Antibiotic type and dose variably affect microbiomes of a disease-resistant Acropora cervicornis genotype

Antibiotic type and dose variably affect microbiomes of a disease-resistant Acropora cervicornis genotype | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Antibiotic type and dose variably affect microbiomes of a disease-resistant Acropora cervicornis genotype Sunni Patton, Denise Silva, Eddie Fuques, Grace Klinges, Erinn Muller, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5384505/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in Environmental Microbiome → Version 1 posted 10 You are reading this latest preprint version Abstract Background As coral diseases become more prevalent and frequent, the need for new intervention strategies also increases to counteract the rapid spread of disease. Recent advances in coral disease mitigation have resulted in increased use of antibiotics on reefs, as their application may halt disease lesion progression. Although efficacious, consequences of deliberate microbiome manipulation resulting from antibiotic administration are less well-understood – especially in non-diseased corals that appear visually healthy. Therefore, to understand how healthy corals are affected by antibiotics, we investigated how three individual antibiotics, and a mixture of the three, impact the microbiome structure and diversity of a disease-resistant Caribbean staghorn coral ( Acropora cervicornis ) genotype. Over a 96-hour, aquarium-based antibiotic exposure experiment, we collected and processed coral tissue and water samples for 16S rRNA gene analysis. Results We found that antibiotic type and dose distinctively impact microbiome alpha diversity, beta diversity, and community composition. In experimental controls, microbiome composition was dominated by an unclassified bacterial taxon from the order Campylobacterales , while each antibiotic treatment significantly reduced the relative abundance of this taxon. Those taxa that persisted following antibiotic treatment largely differed by antibiotic type and dose, thereby indicating that antibiotic treatment may result in varying potential for opportunist establishment. Conclusion Together, these data suggest that antibiotics induce microbiome dysbiosis – hallmarked by the loss of a dominant bacterium and the increase in taxa associated with coral stress responses. Understanding the off-target consequences of antibiotic administration is critical not only for informed, long-term coral restoration practices, but also for highlighting the importance of responsible antibiotic dissemination into natural environments. Acropora cervicornis antibiotics disease-resistance microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Host-associated microorganisms often confer benefits that augment host development and physiology, protect against pathogen infection, or provide other desirable fitness advantages such as feeding adaptations and phenotypic plasticity [ 1 – 7 ]. Microbiome structure and influence are not unilateral, however, and intricate host-microbe-environment interactions each contribute to host fitness, stable microbiome structure, and cohesion between the host and host-associated microbial counterparts (recognized as the holobiont) [ 8 – 10 ]. Under normal circumstances, the resident microbiota of healthy hosts often provides an additional buffer against minor disturbances and environmental fluctuations [ 11 , 12 ]. However, major disruptions to these relationships often support opportunist invasion, infection, and/or microbiome dysbiosis. Across systems, microbiome dysbiosis is thought to be an indicator of imbalance between beneficial and harmful bacteria and is often hallmarked by increased stochastic dispersion and reduced diversity [ 13 – 15 ]. Should a disturbance subside, microbiomes may return to pre-disturbance states, or arrive at new stable states if favorable conditions are met, or even develop a level of resistance to similar subsequent disturbances [ 16 – 19 ]. In other cases, severe dysbiosis may increase host susceptibility to future disturbances and disease or result in alterations in physiological development trajectory [ 20 , 21 ]. Given the range of potential outcomes, it is essential to understand how host-associated microbiomes facilitate disturbance resistance and resilience, and how alterations in microbe-host and microbe-microbe associations may compromise holobiont function – especially in sensitive or endangered species. Sensitive ecosystems such as coral reefs are consistently and increasingly threatened by a suite of local and global environmental perturbations, resulting in holobiont disturbances that jeopardize coral survivability and the biodiverse landscapes and resources they provide. These threats include an array of biotic and abiotic factors such as thermal anomalies, nutrient pollution and imbalance, sedimentation, and macroalgal overgrowth [ 22 – 26 ]. Furthermore, many environmental stressors are not mutually exclusive and often act synergistically with one another to further exacerbate coral health decline through microbiome dysbiosis [ 27 – 32 ], but see Maher et al., 2020 [ 33 ]. Since the first coral diseases were documented in the 1970s, approximately 40 diseases have been described, with only six having known etiological agents [ 10 , 34 , 35 ]. In many cases, validating a causative agent via Koch’s postulates is difficult due to the obvious differences between laboratory and reef conditions, incomplete understanding of transmission dynamics, as well as culturing limitations [ 36 – 38 ]. Additionally, many coral diseases are likely not caused by a single pathogen, as several are considered polymicrobial or the result of a secondary infection [ 39 – 42 ]. Given the complex association of coral microorganisms, many taxa linked to disease are also found in healthy individuals, further obscuring disease etiology [ 43 – 45 ]. Owing to the array of diseases with undetermined causes, and difficult diagnostic methods, restoration efforts largely focus on immediate disease remediation. Two highly transmissible diseases at the forefront of disease remediation efforts are Stony Coral Tissue Loss Disease (SCTLD) and White Band Disease (WBD). First characterized in Florida in 2014, SCTLD is hallmarked by unique disease epidemiology and often results in rapid mortality [ 46 – 48 ]. This disease affects more than 20 coral species throughout the Atlantic – many of which are considered endangered by the International Union for Conservation of Nature Red List [ 46 , 49 ]. Despite the broad species range of SCTLD, this disease has not yet been identified in Acropora corals such as A. cervicornis and A. palmata. These species are instead plagued by WBD, which has been responsible for the large-scale population mortality since the late 1970s [ 50 ]. To control the spread of diseases, recent efforts have focused on utilizing broad-spectrum antibiotics, namely ampicillin and amoxicillin pastes [ 51 – 55 ]. Given the widespread and elusive nature of these diseases, antibiotics are being utilized not only as a way to suggest a bacterial component of the disease, but also to provide an immediate solution while the etiological agent(s) remain unidentified. Amoxicillin pastes have demonstrated clear efficacy in short (~ 2 weeks) and long-term (~ 1 year) in-situ and ex-situ experiments, although treatment efficacy may be dependent on species – likely due, in part, to morphological characteristics not conducive to antibiotic application [ 51 , 53 – 55 ]. Treatment regimens differ across studies, yet each consistently reports that amoxicillin pastes slow or halt disease lesion progression into a quiescent state [ 54 , 55 ]. Despite the greater than 90% success rate in many cases, these intervention strategies are likely only a temporary solution as antibiotic administration does not prevent new lesion formation [ 53 – 55 ]. This may indicate that the current antibiotic does not target the true causative agent, that retreatment strategies require further optimization, or that the waterborne pathogen(s) are transmitted to other parts of the coral colony prior to treatment intervention. In addition to the ecotoxicological and antibiotic resistance concerns of antibiotic contamination in reef systems, several studies have noted that antibiotic-induced disruption of coral microbiomes results in a diminished capacity to withstand subsequent stressors such as heat stress and transplantation to a natural system [ 12 , 56 – 58 ]. Disruptions to the holobiont manifest as increased transcriptional stress responses from both the coral host and algal symbiont and decreased bacterial diversity which, when combined, reduces heat tolerance and upregulates immune response genes [ 57 , 59 ]. Therefore, with the threat of subsequent and concurrent disturbances, it is imperative to pair microbiome studies with antibiotic interventions to understand how both target and non-target microbiomes are affected. In this study, we investigated how a 96-hour exposure to one of two concentrations of ampicillin, streptomycin, ciprofloxacin, and a mixture of the three, affects microbiome composition and diversity in a disease-resistant Acropora cervicornis genotype as a representative off-target species. Our results suggest that antibiotics reduce dominant taxa and allow for potentially harmful bacteria to proliferate. Additionally, they suggest that antibiotic dose range-finding is essential for future disease interventions, as different concentrations of the same antibiotic may result in distinct microbial community profiles. Methods Experimental design A total of 120 coral fragments of a disease-resistant Acropora cervicornis genotype (ML-7) were collected from the offshore coral nursery at Mote Marine Laboratory’s International Center for Coral Reef Research and Restoration (IC2R3) in Summerland Key, Florida. Fragments were attached to ceramic plugs using cyanoacrylate glue and acclimated to ex-situ raceway conditions for 14 days in IC2R3’s Climate and Ocean Acidification Simulator (CAOS) system before experimentation. Following acclimation, coral fragments were each randomly assigned to one of nine experimental treatments: ‘blank’ (no treatment), ‘ampicillin low’ and ‘ampicillin high’ (final tank concentrations of 10 mg/L and 100 mg/L, respectively), ‘streptomycin low’ and ‘streptomycin high’ (10 mg/L and 100 mg/L), ‘ciprofloxacin low’ and ‘ciprofloxacin high’ (due to high potency, 1 mg/L and 10 mg/L concentrations were used for low and high doses, respectively), and ‘mixture low’ and 'mixture high’. ‘Mixture low’ and ‘mixture high’ were comprised of a combination of low or high doses of ampicillin, streptomycin, and ciprofloxacin, respectively. Fragments were then added to corresponding non-flow-through 5-gallon aquaria containing 6 L of water. Aside from the no treatment which had a total of six tanks, each treatment had three replicate tanks with four coral fragments in each tank (i.e. n = 24 for ‘blank’ treatment and n = 12 for each other treatment). Each tank was then randomly distributed across three outdoor raceways. Samples were collected prior to antibiotic treatment (Time 0), and at 12, 24, 48, and 96 hours during treatment (Supplementary file 1 Figure S1 ). Because experimental aquaria were enclosed, tank water was manually refreshed by replacing half of the volume of water at each sampling time point and 72 hours after initial antibiotic treatment. To ensure a consistent, four-day antibiotic challenge, additional half-doses of antibiotics were added each time the water was changed, such that the final antibiotic concentration in the tanks was consistent throughout the experiment. After time 0 sampling, all four coral fragments within one of the control tanks (Blank 1), were sacrificed for other analyses; therefore, at each subsequent time point, only five control tanks were sampled (n = 20). Antibiotic preparation and dosing Three broad-spectrum antibiotics (ampicillin, streptomycin sulfate, and ciprofloxacin anhydrous) were chosen due to their diverse mechanisms of action and bactericidal nature. Ampicillin is a beta-lactam antibiotic that inhibits cell wall synthesis [ 60 ]; streptomycin is an aminoglycoside that interferes with protein synthesis [ 61 ]; and ciprofloxacin is a fluoroquinolone that inhibits DNA gyrase which ultimately impedes DNA replication [ 62 ]. Concentrated stock solutions of ampicillin and streptomycin (120 g/L), and ciprofloxacin (12 g/L) were made using 0.2 µm filter-sterilized seawater. For the initial, full-potency antibiotic dose (immediately after T0), 15 mL high-dose (40 g/L for ampicillin and streptomycin, and 4 g/L for ciprofloxacin) working solutions were prepared. 15 mL low-dose working solutions were also prepared by diluting 1.5 mL of the high-dose concentration in filter-sterilized seawater. When added to the tank, the low-dose concentrations were tenfold lower than the high. High-dose antibiotic mixture doses were prepared by combining 5 mL of each concentrated stock solution, and low doses of the antibiotic mixture were again prepared by diluting 1.5 mL of the high-dose concentration in filter-sterilized seawater. 7.5 mL of the working solutions were used for all subsequent doses. At each time point, blank tanks were supplemented with equal volumes of the same filter-sterilized seawater that was used to make the antibiotic solutions. Sample collection and processing At each time point, before sampling coral fragments, three liters of aquaria water were removed from each tank to conduct a half-tank water change – one liter of which was retained and filtered using a peristaltic pump and 0.22 µm Sterivex filter unit (model SVGP01050 Millepore Sigma) for 16S rRNA gene analysis of the bacteria in the water column. Each Sterivex filter was placed in sterile bags (Whirl-Pak) and stored at -80°C until processing. Avoiding the apical polyp and any previous wounds, two verrucae were snipped from each coral using sterile bone cutters and placed in a 1.2 mL cryogenic tube in 500 µL of DNA/RNA Shield (Zymo Research, Irvine, CA, USA). The samples were promptly stored at -80°C until they were processed using the DNeasy 96 PowerSoil Pro kit (Qiagen) using the OT-2 liquid handling system (Opentrons). DNA extraction of water samples Sterivex filter cartridges were defrosted and sealed at one end using a Luer-Lok cap. Then 460 µL of extraction buffer solution (composed of 40 µL proteinase K, 200 µL of buffer AL provided by the Qiagen Blood & Tissue kit, and 220 µL of PBS) was added to the column. The other end was then sealed with another Luer-Lok cap, and both ends were wrapped in parafilm to avoid leakage. The filled filter cartridges were then attached horizontally to the rotator inside of a hybridization incubator (Robbins Scientific Model 400) and incubated at 56°C for 4 hours while rotating at 20 rpm. After incubation, the inlet cap was removed, and the inlet port of the cartridge was placed into a 2 mL tube and sealed with parafilm. The filter cartridge with the attached 2 mL tube was placed into a sterile 50 mL conical tube and centrifuged at 5,000 x g for 2 min to elute the extracted DNA from the filter cartridge. After centrifugation, the 2 mL tube was detached from the filter cartridge and stored for downstream use. 16S rRNA gene amplicon library preparation The V4 region of the 16S rRNA gene from coral and seawater samples was amplified via a one-step polymerase chain reaction (PCR) approach. 25 µL PCR reactions were made using 10 µL of Platinum II Taq Hot-Start PCR Master Mix (2x) (Invitrogen) master mix, 2.5 µL each of 10 µM primers 515F (5’ - GTGYCAGCMGCCGCGGTAA − 3’) and 806R (5’ - GGACTACNVGGGTWTCTAAT − 3’) [ 63 ] with attached barcodes for dual-indexed libraries (for details see Silva et al., 2023 [ 64 ]). Three negative controls were included in each 96-well plate for a total of 21 negative controls. The template DNA was amplified using the following thermocycler parameters: initial denaturation at 94°C for 2 minutes, followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 60°C for 30 seconds, and extension at 68°C for 60 seconds, followed by a single final extension step at 68°C for 10 minutes. Successful amplification was confirmed by visualizing the PCR product on a 1.5% agarose gel. Amplified PCR products were then purified using Agencourt AMPure XP beads (Beckman Coulter) following the manufacturer’s guidelines. However, 80% ethanol was used for the washing steps, and a 5-minute drying step was included after the final ethanol wash to evaporate excess ethanol. Purified libraries were again visualized via gel electrophoresis before quantifying DNA concentration using the BioTek Synergy H1 multi-mode plate reader. Libraries were pooled at equimolar concentrations before paired-end 2x300 bp sequencing using the Illumina NextSeq 2000 P1 system at Oregon State University’s Center for Qualitative Life Science (CQLS). Raw read quality control and sequence preprocessing A total of 602 samples (21 of which were negative controls) were demultiplexed by the CQLS. Individual forward and reverse quality profiles were assessed using FastQC and MultiQC [ 65 ]. Primer sequences were removed from forward and reverse reads using a two-step cutadapt approach to remove forward and reverse primer sequences, as well as their reverse complements [ 66 ]. Reads were imported into RStudio (v. 4.3.0) for subsequent quality control processing. Using DADA2 (v. 1.28.0) [ 67 ], low-quality sequences were edited and filtered using several steps: 1) First, to remove low-quality bases, reads were truncated at the 3’ end at 245 bp and 230 bp for the forward and reverse reads, respectively, based on MultiQC reports. 2) Then low-quality reads were removed when a quality score of 2 was identified. 3) Then maximum expected error was calculated and any reads that exceeded a maximum error rate of 2 were also removed. To account for the large number of samples and reads, we used five times the number of bases to estimate the sequencing error than the default. Sample sequence identity was then inferred by DADA2:: dada using default parameters. Contigs were then assembled, and only those within the amplicon size target range (251–255 bp for coral samples and 253–254 bp for seawater samples) were used in further analyses. Before taxonomic assignment, sequences identified by DADA2 as chimeras were omitted from subsequent analysis. Taxonomy was assigned down to the genus level using the SILVA nr 99 v138.1 training set [ 68 ]. When possible, species-level taxonomy was also assigned using the SILVA Species Assignment v138.1 [ 68 ]. Those sequences identified as chloroplast or mitochondria, or those that were not annotated beyond the Kingdom level, were excluded. A phyloseq object was then created using the phyloseq package (v. 1.44.0) in R [ 69 ]. Through the ‘combined detection method’ in the decontam package (v. 1.20.0; Supplementary file 1), contaminants were identified on the basis of both prevalence and quantification thresholds from negative control samples and were subsequently removed from all samples [ 70 ]. After contaminant ASVs were removed, negative controls were excluded from downstream analysis. Due to high experimental replication, taxa that had both low frequency (appearing in only one sample) and low abundance (reads within the first quartile of read distribution) were removed. Samples with fewer than 1000 reads after all quality control filtering (8) were removed from the analysis. After filtering, 5,660,802 reads and 2,962 ASVs across 573 coral samples remained. Antibiotic treatment had a significant effect on library size (i.e., number of reads) across treatment groups at each time point except for T0 (p = 2.06e − 7 , p = 6.92e − 8 , p = 9.01e − 9 , and p = 0.0005 for T12, T24, T48, and T96, respectively; Kruskal-Wallis), with trends becoming especially apparent after all quality control steps (Supplementary file 1 Figure S2 ). Therefore, to account for differences in library size among samples, the rrarefy function from the vegan package [ 71 ] was used to randomly subsample counts data to 5000 reads, as a majority of the observed richness was captured by 5000 reads (Supplementary file 1 Figure S3). For samples with fewer than 5000 total reads (70 samples with an average read depth of 3,404 reads), no random subsampling occurred, and all reads were used. Microbiome and Statistical Analyses Alpha Diversity and Microbiome Relative Abundance Observed Richness, Shannon Diversity, Inverse Simpson, and Faith’s Phylogenetic Diversity (PD) were all calculated at the genus level using the estimate_richness function in the phyloseq package. All alpha diversity measures were calculated using the rarefied phyloseq object described above. For brevity, Shannon Diversity is presented here at the genus level, although other metrics can be seen in Figure S5 in Supplementary file 1. To quantitatively evaluate how coral fragments responded to each antibiotic treatment over time, a linear mixed-effect model was created using the lme4 R package (v. 1.1.35.1) [ 72 ]. In this model, time, treatment (antibiotic plus dose), and their interaction were set as fixed effects, while tank and coral sample ID were set as nested, random effects (Supplementary file 1). Pairwise comparisons were made using the emmeans package (v. 1.10.0), with the Tukey-Kramer p-adjustment method [ 73 ]. Pruned, rarefied data were also used to calculate relative abundance measures for each antibiotic treatment group. After taxa counts were transformed to relative abundances, the top ten most abundant taxa across the samples were determined. Treatment replicates were then merged to calculate the mean relative abundance of the top ten most abundant taxa across all samples. Beta Diversity and Dispersion Beta diversity analyses were performed using pruned, unrarefied data that were robust centered log-ratio (rCLR) transformed data using the microViz package to account for data compositionality and sparsity [ 74 , 75 ]. Transformed data were ordinated using a Principal Component Analysis (PCA). Differences in beta diversity between sample groups within time points were identified via a permutational analysis of variance (PERMANOVA) using adonis2 . The pairwise.adonis package was used for pairwise comparisons, and p-values were adjusted according to the false discovery rate (fdr) formula [ 76 ]. Beta dispersion (as distance to centroid) was determined using Euclidean distances that were calculated from the rCLR-transformed dataset, thereby producing robust Aitchison distances. Statistically significant differences were determined using the betadisper and permutest functions in the vegan package with fdr p-value correction [ 71 ]. Differential Abundance Differential abundance analysis was performed using ANCOM-BC2 on pruned, unrarefied data to identify taxa whose relative abundances were significantly different [ 77 ]. For each treatment, all time points (T12 - T96) were combined and compared to all pretreatment (T0) samples. Repeated sampling was accounted for by including coral ID as a random effect in the differential abundance models. Network Analysis Data subsets were created for each treatment using the original, unrarefied, unpruned phyloseq object. Time 0 samples were excluded from analysis, as corals at this time point had not yet been exposed to antibiotics. Before network construction, taxa that only appeared in one sample were removed. Microbial co-occurrence networks were then created using the microeco R package [ 78 ]. Networks were created using the SpiecEasi method with Meinhausen and Bühlmann (MB) neighborhood selection to calculate taxon co-occurrence at the ASV level [ 79 ]. Network centrality measures were calculated using functions within the igraph and meconetcomp packages [ 80 , 81 ]. Networks were then filtered to only include the top three most relatively abundant ASVs and their interactions to understand how each treatment affected the co-occurrence relationships among them. These ASVs included Campylobacterales (ASV1), Helicobacteraceae Family (ASV3), Phaeobacter (ASV4). Full networks were visualized using Cytoscape [ 82 ] and can be viewed in Figure S8 in Supplementary file 1 or as interactive network plots on NDEx following the link provided in the availability of data and materials section below. Results Antibiotics reduce a dominant, unclassified Campylobacterales ASV Microbiomes of T0 (pre-treatment) corals, and those of all time points within the ‘blank’ (control) treatment group, were dominated by a single bacterial taxon from the order Campylobacterales (ASV1) (Fig. 1 ). The mean relative abundance of this bacterium in ‘blank’ samples remained high over the course of the experiment, with mean relative abundance ranging from 28.25 ± 31.00% at T0 to 60.12 ± 29.18% at T96 (Fig. 1 and Supplementary file 2 Table S1 ). However, in all antibiotic treatment groups, regardless of dose, the mean relative abundance of ASV1 was markedly reduced (Fig. 1 ). Most antibiotic treatment groups showed a reduction of this taxon by 12 hours (T12) after the initial antibiotic dose, and this reduction was maintained throughout the remaining time points. Despite each antibiotic reducing the mean relative abundance of the uncharacterized Campylobacterales ASV, the bacterial taxa that then increased in relative abundance differed among antibiotic type and dose. For example, the ‘mixture low’ samples displayed higher relative abundances of the genus P30B-42 (13.15 ± 21.20% at T12) than ‘blank’ samples (1.07 ± 2.90% at T12) while a single Alteromonas taxon became more dominant in ‘ampicillin low’ and ‘streptomycin high’ samples after the initial antibiotic administration compared to ‘blank’, mixture, and ciprofloxacin treated samples (Fig. 1 ). Although already found at relatively low abundance in both T0 (5.94 ± 10.42%) and ‘blank’ samples over time (5.04 ± 11.84%), a single ASV from the Helicobacteraceae family (ASV3) was almost entirely eliminated by T96 by all antibiotic treatments, except for in the ciprofloxacin treatment group in which the reverse pattern was identified (Fig. 1 ). By T96 for both the ‘ciprofloxacin low’ and ‘high’ treatment groups, this Helicobacteraceae taxon became the dominant taxon (30.52 ± 43.57% and 26.31 ± 26.59%, respectively) (Fig. 1 ). Detailed relative abundance patterns for individual corals are shown in Supplementary file 1 Figure S4. Time and antibiotic treatment differentially impact coral microbiome alpha diversity Statistical analyses of Shannon diversity revealed that time, and the interaction of time and treatment, significantly affected the combined richness and evenness (Shannon diversity) (LMEM, p = 0.002; Supplementary file 2 Table S2 ). Upon further pairwise analysis, although time was a significant driver of differences in Shannon diversity, no significant differences were observed in the ‘blank’ treatment group over time (Fig. 2 , Supplementary file 2 Table S3). Similarly, no significant differences in Shannon diversity between time points in ciprofloxacin-treated samples were identified for either dose (Fig. 2 ). For ampicillin and streptomycin, changes in alpha diversity appeared to be largely dose-dependent, as significant increases in Shannon diversity were observed only in the low doses of each (Fig. 2 ). In ‘ampicillin low’ samples, at time points T12, T24, and T96, Shannon diversity increased significantly compared to T0 (Fig. 2 , Pairwise EMM, p = 0.001, p = 0.002, and p = 0.02, respectively; Supplementary file 2 Table S3). ‘Streptomycin low’ samples similarly displayed increased alpha diversity over time compared to the pre-treatment time point with significant differences at T12, T24, and T48 (Fig. 2 , Pairwise EMM, p = 0.006, p = 0.018, p = 0.03, respectively; Supplementary file 2 Table S3). Unlike ampicillin and streptomycin, where significant increases in alpha diversity were observed in the low dose, one significant comparison was found in the high-dose mixture treatment group between T24 and T96 in which diversity was reduced in the later time point (Fig. 2 , Pairwise EMM, p = 0.026, Supplementary file 2 Table S3). Although not statistically significant, ‘mixture high’ was the only treatment group that displayed decreased diversity at T96 compared to T0 (Fig. 2 ). Antibiotic treatment results in distinct shifts in coral microbiome beta diversity PERMANOVA identified time, treatment, and the interaction between time and treatment as drivers of differences in beta diversity (Supplementary file 2 Table S4). Time also significantly affected beta diversity in ‘blank’ samples (Supplementary file 2 Table S4). Therefore, comparisons of community dissimilarity were made within time points, to identify significant differences between antibiotic doses compared to untreated samples subjected to the same tank residence time. At each time point, aside from T0, significant differences in beta diversity were observed between both doses of the antibiotic mixture and streptomycin relative to ‘blank’ samples (Fig. 3 ). Dose-dependent community differences were detected at T96 between ‘mixture low’ and ‘mixture high’ (p = 0.03; Fig. 3 ; Supplementary file 2 table 5), and at T24, T48, and T96 between ‘streptomycin low’ and ‘streptomycin high’ (p = 0.001; Fig. 3 ; Supplementary file 2 table 5). For ampicillin-treated samples, significant differences in beta diversity were detected beginning at T24 where ‘ampicillin low’ and ‘ampicillin high’ community profiles were distinct from ‘blank’ samples (p = 0.002), yet not significantly different from one another (Fig. 3 , Supplementary file 2 Table S5). At times 48 and 96, however, ‘ampicillin low’, ‘ampicillin high’, and ‘blank’ groups displayed significantly different microbiome community structures – indicating both treatment and dose-dependent responses at these later time points (p = 0.001 at T48, p = 0.002 at T96, Fig. 3 , Supplementary file 2 Table S5). The community composition of the ‘ciprofloxacin high’ samples significantly differed from that of the ‘blank’ samples at T12 (p = 0.012, Fig. 3 ), yet not for the low dose. At all subsequent time points, both ‘ciprofloxacin high’ and ‘ciprofloxacin low’ samples exhibited distinct community clustering from untreated samples (p = 0.002, Fig. 3 ), although no dose-dependent differences were observed. All antibiotics also displayed significant differences in within-group variation, or beta dispersion, in at least one time point. The direction of differences in dispersion, however, depended on the antibiotic as well as dose. At time T48, ‘mixture high’ samples displayed significantly reduced dispersion compared to ‘blank’ samples (p = 0.027, Fig. 3 ), while ‘ampicillin high’ displayed significantly increased dispersion compared to ‘blank’ and ‘ampicillin low’ samples (p = 0.03 and p = 0.003, respectively, Fig. 3 ). ‘Streptomycin low’ samples had significantly lower beta dispersion compared to ‘blank’ samples at T96 (p = 0.02, Fig. 3 , Supplementary file 2 Table S6), while ‘streptomycin high’ was not distinct from the ‘blank’ group. Low-dose ciprofloxacin samples displayed increased dispersion compared to ‘blank’ and high-dose samples at both T24 (p = 0.018 and 0.03, respectively) and T96 (p = 0.0015, Fig. 3 ). Patterns in coral bacterial differential abundance are dependent upon antibiotic type and dose No taxa were differentially abundant across time in the ‘blank’ samples (Fig. 4 , Supplementary file 1 Figure S6). However, variable patterns in differential abundance were seen based on antibiotic type and dose. Every antibiotic treatment, at all doses, resulted in a significant reduction of the unclassified Campylobacterales ASV1 (Fig. 4 ), with the largest negative log-fold change occurring in ‘mixture low’ with a log-fold change of -3.9 (Supplemental Table 7). At least one dose within each antibiotic group, with the exception of ciprofloxacin, also resulted in the reduction of ASV3 from the Helicobacteraceae family (also within the Campylobacterales order) (Fig. 4 ). ‘Mixture high’ and ‘mixture low’ and ‘ciprofloxacin high’ and ‘ciprofloxacin low’ also all displayed reduced abundance of Alteromonas (ASV2), while ‘streptomycin high’ was the only group in which this taxon increased in relative abundance. Overall, samples treated with the antibiotic mixture displayed the largest number of taxa that were significantly reduced, with five and four taxa reduced in ‘mixture low’ and ‘mixture high’ groups, respectively (Fig. 4 ). A taxon from the genus Phaeobacter (ASV4) (formerly Nautella ) was reduced in both mixture doses, and a taxon from the Altermonadaceae family (ASV18) decreased in ‘mixture low’ (Fig. 4 ). Apart from the Alteromonas taxon (ASV2) that had significantly higher relative abundance in ‘streptomycin high’ samples, only three other taxa were significantly increased by antibiotic treatments. A taxon from the family Hyphomonadaceae (ASV12) increased significantly in ‘mixture low’, ‘ampicillin low’, and ‘ciprofloxacin low’ samples relative to pre-treatment samples. ‘Mixture high’ and ‘ampicillin low’ both displayed elevated abundances of a taxon from the Caedibacter taeniospiralis group (ASV9) (Fig. 4 ). Differential abundance patterns for each treatment over time can be seen in Supplemental Fig. 6. Water and coral microbiome compositions remain distinct from one another before and after antibiotic treatment Prior to antibiotic treatment, coral and water samples displayed distinct microbiome structures (Fig. 5 A and 5 B). After the 96-hour antibiotic exposure experiment, the coral and water samples from the ‘blank’ samples maintained this separation (Fig. 5 ). Furthermore, antibiotic exposure appeared to shift both water and coral microbial communities away from their respective untreated control groups (Fig. 5 A). Interestingly, antibiotic administration appeared to somewhat homogenize the bacterial communities between coral and water samples, although each group remained significantly dissimilar from one another (Fig. 5 A, Supplementary file 2 Table S8). This convergence, however, appears to be driven by the ampicillin groups, as the coral and water were more similar in this treatment than in other antibiotic groups (Supplemental Fig. 7), or due to the residence time of the corals in the aquaria at the final time point. Additionally, we found that the antibiotic-induced loss of the dominant Campylobacterales ASV in corals was not mirrored in the water samples, nor was that taxon found to be established in the water column following depletion from the coral host (Fig. 5 C). In fact, the Campylobacterales ASV was present at very low abundance and prevalence in all water samples. This ASV was found in 83% of coral samples and accounted for 23.6% of the total reads, while it was found in 48.6% of water samples and only contributed to 0.04% of the total reads. Genera such as Alteromonas and Phaeobacter that were identified in coral samples, were identified in relatively high abundance in water samples (Fig. 5 ). Yet, interestingly, these taxa were largely eliminated from the water column by each antibiotic treatment, whereas the reduction of these taxa was more varied across treatment groups in the coral samples (Fig. 1 and Fig. 5 ). Minor taxa primarily drive network structure and function Despite being the most abundant taxon in the control samples, Campylobacterales ASV1 was not identified as a microbiome network hub node in any of the five networks according to centrality metrics such as degree, closeness centrality, or eigenvector centrality (Supplemental Table 10). This taxon had eigenvector centrality scores of 7.99e-08, 4.77e-03, 2.37e-04, and 2.7e-04 (scores range from 0–1, with more influential taxa having scores closer to 1) for blank, ampicillin, streptomycin, and ciprofloxacin networks, respectively, and did not have any significant connections in the mixture network. Instead, those taxa with eigenvector centrality scores of 0.75 or higher were considered hub nodes. These nodes were identified as Chryseobacterium (ASV664) and Winogradskyella (ASV25) in the blank network, Caenarcaniphilales Order ASV406 and Woesia (ASV232) in the mixture network, Aquibacter (ASV37), ASV654 from the Stappiaceae family, and Labrenzia alexandrii (ASV367) in the ampicillin network, Pseudoaminobacter (ASV86) and Aestuariibacter (ASV1057) in the streptomycin network, and Tropicibacter (ASV329) and ASV411 from the Cryomorphaceae family in the ciprofloxacin network. Degree was also measured to identify highly connected taxa. The taxa with the most connections in the blank, mixture, ampicillin, streptomycin, and ciprofloxacin networks were Blastopirellula (ASV503) and MBIC10086 (ASV2773) (degree = 11), Dadabacterales order ASV485 (degree = 8), Muricauda ASV21 (degree = 11), Pseudoalteromonas (ASV555) (degree = 14), Rhodobacteraceae family (ASV263) (degree = 16), respectively. Each network was then subset to only include the top three most abundant taxa ( Campylobacterales order ASV1, Alteromonas ASV2, and Helicobacteraceae family ASV3) to observe how their relationships changed across networks. Several positive co-occurrence relationships observed in the control network were also seen across the treatment networks. The co-occurrence between the Alteromonas ASV2 and an unclassified ASV18 from the Alteromonadaceae family was seen in each of the networks (Fig. 6 ), and was, in fact, the only relationship conserved in all networks. Interestingly, although this interaction was present in each of the networks, its structural and/or functional importance does not appear consistent among the networks, as the relationship between ASV18 and ASV2 forms a distinct cluster away from the main network in the mixture network, whereas the co-occurrence relationship is more central in all other networks (Supplemental Fig. 8). The positive relationship between ASV1 and ASV3 ( Helicobacteraceae family) in the blank network was also detected in the streptomycin network. The co-occurrence of ASV1 and JGI_0000069-P22 (ASV20) was detected in both blank and ciprofloxacin networks, while the co-occurrence of the Helicobacteraceae ASV3 and ASV20 was identified in the blank, mixture, and ampicillin networks. Lastly, the blank and ampicillin networks shared the positive co-occurrence between Helicobacteraceae ASV3 and Patescibacteria ASV14. Interestingly, Alteromonas ASV2 and Phaeobacter ASV4 negatively co-occurred in the blank network, while the opposite relationship was observed in the ampicillin network. In the ampicillin network, ASV1 had a negative co-occurrence with ASV9 from the Caedibacter taeniospiralis group. Discussion Campylobacterales in disease-resistant Acropora cervicornis Genera within the order Campylobacterales , namely Campylobacter and Arcobacter , have been associated with a range of diseases in various coral species [ 36 , 83 – 88 ], although none have been implicated as primary pathogens. Furthermore, Campylobacterales metagenome-assembled genomes (MAGs) most closely related to the genus Arcobacter were enriched in diseased coral tissue compared to apparently healthy tissue samples [ 85 ]. However, since 2019, an uncharacterized ASV belonging to the order Campylobacterales dominated the microbiomes of some specific genotypes of A. cervicornis , particularly in the same genotype studied here; however, there is no evidence suggesting that these corals are diseased [ 89 ]. In fact, this genotype is one of the few A. cervicornis genotypes found to be disease-resistant both ex situ and in situ [90–92, E. Bartels, pers. comm. ]. Interestingly, despite the high relative abundance of Campylobacterales , this dominance is thought to represent a shift over time, as corals of this same genotype displayed far more even and diverse microbiome with very low abundance of this Campylobacterales ASV in 2015 [ 93 ]. Given this shift toward a Campylobacterales -dominated microbiome, there may exist at least two microbiome states that are capable of supporting disease resistance in A. cervicornis , yet the implications of reduced microbiome evenness remain unclear. It is possible that the shift toward single-taxon dominance may ultimately be detrimental to the system, as observed in disease-susceptible A. cervicornis genotypes dominated by the intracellular bacterial parasite Ca . Aquarickettsia rohweri [ 93 ]. More likely, the association of this Campylobacterales taxon may be indicative of a newly established, potentially beneficial, symbiotic relationship in response to chronic nutrient enrichment on reefs, as many taxa within the Campylobacterales order are important contributors to sulfur and nitrogen cycling [ 94 – 96 ]. Some members reduce nitrate to ammonium, which is potentially important on reefs as nitrate enrichment alone is known to exacerbate thermal bleaching outcomes and hinder coral growth rates, while slight ammonium enrichment, coupled with natural sources of phosphorous, may be beneficial to coral growth [ 89 , 94 , 97 , 98 ]. In a recent study, the microbiome of the same A. cervicornis genet used in the present study was stable in response to acute nutrient enrichment, including the maintenance of Campylobacterales dominance throughout treatment [ 89 ]. Therefore, it was hypothesized that the microbiome structure of this disease-resistant genotype may provide some tolerance to environmental stressors [ 89 ]. Interestingly, despite the significant loss of this dominant ASV following the antibiotic challenge, there were no immediate signs of host health decline, no striking changes in co-occurrence relationships of this taxon among networks, nor were there drastic shifts in the number of taxa co-occurring with this ASV. This may indicate that the Campylobacterales ASV1 is not a species generalist in the microbiome, as it only interacts with a select few other taxa in the networks [ 99 ]. Given its rapid loss, yet concomitant apparent host health stability, we hypothesize that the Campylobacterales taxon itself is not required to maintain disease resistance and is rather a nonobligatory association. Reduction in a Campylobacterales ASV potentially supports an increase of other taxa In this study, we show that the Campylobacterales ASV1 was highly susceptible to all antibiotic treatments (Fig. 4 ) and that taxa associated with coral stress response increase in relative abundance to presumably inhabit the niche space that the Campylobacterales occupied, or simply appear to increase given the constraints of relative abundance analyses and compositional data. The second-most reduced taxon in this study was from the Helicobacteraceae family, which is also within the Campylobacterales order, and which was reduced in all treatment groups except for ciprofloxacin and ‘streptomycin low’ (Fig. 1 and Fig. 4 ). Among the taxa that were positively enriched in response to antibiotic treatment, P3OB-42 (ASV5; Myxococcales ) and Caedibacter taeniospiralis (ASV9) are of particular interest. The taxon P3OB-42 is hypothesized to play a role in pathogen regulation in A. cervicornis , as higher relative abundances of this taxon were associated with corals that were exposed to disease, yet remained visually unaffected [ 100 ]. In an agricultural system, Myxococcales spp. were also shown to inhibit phytopathogen infection [ 101 ]. Given the putative commensal nature of Myxococcales , the increase in this taxon may result from antibiotic-induced disruption to the coral surface mucus layer (SML), thereby exposing the coral to potential invasion as the SML normally serves as a first line of defense via niche occlusion and antimicrobial properties [ 102 , 103 ]. Interestingly, species within the P3OB-42 genus are also known antibiotic degraders – specifically sulfonamides and beta-lactam antibiotics [ 104 , 105 ] – which may explain the significant enrichment of this taxon in response to a low dose of ampicillin and high dose of the antibiotic mixture (Fig. 4 ). The other notable bacterium that increased in relative abundance following antibiotic treatment was from the Caedibacter taeniospiralis group (closely related to the genus Cysteiniphilum : BLASTn 100% sequence identity), which is a known obligate intracellular symbiont of the paramecium Paramecium tetraurelia , capable of conferring a fitness advantage to the host by producing refractile bodies that kill other paramecia [ 106 , 107 ]. Although this paramecium has not been documented in corals – healthy or diseased – ciliated protozoans are often associated with disease incidence [ 87 , 108 , 109 ]. Recently, the Caedibacter taeniospiralis group was also identified at higher abundances in diseased corals exposed to both white band disease type I and the coral pathogen, Serratia marcescens [ 110 ]. In the present study, and in Young et al. 2023, the Caedibacter taeniospiralis group had lower abundance in controls. Similarly, the closely related taxon, Cysteiniphilum litorale was found in significantly higher relative abundance in WBD-afflicted A. cervicornis (77.5% ± 5.1% SE) compared to healthy corals (2.9% ± 1.2% SE) [ 111 ]. Using machine learning and transmission experiments, this ASV was further identified as a potential WBD pathogen [ 111 ], thereby indicating that the increase of this taxon is likely due to burgeoning opportunistic establishment. Dose-dependent differences in bacterial responses Our results suggest that care must be taken to identify the most appropriate antibiotic dose to treat coral health problems, as we found that different antibiotics result in dramatically different microbial community structures compared to control samples. Further, we found that increased dose does not simply magnify the effect of change but rather can result in strikingly different community compositions – especially after prolonged exposure (Fig. 3 ). This dose-dependent effect was demonstrated by an Alteromonas ASV, which was reduced by the low dose of streptomycin (although not significantly), yet paradoxically proliferated in the high dose (Fig. 1 and Fig. 4 ). A BLASTn search revealed that this ASV shared 100% sequence identity with Alteromonas macleodii (e-value = 4e − 127 ), and strains within this species are known to be either fully resistant to antibiotics including streptomycin or only slightly sensitive to others such as ampicillin [ 112 ]. Given this resistance, it is likely that, in conjunction with the effects of antibiotic photodegradation, the low streptomycin treatment may have been administered at an effective dose. In the high dose, however, resistant strains of this taxon were possibly able to overcome the high concentration of antibiotics and then actively outcompete other bacteria [ 113 ]. It must be noted, however, that there is great diversity at the strain level within A. macleodii ; therefore, without additional genomic information, the mechanisms of this shift remain largely unknown [ 114 ]. Implications of antibiotic use Despite their persistence and accumulation in the environment, antibiotics in aquatic systems are subject to various degradation mechanisms that further affect concentration. Photodegradation, hydrolysis, microbial degradation, and changes in pH and temperature, all contribute to reducing an antibiotic’s half-life [ 115 – 117 ]. In targeted therapeutic applications, these processes pose additional challenges for proper dosing which, as seen in this study, can drastically affect microbiome diversity and composition. Many of these processes, however, do not eliminate antibiotics entirely. Instead, these antibiotics are often reduced to subinhibitory concentrations, which may favor a shift toward microbial antibiotic tolerance and persistence in the system, thereby further complicating disease control [ 118 ]. Many coral diseases either have unknown etiological agents or are thought to be polymicrobial [ 36 , 43 , 83 ]. For this reason, broad-spectrum antibiotics are often employed to target a wide range of bacteria, but their use may have unintended ramifications. Given the variable range of inhibitory concentrations that antibiotics have on specific bacteria, it is difficult to develop a directed therapy that evenly targets each taxon of interest. Perhaps an antibiotic effectively targets one member of the polymicrobial consortia, which in turn provides the opportunity for an antibiotic-degrading bacteria to flourish, and potentially shield the other disease-associated taxa. In a recent study, researchers reported that normally commensal, beta-lactam degrading bacteria in the mouse gut may inadvertently protect a normally antibiotic-sensitive pathogen from the effects of ampicillin through commensal-mediated pathogen shielding [ 119 ]. Although the aforementioned study was conducted in a mouse model, similar mechanisms may be present in other systems. Given that antibiotic treatment does not always result in permanent disease cessation, this may indicate that one or more of the suspected pathogens may be initially susceptible but ultimately receive a level of protection due to antibiotic-degrading bacteria, thereby allowing the pathogen(s) to proliferate further. Disturbances such as antibiotic treatment can destabilize a microbiome not only due to the direct bactericidal effects, but also in ways that transform microbe-microbe interactions. Increased positive co-occurrence patterns may present increased opportunities for positive feedback loops and unchecked proliferation in the microbiome, which are hypothesized to negatively affect microbiome stability, whereas competitive relationships are thought to assist in stability [ 11 , 120 ]. Therefore, in addition to understanding how antibiotics affect the microbiome composition of target and off-target species, intervention strategies must also understand the effects these treatments have on microbial interactions as a whole. Conclusions In this study, we found that following antibiotic perturbation, the abundance of a dominant, unclassified Campylobacterales taxon was significantly reduced by each antibiotic and dose. Despite varying implications of Campylobacterales in coral disease, the taxon described here does not appear to be associated with negative health effects in this coral genotype, although its capacity for commensalism and the implications of its loss remain unknown. Given the ecologically threatened state of many corals, antibiotics provide a reasonable and productive short-term approach to slow disease progression, yet dose range finding and off-target effects must be taken into consideration when assessing the risks and rewards of this approach, as well as how microbiome manipulation may affect a host's long-term ability to combat future disturbances. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Scripts used for bioinformatic and statistical analyses can be found at: https://github.com/pattonsunni/RoL_Antibiotics_G7. The 16S rRNA gene dataset supporting the conclusions of this article is available in the NCBI Sequence Read Archive (SRA) repository under the BioProject accession number PRJNA1165811. Interactive networks can be accessed by the following link https://www.ndexbio.org/#/networkset/bec779a5-7d0f-11ef-ad6c-005056ae3c32?accesskey=ee1db6b84a489b391e5fbcbc8cdf1ac71becd0eabcc5b84ee52b30822da26a74. Competing interests The authors declare that they have no competing interests Funding This work was funded by the U.S. National Science Foundation (NSF) grant awarded to Rebecca L. Vega Thurber (Award Number 2025457), as well as by an NSF Graduate Research Fellowship Program award granted to Sunni Patton (Award Number 2139319). Authors’ contributions S.P. conceptualized the study, performed the investigation, conducted the formal analysis, generated all figures, wrote the original draft of the manuscript, reviewed and edited the manuscript, and acquired funding. 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Williams SD, Klinges JG, Zinman S, Clark AS, Bartels E, Villoch Diaz Maurino M, et al. Geographically driven differences in microbiomes of Acropora cervicornis originating from different regions of Florida’s Coral Reef. PeerJ. 2022;10:e13574. Klinges G, Maher RL, Vega Thurber RL, Muller EM. Parasitic ‘Candidatus Aquarickettsia rohweri’ is a marker of disease susceptibility in Acropora cervicornis but is lost during thermal stress. Environ Microbiol. 2020;22:5341–55. Luijten MLGC, de Weert J, Smidt H, Boschker HTS, de Vos WM, Schraa G, et al. Description of Sulfurospirillum halorespirans sp. nov., an anaerobic, tetrachloroethene-respiring bacterium, and transfer of Dehalospirillum multivorans to the genus Sulfurospirillum as Sulfurospirillum multivorans comb. nov. Int J Syst Evol MicroBiol. 2003;53:787–93. Scholz-Muramatsu H, Neumann A, Meßmer M, Moore E, Diekert G. 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Microbiome differences in disease-resistant vs. susceptible Acropora corals subjected to disease challenge assays. Sci Rep. 2019;9:18279. Dobrovol’skaya TG, Khusnetdinova KA, Manucharova NA, Balabko PN. The structure and functions of bacterial communities in an agrocenosis. Eurasian Soil Sc. 2016;49:70–6. Shnit-Orland M, Kushmaro A. Coral mucus-associated bacteria: a possible first line of defense. FEMS Microbiol Ecol. 2009;67:371–80. Krediet CJ, Ritchie KB, Paul VJ, Teplitski M. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proceedings of the Royal Society B: Biological Sciences. 2013;280:20122328. Chen J, Gao M, Zhao Y, Guo L, Jin C, Ji J, et al. Nitrogen and sulfamethoxazole removal in a partially saturated vertical flow constructed wetland treating synthetic mariculture wastewater. Bioresour Technol. 2022;358:127401. Guo N, Liu M, Yang Z, Wu D, Chen F, Wang J, et al. The synergistic mechanism of β-lactam antibiotic removal between ammonia-oxidizing microorganisms and heterotrophs. Environ Res. 2023;216:114419. Beale GH, Jurand A, Preer JR. The classes of endosymbiont of Paramecium Aurelia. J Cell Sci. 1969;5:65–91. Beier CL, Horn M, Michel R, Schweikert M, Görtz H-D, Wagner M. The Genus Caedibacter Comprises Endosymbionts of Paramecium spp. Related to the Rickettsiales (Alphaproteobacteria) and to Francisella tularensis (Gammaproteobacteria). Appl Environ Microbiol. 2002;68:6043–50. Bourne DG, Boyett HV, Henderson ME, Muirhead A, Willis BL. Identification of a Ciliate (Oligohymenophorea: Scuticociliatia) Associated with Brown Band Disease on Corals of the Great Barrier Reef. Appl Environ Microbiol. 2008;74:883–8. Cróquer A, Bastidas C, Lipscomp D, Rodríguez-Martínez RE, Jordan-Dahlgren E, Guzman HM. First report of folliculinid ciliates affecting Caribbean scleractinian corals. Coral Reefs. 2006;25:187–91. Young BD, Rosales SM, Enochs IC, Kolodziej G, Formel N, Moura A, et al. Different disease inoculations cause common responses of the host immune system and prokaryotic component of the microbiome in Acropora palmata. PLoS ONE. 2023;18:e0286293. Selwyn JD, Despard BA, Vollmer MV, Trytten EC, Vollmer SV. Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning. Environ Microbiol. 2024;26:e16700. El-Moselhy KM, Shaaban MT, Ibrahim HAH, Abdel-Mongy AS. Biosorption of cadmium by the multiple-metal resistant marine bacterium Alteromonas macleodii ASC1 isolated from Hurghada harbour, Red Sea. Archives Des Sci. 2013;66. Rypien KL, Ward JR, Azam F. Antagonistic interactions among coral-associated bacteria. Environ Microbiol. 2010;12:28–39. Koch H, Germscheid N, Freese HM, Noriega-Ortega B, Lücking D, Berger M, et al. Genomic, metabolic and phenotypic variability shapes ecological differentiation and intraspecies interactions of Alteromonas macleodii. Sci Rep. 2020;10:809. Cardoza LA, Knapp CW, Larive CK, Belden JB, Lydy M, Graham DW. Factors Affecting the Fate of Ciprofloxacin in Aquatic Field Systems. Water Air Soil Pollut. 2005;161:383–98. Rodríguez-López L, Cela-Dablanca R, Núñez-Delgado A, Álvarez-Rodríguez E, Fernández-Calviño D, Arias-Estévez M. Photodegradation of Ciprofloxacin, Clarithromycin and Trimethoprim: Influence of pH and Humic Acids. Molecules. 2021;26:3080. Shen Y, Zhao W, Zhang C, Shan Y, Shi J. Degradation of streptomycin in aquatic environment: kinetics, pathway, and antibacterial activity analysis. Environ Sci Pollut Res. 2017;24:14337–45. Andersson DI, Hughes D. Evolution of antibiotic resistance at non-lethal drug concentrations. Drug Resist Updates. 2012;15:162–72. Gjonbalaj M, Keith JW, Do MH, Hohl TM, Pamer EG, Becattini S. Antibiotic Degradation by Commensal Microbes Shields Pathogens. Infect Immun. 2020;88. 10.1128/iai.00012–20 . Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34. Williams SD, Klinges JG, Zinman S, Clark AS, Bartels E, Maurino MVD, et al. Geographically driven differences in microbiomes of Acropora cervicornis originating from different regions of Florida’s Coral Reef. PeerJ. 2022;10:e13574. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.Figuresandmethods.docx Supplementaryfile2.Tables.xlsx Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in Environmental Microbiome → Version 1 posted Editorial decision: Revision requested 11 Mar, 2025 Reviews received at journal 25 Feb, 2025 Reviews received at journal 08 Feb, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers agreed at journal 06 Feb, 2025 Reviewers agreed at journal 05 Jan, 2025 Reviewers invited by journal 27 Dec, 2024 Editor assigned by journal 19 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 03 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5384505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374467203,"identity":"6126ac74-4481-49ca-b6c6-c544b2ef880d","order_by":0,"name":"Sunni Patton","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYLCChAogIQHEPMRrOUOyFsY2UrTIz8g99uDhvMPy8rMbGB+8bSNCi8GNvHSDxG2HDTfcOcBsOJcoLRI5ZhJALQkGEgls0rzEaJGfAdIy53CC/IwE9t9EaWG4AdLScDiB4UYCGzNRWgzOvDGTSDiWDvTLwWbJOeeIcVh7jpnkjxprYIg1H/zwpowYhyEAYwNp6kfBKBgFo2AU4AYA4N4z8htr0dQAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":true,"prefix":"","firstName":"Sunni","middleName":"","lastName":"Patton","suffix":""},{"id":374467204,"identity":"9d65ed20-6da4-4ace-b60b-ee1be48437c2","order_by":1,"name":"Denise Silva","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Denise","middleName":"","lastName":"Silva","suffix":""},{"id":374467205,"identity":"87afd390-3fbf-4523-8870-e5c80437f778","order_by":2,"name":"Eddie Fuques","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Eddie","middleName":"","lastName":"Fuques","suffix":""},{"id":374467206,"identity":"9d7ef001-12f5-428e-ab38-c063a59caf31","order_by":3,"name":"Grace Klinges","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"","lastName":"Klinges","suffix":""},{"id":374467207,"identity":"5181dc5c-053e-4ba6-9fb6-9dd0ae972953","order_by":4,"name":"Erinn Muller","email":"","orcid":"","institution":"Mote Marine Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Erinn","middleName":"","lastName":"Muller","suffix":""},{"id":374467208,"identity":"701448a2-be10-4263-a3b0-e3f0a54a05d4","order_by":5,"name":"Rebecca Vega Thurber","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"Vega","lastName":"Thurber","suffix":""}],"badges":[],"createdAt":"2024-11-04 03:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5384505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5384505/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40793-025-00709-2","type":"published","date":"2025-05-02T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69322386,"identity":"9a40b7b7-7e17-4b46-9d62-2ba6702969e5","added_by":"auto","created_at":"2024-11-19 07:16:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":584319,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance of the top ten most abundant taxa across all antibiotic treatment groups. Each bar represents the average of a minimum of 19 replicates in the ‘blank’ treatment group, and a minimum of 9 replicates in each of the antibiotic treatment groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/2e4ff0e692b12a488bd028c7.png"},{"id":69322388,"identity":"9589fc41-166f-4fc3-8d2f-bd60a32b047c","added_by":"auto","created_at":"2024-11-19 07:16:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":652330,"visible":true,"origin":"","legend":"\u003cp\u003eShannon Diversity calculated at the genus level. Statistical significance was determined by a LMM in which treatment, time, and their interaction were set as fixed effects, while tank and unique coral ID were set as nested, random effects. Pairwise comparisons of estimated marginal means were calculated and the p-value was adjusted using the Tukey method. Significance codes are as follows: 0 ‘***’, 0.001 ‘**’, and 0.01 ‘0.01’.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/6d39aa4bd16836f54f066bba.png"},{"id":69323159,"identity":"915f34e0-48c1-45dd-9aab-8cd93590f095","added_by":"auto","created_at":"2024-11-19 07:24:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":789822,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Principal components analysis ordination of beta diversity based on Euclidean distances of robust centered log-ratio transformed data. ‘Blank’, high, and low doses of each antibiotic were compared within each time point. Ellipses display 95% confidence intervals. Black dots and ellipses at time 0 represent all samples prior to the treatment. Right: Beta dispersion, as distance-to-centroid, by antibiotic treatment group within a time point. Significance levels reflect fdr-adjusted p-values. Significance codes are as follows: 0 ‘***’, 0.001 ‘**’, and 0.01 ‘*’.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/eb63056d9cf65b75233fec06.png"},{"id":69322383,"identity":"c6f74c61-d11c-452a-8f24-3adc7f8d4ebc","added_by":"auto","created_at":"2024-11-19 07:16:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":476715,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot depicting differentially abundant taxa by treatment, as determined by ANCOM-BC2. Each treatment group was compared against all pretreatment samples (time 0). Taxa below the horizontal dotted line were not significantly differentially abundant. Each panel contains all samples from times 12 through 96 within that antibiotic treatment group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/a20a38960fa4293bce6eef43.png"},{"id":69322385,"identity":"9c7f0270-5ee6-4c2d-aaa8-b741a5a844f2","added_by":"auto","created_at":"2024-11-19 07:16:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":676738,"visible":true,"origin":"","legend":"\u003cp\u003eCombined PCA ordination (A) and mean relative abundance plot depicting the top ten most abundant taxa in coral and top ten most abundant taxa in water samples at time 0 (B) and time 96 (C). Beta diversity was calculated based on Euclidean distances of robust centered log-ration transformed data. Ellipses represent 95% confidence intervals. In panel B, each bar represents the average of six samples for water, and 119 samples for coral. In panel C, each water sample bar represents the average of three samples for experimental treatments and five samples for ‘blank’. Each coral sample bar represents the average of 19 samples for ‘blank’, 12 samples for ‘ampicillin low’, ‘ampicillin high’, ‘ciprofloxacin low’, ‘mixture low’, and ‘streptomycin high’, 11 samples for ‘ciprofloxacin high’ and ‘streptomycin low’, and 9 samples for ‘mixture high’.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/52379c178917f4d478908220.png"},{"id":69322390,"identity":"d606993d-8fb3-4166-bf4e-33917f5c710b","added_by":"auto","created_at":"2024-11-19 07:16:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":548314,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot displaying positive and negative microbial co-occurrence patterns among the top three most relatively abundant taxa (\u003cem\u003eCampylobacterales \u003c/em\u003eorder ASV1, \u003cem\u003eAlteromonas \u003c/em\u003eASV2, and \u003cem\u003eHelicobacteraceae \u003c/em\u003efamily ASV3). Co-occurrence patterns were determined using SpiecEasi.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/f3ae531cb4dc4bac4edddaa6.png"},{"id":81987993,"identity":"d8ccb5a2-3748-4bd9-93e2-793dca7d3f78","added_by":"auto","created_at":"2025-05-05 16:07:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4837025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/618225a8-877b-4bdc-b529-97094d1997be.pdf"},{"id":69322391,"identity":"ab2aec0d-3f67-4e58-9e84-97468dfa99c9","added_by":"auto","created_at":"2024-11-19 07:16:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5071635,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.Figuresandmethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/7e5d03621e8cf5ece7023c27.docx"},{"id":69322389,"identity":"e12c93e9-8b96-4d2c-a88f-b913d5f120f1","added_by":"auto","created_at":"2024-11-19 07:16:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1052022,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.Tables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5384505/v1/85b366252f13da82965a130d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antibiotic type and dose variably affect microbiomes of a disease-resistant Acropora cervicornis genotype","fulltext":[{"header":"Background","content":"\u003cp\u003eHost-associated microorganisms often confer benefits that augment host development and physiology, protect against pathogen infection, or provide other desirable fitness advantages such as feeding adaptations and phenotypic plasticity [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Microbiome structure and influence are not unilateral, however, and intricate host-microbe-environment interactions each contribute to host fitness, stable microbiome structure, and cohesion between the host and host-associated microbial counterparts (recognized as the holobiont) [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Under normal circumstances, the resident microbiota of healthy hosts often provides an additional buffer against minor disturbances and environmental fluctuations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, major disruptions to these relationships often support opportunist invasion, infection, and/or microbiome dysbiosis. Across systems, microbiome dysbiosis is thought to be an indicator of imbalance between beneficial and harmful bacteria and is often hallmarked by increased stochastic dispersion and reduced diversity [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Should a disturbance subside, microbiomes may return to pre-disturbance states, or arrive at new stable states if favorable conditions are met, or even develop a level of resistance to similar subsequent disturbances [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In other cases, severe dysbiosis may increase host susceptibility to future disturbances and disease or result in alterations in physiological development trajectory [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given the range of potential outcomes, it is essential to understand how host-associated microbiomes facilitate disturbance resistance and resilience, and how alterations in microbe-host and microbe-microbe associations may compromise holobiont function \u0026ndash; especially in sensitive or endangered species.\u003c/p\u003e \u003cp\u003eSensitive ecosystems such as coral reefs are consistently and increasingly threatened by a suite of local and global environmental perturbations, resulting in holobiont disturbances that jeopardize coral survivability and the biodiverse landscapes and resources they provide. These threats include an array of biotic and abiotic factors such as thermal anomalies, nutrient pollution and imbalance, sedimentation, and macroalgal overgrowth [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, many environmental stressors are not mutually exclusive and often act synergistically with one another to further exacerbate coral health decline through microbiome dysbiosis [\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], but see Maher et al., 2020 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the first coral diseases were documented in the 1970s, approximately 40 diseases have been described, with only six having known etiological agents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In many cases, validating a causative agent via Koch\u0026rsquo;s postulates is difficult due to the obvious differences between laboratory and reef conditions, incomplete understanding of transmission dynamics, as well as culturing limitations [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, many coral diseases are likely not caused by a single pathogen, as several are considered polymicrobial or the result of a secondary infection [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Given the complex association of coral microorganisms, many taxa linked to disease are also found in healthy individuals, further obscuring disease etiology [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOwing to the array of diseases with undetermined causes, and difficult diagnostic methods, restoration efforts largely focus on immediate disease remediation. Two highly transmissible diseases at the forefront of disease remediation efforts are Stony Coral Tissue Loss Disease (SCTLD) and White Band Disease (WBD). First characterized in Florida in 2014, SCTLD is hallmarked by unique disease epidemiology and often results in rapid mortality [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This disease affects more than 20 coral species throughout the Atlantic \u0026ndash; many of which are considered endangered by the International Union for Conservation of Nature Red List [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Despite the broad species range of SCTLD, this disease has not yet been identified in \u003cem\u003eAcropora\u003c/em\u003e corals such as \u003cem\u003eA. cervicornis\u003c/em\u003e and \u003cem\u003eA. palmata.\u003c/em\u003e These species are instead plagued by WBD, which has been responsible for the large-scale population mortality since the late 1970s [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo control the spread of diseases, recent efforts have focused on utilizing broad-spectrum antibiotics, namely ampicillin and amoxicillin pastes [\u003cspan additionalcitationids=\"CR52 CR53 CR54\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Given the widespread and elusive nature of these diseases, antibiotics are being utilized not only as a way to suggest a bacterial component of the disease, but also to provide an immediate solution while the etiological agent(s) remain unidentified. Amoxicillin pastes have demonstrated clear efficacy in short (~\u0026thinsp;2 weeks) and long-term (~\u0026thinsp;1 year) \u003cem\u003ein-situ\u003c/em\u003e and \u003cem\u003eex-situ\u003c/em\u003e experiments, although treatment efficacy may be dependent on species \u0026ndash; likely due, in part, to morphological characteristics not conducive to antibiotic application [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Treatment regimens differ across studies, yet each consistently reports that amoxicillin pastes slow or halt disease lesion progression into a quiescent state [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Despite the greater than 90% success rate in many cases, these intervention strategies are likely only a temporary solution as antibiotic administration does not prevent new lesion formation [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This may indicate that the current antibiotic does not target the true causative agent, that retreatment strategies require further optimization, or that the waterborne pathogen(s) are transmitted to other parts of the coral colony prior to treatment intervention.\u003c/p\u003e \u003cp\u003eIn addition to the ecotoxicological and antibiotic resistance concerns of antibiotic contamination in reef systems, several studies have noted that antibiotic-induced disruption of coral microbiomes results in a diminished capacity to withstand subsequent stressors such as heat stress and transplantation to a natural system [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Disruptions to the holobiont manifest as increased transcriptional stress responses from both the coral host and algal symbiont and decreased bacterial diversity which, when combined, reduces heat tolerance and upregulates immune response genes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Therefore, with the threat of subsequent and concurrent disturbances, it is imperative to pair microbiome studies with antibiotic interventions to understand how both target and non-target microbiomes are affected.\u003c/p\u003e \u003cp\u003eIn this study, we investigated how a 96-hour exposure to one of two concentrations of ampicillin, streptomycin, ciprofloxacin, and a mixture of the three, affects microbiome composition and diversity in a disease-resistant \u003cem\u003eAcropora cervicornis\u003c/em\u003e genotype as a representative off-target species. Our results suggest that antibiotics reduce dominant taxa and allow for potentially harmful bacteria to proliferate. Additionally, they suggest that antibiotic dose range-finding is essential for future disease interventions, as different concentrations of the same antibiotic may result in distinct microbial community profiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design\u003c/h2\u003e \u003cp\u003eA total of 120 coral fragments of a disease-resistant \u003cem\u003eAcropora cervicornis\u003c/em\u003e genotype (ML-7) were collected from the offshore coral nursery at Mote Marine Laboratory\u0026rsquo;s International Center for Coral Reef Research and Restoration (IC2R3) in Summerland Key, Florida. Fragments were attached to ceramic plugs using cyanoacrylate glue and acclimated to \u003cem\u003eex-situ\u003c/em\u003e raceway conditions for 14 days in IC2R3\u0026rsquo;s Climate and Ocean Acidification Simulator (CAOS) system before experimentation. Following acclimation, coral fragments were each randomly assigned to one of nine experimental treatments: \u0026lsquo;blank\u0026rsquo; (no treatment), \u0026lsquo;ampicillin low\u0026rsquo; and \u0026lsquo;ampicillin high\u0026rsquo; (final tank concentrations of 10 mg/L and 100 mg/L, respectively), \u0026lsquo;streptomycin low\u0026rsquo; and \u0026lsquo;streptomycin high\u0026rsquo; (10 mg/L and 100 mg/L), \u0026lsquo;ciprofloxacin low\u0026rsquo; and \u0026lsquo;ciprofloxacin high\u0026rsquo; (due to high potency, 1 mg/L and 10 mg/L concentrations were used for low and high doses, respectively), and \u0026lsquo;mixture low\u0026rsquo; and 'mixture high\u0026rsquo;. \u0026lsquo;Mixture low\u0026rsquo; and \u0026lsquo;mixture high\u0026rsquo; were comprised of a combination of low or high doses of ampicillin, streptomycin, and ciprofloxacin, respectively. Fragments were then added to corresponding non-flow-through 5-gallon aquaria containing 6 L of water. Aside from the no treatment which had a total of six tanks, each treatment had three replicate tanks with four coral fragments in each tank (i.e. n\u0026thinsp;=\u0026thinsp;24 for \u0026lsquo;blank\u0026rsquo; treatment and n\u0026thinsp;=\u0026thinsp;12 for each other treatment). Each tank was then randomly distributed across three outdoor raceways. Samples were collected prior to antibiotic treatment (Time 0), and at 12, 24, 48, and 96 hours during treatment (Supplementary file 1 Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Because experimental aquaria were enclosed, tank water was manually refreshed by replacing half of the volume of water at each sampling time point and 72 hours after initial antibiotic treatment. To ensure a consistent, four-day antibiotic challenge, additional half-doses of antibiotics were added each time the water was changed, such that the final antibiotic concentration in the tanks was consistent throughout the experiment. After time 0 sampling, all four coral fragments within one of the control tanks (Blank 1), were sacrificed for other analyses; therefore, at each subsequent time point, only five control tanks were sampled (n\u0026thinsp;=\u0026thinsp;20).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntibiotic preparation and dosing\u003c/h3\u003e\n\u003cp\u003eThree broad-spectrum antibiotics (ampicillin, streptomycin sulfate, and ciprofloxacin anhydrous) were chosen due to their diverse mechanisms of action and bactericidal nature. Ampicillin is a beta-lactam antibiotic that inhibits cell wall synthesis [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]; streptomycin is an aminoglycoside that interferes with protein synthesis [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]; and ciprofloxacin is a fluoroquinolone that inhibits DNA gyrase which ultimately impedes DNA replication [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Concentrated stock solutions of ampicillin and streptomycin (120 g/L), and ciprofloxacin (12 g/L) were made using 0.2 \u0026micro;m filter-sterilized seawater. For the initial, full-potency antibiotic dose (immediately after T0), 15 mL high-dose (40 g/L for ampicillin and streptomycin, and 4 g/L for ciprofloxacin) working solutions were prepared. 15 mL low-dose working solutions were also prepared by diluting 1.5 mL of the high-dose concentration in filter-sterilized seawater. When added to the tank, the low-dose concentrations were tenfold lower than the high. High-dose antibiotic mixture doses were prepared by combining 5 mL of each concentrated stock solution, and low doses of the antibiotic mixture were again prepared by diluting 1.5 mL of the high-dose concentration in filter-sterilized seawater. 7.5 mL of the working solutions were used for all subsequent doses. At each time point, blank tanks were supplemented with equal volumes of the same filter-sterilized seawater that was used to make the antibiotic solutions.\u003c/p\u003e\n\u003ch3\u003eSample collection and processing\u003c/h3\u003e\n\u003cp\u003eAt each time point, before sampling coral fragments, three liters of aquaria water were removed from each tank to conduct a half-tank water change \u0026ndash; one liter of which was retained and filtered using a peristaltic pump and 0.22 \u0026micro;m Sterivex filter unit (model SVGP01050 Millepore Sigma) for 16S rRNA gene analysis of the bacteria in the water column. Each Sterivex filter was placed in sterile bags (Whirl-Pak) and stored at -80\u0026deg;C until processing. Avoiding the apical polyp and any previous wounds, two verrucae were snipped from each coral using sterile bone cutters and placed in a 1.2 mL cryogenic tube in 500 \u0026micro;L of DNA/RNA Shield (Zymo Research, Irvine, CA, USA). The samples were promptly stored at -80\u0026deg;C until they were processed using the DNeasy 96 PowerSoil Pro kit (Qiagen) using the OT-2 liquid handling system (Opentrons).\u003c/p\u003e\n\u003ch3\u003eDNA extraction of water samples\u003c/h3\u003e\n\u003cp\u003eSterivex filter cartridges were defrosted and sealed at one end using a Luer-Lok cap. Then 460 \u0026micro;L of extraction buffer solution (composed of 40 \u0026micro;L proteinase K, 200 \u0026micro;L of buffer AL provided by the Qiagen Blood \u0026amp; Tissue kit, and 220 \u0026micro;L of PBS) was added to the column. The other end was then sealed with another Luer-Lok cap, and both ends were wrapped in parafilm to avoid leakage. The filled filter cartridges were then attached horizontally to the rotator inside of a hybridization incubator (Robbins Scientific Model 400) and incubated at 56\u0026deg;C for 4 hours while rotating at 20 rpm. After incubation, the inlet cap was removed, and the inlet port of the cartridge was placed into a 2 mL tube and sealed with parafilm. The filter cartridge with the attached 2 mL tube was placed into a sterile 50 mL conical tube and centrifuged at 5,000 x g for 2 min to elute the extracted DNA from the filter cartridge. After centrifugation, the 2 mL tube was detached from the filter cartridge and stored for downstream use.\u003c/p\u003e\n\u003ch3\u003e16S rRNA gene amplicon library preparation\u003c/h3\u003e\n\u003cp\u003eThe V4 region of the 16S rRNA gene from coral and seawater samples was amplified via a one-step polymerase chain reaction (PCR) approach. 25 \u0026micro;L PCR reactions were made using 10 \u0026micro;L of Platinum II \u003cem\u003eTaq\u003c/em\u003e Hot-Start PCR Master Mix (2x) (Invitrogen) master mix, 2.5 \u0026micro;L each of 10 \u0026micro;M primers 515F (5\u0026rsquo; - GTGYCAGCMGCCGCGGTAA \u0026minus;\u0026thinsp;3\u0026rsquo;) and 806R (5\u0026rsquo; - GGACTACNVGGGTWTCTAAT \u0026minus;\u0026thinsp;3\u0026rsquo;) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] with attached barcodes for dual-indexed libraries (for details see Silva et al., 2023 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]). Three negative controls were included in each 96-well plate for a total of 21 negative controls. The template DNA was amplified using the following thermocycler parameters: initial denaturation at 94\u0026deg;C for 2 minutes, followed by 35 cycles of denaturation at 94\u0026deg;C for 30 seconds, annealing at 60\u0026deg;C for 30 seconds, and extension at 68\u0026deg;C for 60 seconds, followed by a single final extension step at 68\u0026deg;C for 10 minutes. Successful amplification was confirmed by visualizing the PCR product on a 1.5% agarose gel. Amplified PCR products were then purified using Agencourt AMPure XP beads (Beckman Coulter) following the manufacturer\u0026rsquo;s guidelines. However, 80% ethanol was used for the washing steps, and a 5-minute drying step was included after the final ethanol wash to evaporate excess ethanol. Purified libraries were again visualized via gel electrophoresis before quantifying DNA concentration using the BioTek Synergy H1 multi-mode plate reader. Libraries were pooled at equimolar concentrations before paired-end 2x300 bp sequencing using the Illumina NextSeq 2000 P1 system at Oregon State University\u0026rsquo;s Center for Qualitative Life Science (CQLS).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRaw read quality control and sequence preprocessing\u003c/h2\u003e \u003cp\u003eA total of 602 samples (21 of which were negative controls) were demultiplexed by the CQLS. Individual forward and reverse quality profiles were assessed using FastQC and MultiQC [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Primer sequences were removed from forward and reverse reads using a two-step \u003cem\u003ecutadapt\u003c/em\u003e approach to remove forward and reverse primer sequences, as well as their reverse complements [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Reads were imported into RStudio (v. 4.3.0) for subsequent quality control processing. Using DADA2 (v. 1.28.0) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], low-quality sequences were edited and filtered using several steps: 1) First, to remove low-quality bases, reads were truncated at the 3\u0026rsquo; end at 245 bp and 230 bp for the forward and reverse reads, respectively, based on MultiQC reports. 2) Then low-quality reads were removed when a quality score of 2 was identified. 3) Then maximum expected error was calculated and any reads that exceeded a maximum error rate of 2 were also removed.\u003c/p\u003e \u003cp\u003eTo account for the large number of samples and reads, we used five times the number of bases to estimate the sequencing error than the default. Sample sequence identity was then inferred by DADA2::\u003cem\u003edada\u003c/em\u003e using default parameters. Contigs were then assembled, and only those within the amplicon size target range (251\u0026ndash;255 bp for coral samples and 253\u0026ndash;254 bp for seawater samples) were used in further analyses. Before taxonomic assignment, sequences identified by DADA2 as chimeras were omitted from subsequent analysis. Taxonomy was assigned down to the genus level using the SILVA nr 99 v138.1 training set [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. When possible, species-level taxonomy was also assigned using the SILVA Species Assignment v138.1 [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Those sequences identified as chloroplast or mitochondria, or those that were not annotated beyond the Kingdom level, were excluded.\u003c/p\u003e \u003cp\u003eA phyloseq object was then created using the \u003cem\u003ephyloseq\u003c/em\u003e package (v. 1.44.0) in R [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Through the \u0026lsquo;combined detection method\u0026rsquo; in the \u003cem\u003edecontam\u003c/em\u003e package (v. 1.20.0; Supplementary file 1), contaminants were identified on the basis of both prevalence and quantification thresholds from negative control samples and were subsequently removed from all samples [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. After contaminant ASVs were removed, negative controls were excluded from downstream analysis. Due to high experimental replication, taxa that had both low frequency (appearing in only one sample) and low abundance (reads within the first quartile of read distribution) were removed. Samples with fewer than 1000 reads after all quality control filtering (8) were removed from the analysis. After filtering, 5,660,802 reads and 2,962 ASVs across 573 coral samples remained.\u003c/p\u003e \u003cp\u003eAntibiotic treatment had a significant effect on library size (i.e., number of reads) across treatment groups at each time point except for T0 (p\u0026thinsp;=\u0026thinsp;2.06e\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;6.92e\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;9.01e\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e, and p\u0026thinsp;=\u0026thinsp;0.0005 for T12, T24, T48, and T96, respectively; Kruskal-Wallis), with trends becoming especially apparent after all quality control steps (Supplementary file 1 Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Therefore, to account for differences in library size among samples, the \u003cem\u003errarefy\u003c/em\u003e function from the \u003cem\u003evegan\u003c/em\u003e package [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] was used to randomly subsample counts data to 5000 reads, as a majority of the observed richness was captured by 5000 reads (Supplementary file 1 Figure S3). For samples with fewer than 5000 total reads (70 samples with an average read depth of 3,404 reads), no random subsampling occurred, and all reads were used.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobiome and Statistical Analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAlpha Diversity and Microbiome Relative Abundance\u003c/h2\u003e \u003cp\u003eObserved Richness, Shannon Diversity, Inverse Simpson, and Faith\u0026rsquo;s Phylogenetic Diversity (PD) were all calculated at the genus level using the \u003cem\u003eestimate_richness\u003c/em\u003e function in the \u003cem\u003ephyloseq\u003c/em\u003e package. All alpha diversity measures were calculated using the rarefied phyloseq object described above. For brevity, Shannon Diversity is presented here at the genus level, although other metrics can be seen in Figure S5 in Supplementary file 1.\u003c/p\u003e \u003cp\u003eTo quantitatively evaluate how coral fragments responded to each antibiotic treatment over time, a linear mixed-effect model was created using the \u003cem\u003elme4\u003c/em\u003e R package (v. 1.1.35.1) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In this model, time, treatment (antibiotic plus dose), and their interaction were set as fixed effects, while tank and coral sample ID were set as nested, random effects (Supplementary file 1). Pairwise comparisons were made using the \u003cem\u003eemmeans\u003c/em\u003e package (v. 1.10.0), with the Tukey-Kramer p-adjustment method [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePruned, rarefied data were also used to calculate relative abundance measures for each antibiotic treatment group. After taxa counts were transformed to relative abundances, the top ten most abundant taxa across the samples were determined. Treatment replicates were then merged to calculate the mean relative abundance of the top ten most abundant taxa across all samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBeta Diversity and Dispersion\u003c/h2\u003e \u003cp\u003eBeta diversity analyses were performed using pruned, unrarefied data that were robust centered log-ratio (rCLR) transformed data using the \u003cem\u003emicroViz\u003c/em\u003e package to account for data compositionality and sparsity [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Transformed data were ordinated using a Principal Component Analysis (PCA). Differences in beta diversity between sample groups within time points were identified via a permutational analysis of variance (PERMANOVA) using \u003cem\u003eadonis2\u003c/em\u003e. The \u003cem\u003epairwise.adonis\u003c/em\u003e package was used for pairwise comparisons, and p-values were adjusted according to the false discovery rate (fdr) formula [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Beta dispersion (as distance to centroid) was determined using Euclidean distances that were calculated from the rCLR-transformed dataset, thereby producing robust Aitchison distances. Statistically significant differences were determined using the \u003cem\u003ebetadisper\u003c/em\u003e and \u003cem\u003epermutest\u003c/em\u003e functions in the \u003cem\u003evegan\u003c/em\u003e package with fdr p-value correction [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Abundance\u003c/h2\u003e \u003cp\u003eDifferential abundance analysis was performed using ANCOM-BC2 on pruned, unrarefied data to identify taxa whose relative abundances were significantly different [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. For each treatment, all time points (T12 - T96) were combined and compared to all pretreatment (T0) samples. Repeated sampling was accounted for by including coral ID as a random effect in the differential abundance models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis\u003c/h2\u003e \u003cp\u003eData subsets were created for each treatment using the original, unrarefied, unpruned phyloseq object. Time 0 samples were excluded from analysis, as corals at this time point had not yet been exposed to antibiotics. Before network construction, taxa that only appeared in one sample were removed. Microbial co-occurrence networks were then created using the \u003cem\u003emicroeco\u003c/em\u003e R package [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Networks were created using the SpiecEasi method with Meinhausen and B\u0026uuml;hlmann (MB) neighborhood selection to calculate taxon co-occurrence at the ASV level [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Network centrality measures were calculated using functions within the \u003cem\u003eigraph\u003c/em\u003e and \u003cem\u003emeconetcomp\u003c/em\u003e packages [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Networks were then filtered to only include the top three most relatively abundant ASVs and their interactions to understand how each treatment affected the co-occurrence relationships among them. These ASVs included \u003cem\u003eCampylobacterales\u003c/em\u003e (ASV1), \u003cem\u003eHelicobacteraceae\u003c/em\u003e Family (ASV3), \u003cem\u003ePhaeobacter\u003c/em\u003e (ASV4). Full networks were visualized using Cytoscape [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] and can be viewed in Figure S8 in Supplementary file 1 or as interactive network plots on NDEx following the link provided in the availability of data and materials section below.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAntibiotics reduce a dominant, unclassified\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eCampylobacterales\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eASV\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMicrobiomes of T0 (pre-treatment) corals, and those of all time points within the \u0026lsquo;blank\u0026rsquo; (control) treatment group, were dominated by a single bacterial taxon from the order \u003cem\u003eCampylobacterales\u003c/em\u003e (ASV1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean relative abundance of this bacterium in \u0026lsquo;blank\u0026rsquo; samples remained high over the course of the experiment, with mean relative abundance ranging from 28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;31.00% at T0 to 60.12\u0026thinsp;\u0026plusmn;\u0026thinsp;29.18% at T96 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary file 2 Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, in all antibiotic treatment groups, regardless of dose, the mean relative abundance of ASV1 was markedly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most antibiotic treatment groups showed a reduction of this taxon by 12 hours (T12) after the initial antibiotic dose, and this reduction was maintained throughout the remaining time points.\u003c/p\u003e \u003cp\u003eDespite each antibiotic reducing the mean relative abundance of the uncharacterized \u003cem\u003eCampylobacterales\u003c/em\u003e ASV, the bacterial taxa that then increased in relative abundance differed among antibiotic type and dose. For example, the \u0026lsquo;mixture low\u0026rsquo; samples displayed higher relative abundances of the genus P30B-42 (13.15\u0026thinsp;\u0026plusmn;\u0026thinsp;21.20% at T12) than \u0026lsquo;blank\u0026rsquo; samples (1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90% at T12) while a single \u003cem\u003eAlteromonas\u003c/em\u003e taxon became more dominant in \u0026lsquo;ampicillin low\u0026rsquo; and \u0026lsquo;streptomycin high\u0026rsquo; samples after the initial antibiotic administration compared to \u0026lsquo;blank\u0026rsquo;, mixture, and ciprofloxacin treated samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although already found at relatively low abundance in both T0 (5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42%) and \u0026lsquo;blank\u0026rsquo; samples over time (5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.84%), a single ASV from the \u003cem\u003eHelicobacteraceae\u003c/em\u003e family (ASV3) was almost entirely eliminated by T96 by all antibiotic treatments, except for in the ciprofloxacin treatment group in which the reverse pattern was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By T96 for both the \u0026lsquo;ciprofloxacin low\u0026rsquo; and \u0026lsquo;high\u0026rsquo; treatment groups, this \u003cem\u003eHelicobacteraceae\u003c/em\u003e taxon became the dominant taxon (30.52\u0026thinsp;\u0026plusmn;\u0026thinsp;43.57% and 26.31\u0026thinsp;\u0026plusmn;\u0026thinsp;26.59%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Detailed relative abundance patterns for individual corals are shown in Supplementary file 1 Figure S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTime and antibiotic treatment differentially impact coral microbiome alpha diversity\u003c/h2\u003e \u003cp\u003eStatistical analyses of Shannon diversity revealed that time, and the interaction of time and treatment, significantly affected the combined richness and evenness (Shannon diversity) (LMEM, p\u0026thinsp;=\u0026thinsp;0.002; Supplementary file 2 Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Upon further pairwise analysis, although time was a significant driver of differences in Shannon diversity, no significant differences were observed in the \u0026lsquo;blank\u0026rsquo; treatment group over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary file 2 Table S3). Similarly, no significant differences in Shannon diversity between time points in ciprofloxacin-treated samples were identified for either dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For ampicillin and streptomycin, changes in alpha diversity appeared to be largely dose-dependent, as significant increases in Shannon diversity were observed only in the low doses of each (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In \u0026lsquo;ampicillin low\u0026rsquo; samples, at time points T12, T24, and T96, Shannon diversity increased significantly compared to T0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Pairwise EMM, p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.002, and p\u0026thinsp;=\u0026thinsp;0.02, respectively; Supplementary file 2 Table S3). \u0026lsquo;Streptomycin low\u0026rsquo; samples similarly displayed increased alpha diversity over time compared to the pre-treatment time point with significant differences at T12, T24, and T48 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Pairwise EMM, p\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;=\u0026thinsp;0.018, p\u0026thinsp;=\u0026thinsp;0.03, respectively; Supplementary file 2 Table S3). Unlike ampicillin and streptomycin, where significant increases in alpha diversity were observed in the low dose, one significant comparison was found in the high-dose mixture treatment group between T24 and T96 in which diversity was reduced in the later time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Pairwise EMM, p\u0026thinsp;=\u0026thinsp;0.026, Supplementary file 2 Table S3). Although not statistically significant, \u0026lsquo;mixture high\u0026rsquo; was the only treatment group that displayed decreased diversity at T96 compared to T0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAntibiotic treatment results in distinct shifts in coral microbiome beta diversity\u003c/h2\u003e \u003cp\u003ePERMANOVA identified time, treatment, and the interaction between time and treatment as drivers of differences in beta diversity (Supplementary file 2 Table S4). Time also significantly affected beta diversity in \u0026lsquo;blank\u0026rsquo; samples (Supplementary file 2 Table S4). Therefore, comparisons of community dissimilarity were made within time points, to identify significant differences between antibiotic doses compared to untreated samples subjected to the same tank residence time.\u003c/p\u003e \u003cp\u003eAt each time point, aside from T0, significant differences in beta diversity were observed between both doses of the antibiotic mixture and streptomycin relative to \u0026lsquo;blank\u0026rsquo; samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Dose-dependent community differences were detected at T96 between \u0026lsquo;mixture low\u0026rsquo; and \u0026lsquo;mixture high\u0026rsquo; (p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary file 2 table 5), and at T24, T48, and T96 between \u0026lsquo;streptomycin low\u0026rsquo; and \u0026lsquo;streptomycin high\u0026rsquo; (p\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary file 2 table 5). For ampicillin-treated samples, significant differences in beta diversity were detected beginning at T24 where \u0026lsquo;ampicillin low\u0026rsquo; and \u0026lsquo;ampicillin high\u0026rsquo; community profiles were distinct from \u0026lsquo;blank\u0026rsquo; samples (p\u0026thinsp;=\u0026thinsp;0.002), yet not significantly different from one another (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary file 2 Table S5). At times 48 and 96, however, \u0026lsquo;ampicillin low\u0026rsquo;, \u0026lsquo;ampicillin high\u0026rsquo;, and \u0026lsquo;blank\u0026rsquo; groups displayed significantly different microbiome community structures \u0026ndash; indicating both treatment and dose-dependent responses at these later time points (p\u0026thinsp;=\u0026thinsp;0.001 at T48, p\u0026thinsp;=\u0026thinsp;0.002 at T96, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary file 2 Table S5). The community composition of the \u0026lsquo;ciprofloxacin high\u0026rsquo; samples significantly differed from that of the \u0026lsquo;blank\u0026rsquo; samples at T12 (p\u0026thinsp;=\u0026thinsp;0.012, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), yet not for the low dose. At all subsequent time points, both \u0026lsquo;ciprofloxacin high\u0026rsquo; and \u0026lsquo;ciprofloxacin low\u0026rsquo; samples exhibited distinct community clustering from untreated samples (p\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), although no dose-dependent differences were observed.\u003c/p\u003e \u003cp\u003eAll antibiotics also displayed significant differences in within-group variation, or beta dispersion, in at least one time point. The direction of differences in dispersion, however, depended on the antibiotic as well as dose. At time T48, \u0026lsquo;mixture high\u0026rsquo; samples displayed significantly reduced dispersion compared to \u0026lsquo;blank\u0026rsquo; samples (p\u0026thinsp;=\u0026thinsp;0.027, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while \u0026lsquo;ampicillin high\u0026rsquo; displayed significantly increased dispersion compared to \u0026lsquo;blank\u0026rsquo; and \u0026lsquo;ampicillin low\u0026rsquo; samples (p\u0026thinsp;=\u0026thinsp;0.03 and p\u0026thinsp;=\u0026thinsp;0.003, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u0026lsquo;Streptomycin low\u0026rsquo; samples had significantly lower beta dispersion compared to \u0026lsquo;blank\u0026rsquo; samples at T96 (p\u0026thinsp;=\u0026thinsp;0.02, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary file 2 Table S6), while \u0026lsquo;streptomycin high\u0026rsquo; was not distinct from the \u0026lsquo;blank\u0026rsquo; group. Low-dose ciprofloxacin samples displayed increased dispersion compared to \u0026lsquo;blank\u0026rsquo; and high-dose samples at both T24 (p\u0026thinsp;=\u0026thinsp;0.018 and 0.03, respectively) and T96 (p\u0026thinsp;=\u0026thinsp;0.0015, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePatterns in coral bacterial differential abundance are dependent upon antibiotic type and dose\u003c/h2\u003e \u003cp\u003eNo taxa were differentially abundant across time in the \u0026lsquo;blank\u0026rsquo; samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary file 1 Figure S6). However, variable patterns in differential abundance were seen based on antibiotic type and dose. Every antibiotic treatment, at all doses, resulted in a significant reduction of the unclassified \u003cem\u003eCampylobacterales\u003c/em\u003e ASV1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with the largest negative log-fold change occurring in \u0026lsquo;mixture low\u0026rsquo; with a log-fold change of -3.9 (Supplemental Table\u0026nbsp;7). At least one dose within each antibiotic group, with the exception of ciprofloxacin, also resulted in the reduction of ASV3 from the \u003cem\u003eHelicobacteraceae\u003c/em\u003e family (also within the \u003cem\u003eCampylobacterales\u003c/em\u003e order) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u0026lsquo;Mixture high\u0026rsquo; and \u0026lsquo;mixture low\u0026rsquo; and \u0026lsquo;ciprofloxacin high\u0026rsquo; and \u0026lsquo;ciprofloxacin low\u0026rsquo; also all displayed reduced abundance of \u003cem\u003eAlteromonas\u003c/em\u003e (ASV2), while \u0026lsquo;streptomycin high\u0026rsquo; was the only group in which this taxon increased in relative abundance. Overall, samples treated with the antibiotic mixture displayed the largest number of taxa that were significantly reduced, with five and four taxa reduced in \u0026lsquo;mixture low\u0026rsquo; and \u0026lsquo;mixture high\u0026rsquo; groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A taxon from the genus \u003cem\u003ePhaeobacter\u003c/em\u003e (ASV4) (formerly \u003cem\u003eNautella\u003c/em\u003e) was reduced in both mixture doses, and a taxon from the \u003cem\u003eAltermonadaceae\u003c/em\u003e family (ASV18) decreased in \u0026lsquo;mixture low\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Apart from the \u003cem\u003eAlteromonas\u003c/em\u003e taxon (ASV2) that had significantly higher relative abundance in \u0026lsquo;streptomycin high\u0026rsquo; samples, only three other taxa were significantly increased by antibiotic treatments. A taxon from the family \u003cem\u003eHyphomonadaceae\u003c/em\u003e (ASV12) increased significantly in \u0026lsquo;mixture low\u0026rsquo;, \u0026lsquo;ampicillin low\u0026rsquo;, and \u0026lsquo;ciprofloxacin low\u0026rsquo; samples relative to pre-treatment samples. \u0026lsquo;Mixture high\u0026rsquo; and \u0026lsquo;ampicillin low\u0026rsquo; both displayed elevated abundances of a taxon from the \u003cem\u003eCaedibacter taeniospiralis\u003c/em\u003e group (ASV9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Differential abundance patterns for each treatment over time can be seen in Supplemental Fig.\u0026nbsp;6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWater and coral microbiome compositions remain distinct from one another before and after antibiotic treatment\u003c/h2\u003e \u003cp\u003ePrior to antibiotic treatment, coral and water samples displayed distinct microbiome structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). After the 96-hour antibiotic exposure experiment, the coral and water samples from the \u0026lsquo;blank\u0026rsquo; samples maintained this separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, antibiotic exposure appeared to shift both water and coral microbial communities away from their respective untreated control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Interestingly, antibiotic administration appeared to somewhat homogenize the bacterial communities between coral and water samples, although each group remained significantly dissimilar from one another (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary file 2 Table S8). This convergence, however, appears to be driven by the ampicillin groups, as the coral and water were more similar in this treatment than in other antibiotic groups (Supplemental Fig.\u0026nbsp;7), or due to the residence time of the corals in the aquaria at the final time point. Additionally, we found that the antibiotic-induced loss of the dominant \u003cem\u003eCampylobacterales\u003c/em\u003e ASV in corals was not mirrored in the water samples, nor was that taxon found to be established in the water column following depletion from the coral host (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In fact, the \u003cem\u003eCampylobacterales\u003c/em\u003e ASV was present at very low abundance and prevalence in all water samples. This ASV was found in 83% of coral samples and accounted for 23.6% of the total reads, while it was found in 48.6% of water samples and only contributed to 0.04% of the total reads. Genera such as \u003cem\u003eAlteromonas\u003c/em\u003e and \u003cem\u003ePhaeobacter\u003c/em\u003e that were identified in coral samples, were identified in relatively high abundance in water samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Yet, interestingly, these taxa were largely eliminated from the water column by each antibiotic treatment, whereas the reduction of these taxa was more varied across treatment groups in the coral samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMinor taxa primarily drive network structure and function\u003c/h2\u003e \u003cp\u003eDespite being the most abundant taxon in the control samples, \u003cem\u003eCampylobacterales\u003c/em\u003e ASV1 was not identified as a microbiome network hub node in any of the five networks according to centrality metrics such as degree, closeness centrality, or eigenvector centrality (Supplemental Table\u0026nbsp;10). This taxon had eigenvector centrality scores of 7.99e-08, 4.77e-03, 2.37e-04, and 2.7e-04 (scores range from 0\u0026ndash;1, with more influential taxa having scores closer to 1) for blank, ampicillin, streptomycin, and ciprofloxacin networks, respectively, and did not have any significant connections in the mixture network. Instead, those taxa with eigenvector centrality scores of 0.75 or higher were considered hub nodes. These nodes were identified as \u003cem\u003eChryseobacterium\u003c/em\u003e (ASV664) and \u003cem\u003eWinogradskyella\u003c/em\u003e (ASV25) in the blank network, \u003cem\u003eCaenarcaniphilales\u003c/em\u003e Order ASV406 and \u003cem\u003eWoesia\u003c/em\u003e (ASV232) in the mixture network, \u003cem\u003eAquibacter\u003c/em\u003e (ASV37), ASV654 from the \u003cem\u003eStappiaceae\u003c/em\u003e family, and \u003cem\u003eLabrenzia alexandrii\u003c/em\u003e (ASV367) in the ampicillin network, \u003cem\u003ePseudoaminobacter\u003c/em\u003e (ASV86) and \u003cem\u003eAestuariibacter\u003c/em\u003e (ASV1057) in the streptomycin network, and \u003cem\u003eTropicibacter\u003c/em\u003e (ASV329) and ASV411 from the \u003cem\u003eCryomorphaceae\u003c/em\u003e family in the ciprofloxacin network. Degree was also measured to identify highly connected taxa. The taxa with the most connections in the blank, mixture, ampicillin, streptomycin, and ciprofloxacin networks were \u003cem\u003eBlastopirellula\u003c/em\u003e (ASV503) and \u003cem\u003eMBIC10086\u003c/em\u003e (ASV2773) (degree\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eDadabacterales\u003c/em\u003e order ASV485 (degree\u0026thinsp;=\u0026thinsp;8), \u003cem\u003eMuricauda\u003c/em\u003e ASV21 (degree\u0026thinsp;=\u0026thinsp;11), \u003cem\u003ePseudoalteromonas\u003c/em\u003e (ASV555) (degree\u0026thinsp;=\u0026thinsp;14), \u003cem\u003eRhodobacteraceae\u003c/em\u003e family (ASV263) (degree\u0026thinsp;=\u0026thinsp;16), respectively.\u003c/p\u003e \u003cp\u003eEach network was then subset to only include the top three most abundant taxa (\u003cem\u003eCampylobacterales\u003c/em\u003e order ASV1, \u003cem\u003eAlteromonas\u003c/em\u003e ASV2, and \u003cem\u003eHelicobacteraceae\u003c/em\u003e family ASV3) to observe how their relationships changed across networks. Several positive co-occurrence relationships observed in the control network were also seen across the treatment networks. The co-occurrence between the \u003cem\u003eAlteromonas\u003c/em\u003e ASV2 and an unclassified ASV18 from the \u003cem\u003eAlteromonadaceae\u003c/em\u003e family was seen in each of the networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and was, in fact, the only relationship conserved in all networks. Interestingly, although this interaction was present in each of the networks, its structural and/or functional importance does not appear consistent among the networks, as the relationship between ASV18 and ASV2 forms a distinct cluster away from the main network in the mixture network, whereas the co-occurrence relationship is more central in all other networks (Supplemental Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003eThe positive relationship between ASV1 and ASV3 (\u003cem\u003eHelicobacteraceae\u003c/em\u003e family) in the blank network was also detected in the streptomycin network. The co-occurrence of ASV1 and JGI_0000069-P22 (ASV20) was detected in both blank and ciprofloxacin networks, while the co-occurrence of the \u003cem\u003eHelicobacteraceae\u003c/em\u003e ASV3 and ASV20 was identified in the blank, mixture, and ampicillin networks. Lastly, the blank and ampicillin networks shared the positive co-occurrence between \u003cem\u003eHelicobacteraceae\u003c/em\u003e ASV3 and \u003cem\u003ePatescibacteria\u003c/em\u003e ASV14. Interestingly, \u003cem\u003eAlteromonas\u003c/em\u003e ASV2 and \u003cem\u003ePhaeobacter\u003c/em\u003e ASV4 negatively co-occurred in the blank network, while the opposite relationship was observed in the ampicillin network. In the ampicillin network, ASV1 had a negative co-occurrence with ASV9 from the \u003cem\u003eCaedibacter taeniospiralis\u003c/em\u003e group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eCampylobacterales\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ein disease-resistant\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAcropora cervicornis\u003c/span\u003e\u003c/p\u003e \u003cp\u003eGenera within the order \u003cem\u003eCampylobacterales\u003c/em\u003e, namely \u003cem\u003eCampylobacter\u003c/em\u003e and \u003cem\u003eArcobacter\u003c/em\u003e, have been associated with a range of diseases in various coral species [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR84 CR85 CR86 CR87\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], although none have been implicated as primary pathogens. Furthermore, \u003cem\u003eCampylobacterales\u003c/em\u003e metagenome-assembled genomes (MAGs) most closely related to the genus \u003cem\u003eArcobacter\u003c/em\u003e were enriched in diseased coral tissue compared to apparently healthy tissue samples [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. However, since 2019, an uncharacterized ASV belonging to the order \u003cem\u003eCampylobacterales\u003c/em\u003e dominated the microbiomes of some specific genotypes of \u003cem\u003eA. cervicornis\u003c/em\u003e, particularly in the same genotype studied here; however, there is no evidence suggesting that these corals are diseased [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. In fact, this genotype is one of the few \u003cem\u003eA. cervicornis\u003c/em\u003e genotypes found to be disease-resistant both \u003cem\u003eex situ\u003c/em\u003e and \u003cem\u003ein situ\u003c/em\u003e [90\u0026ndash;92, E. Bartels, \u003cem\u003epers. comm.\u003c/em\u003e]. Interestingly, despite the high relative abundance of \u003cem\u003eCampylobacterales\u003c/em\u003e, this dominance is thought to represent a shift over time, as corals of this same genotype displayed far more even and diverse microbiome with very low abundance of this \u003cem\u003eCampylobacterales\u003c/em\u003e ASV in 2015 [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven this shift toward a \u003cem\u003eCampylobacterales\u003c/em\u003e-dominated microbiome, there may exist at least two microbiome states that are capable of supporting disease resistance in \u003cem\u003eA. cervicornis\u003c/em\u003e, yet the implications of reduced microbiome evenness remain unclear. It is possible that the shift toward single-taxon dominance may ultimately be detrimental to the system, as observed in disease-susceptible \u003cem\u003eA. cervicornis\u003c/em\u003e genotypes dominated by the intracellular bacterial parasite \u003cem\u003eCa\u003c/em\u003e. \u003cem\u003eAquarickettsia rohweri\u003c/em\u003e [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. More likely, the association of this \u003cem\u003eCampylobacterales\u003c/em\u003e taxon may be indicative of a newly established, potentially beneficial, symbiotic relationship in response to chronic nutrient enrichment on reefs, as many taxa within the \u003cem\u003eCampylobacterales\u003c/em\u003e order are important contributors to sulfur and nitrogen cycling [\u003cspan additionalcitationids=\"CR95\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Some members reduce nitrate to ammonium, which is potentially important on reefs as nitrate enrichment alone is known to exacerbate thermal bleaching outcomes and hinder coral growth rates, while slight ammonium enrichment, coupled with natural sources of phosphorous, may be beneficial to coral growth [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. In a recent study, the microbiome of the same \u003cem\u003eA. cervicornis\u003c/em\u003e genet used in the present study was stable in response to acute nutrient enrichment, including the maintenance of \u003cem\u003eCampylobacterales\u003c/em\u003e dominance throughout treatment [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Therefore, it was hypothesized that the microbiome structure of this disease-resistant genotype may provide some tolerance to environmental stressors [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Interestingly, despite the significant loss of this dominant ASV following the antibiotic challenge, there were no immediate signs of host health decline, no striking changes in co-occurrence relationships of this taxon among networks, nor were there drastic shifts in the number of taxa co-occurring with this ASV. This may indicate that the \u003cem\u003eCampylobacterales\u003c/em\u003e ASV1 is not a species generalist in the microbiome, as it only interacts with a select few other taxa in the networks [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Given its rapid loss, yet concomitant apparent host health stability, we hypothesize that the \u003cem\u003eCampylobacterales\u003c/em\u003e taxon itself is not required to maintain disease resistance and is rather a nonobligatory association.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eReduction in a\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eCampylobacterales\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eASV potentially supports an increase of other taxa\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn this study, we show that the \u003cem\u003eCampylobacterales\u003c/em\u003e ASV1 was highly susceptible to all antibiotic treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and that taxa associated with coral stress response increase in relative abundance to presumably inhabit the niche space that the \u003cem\u003eCampylobacterales\u003c/em\u003e occupied, or simply appear to increase given the constraints of relative abundance analyses and compositional data. The second-most reduced taxon in this study was from the \u003cem\u003eHelicobacteraceae\u003c/em\u003e family, which is also within the \u003cem\u003eCampylobacterales\u003c/em\u003e order, and which was reduced in all treatment groups except for ciprofloxacin and \u0026lsquo;streptomycin low\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the taxa that were positively enriched in response to antibiotic treatment, P3OB-42 (ASV5; \u003cem\u003eMyxococcales\u003c/em\u003e) and \u003cem\u003eCaedibacter taeniospiralis\u003c/em\u003e (ASV9) are of particular interest. The taxon P3OB-42 is hypothesized to play a role in pathogen regulation in \u003cem\u003eA. cervicornis\u003c/em\u003e, as higher relative abundances of this taxon were associated with corals that were exposed to disease, yet remained visually unaffected [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. In an agricultural system, \u003cem\u003eMyxococcales\u003c/em\u003e spp. were also shown to inhibit phytopathogen infection [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. Given the putative commensal nature of \u003cem\u003eMyxococcales\u003c/em\u003e, the increase in this taxon may result from antibiotic-induced disruption to the coral surface mucus layer (SML), thereby exposing the coral to potential invasion as the SML normally serves as a first line of defense via niche occlusion and antimicrobial properties [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Interestingly, species within the P3OB-42 genus are also known antibiotic degraders \u0026ndash; specifically sulfonamides and beta-lactam antibiotics [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] \u0026ndash; which may explain the significant enrichment of this taxon in response to a low dose of ampicillin and high dose of the antibiotic mixture (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe other notable bacterium that increased in relative abundance following antibiotic treatment was from the \u003cem\u003eCaedibacter taeniospiralis\u003c/em\u003e group (closely related to the genus \u003cem\u003eCysteiniphilum\u003c/em\u003e: BLASTn 100% sequence identity), which is a known obligate intracellular symbiont of the paramecium \u003cem\u003eParamecium tetraurelia\u003c/em\u003e, capable of conferring a fitness advantage to the host by producing refractile bodies that kill other paramecia [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Although this paramecium has not been documented in corals \u0026ndash; healthy or diseased \u0026ndash; ciliated protozoans are often associated with disease incidence [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Recently, the \u003cem\u003eCaedibacter taeniospiralis group\u003c/em\u003e was also identified at higher abundances in diseased corals exposed to both white band disease type I and the coral pathogen, \u003cem\u003eSerratia marcescens\u003c/em\u003e [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. In the present study, and in Young et al. 2023, the \u003cem\u003eCaedibacter taeniospiralis group\u003c/em\u003e had lower abundance in controls. Similarly, the closely related taxon, \u003cem\u003eCysteiniphilum litorale\u003c/em\u003e was found in significantly higher relative abundance in WBD-afflicted \u003cem\u003eA. cervicornis\u003c/em\u003e (77.5% \u0026plusmn; 5.1% SE) compared to healthy corals (2.9% \u0026plusmn; 1.2% SE) [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Using machine learning and transmission experiments, this ASV was further identified as a potential WBD pathogen [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], thereby indicating that the increase of this taxon is likely due to burgeoning opportunistic establishment.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDose-dependent differences in bacterial responses\u003c/h2\u003e \u003cp\u003eOur results suggest that care must be taken to identify the most appropriate antibiotic dose to treat coral health problems, as we found that different antibiotics result in dramatically different microbial community structures compared to control samples. Further, we found that increased dose does not simply magnify the effect of change but rather can result in strikingly different community compositions \u0026ndash; especially after prolonged exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This dose-dependent effect was demonstrated by an \u003cem\u003eAlteromonas\u003c/em\u003e ASV, which was reduced by the low dose of streptomycin (although not significantly), yet paradoxically proliferated in the high dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A BLASTn search revealed that this ASV shared 100% sequence identity with \u003cem\u003eAlteromonas macleodii\u003c/em\u003e (e-value\u0026thinsp;=\u0026thinsp;4e\u003csup\u003e\u0026minus;\u0026thinsp;127\u003c/sup\u003e), and strains within this species are known to be either fully resistant to antibiotics including streptomycin or only slightly sensitive to others such as ampicillin [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Given this resistance, it is likely that, in conjunction with the effects of antibiotic photodegradation, the low streptomycin treatment may have been administered at an effective dose. In the high dose, however, resistant strains of this taxon were possibly able to overcome the high concentration of antibiotics and then actively outcompete other bacteria [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. It must be noted, however, that there is great diversity at the strain level within \u003cem\u003eA. macleodii\u003c/em\u003e; therefore, without additional genomic information, the mechanisms of this shift remain largely unknown [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications of antibiotic use\u003c/h2\u003e \u003cp\u003eDespite their persistence and accumulation in the environment, antibiotics in aquatic systems are subject to various degradation mechanisms that further affect concentration. Photodegradation, hydrolysis, microbial degradation, and changes in pH and temperature, all contribute to reducing an antibiotic\u0026rsquo;s half-life [\u003cspan additionalcitationids=\"CR116\" citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. In targeted therapeutic applications, these processes pose additional challenges for proper dosing which, as seen in this study, can drastically affect microbiome diversity and composition. Many of these processes, however, do not eliminate antibiotics entirely. Instead, these antibiotics are often reduced to subinhibitory concentrations, which may favor a shift toward microbial antibiotic tolerance and persistence in the system, thereby further complicating disease control [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany coral diseases either have unknown etiological agents or are thought to be polymicrobial [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. For this reason, broad-spectrum antibiotics are often employed to target a wide range of bacteria, but their use may have unintended ramifications. Given the variable range of inhibitory concentrations that antibiotics have on specific bacteria, it is difficult to develop a directed therapy that evenly targets each taxon of interest. Perhaps an antibiotic effectively targets one member of the polymicrobial consortia, which in turn provides the opportunity for an antibiotic-degrading bacteria to flourish, and potentially shield the other disease-associated taxa. In a recent study, researchers reported that normally commensal, beta-lactam degrading bacteria in the mouse gut may inadvertently protect a normally antibiotic-sensitive pathogen from the effects of ampicillin through commensal-mediated pathogen shielding [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. Although the aforementioned study was conducted in a mouse model, similar mechanisms may be present in other systems. Given that antibiotic treatment does not always result in permanent disease cessation, this may indicate that one or more of the suspected pathogens may be initially susceptible but ultimately receive a level of protection due to antibiotic-degrading bacteria, thereby allowing the pathogen(s) to proliferate further.\u003c/p\u003e \u003cp\u003eDisturbances such as antibiotic treatment can destabilize a microbiome not only due to the direct bactericidal effects, but also in ways that transform microbe-microbe interactions. Increased positive co-occurrence patterns may present increased opportunities for positive feedback loops and unchecked proliferation in the microbiome, which are hypothesized to negatively affect microbiome stability, whereas competitive relationships are thought to assist in stability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. Therefore, in addition to understanding how antibiotics affect the microbiome composition of target and off-target species, intervention strategies must also understand the effects these treatments have on microbial interactions as a whole.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we found that following antibiotic perturbation, the abundance of a dominant, unclassified \u003cem\u003eCampylobacterales\u003c/em\u003e taxon was significantly reduced by each antibiotic and dose. Despite varying implications of \u003cem\u003eCampylobacterales\u003c/em\u003e in coral disease, the taxon described here does not appear to be associated with negative health effects in this coral genotype, although its capacity for commensalism and the implications of its loss remain unknown. Given the ecologically threatened state of many corals, antibiotics provide a reasonable and productive short-term approach to slow disease progression, yet dose range finding and off-target effects must be taken into consideration when assessing the risks and rewards of this approach, as well as how microbiome manipulation may affect a host's long-term ability to combat future disturbances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eScripts used for bioinformatic and statistical analyses can be found at: https://github.com/pattonsunni/RoL_Antibiotics_G7. The 16S rRNA gene dataset supporting the conclusions of this article is available in the NCBI Sequence Read Archive (SRA) repository under the BioProject accession number PRJNA1165811. Interactive networks can be accessed by the following link https://www.ndexbio.org/#/networkset/bec779a5-7d0f-11ef-ad6c-005056ae3c32?accesskey=ee1db6b84a489b391e5fbcbc8cdf1ac71becd0eabcc5b84ee52b30822da26a74.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the U.S. National Science Foundation (NSF) grant awarded to Rebecca L. Vega Thurber (Award Number 2025457), as well as by an NSF Graduate Research Fellowship Program award granted to Sunni Patton (Award Number 2139319).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026rsquo; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eS.P. conceptualized the study, performed the investigation, conducted the formal analysis, generated all figures, wrote the original draft of the manuscript, reviewed and edited the manuscript, and acquired funding. D.P.P. conceptualized the study, performed the investigation, conducted the formal analysis, and reviewed and edited the manuscript. E.F. conceptualized the study, performed the investigation, and reviewed and edited the manuscript. G.K. Reviewed and edited the manuscript. E.M.M. reviewed and edited the manuscript. R.L.V.T. conceptualized the study, performed the investigation, acquired funding, provided supervision, wrote the original draft of the manuscript, and reviewed and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge staff at Mote Marine Laboratory\u0026rsquo;s IC2R3 for assisting with specimen collection and CAOS system monitoring. We would also like to thank Oregon State University\u0026rsquo;s Center for Quantitative Life Sciences for next-generation sequencing services.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSommer F, B\u0026auml;ckhed F. The gut microbiota \u0026mdash; masters of host development and physiology. Nat Rev Microbiol. 2013;11:227\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith K, McCoy KD, Macpherson AJ. 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Infect Immun. 2020;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/iai.00012\u0026ndash;20\u003c/span\u003e\u003cspan address=\"10.1128/iai.00012\u0026ndash;20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams SD, Klinges JG, Zinman S, Clark AS, Bartels E, Maurino MVD, et al. Geographically driven differences in microbiomes of Acropora cervicornis originating from different regions of Florida\u0026rsquo;s Coral Reef. PeerJ. 2022;10:e13574.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acropora cervicornis, antibiotics, disease-resistance, microbiome","lastPublishedDoi":"10.21203/rs.3.rs-5384505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5384505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs coral diseases become more prevalent and frequent, the need for new intervention strategies also increases to counteract the rapid spread of disease. Recent advances in coral disease mitigation have resulted in increased use of antibiotics on reefs, as their application may halt disease lesion progression. Although efficacious, consequences of deliberate microbiome manipulation resulting from antibiotic administration are less well-understood \u0026ndash; especially in non-diseased corals that appear visually healthy. Therefore, to understand how healthy corals are affected by antibiotics, we investigated how three individual antibiotics, and a mixture of the three, impact the microbiome structure and diversity of a disease-resistant Caribbean staghorn coral (\u003cem\u003eAcropora cervicornis\u003c/em\u003e) genotype. Over a 96-hour, aquarium-based antibiotic exposure experiment, we collected and processed coral tissue and water samples for 16S rRNA gene analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that antibiotic type and dose distinctively impact microbiome alpha diversity, beta diversity, and community composition. In experimental controls, microbiome composition was dominated by an unclassified bacterial taxon from the order \u003cem\u003eCampylobacterales\u003c/em\u003e, while each antibiotic treatment significantly reduced the relative abundance of this taxon. Those taxa that persisted following antibiotic treatment largely differed by antibiotic type and dose, thereby indicating that antibiotic treatment may result in varying potential for opportunist establishment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTogether, these data suggest that antibiotics induce microbiome dysbiosis \u0026ndash; hallmarked by the loss of a dominant bacterium and the increase in taxa associated with coral stress responses. Understanding the off-target consequences of antibiotic administration is critical not only for informed, long-term coral restoration practices, but also for highlighting the importance of responsible antibiotic dissemination into natural environments.\u003c/p\u003e","manuscriptTitle":"Antibiotic type and dose variably affect microbiomes of a disease-resistant Acropora cervicornis genotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 07:16:24","doi":"10.21203/rs.3.rs-5384505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-11T14:48:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-25T16:24:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-09T02:43:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319600128444755375068843242314646027770","date":"2025-02-08T11:42:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113577481854650599267577382712042990793","date":"2025-02-06T12:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308449130124631873687955578043267013326","date":"2025-01-05T23:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-27T11:44:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-19T14:49:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-05T16:02:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2024-11-04T03:23:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65291655-6817-4943-8786-1c3ee6146e8b","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:04:25+00:00","versionOfRecord":{"articleIdentity":"rs-5384505","link":"https://doi.org/10.1186/s40793-025-00709-2","journal":{"identity":"environmental-microbiome","isVorOnly":false,"title":"Environmental Microbiome"},"publishedOn":"2025-05-02 15:57:47","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2024-11-19 07:16:24","video":"","vorDoi":"10.1186/s40793-025-00709-2","vorDoiUrl":"https://doi.org/10.1186/s40793-025-00709-2","workflowStages":[]},"version":"v1","identity":"rs-5384505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5384505","identity":"rs-5384505","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}