binding strengths are different, resulting in differences in their efficiencies to accept electrons. The acceptor 2-phenyl-1,4-benzoquinone binds most weakly to the QB site and showed the highest oxygen-evolving activity, implying a reverse relationship between the binding strength and oxygen-evolving activity. In addition, a novel quinone-binding site, designated the QD site, was discovered, which is located in the vicinity of QB site and close to QC site, a binding site reported previously. This QD site is expected to play a role as a channel or a storage site for quinones to be transported to the QB site. These results provide the structural basis for elucidating the actions of AEAs and exchange mechanism of QB in PSII and also provide information for the design of more efficient electron acceptors.Photosystem II (PSII) utilizes light energy to split water, and the electrons extracted from water are transferred to Q Thermostichus (Thermosynechococcus) vulcanus, which revealed the arrangement of all protein subunits and the precise location of all cofactors including chlorophylls (Chls), carotenoids, and an oxygen-evolving manganese–calcium cluster, providing a basis for elucidating the function of PSII. Recently, single-particle analysis using cryoelectron microscopy revealed the PSII structure in solution at 1.93 to 1.95 Å resolution, which is closer to the in vivo state of PSII and was found to be essentially identical to PSII in its crystalline state with some minor differences (Photosystem II (PSII) exists in thylakoid membranes of oxygenic photosynthetic organisms and performs light-induced electron transfer reactions coupled with water splitting to produce electrons, protons, and oxygen molecules , 2, 3. Tferences , 7.+. P680+ pulls electrons from a catalytic center called a manganese–calcium cluster (Mn4CaO5 cluster) via a redox-active, nearby tyrosine residue D1-Tyr161 . After accepting two electrons, QB is protonated with two protons transferred from the cytosol (stroma) through a hydrogen-bonding network formed by surrounding amino acids, bicarbonate (HCO3−), and water molecules, resulting in the formation of PQH2 (B-binding site (QB site) is refilled with a new PQ from the PQ pool within the thylakoid membrane; in this way PQ is reduced one after another  in the QB site. The molecular structure of AEAs is similar to that of the head group of QB (C), and, like natural QB, they can accept electrons to support the redox reaction of PSII , for several hours, and then subjected to X-ray crystallography. Structural analysis showed that all AEAs bind to the QB site and there was a reverse correlation between the binding strength and oxygen-evolving activity. In addition, a novel quinone-binding site was found at a position located slightly away from the QB site as well as the previously found QC site , 2,6-dichloro-1,4-benzoquinone (DCBQ), and 2-phenyl-1,4-benzoquinone (PPBQ), respectively C, for se QC site . These rB molecule omitted showed shapes different from that of the QB molecule at the QB-binding site (QB site) in both AEA-treated conditions (A–C). These maps matched with DBBQ and DCBQ, respectively, in the DBBQ- and DCBQ-treated conditions (D–F), indicating the binding of DBBQ and DCBQ directly to the QB site in place of QB. To examine the exact binding position of DBBQ and DCBQ to the QB site, Br atoms in DBBQ and Cl atoms in DCBQ were detected by X-ray anomalous dispersion, which is a specific and sensitive method to detect heavy atoms in crystals. In the DBBQ-treated condition, three anomalous signals derived from bromide atoms were found in the QB site with the diffraction data collected at 0.9 Å wavelength, which is close to the Br absorption edge (B), indicating that DBBQ adopts two conformations in the QB site. The two conformations that best match with the electron density map and the location of the anomalous signals are shown in Figure 2E, in which the A conformer has a 0.55 occupancy and the B conformer has a 0.45 occupancy, respectively (2. This structure is registered at the Protein Data Bank (PDB) with a PDB code of 8GN0.X-ray diffraction images were collected from PSII crystals treated with either DBBQ or DCBQ, and the images were successfully analyzed at 2.10-Å resolution for the 10 mM DBBQ-treated condition and 2.15-Å resolution for the 10 mM DCBQ-treated condition, respectively . The 2Fonditions , A–C. Thnditions , D–F, inion edge B, indicaectively , and theB site in the anomalous difference map of 10 mM DCBQ-treated PSII crystals collected at 1.8 Å wavelength (C). As shown in Figure 2F, the electron density map and the anomalous signals fit well with a single DCBQ molecule with 1.0 occupancy, and the B-factor is 84.8 Å2. This structure is registered at PDB with a code of 8GN1. However, a positive Fo-Fc map remains in the upper right corner of the DCBQ, and this feature is more pronounced on the B monomer side of the PSII dimer , whereas in the PPBQ-treated conditions, the map changed to a round shape and the map corresponding to the tail of the QB molecule was largely obscured or no longer visible (B and C). B at different angles, in which a large area of the QB tail is obscured in the PPBQ-treated conditions compared with the control condition. Next, maps assigned with the QB molecule (PQ) were compared (A–F). The electron density map observed in the control condition was in good agreement with the shape of the QB molecule. On the other hand, in the PPBQ-added conditions, the maps corresponding to the two methyl groups in the head region and tail of QB were unclear or broken (B and C), so were the tail regions of the molecule from the side view (D–F). These results suggest that the QB molecule has been replaced by the PPBQ molecule in the PPBQ-treated conditions.Since PPBQ does not contain heavy atoms that show anomalous signals, the shape of the electron density map is carefully compared with that of the control condition to reveal the possible binding of PPBQ to the Q control A and S3.d B site , 37, 38. QB site . The QB  PQ head A, wherea visible , B and Ccompared , A–F. Thr broken , B and Cide view , D–F. ThB molecule with the map in each condition, the average B-factors of the QB molecule were compared between the control and PPBQ-treated conditions, assuming QB occupies its position in all the conditions. The average B-factor of the QB molecule was found to be higher under the PPBQ-treated condition than that under the control condition . On the other hand, in the PPBQ-treated conditions, one relatively large Fo-Fc positive area was observed near the neck of the QB head (H and I). Since the crystals were prepared under the same condition except for the addition of PPBQ, a plausible interpretation of this large positive area is the presence of PPBQ. Assuming PPBQ is present at this position, this large positive area corresponds to the second benzene ring of the PPBQ (C).To quantitatively compare the fit of the Qondition . A plausccupancy G. On the QB head , H and Ithe PPBQ C.Table 1B site, PPBQ molecules were assigned to the map for each condition and refined (A–C). Since PPBQ was not added to the crystal in the control condition, two positive Fo-Fc map areas were observed in the region corresponding to the two methyl groups of the QB head region (A) and a negative Fo-Fc area was observed at the location of the second benzene ring. Furthermore, a large and long positive Fo-Fc map is visible in the area corresponding to the tail of QB. On the other hand, in both PPBQ-added conditions, no such strong Fo-Fc areas were observed in the positions observed in the control condition (B and C). These results indicate that it is more reasonable to assign PPBQ to the QB site than to assign only QB under the PPBQ-treated conditions.To verify the binding of PPBQ to the Q refined , A–C. Sid region A and a nondition , B and CB and C, a small Fo-Fc positive area was still observed even after refinement with the assignment of PPBQ. Assuming that this small Fo-Fc area was due to residual QB molecule, the PPBQ and QB multiconformers were assigned with different occupancies and refined. As a result, the placement of PPBQ and QB as multiconformers matched well with the map features (E and F). The ratio of multiconformers was determined based on the Fo-Fc map, and it was found that PPBQ binds to the QB site by replacing up to 60% of QB (D–N). Structures whose occupancy ratio is 0.2 for PPBQ and 0.8 for QB, where the B-factor is not too high . This position is occupied by the tail of an SQDG molecule, which is surrounded by D1-Trp278 and D1-Leu200 in control PSII , and is separated from the QB site by around 15 Å. In the DBBQ-treated PSII, the 2Fo-Fc map at this position showed a DBBQ-like shape different from that observed in the control condition (A and B). Based on this 2Fo-Fc map and the anomalous signals, a DBBQ molecule was assigned to this map. The distance between the two anomalous signals is consistent with the distance between the two Br atoms in the DBBQ molecule (E). This novel quinone-binding site is called the QD-binding site (QD site) in this article to distinguish from the QC site reported previously .Two new, strong heavy-atom anomalous signals were found at a location other than the Q crystal B. This pondition , A and Bmolecule E. This neviously . The disound 8 Å . A QD siome b559 , 40. Theome b559 B.Figure C, F and I). Smaller anomalous signals were observed for the DCBQ-treated crystal, as these signals were collected with a wavelength of 1.8 Å, which is much shorter than the K-edge absorption of Cl atom at 4.4 Å. The shape of the 2Fo-Fc map in the QD site well matches with that of the DCBQ molecule, thus they are assigned as the DCBQ molecule in the DCBQ-treated PSII. Because an occupancy of 1.0 for either the DBBQ or DCBQ-treated PSII exhibited a negative Fo-Fc map, their respective occupancies were investigated for the QD site. The occupancies were determined by the Fo-Fc map to be 0.65 for DBBQ and 0.9 for DCBQ  in place of QB. The binding of DBBQ is well supported by the X-ray anomalous dispersion data measured at 0.9 Å wavelength, which gives rise to the largest anomalous signal of the Br atoms derived from the DBBQ molecule. Two sites were found to bind the DBBQ molecule; these two sites are in close proximity, so they are modeled as multiconformations of a single molecule (B and E). The shape of the 2Fo-Fc map matched well with that of the two DBBQ molecules. The distances of DBBQ to the nearby residues do not change much in the two different conformations. Similar results were obtained for DCBQ (C and F), which bind to a single site in place of QB. Compared with the anomalous signal of the Br atom in DBBQ, the anomalous signal of the Cl atom in DCBQ was smaller. This is because the K absorption edge of the Cl atom is 4.4 Å, whereas the dataset was collected at 1.8 Å wavelength under the DCBQ-added condition. As the Cl ions in the vicinity of the manganese cluster have been identified by the similar approach in previous studies . In native PSII, there is no heavy atom at this position that emits anomalous signals, and the tail of SQDG is assigned in this region in the previous studies (D and G). Therefore, these anomalous signals are most likely derived from the Br atoms of DBBQ. The shape of the 2Fo-Fc density map at this position is consistent with the binding of DBBQ . Thus, a DBBQ molecule was assigned in this position; concomitant with the occupation by DBBQ, the tail of the SQDG molecule shifted away from its original position. A similar result was obtained from the crystal treated with DCBQ . These AEAs are in a position to have π-π stacking interactions with D1-Trp278. The Br atom of DBBQ is at a hydrogen bond distance of 3.18 Å to the carbonyl group of D1-Leu200, and the ketone of DCBQ is at a hydrogen bond distance of 2.69 Å to the carbonyl group of D1-Trp278. These intermolecular interactions are thought to stabilize the AEAs at this position. This site is termed the QD site, which is around 8 Å away from the third quinone-binding site (QC site) found in a previous structural analysis B site, thannel I , one of  QB site , 46, 47.D site was predicted based on a kinetic study of cytochrome b559 reduction -1, 1-dimethylurea bind to the Qynthesis , 22, 23. (B site . It was of 3.2 Å . HoweverThermostichus (Thermosynechococcus) vulcanus was cultured in a liquid medium at 50 °C with bubbling of air containing 3% (v/v) CO2 under LED light in a plant growth chamber (BIOTRON LH-410PF-SP) as described , 20 mM NaCl, 10 mM CaCleviously , 50. AllThe PSII crystals obtained were subjected to the treatment of dehydration and cryoprotection as follows. The crystals were first transferred into a 100 μl buffer solution containing 7% polyethylene glycol (PEG) 1450 in addition to the crystallization buffer. After incubation at 12 °C for 15 min, 100 μl of a buffer containing 0.2% higher concentration of PEG 1450 and new 1.8% DMSO and 0.7% PEG 5000 MME was added. After 15 min incubation, half of the buffer volume was replaced with a new buffer that contained the same increment of PEGs and DMSO. This procedure was repeated every 15 min until the concentrations of DMSO and total PEGs in the final buffer reached 25% and 20%, respectively. For the structural analysis of PSII crystals treated with AEAs, DBBQ, DCBQ, and PPBQ were added to the buffer solutions, respectively. The initial buffer solution did not contain AEAs, and the concentration was increased stepwise in the same way as the increase in the concentration of PEG 5000 and DMSO, so that the final buffer solutions contained 1 mM or 10 mM of AEAs, respectively. The crystals were then dehydrated by evaporation against air at a humidity of 75 to 90% in the final buffer for 1 h in an incubator at 12 °C, flash-frozen in a nitrogen gas stream, and stored in liquid nitrogen. All cryoprotectant replacement and cryocooling procedures were carried out under dim green light.For the PPBQ conditions, dehydration and cryoprotection treatment were also performed using a buffer containing glycerol instead of DMSO. The crystals were first transferred into a 100 μl buffer solution containing 7% PEG 1450 and 2% DMSO in addition to the crystallization buffer. After incubation at 12 °C for 15 min, 100 μl of a buffer containing 0.2% higher concentration of PEG 1450 and new 1.6% glycerol and 0.7% PEG 5000 MME were added. After 15 min incubation, half of the buffer volume was replaced with a new buffer containing the same increment of PEGs and glycerol. This procedure was repeated every 15 min until the concentrations of glycerol and total PEGs in the final buffer reached to 23% and 20%, respectively. The other procedures are the same as described above.1 state because the crystals were made in the dark, and X-rays were given in full darkness. Diffraction datasets were collected at a wavelength of 1.0 Å from untreated, DCBQ-treated, or PPBQ-treated PSII crystals with an oscillation angle of 0.2° over 180°, resulting in 900 images. The datasets collected were processed, integrated, and scaled using X-ray Detector Software (http://www.pymol.org).X-ray diffraction experiments were performed at beamline BL41XU of SPring-8, Japan, at 100 K. Most of the PSII is in the SSoftware . The iniSoftware  of the CSoftware  using thSoftware  of the CSoftware , 56. ModSoftware , 58, andX-ray anomalous dispersion is a specific and sensitive method for detecting heavy atoms in crystals , 60, 61.−1. In addition to 1 mM potassium ferricyanide, 0.4 mM PPBQ, DBBQ, or DCBQ was added as an electron acceptor in the measurements. Ferricyanide was added to maximize oxygen evolution, but AEA activities are largely similar without the addition of ferricyanide (Oxygen evolution of thylakoid membranes and PSII dimers was measured with a Clark-type oxygen electrode (Hansatech Instruments) under continuous, saturating illumination at 30 °C in the storage buffer at a Chl concentration of 10 μg of Chl mlicyanide . MeasureAtomic coordinates for the reported structures have been deposited in the Protein Data Bank under accession codes of 8GN0, 8GN1, and 8GN2 for DCBQ, DBBQ, and PPBQ-bound PSII, respectively. All other data are contained in this paper.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Furthermore, we analyzed the bacterial community structure in both compartments via 16S rRNA gene amplicon sequencing, following the river downstream. Additionally, conventional water parameters and sulfonamide concentrations were measured, and seasonal aspects were considered by comparing the fate of ARGs and bacterial community diversity in the surface water compartment between the summer and winter season. Our results show that biofilm compartments near the WWTP had a higher relative abundance of ARGs (up to 4.7%) than surface waters (<2.8%). Sulfonamide resistance genes were more persistent further downstream (>10 km) of the WWTP in the hot and dry summer season than in winter. This finding is likely a consequence of the higher proportion of wastewater and thus wastewater-derived microorganisms in the river during summer periods. We observed distinct bacterial communities and ARG abundance between the biofilm and surface water compartment, but even greater variations when considering seasonal and spatiotemporal parameters. This underscores the need to consider seasonal aspects when studying the fate of ARGs in aquatic ecosystems.The spread of bacteria with antibiotic resistance genes (ARGs) in aquatic ecosystems is of growing concern as this can pose a risk of transmission to humans and animals. While the impact of wastewater treatment plant (WWTP) effluent on ARG abundance in surface waters has been studied extensively, less is known about the fate of ARGs in biofilms. The proximity and dense growth of microorganisms in combination with the accumulation of higher antibiotic concentrations in biofilms might render biofilms a reservoir for ARGs. Seasonal parameters such as water temperature, precipitation, and antibiotic concentrations should be considered as well, as they may further influence the fate of ARGs in aquatic ecosystems. Here we investigated the effect of WWTP effluent on the abundance of the sulfonamide resistance genes  As a step toward limiting the global spread of antimicrobial resistance, it is crucial to remind ourselves that humans, animals, and the environment are interconnected, which has already been well expressed as the basis of the One Health approach . The fresul1 and sul2 genes, which confer resistance to sulfonamides, are among the earliest used markers and most frequently detected ARGs in WWTP effluent  at all six sites. Rain events were rare and the average rainfall was less than 2.3 mm per day . Water t 1 and 5 , and phy2 of surface area was used for biofilm sampling at Sites 1, 2, 5, and 6. At Sites 3 and 4, which were characterized by dense biofilms, approximately 1 cm2 was sampled. All samples were stored in a thermobox with cold packs and transported to the laboratory within 3 h. Immediately upon arrival, biofilm samples were stored at 8°C until DNA extraction within the next 2 days. Planktonic samples were further treated as follows. One liter of river water from each site except Site 3 (500 mL) was filtered using 0.22 μm pore size PSE PALL filters  and stored at −20°C until DNA extraction. Additionally, 100 mL flow-through was collected for each site and stored at 8°C overnight before solid phase extraction (SPE) and sulfonamide concentration measurements via HPLC-MS.Planktonic samples were collected in sterilized and pre-rinsed 1-L glass flasks at an approximate water depth of 5 cm. Biofilm samples were collected with a spatula from the water-facing side of stones covering the riverbed and transferred into tubes containing 1 mL 1xPBS. To obtain a sufficient amount of biomass, approximately 10 cmFlow-through was concentrated 100-fold using SPE with Oasis HLB 6cc 500 mg columns  before HPLC-MS was carried out as described in g to harvest microorganisms. The supernatant was discarded and the sediment was resuspended in 100 μL BE buffer . For planktonic samples, filters were cut into small pieces and divided into two parallel 2-mL tubes containing 200 μL BE buffer and approximately 0.15 g of 1-mm zirconium beads  each. The individual tubes were agitated for 20 s at 4 m/s on a FastPrep 24 instrument  and centrifuged in a benchtop centrifuge . The supernatant from parallel tubes was collected and pooled. To maximize the final DNA yield, an additional 100 μL of BE buffer was added to the tubes containing the filter pieces, vortexed, and centrifuged again, and the supernatant was pooled with the previous one. DNA extraction was performed using the NucleoSpin Microbial DNA Kit  following the manufacturer’s protocol.The biofilm samples were vortexed and centrifuged for 5 min at 13,000 The diversity of bacterial communities in planktonic and biofilm samples was determined for all sites and sampling campaigns by preparing a 16S rRNA gene amplicon library as described . We condsul1, sul2, intI1, and the 16S rRNA gene were determined with SYBR Green-based quantitative real-time PCR using established primers  was lower than during the winter season (73%), presumably because of high water evaporation together with the lack of precipitation. SDZ and SMZ concentrations did not exceed 1.3 ng/L at any of the sampling sites  resulted in an elevated water temperature of 19°C at Site 4. Downstream at Site 5 (17°C), water temperature increased further, peaking at 22°C at Site 6. The pH in the river changed from pH 7.3 at Site 1 to pH 8.8 at Site 6. Nitrate concentrations were 1.2 mg/L upstream of the WWTP, 4 mg/L in the wastewater effluent, and 2.8 mg/L downstream at Site 5. These values are in the lower range of previously reported nitrate concentrations along the river stretch . Total pp ≤ 0.01 for Site 1, p ≤ 0.05 for Site 2). Downstream of the WWTP, both indices increased steadily, reaching values of 6 and 1,354 at Site 6. The steady increase of both indices downstream the discharge point indicates a recovery of the riverine bacterial community, which was demonstrated in our previous winter monitoring study on the Holtemme using a microbiome recovery model (p ≤ 0.05) with medians of 5.3 and 803 at Site 6. Notably, the biofilm communities at Sites 1, 2, 5, and 6 showed significantly lower alpha diversity in comparison to the corresponding planktonic samples (Dunn’s test: p ≤ 0.05). The number of observed ASVs at Site 1 was the only exception, showing a non-significant difference. Conversely, polluted Sites 3 and 4 show much more uniform alpha diversity in both planktonic and biofilm-forming bacterial communities. We hypothesize that the increase in both indices in the surface water compartment downstream from the WWTP might be attributed to biases during rarefaction of the sequencing data. The high abundance of certain taxa in wastewater may have prevented detection of rare taxa, which were found further downstream in the river with higher evenness  to assess bacterial diversity . In planry model . In contevenness .p ≤ 0.05). NMDS analysis also revealed a statistically significant separation between planktonic and biofilm-forming bacterial communities at all sampling sites (PERMANOVA: p ≤ 0.01). However, the change in community composition seemed to be driven more by the anthropogenic pollution rather than the type of compartment, i.e., surface water vs. biofilm.The composition of bacterial communities along the river stretch was impacted by anthropogenic pollution. This is evident in the dissimilarities of beta diversity across different sites, as shown in the NMDS plot in Alphaproteobacteria, Gammaproteobacteria, Clostridia, and Bacilli. In the biofilm samples, approximately 4% of the high-quality reads were derived from Cyanobacteria, whereas in the planktonic samples, Cyanobacteria were not among the 50 most abundant phylotypes . At the family level, bacteria typically exhibiting an aerobic, heterotrophic metabolism predominated in both compartments . Noteworthy, we found an ASV affiliated with Escherichia-Shigella in every sample from the surface water compartment at Site 1 (average read abundance of 0.3%). Since identical or similar ASVs were not found in any of the other 55 samples from our campaign, it seems unlikely that they are sampling/laboratory contaminants in the planktonic samples from Site 1. While it remains to be elucidated to which genus the Escherichia-Shigella ASV belonged, and whether it was derived from an autochthonous population, the members of that genus could be a source of the occasionally detected sul1, sul2, and intI1 genes at the site (see below). At Site 2 just downstream the city of Wernigerode, the communities of the surface water and biofilm compartments included several phylotypes often associated with warm-blooded animals  in addition to those found at Site 1. The WWTP effluent at Site 3 was dominated by phylotypes of the family Peptostreptococcaceae and the order Lactobacillales in planktonic samples, while in biofilm samples most abundant phylotypes were associated with Peptostreptococcaceae, Nitrospiraceae, and Rhodobacteraceae. Downstream the discharge point, Peptostreptococcaceae remained a dominant family, but Sphingomonadaceae and Xenococcaceae were also detected with increasing abundance. Arcobacteraceae (phylum Campilobacterota) became highly abundant in biofilm samples . Summer communities cluster more than those acquired in winter season, which is most likely related to the shorter sampling period. This becomes evident in the spatial arrangement of data points in the NMDS plot depending on the sampling date in the winter season, where samples were collected over approximately 3 months. The summer sampling campaign lasted only about 1 month. In contrast to upstream sites, the bacterial community in the WWTP effluent were more stable over time and in between seasons, as data points are located close to each other in the plot. As WWTP performances are often stable over time, one would expect the bacterial community structure to be distributed more closely around an average, as previously shown by Differences in beta diversity of planktonic communities from this sampling campaign and those analyzed in our study of the winter season 2020/2021  are showsul1, sul2, intI1, and the 16S rRNA gene in planktonic samples. All four genes showed a similar pattern with low CN or CN below limit of quantification at Sites 1 and 2, highest CN in wastewater and decreasing CN downstream of the WWTP . The median CN of 16S rRNA gene at Site 6 was only 16% of the CN at Site 4. Similar declines were observed for sul1, sul2, and intI1 . The absolute CN of the 16S rRNA gene was higher at the wastewater discharge point (median 4.1 × 107 CN/100 mL) than in the river water with a median of 4.3 × 106 CN/100 mL at Site 5. In contrast to our previous monitoring study in the winter season 2020/2021, the absolute abundance of all four target genes in summer season declined more rapidly from Site 4 to 5. This observation is of special interest, as we would have expected the opposite, a faster decrease in the absolute abundance during winter season, due to dilution effects. Absolute abundance for biofilm samples is not depicted because a normalization to CN/sample does not allow for direct comparison between different sampling sites. In the summer season, relative abundance against 16S rRNA gene showed a similar pattern for all three target genes sul1, sul2, and intI1 in planktonic samples, with the highest relative abundance at Site 3  and decreasing relative abundance from Site 4 to 5 (sul1 and sul2 when compared to both upstream sites (Dunn’s test: p ≤ 0.05). The decrease in absolute and relative abundance downstream the WWTP is most likely attributed to dilution effects, but additional removal mechanisms like sedimentation, cell death by predation or inactivation by UV light might also occur . This increase is most likely a consequence of higher abundance of microorganisms in the WWTP during summer season. The relative abundance of sul1 and sul2 was also higher in the summer season at Site 3 and downstream the WWTP. For sul2 the difference was statistically significant (Dunn’s test: p ≤ 0.05). We estimate an increase in the proportion of WWTP effluent at Site 4 from ∼27% in winter to about 75% in the summer season (see above). Therefore, seasonal differences like the increased proportion of wastewater and thus ARG-carrying wastewater-derived microorganisms in the river increased the abundance of sulfonamide resistance genes. In the face of climate change with less average precipitation in summer seasons in Europe, one must assume that the proportion of wastewater in rivers will increase even further  and 13 km (Site 6) downstream the WWTP. These observations show that ARGs can be persistent in the aquatic environment even far downstream the point source of pollution (>10 km).  further , leadingsul1 and sul2, however, at higher relative abundance at Site 3 (both genes at 3%) and Site 4 (2 and 4.7%) and a more rapid drop from Site 4 to Site 5 (both 0.3%). The relative abundance of both genes was significantly increased at Site 3 and 4 when compared to the pristine Site 1 (Dunn’s test: p ≤ 0.01). The elevated relative abundance in vicinity to the discharge point indicates that ARG-carrying microorganisms were mainly accumulated in biofilm directly at the WWTP, whereas those remaining in surface waters over longer distance did not or only barely enter biofilms at a later stage. Similar findings were reported by sul1 and sul2 in biofilms in close vicinity to the discharge point suggests that this was not the case in our study.In biofilm samples, similar relative abundances were observed for intI1 was detectable upstream and downstream of the WWTP without significant differences in the relative abundance between sampling sites. However, wastewater discharge increased its relative abundance from a median of 0.9% at Site 2 to 1.5% at Site 4. All anthropogenically impacted ecosystems have become polluted with the clinical class 1 integron in recent years  can be found below: SH, NM, JM, and H-HR conceived the study and the experimental design. SH performed the sampling, sample treatment, and data analysis and wrote the manuscript with contributions from NM and JM. NM and SH supervised during the whole study duration. All authors contributed to the manuscript revision and read and approved the submitted version.