Bladder overdistension in labor may lead to prolonged postpartum urinary retention. We hypothesized that nulliparas mobilizing to toilet is more likely to achieve satisfactory micturition.One hundred sixteen (58 in each arm) term nulliparas in labor with filled bladders were randomized to mobilizing to the toilet or using bedpan to micturate. Primary outcome was satisfactory micturition defined as ultrasound derived post-void bladder volume < 150 ml. Following unsatisfactory micturition, participants crossover to the opposed intervention. Participants were catheterized if after crossover, residual bladder volume was ≥250 ml.b 4.8 95%CI 3.0–12.4 P = 0.008, failure to micturate 1/58 (2%) vs. 8/58 (14%) RR 0.13 95%CI 0.02–0.97 NNTb 8.3 95%CI 4.6–38.7 P = 0.047. After cross over following unsatisfactory bladder voiding, satisfactory micturition rates were 0/3 (0%) vs 13/15 (87%) P = 0.024, bladder catheterization rates were 3/58 (5%) vs. 2/58 (4%) RR 95%CI 1.5 (0.26–8.65) P = 0.648, maternal satisfaction with allocated intervention 55/58 (95%) vs. 9/58 (16%) RR 95%CI 6.1 (3.3–11.2) NNTb 95%CI 1.3 (1.1–1.5) P <  0.0001 and preference for mobilizing to the toilet if micturition was needed again during labor 55/58 (95%) vs. 53/58 (92%) for mobilizing to the toilet compared to bedpan use arms respectively. Labor and neonatal outcomes were similar.Satisfactory micturition rates were 55/58 (95%) vs. 43/58 (74%) RR 1.28 95%CI 1.08–1.51 NNTSatisfactory micturition was more frequently achieved with mobilization to the toilet than bedpan use. Women in both arms overwhelmingly prefer to mobilize to the toilet to urinate.ISRCTN17787339. First participant was recruited on 31/07/2019. The last patient was recruited on 18/12/2019.This study was registered with ISRCTN on 17/07/2019 with trial identification number: The online version contains supplementary material available at 10.1186/s12884-022-05162-4. Overstretching the bladder wall during pregnancy or delivery can result in severe detrusor damage followed by voiding dysfunction . Acute cNulliparas due to their tendency for labor dystocia, often need oxytocin augmentation , 8 with Women’s toileting behavior related to micturition can be defined as voluntary actions related to the physiological event of emptying the bladder comprising specific attributes of voiding place, voiding time, voiding position and voiding style influenced also by the physical and social environments . For thehttps://doi.org/10.1186/ISRCTN17787339 on 17/07/2019). The trial was performed in accordance with International Conference on Harmonization – Guidelines for Good Clinical Practice (ICH-GCP) and Declaration of Helsinki. Informed consents were obtained from all participants.This trial was approved by the Medical Ethics Committee of University Malaya Medical Centre (reference number: 2019330–7272 on 04/07/2019) and registered with ISRCTN  in labor  who had the urge to micturate or a palpable bladder and ultrasound scan bladder volume > 300 ml, aged > 18 years old with a singleton live fetus in cephalic presentation for trial participation. An adult functional bladder can comfortably hold between 300 and 400 ml . Wall prPre and postvoid ultrasound for bladder volume was performed (solely by investigator WKC) using a 3.5 MHz convex transducer with the participant in semi-recumbent position. Bladder measurements were performed in two planes  in between contractions. For transverse image, the transducer is positioned horizontally along the long axis of the bladder, and adjusted until the maximum transverse section is found and the maximum transverse width measured . For the sagittal image, the transducer is rotated 90-degree into the sagittal plane, position adjusted until the maximum sagittal section is obtained and sagittal depth and height were then measured. Sonographic measurements were recorded for sagittal height, sagittal depth and transverse width of the bladder . Using the prolate ellipsoid method , volume random.org by investigator (PCT) who was not involved with recruitment.Prior to randomization women were asked on their preferred method to urinate using bedpan, mobilize to toilet, catheterization or no preference. Participants were randomized to micturition by mobilizing to toilet or using bedpan by opening the lowest numbered, sealed and opaque envelope that remained. The randomization sequence was computer generated in blocks of 4 or 8 using Participants randomized to mobilizing to toilet were disconnected from medical devices and can choose to go to the toilet with a companion (birth partner or the midwife) or by themselves with the door closed, for up to 10 min to micturate on a standard sitting toilet. The voided urine was collected into a disposable plastic bag that was hold firmly under the toilet lid. The voided urine volume was measured after decanting to a measuring jug. Participants in the bedpan arm was provided with the bedpan and be in any position that was comfortable to her (lying or sitting on the bed) for up to 10 min to micturate. The voided volume was measured after decanting to a measuring jug.Primary outcome of satisfactory micturition is defined as residual urine volume < 150 ml by ultrasound. A residual bladder volume ≥ 150 ml typically defines covert postpartum urinary retention , 18. ParImmediately after completion of their micturition attempt, participants provided a 5-grade Likert’s scale response of their satisfaction with their allocated intervention and stated their preferred bladder voiding method to “if you have to pass urine again during labor, what would you prefer?”. Labor and neonatal outcomes were retrieved from participants’ charts. Prespecified secondary outcomes were in-out catheterization, duration of the second stage of labor, mode of delivery, estimated delivery blood loss and maternal satisfaction after allocated intervention prior to any crossover.Participants, investigator (WKC) who performed the ultrasound and care providers were not masked to the interventions as it was impractical due to its obvious nature.N = 116) using the PS: Power and Sample Size Calculation [As there was no direct pilot data available, sample size calculation was based on in-out catheterization rates of 19% vs 54% in recovery room among postoperative patients (early mobilization vs bedpan) [culation .P values were reported and p <  0.05 was regarded as significant. Analysis was based on intention-to-treat.Data were entered into SPSS . Student t test was applied for means with normally distributed continuous data, Mann-Whitney U test for non-normally distributed data or ordinal data, Chi-square test for categorical data across trial arms and paired t test for continuous data within trial arms. Two-sided Figure Table b 95%CI 4.8 (3.0–12.4) P = 0.008 and prespecified secondary outcome bladder catheterization 3/58 (5%) vs. 2/58 (4%) RR 95%CI 1.5 (0.26–8.65) P = 0.648 for toilet vs. bedpan respectively. Other (post hoc) significant secondary outcomes of failure to micturate (0 ml voided) were 1/58 (2%) vs. 8/58 (14%) RR 95%CI 0.13 (0.02–0.97) NNTb 95%CI 8.3 (4.6–38.7) P = 0.047, and after cross-over, successful micturition was 0/3 (0%) vs. 13/15 (87%) P = 0.024 for toilet vs. bedpan respectively.Table b 95%CI 1.3 (1.1–1.5) P < 0.0001, heavily favored mobilization to the toilet arm, but duration of the second stage of labor, mode of delivery and estimated delivery blood loss were not different. When asked to state a preference if they need to micturate in labor again, 55/58 (95%) mobilizing arm vs. 53/58 (92%) bedpan arm P = 0.554 responded overwhelmingly and uniformly in favor of mobilization to the toilet in both trial arms. Neonatal outcomes were similar.Table P = 0.151 , 406 ± 138 vs. 390 ± 143 ml P = 0.058 (mobilize to toilet) and 322 ± 202 vs. 321 ± 215 ml P = 0.946 (bedpan); no significant difference between ultrasound derived to measured voided volumes in all three analyses indicating that ultrasound derived parameters are accurate and closely comparable to measured volumes. With accuracy defined as concordance within 20% range of ultrasound to measured actual voided volume in participants who managed to void , the accuracy rate by ultrasound 42/56 (75%) vs. 37/48 (77%) RR 95%CI 0.89 (0.36–2.20) P = 0.805 for toilet and bedpan arms respectively with no indication of operator bias across trial arms.We sought to evaluate a) difference in ultrasound derived mean voided volume  and the measured actual voided volume using paired t test for the entire trial population and separately for within trial arms and; b) the accuracy of ultrasound (US) over measured actual voided volume with the use of the ratio of ultrasound derived voided volume to measured actual voided volume (Supplementary Table b 4.8) and less likely to have overt urinary retention (NNTb 8.3). Amongst women who had overt urinary retention or complete failure to void with their allocated intervention, 13/15 (87%) who cross-over to the toilet after failure with the bedpan managed to micturate satisfactorily whilst no participant 0/3 (0%) who cross-over to the bedpan after failure in the toilet managed to micturate satisfactorily. However, after cross-over was permitted, the need for catheterization was low (toilet 5% vs. bedpan 4%) and not significantly different across trial arms based on original intention-to-treat analysis.Nulliparas in labor randomized to mobilizing to the toilet were more likely to micturate satisfactorily (NNThttps://pubmed.ncbi.nlm.nih.gov/) was performed on April 6 2021 using search terms “toilet bedpan labor trial” retrieved only a solitary trial report [A PubMed search (b 1.3) and 95% would prefer to mobilize to the toilet to urinate if they needed to do so again. In women randomized to bedpan, although 74% achieved satisfactory micturition with bedpan, 91% would still prefer to mobilize to toilet to micturate, which suggested that the preference for the toilet was not motivated by a bad micturition performance with bedpan, but a positive personal choice and a near universal one at that. The bedpan is a subject of everyday nursing practice but little research was found concerning this issue; its use has been described by patients as uncomfortable, embarrassing [Participants in mobilization to the toilet arm were more likely to report satisfaction (NNTrrassing , 23 and Our primary outcome was predicated on ultrasound derived post-void residual volume which was calculated using the prolate ellipsoid method  We used The overall urinary catheterization rate in our trial was low at about 5% and similar across trial arms due to an almost 90% cross-over success rate after unsatisfactory micturition with the bedpan, whereas after unsatisfactory micturition in the toilet, cross-over success rate was 0%. This finding reinforced our primary finding of the toilet being the better set-up for micturition. Our data indicated that a two-step approach of bedpan and if unsatisfactory, switch-over to toilet compared to mobilization to the toilet might still be viable but cumbersome option with the catheterization rate as the endpoint. Use of the bedpan can be convenient but the privacy of the toilet appeared the most suitable for micturition.With regard to strength, this is an original and powered study to evaluate the micturition performance in toilet or by bedpan of nulliparas in labor without neuraxial analgesia with positive finding on the primary and a number of supportive secondary outcomes in favor of the toilet environment. The study was powered and data replete with no drop-out. A single investigator was involved, eliminating inter-operator bias of ultrasound assessment. The investigator had 6 years of experience after certified training in obstetrics and gynecologic ultrasound. We believed our findings to be generalizable.With regards to limitations, this study was not blinded to the investigator-sonographer. This could be important as our primary outcome was ultrasound derived post-void residual urine volume and there could be investigator bias in image capture and measurements. Post hoc analyses of our data on ultrasound accuracy did not suggest operator bias across trial arms. There is continuing debate about the accuracy of ultrasound estimation of urine volume particularly during labor compared to measured catheterized volumes and 2-dimensional vs. 3-dimensional bladder volumes using ultrasound , 24–26. In conclusion, nulliparas in labor are more likely to have satisfactory micturition and far higher maternal satisfaction when mobilized to the toilet compared to bedpan use. However, with cross-over to go to the toilet if bedpan use is unsatisfactory, urinary catheterization rates are similar indicating a two-step approach with initial bedpan may still be viable. As a woman-centered quality of care issue, mobilizing to the toilet in labor to micturate is overwhelmingly preferred over the alternatives of the bedpan or catheterization.Additional file 1.


Natrinema and Halarchaeum in the lysis-resistant fraction could hint at the production of cyst-like cells or other resistant cells. These results can guide future studies aiming to isolate and broaden the characterization of lysis-resistant archaea.The production of specialized resting cells is a remarkable strategy developed by several organisms to survive unfavorable environmental conditions. Spores are specialized resting cells that are characterized by low to absent metabolic activity and higher resistance. Spore-like cells are known from multiple groups of bacteria, which can form spores under suboptimal growth conditions . In contrast, little is known about the production of specialized resting cells in archaea. In this study, we applied a culture-independent method that uses physical and chemical lysis, to assess the diversity of lysis-resistant bacteria and archaea and compare it to the overall prokaryotic diversity (direct DNA extraction). The diversity of lysis-resistant cells was studied in the polyextreme environment of the Salar de Huasco. The Salar de Huasco is a high-altitude athalassohaline wetland in the Chilean Altiplano. Previous studies have shown a high diversity of bacteria and archaea in the Salar de Huasco, but the diversity of lysis-resistant microorganisms has never been investigated. The underlying hypothesis was that the combination of extreme abiotic conditions might favor the production of specialized resting cells. Samples were collected from sediment cores along a saline gradient and microbial mats were collected in small surrounding ponds. A significantly different diversity and composition were found in the sediment cores or microbial mats. Furthermore, our results show a high diversity of lysis-resistant cells not only in bacteria but also in archaea. The bacterial lysis-resistant fraction was distinct in comparison to the overall community. Also, the ability to survive the lysis-resistant treatment was restricted to a few groups, including known spore-forming phyla such as Firmicutes and Actinobacteria. In contrast to bacteria, lysis resistance was widely spread in archaea, hinting at a generalized resistance to lysis, which is at least comparable to the resistance of dormant cells in bacteria. The enrichment of  Natural environments can experience fluctuation in biotic and abiotic factors, which impacts the growth and survival of the organisms living within them. In response, organisms must develop strategies to survive under environmental conditions that are suboptimal for their growth and reproduction . One comMyxococcales  were collected from the main lake, following a saline gradient, with salinity ranging from 53 to 0.706 PSU . The cor® disperser tubes . Then, 15 ml of Na-Hexametaphosphate (1%) were added and the mixture was homogenized two times at 3,000 rpm for 1 min with the ULTRA-TURRAX® tube disperser workstation system. The sample was left to precipitate for 10 min and the supernatant was collected. This initial step was repeated two times. The two 15 ml supernatants were pooled, centrifuged at 20 × g for 1 min to precipitate large particles, and the remaining supernatant was filtrated through a 0.2 μm sterile nitrocellulose filter. For each sample, the filter was cut in half. One half was used to assess the total microbial community by directly extracting DNA and the second half was subjected to the lysis-resistant enrichment method to assess the lysis-resistant fraction. This way the same filter was used to assess both communities (Attachment to inorganic particles such as sediment can bias the separation of the lysis-resistant fraction by protecting cells from lysis or from enzymatic degradation of DNA (a key step to degrade released DNA from easy to lyse cells during the “lysis-resistant enrichment method”). Therefore, indirect DNA extraction was applied. For each sample, 3 g of material were weighted in ULTRA-TURRAXmunities .® SPIN kit for soil  was used with a modified protocol that included three successive bead-beating rounds in the first step. The final DNA extracts from the successive bead-beating steps were pooled together .To the cellular material on one half of the filter, 900 μl of 1× TE (Tris-EDTA) buffer were added and incubated at 65°C for 10 min shaking at 80 rpm. Afterward, 100 μl of 20 mg/ml lysozyme were added and the sample was incubated at 37°C for 60 min shaking at 80 rpm. Later, 250 μl of 3 N sodium hydroxide (NaOH) and 250 μl of 6% sodium dodecyl sulfate (SDS) solutions were added and the sample was incubated at room temperature for 60 min shaking at 80 rpm. Next, the sample was filtrated in a 0.2 μm sterile nitrocellulose filter, which was then washed two times with 2 ml of sterile physiological water to remove any residue of the solutions used. Once the filter was dry, 450 μl of sterile water, 50 μl of DNase reaction buffer (1×) and 0.5 μl DNase enzyme were added and let stand for 15 min on the filter. The addition of the DNase enzyme and DNase reaction buffer is a vital step to degrade the DNA released by the easy-to-lyse cells prior to the DNA extraction from the lysis-resistant fraction. After 15 min, the solution was filtrated, and once more, the residue was washed with 1 ml of sterile physiological water that was filtrated to wash away the degraded DNA as well as the DNAase solution. Finally, the filters (containing the lysis resistant fraction only) were stored at −20°C until the DNA extraction was performed . The samtogether . The DNAThe purified DNA extracts were sent to Fasteris  for bacterial and archaeal 16S rDNA amplicon sequencing using an Illumina MiSeq platform , generating 300 bp paired-end reads. For the bacterial 16S rDNA, the V3–V4 region was amplified using the universal primers Bakt_341F (5′-CCT ACG GGN GGC WGC AG-3′) and Bakt_805R (5′-GAC TACHVG GGT ATCTAA TCC-3′) . For theIn this study, we analyzed two types of environments: sediment cores from the main saline lake and microbial mats from the small ponds surrounding it. An ordination of the communities using a principal coordinate analysis (PCoA) showed that both the lysis-resistant  and a total fraction  grouped mostly by the type of environment . AlthougSeven sediment core samples were collected along the salinity gradient ranging from 53 to 0.706 PSU in the main saline lake. The pH in the lake ranged between 8.22 and 8.43 (not following a specific gradient). From these samples, sample 5 could not be analyzed as there was not enough sediment to perform the analysis. From the sequencing, we were not able to generate any products for the archaea sample 6 (lysis-resistant only). Although the community analysis was performed in all the samples, for the comparison, only samples that could be paired total vs. lysis-resistant were retained.p-value = 0.18) between the Shannon alpha diversity of the lysis-resistant and the total fractions  between the samples after the lysis treatment and the samples from the direct DNA extraction  and Methanospirillum (anaerobic methanogenic) were only present in two and one cores in the lysis-resistant fraction, respectively. In the total community, the presence of Methanolobus and Methanosaeta was observed.The Shannon alpha diversity of the archaea samples did not show a statistical difference . Both Nitrososphaera and Candidatus Nitrosocosmicus are soil ammonia oxidizers from which no resistant cell form is known. Although the Euryarchaeota phyla did not show significant enrichment in the lysis-resistant fraction , and samples 8, 15, and 18 . Although the community analysis was performed in all the samples, for the comparison only samples that could be paired, total vs. lysis-resistant were retained.The PCoA of the microbial mat samples showed the same clustering as the one observed in the lake sediments . The totCandidatus Omnitrophicaeota, and Caldiserica showed the same distribution. On the other hand, the phyla Candidatus Eremiobacterota, and Elusimicrobia were only present in the lysis-resistant fraction. Nitrospirae and BRC1 were only present in the total fraction in the lake sediments, while in the microbial mat they are present in both fractions. The opposite was true for the phyla Hydrogenedentes, Cloacimonetes, and Aegiribacteria, which were only present in the total fraction in the microbial mats but were present in both fractions in the lake sediments. A more detailed assessment of the enriched phyla of the microbial mats is shown in Clostridium, Bacillus, Paenibacillus, and Sporosarcina are known spore formers found among those genera enriched. In addition, genera not known to form spores but with higher abundance in the lysis-resistant fraction, such as Enterococcus and Dorea, were also detected. In the case of Actinobacteria . The two microbial mats collected from pond 4 were highly structured, pink submerged mats with a salinity of 0.738 PSU and a pH of 7.89, while the sample from pond 5 was a brown submerged mat presenting the same salinity and pH as pond 4. The genus Methanosarcina was only found in ponds 3  and 4. Meanwhile, the top 50 ASVs of ponds 5 and 6  were dominated by the genus Methanosaeta in the total fraction, whereas the lysis-resistant fraction of the same ponds also presented the genera, Candidatus Nitrosocosmicus, Halalkalicoccus, Methanobacterium, and Methanocalculus. A larger archaeal diversity was revealed when assessing the enrichment of each phylum . In the case of the Euryarchaeota phylum, many genera showed no enrichment in either fraction, but the same relative abundance in both  is in agreement with previous studies assessing the bacterial diversity in the site , as well studies . On the our case . A high our case .Gracilibacter  was unproven . The reslibacter  and Enterococcus  in Firmiliruptor  and Micracterium  in ActinAzotobacter genus  was enriched in the lake sediment samples. The formation of exospores  has been described in Thermosporothrix hazakensis , and Elusimicrobia is noteworthy. Although for none of them spore-like cells are known, the extent to which culturable type strains are a good representation of their environmental counterparts is still unclear. It has been shown that the “domestication” of bacterial isolates can affect their metabolism, morphology, fitness, sporulation, pathogenesis, and resistance  are still needed. Furthermore, in the face of a future in which the planet has an even more unpredictable and changing climate, understanding the diversity of microorganisms with the ability to survive highly fluctuating environmental conditions might help us predict the response of microbial communities to climate change.PRJNA786740.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI GenBank – AC, PJ, GC, MF, and CD made major contributions to the acquisition of the data. AC and PJ made major contributions to the analysis or interpretation of the data. All authors proofread and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples.k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 . On semi-synthetic metagenomic data—external test—on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols.We present AMR-meta, a database-free and alignment-free approach, based on AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools. MEGARes [AMR prediction methods for metagenomics rely on comparison to databases of AMR genes. Two comprehensive and widely used AMR databases are the Comprehensive Antibiotic Resistance Database (CARD) , 8 and M MEGARes —and its  MEGARes —is a hanFor AMR classification of metagenomic samples from high-throughput sequencing, 1 class of methods is based on the use of sequence read aligners. One widely used tool in this category is AMRPlusPlus , which ak-mer) matching and machine learning. ResFinder [k-length subsequences  from the dataset. ResFinder 4.0 compares the set of unique k-mers to detect AMR genes and AMR-related chromosomal gene mutations based on an reference database built on a collection of chromosomal point mutations in bacterial pathogens [k-mers to an ad hoc gene AMR reference database derived from the literature [k-mer matching between a query sequence and the database, with a “winner takes all” strategy, i.e., multiple k-mer occurrences on different genes are resolved by selecting the one with highest frequency. Next, a quality measure of a whole AMR gene match is defined as a probability function of coverage  and depth . Similar to alignment-based methods, ResFinder and KmerResistance are also restricted to identifying genes that are found in a specified database and, therefore, have limited ability to detect putative AMR sequences. Another limitation of the k-mer–based approaches is their low flexibility with respect to sequencing errors [Another class of methods for AMR characterization is alignment-free, using a variety of approaches including substring  in classification performance and execution speed. Notably, AMR-meta captures resistance mechanics complementary to those found by other tools, which instead are more correlated to each other.In this article, we develop AMR-meta, a novel, alignment-free, AMR classification approach for high-throughput metagenomic data, based on k-mers and k-mer–derived metafeatures) are trained and tested accordingly . Second, we generate 2 semi-synthetic external datasets, drawing bacterial genomes from the Pathosystems Resource Integration Center (PATRIC) [moland PSScla, respectively. This 2-fold design allows us to benchmark AMR-meta against other existing tools—AMRPlusPlus 2.0, Meta-MARC, and DeepARG—in a flexible way because their output levels vary among antibiotic classes and more specific mechanisms. We use PSSmol to score the AMR predictions and PSScla to estimate the concordance of AMR-meta class predictions with those of other methods. Finally, we combine AMR-meta with the other tools and evaluate their predictions on 2 functional metagenomic datasets that were sampled from a clinical and an environmental setting. Our internal/external workflow is summarized in Fig. AMR-meta is trained and tested first on an internal dataset that—differently from other approaches—includes both AMR (named "resistant") and non-AMR genes (named "susceptible"). The AMR genes are taken from MEGARes 2.0 , while n(PATRIC) , and simk-mers as input. Each model uses the whole class-specific k-mer spectrum (derived from the collated positive/negative training datasets), where each feature is a binary value, representing the presence or absence of a particular k-mer in the dataset. Given the high-dimensionality of the k-mer spectrum, we use least absolute shrinkage and selection operator (LASSO) regularization to reduce the feature space, optimizing the shrinkage operator via cross-validation [k-mer LASSO regressors.The baseline models of AMR-meta are logistic regressors (1 for each antibiotic class) that use raw lidation . Given tk-mer LASSO regression is that a single linear combination of k-mer features might not be able to explain the variance of the entire dataset, even if discrimination performance is good for the majority of genes in 1 class. A way to increase the portion of variance explained is to use >1 linear combination, e.g., the first mth vectors of a principal component analysis. In this way, multiple independent combinations of k-mers can more effectively represent the genetic diversity within antibiotic classes.One possible problem with k-mer spectrum that identifies a set of  multiple features, i.e., metafeatures, each as an independent combination of the original k-mers contributing to a cumulative portion of the data variance. To do so, we apply a matrix factorization approach, which has been previously shown apt to tackle complex feature extraction problems, e.g., oncology and proteomics [Accordingly, we explore a space transformation—with concomitant dimension reduction—of the oteomics ,37. The oteomics . The algk-mers and genes. A k-mer is related to a gene if it is present in the gene sequence. Let us denote the total number of genes with g; the total number of k-mers with t; a matrix of r rows and c columns having all values equal to zero with k-mer per column with g, tR, and A with superscript T as AT in the rest of this article. We express the relation between the 2 domains by a symmetrical, 4-block matrix:Here we consider 2 data domains, namely, k-mers and genes. Note that in this context, the relation between elements is defined by design: We set the value of R at an entry to 1 if the corresponding k-mer is present in the corresponding gene, and 0 otherwise.where non-diagonal block matrices represent the relation (intersections) between k-mer metafeatures and the number of gene metafeatures as tm and gm, respectively. The factorization procedure decomposes R into the product of 3 matrices G, S, and GT, such that G × S × GT will approximate R by reducing the error up to a user-defined lower bound set as the difference between 2 consecutive iterations (denoted with R ≈ GSGT). Here G represents the relation between the original domains  and their metafeatures; and S represents the relation between the metafeatures, i.e., how 1 domain is mapped to the other. The matrices G and S have the following form, both expressed as 4-block matrices:We denote the number of k-mer/k-mer relations. Therefore, Θ is an R × R matrix. The empty blocks of Θ are the non-diagonal blocks. We use the intersection between the data of the same domain as constraints in the factorization process; i.e., each domain has a block, symmetrical constraint. We define the matrix Θ to represent the self-domain relations, i.e., gene/gene and t block we consider each k-mer identical to itself , while we make no assumption about the relation with 2 different k-mers . In the Θg block, we consider all the genes of each class to be related (−1), and different from the genes of other classes (1).In Θ we set each entry to −1 if the corresponding row and column elements share a relation; 1 if unrelated; and 0 if the relation is unknown. In this application, in the Θ−2 as the difference between consecutive iterations, or reaching 5,000 iterations, as stop criteria. Previous works discuss the method in detail [k-mers to their metafeatures. For each short-read pair encoded as binary vector of k-mer occurrences srt1, , we calculate its metafeatures as tm = 100 and gm = 25 metafeatures. After factorization, we feed the metafeatures to a logistic regression, optimizing the coefficients with a ridge approach. Figure The goal of the factorization is to minimize the following objective function:hreshold . We fix n detail ,37; a den detail . The facfeatures ,37, for entclass1pt{minimaWe collate AMR genes from MEGARes 2.0 , constitWe include putative non-AMR genes from the RefSeq database . Using Bnmatch nucleotides between target and query AMR gene, we extend the match by nmatch/2 nucleotides in both directions on the target MEGARes sequence. The underlying assumption here is that matches of bacterial AMR genes on vertebrate genomes are spurious or not functional and therefore do not provide AMR. Of note, this set-up is similar to the test set derivation presented in DeepARG [To mimic genes that likely do not provide AMR but share a significant similarity with AMR genes, we assemble a dataset selecting AMR-homologous genes and gene fragments from the human genome (GRCh38), and all the contigs in RefSeq labeled as "vertebrate mammalian" and "vertebrate other" assemblies. To do so, we run an ungapped BLAST search of all MEGARes genes against these human and vertebrate sequences . We use each unique sequence match, and add the flanking region to each match, elongating the matched sequence to be equal in length to the corresponding resistant MEGARes gene. Specifically, with a match of  DeepARG .k-mers present in the genes of the training datasets, excluding any sample reserved for validation (see next subsection), are considered and counted using different values of k, from 13 to 77 based on prior literature evidence [k is chosen incrementally on the basis of internal validation performance, stopping when performance decreases. Next, we stratify the training samples by class. We remove all k-mers with a frequency less than a given cut-off f in a single class . We also exclude AMR classes with <10 k-mers after frequency filtering.All evidence . The besWe use the AMR datasets described above to generate short reads, labeling each as resistant or susceptible to an antibiotic class. For each MEGARes class, we generate short-read datasets providing 10× base coverage of the original full-gene data. These datasets allow the evaluation of both false-positive and false-negative results.k-mer spectrum in the external test set is not guaranteed to be balanced as in the training, we re-calibrate the k-mer and metafeature probability threshold for external validation using the internal validation dataset and a number of samples where the k-mer and metafeature vectors are empty; i.e., they represent the non-AMR gene background. The ratio is optimized between 1:0.05 and 1:10, picking the first that meets the calibration target, i.e., a prediction with a score <0.5 for a feature vector without any k-mer belonging to our model.We use 4 independent external datasets, 2 semi-synthetic (made similarly to the training set) and 2 from functional metagenomic experiments. Because the prevalence of AMR and the We create the semi-synthetic datasets from PATRIC, downloading via FTP full bacterial genomes and summary metadata ,30. We rmol) and MEGARes classes (PSScla), respectively.We then generate 2 PATRIC semi-synthetic datasets (PSS), based on PATRIC antibiotic molecule labels (PSSmol to assess the performance of our approach and the concurrent methods on molecule-specific data. We retain genomes that are resistant (or susceptible) to ≥1 MEGARes class. We rank the PATRIC drug labels on the basis of number of associated genomes, and we select the top ones on the basis of the associated MEGARes classes. We exclude labels with <250 genomes or labels not referring to a specific molecule . We generate 250,000 short reads for each PATRIC label, equally divided between resistant and susceptible. Note that for PSSmol, because the PATRIC labels refer to genome , it is not possible to determine the ground truth, i.e., whether a short read comes from a resistant or a susceptible gene. To assess each method's performance, in the absence of such ground truth, we develop a scoring system based on the assumption that a method should find more resistant read pairs from resistant genomes and fewer from susceptible genomes. With srres, res defined as the number of short-read pairs coming from resistant genomes and classified as resistant, and with srres, sus as the number of short-read pairs coming from susceptible genomes and classified as resistant, we define the S-score as S = srres, res − srres, sus. A higher S-score thus denotes better performance, and a negative value implies that the method finds more resistant short-read pairs among the susceptible ones.We use PSScla is collated at the class level. Unlike PSSmol, each short read from PSScla has a known label that indicates whether it comes from a resistant or susceptible gene. To generate PSScla, first we remove PATRIC genomes presenting inconsistent class annotations, i.e., that are annotated as both resistant and susceptible to antibiotics belonging to the same class. Second, to consider only genomes that are resistant (or susceptible) to the range of antibiotics within a given MEGARes class, we rank each genome in decreasing order of the total number of annotations of resistance (or susceptibility) to multiple drugs within the same class. On the basis of this ranking, we retain only genomes that rank above the 90th percentile. Third, we perform a class-by-class BLAST filtering  of the selected PATRIC genomes against MEGARes genes, retaining and clipping the unique genes of PATRIC genomes that match MEGARes. The objective is to extract a set of PATRIC genes that match to MEGARes genes but are not exact matches. In fact, genes similar to known resistant genes coming from antibiotic susceptible—by a phenotypic test—genomes represent excellent candidates to test the ability of a classifier to recognize true/false-positive results. From these selected PATRIC genes, we generate short reads covering the selected genes, and capping the number of resistant or susceptible paired reads at ≤100,000 per AMR class . We reckon that with this procedure, we are able to uniquely label each PATRIC instance that passes the filter; however, in the BLAST alignment, there could be flanking regions or inserts that produce artifact matches. Nonetheless, given the strict parameters used, we we deem these cases to be rare. A resistant sample likely contains only resistant reads, and vice versa for a susceptible sample. Therefore, it is possible to calculate sample-wide performance by counting the proportion of resistant-within-resistant and susceptible-within-susceptible reads in each test sample. After filtering, glycopeptides and lipopeptides are excluded because there are <15 resistant genomes. Sulfonamides are excluded because no susceptible genomes are retained by our filtering procedure.PSSEscherichia coli(DH10B) and consist of of 219 and 169 samples with an average of 1.98 and 1.12 million paired-end short reads, respectively, sequenced with Illumina Genome Analyzer IIx technology. We use the aforementioned PATRIC annotation look-up table to pair antibiotic annotations to MEGARes classes. For testing classifier performance, we randomly select 100,000 short-read pairs for each class as for the PATRIC datasets.We benchmarked our method against 2 functional metagenomic datasets, which we refer to as the Pediatric and the Soil datasets . A functional metagenomics experiments is made by cloning metagenomic DNA fragments into bacterial vectors grown on antibiotic-laden media. The cultured bacteria surviving the antibiotic exposure are sequenced using a clonally amplified high-throughput sequence library. As per experimental design, for each fosmid, all sequence reads contain ≥1 AMR gene (known or not yet discovered) resistant to a known antibiotic. Therefore, each sequencing experiment has a known antibiotic resistance label. However, because the original metagenomics fragments can be longer than a single AMR gene, a single fosmid might contain multiple AMR genes, or contain unknown genes. The Pediatric and Soil datasets include fosmids from mol and PSScla datasets, and the experimental functional metagenomics data through in-house UNIX scripts, off-the-shelf bioinformatics tools including BLAST, R, and Bioconductor. The k-mer LASSO and the metafeature regression are developed in R, bash, and C++. We download the functional metagenomics datasets using NCBI’s sra-toolkit. For short read generation, we use InSilicoSeq [We process the training/validation data, the semi-synthetic PSSilicoSeq , simulatk-mers after frequency filtering. Upon internal validation, the best k-mer length k and frequency threshold f are 13 and 5, respectively (the performance decreases at k = 31 and for f= 3 with the same or higher k). Upon optimization of the k-value, the total number of unique 13-mers is 138,260, and the median number per class is 3,645 . The matrix factorization includes 5,175 training genes, yielding a matrix R of 138,260 + 5,175 = 143,435 rows and columns, and a k-mer/metafeature matrix of 138, 260 × 100 elements.We generate 13 datasets, corresponding to the following antibiotic classes (according to the MEGARes ontology): aminoglycosides, β-lactamases, drug and biocide resistance, fluoroquinolones, glycopeptides, lipopeptides, macrolide-lincosamide-streptogramin (MLS), multi-biocide resistance, multi-drug resistance, multi-metal resistance, phenicols, sulfonamides, and tetracyclines. We exclude classes with <10 k-mer and metafeature regression on the internal validation sets. On the internal validation set, the k-mer LASSO and the metafeature regression exhibit a good trade-off between sensitivity and specificity at both k-values. The median number of features selected by k-mer LASSO is 12,783 . As expected, the highest number of non-zero coefficients is found in the β-lactamase class, which is the class with higher diversity and number of resistant genes in MEGARes. The same holds for the highest number of metafeatures with positive coefficients . In terms of performance, for LASSO, the median (IQR) f-measure across all classes is 0.7 (0.2–0.9), while for the metafeature regression, the median f-measure is 0.4 (0.2–0.7). For both methods, the best-performing classes are β-lactamases and fluoroquinolones, while the most problematic are MLS, and multi-biocide, -drug, and -metal resistance. Despite the k-mer LASSO having a higher median f-measure, the metafeature regression performs better in the problematic MLS and drug and biocide classes and shows better sensitivity in glycopeptides and better specificity in fluoroquinolones and lipopetides. For reference comparison, the median f-measure across classes is 0.5  for DeepARG and 0.9  for Meta-MARC, based on the original articles’ validation results. AMRPlusPlus 2.0 does not report per-class results on test sets.Table mol dataset includes 12 molecule labels incorporated into antibiotic classes, namely, ciprofloxacin and levofloxacin (fluoroquinolones), gentamicin and amikacin (aminoglycosides), ceftriaxone and ampicillin (β-lactamases), chloramphenicol (phenicols), sulfisoxazole (sulfonamides), erythromycin and azithromycin (MLS), tigecycline (tetracyclines), and vancomycin (glycopeptides). Performance results in terms of S-score, which summarizes the correct resistance and susceptible hits (the higher the better), are shown in Fig. S-score for the k-mer LASSO is 285.5 , and for the metafeature regression is 322 . Meta-MARC scores 250 , DeepARG scores 144.5 , and AMRPlusPlus 2.0 scores −29 . Overall, our metafeature approach shows both the highest performance and stability, also exhibiting a positive score in the levofloxacine molecule, whereas all the other methods produce a negative score. The k-mer LASSO component ranks second, followed by the other off-the-shelf tools.The PSSmol does not have a per-gene defined ground truth, we assemble PSScla. The PSScla dataset includes 6 of the 13 MEGARes classes, namely, aminoglycosides, β-lactamases, fluoroquinolones, MLS, phenicols, and tetracyclines. PSScla has instances from both positive (resistant) and negative (susceptible) genes. When we look at the class-specific concordance for each pair of tools using the the Spearman rank correlation , while they have low correlation with the k-mer LASSO and the metafeature regression (range 0.04–0.12)—which in turn show mild-low correlation (range 0.12–0.49). Thus, both k-mer LASSO and metafeature regression stand distant from each other and the other methods. The PSScla dataset is explicitly constructed to measure class-specific concordance, with very similar resistant and susceptible instances. However, for this reason, the PSScla becomes by design a challenging dataset for classification because the reads derived from susceptible genes all align well with other resistant genes in the same AMR class. Thus, the performance of all algorithms will tend to flatten. Nonetheless, the metafeature approach exhibits the highest median accuracy. Overall—pooling both resistant and susceptible for each AMR class—the k-mer LASSO median rate of correct predictions is 44% , the metafeature ridge 46% , DeepARG 44% , AMRPlusPlus 2.0 45% , and Meta-MARC 44% .Next, we measure the correlation between the predictions of the 2 AMR-meta modules and the ones from the other algorithms. Because PSSion Fig. , PSScla cla, we sample the negative genomes based on their similarity with the positive ones, using increasing matching thresholds, i.e., 70–75%, >75–80%, >80–85%, and >85–90%. The hypothesis is that the false-positive rate correlates with the threshold; i.e., algorithms tend to mis-classify non-AMR reads/genes that share high similarity with AMR genes. Because AMR-meta is specifically trained on both negative and positive examples, the expectation is that the algorithm will pick fewer false-positive results than other methods. We thus assemble datasets for each AMR class and for each of the 4 ranges of similarity percentage, with a cap of 250 random genomes per class. As expected, the false-positive rate increases with similarity, and the metafeature model is the method with the lowest median false-positive rate (0.02), followed by DeepARG (0.06), Meta-MARC (0.2), k-mer LASSO (0.23), and AMRPlusPlus (0.3). The full results, stratified by class and threshold ranges, are provided in As a sensitivity analysis, to study how the different algorithms behave with the negative samples in PSSk-mer LASSO, the metafeature regression, and the individual models’ predictions as input features (requiring ≥2 concordant predictions for classifying resistance).The Soil and Pediatric datasets come from functional metagenomics experiments that by design guarantee the presence of antibiotic resistance in a sequence sample because the sample is cultured on antibiotic-laden medium. However, sequenced reads can also contain other or unknown genes, which cannot be quantified. We consider here the hit rate, i.e., the proportion of sequence reads classified as resistant. Caution: a higher hit rate can signify that a method finds more AMR genes but also that a method finds more false-positive genes. Given that AMR-meta is designed to decrease the false-positive rate, we expect it to be the most conservative. Yet, to empirically identify a trade-off between the approaches, in addition to running each single model, we also built an ensemble using voting with k-mer approach with 1.94% , and the metafeature approach with 0.08% . On Pediatric, Meta-MARC achieves the highest hit rate with a median of 8.51% , followed by the k-mer approach with 0.27% , the voting ensemble with 0.27% , AMRPlusPlus 2.0 with 0.2% , DeepARG with 0.19% , and the metafeature approach with 0.01% . We observe large variations in each method depending on the class considered. It has to be noted that Meta-MARC’s threshold was previously re-calibrated on these datasets, and its standard threshold is much more conservative. As expected, the metafeature module is the most conservative on both datasets, while the voting ensemble offers a balanced alternative in all cases. Interestingly, the k-mer approach is one of the least conservative on the Pediatric set. Detailed results on the external Pediatric and Soil functional metagenomics datasets are illustrated in Fig. On Soil, the voting ensemble achieves the highest hit rate, with a median fraction of read pairs identified as resistant of 7.72% , followed by AMRPlusPlus 2.0 with 7.03% , DeepARG with 6.27% , Meta-MARC with 4.97% , the k-mer LASSO are the fastest tools, with a time of execution difference within minutes up to 5 GB load. , and Meta-MARC is considerably slower (30-fold), hitting the 24-hour wall time for files >1 GB.To compare execution times, we create benchmark datasets of increasing size by selecting reads drawn from the semi-synthetic PATRIC data , generating files of 1, 2, 5, and 10 GB of paired short-read files. We run all algorithms on the University of Florida’s High-Performance Cluster—HiPerGator 3.0—using a single node, composed by 4 AMD Opteron 6378 cores, with 32 GB of RAM. Table k-mer– and metafeature-based AMR classifier for short-read metagenomics data. AMR-meta uses an augmented training strategy based on non-AMR and AMR-homologous genes, providing relevant classification performance increment across various antibiotic classes.In this work, we present AMR-meta, an alignment-free, k-mer representation paradigm. For instance, Clark weighs differently k-mers that are found only in specific species, as compared to those that are shared by different species or genera [in vitro susceptibility, have shown high discriminating ability and capacity to identify potential new resistance features [Historically, the main objective of AMR characterization by metagenomics sequencing has been to identify known AMR genes, using comprehensive and up-to-date databases. However, the absence of non-AMR genes (negative examples) and of AMR-homologous sequences that do not have a role in resistance can hamper AMR classification accuracy and affect the trade-off between sensitivity and specificity. Notably, there are metagenomics classification tools that exploited the negative-positive r genera . Other sfeatures , 29.mol and PSScla. In addition, summarizing results over antibiotic classes can also introduce bias, given the high class imbalance in terms of antibiotics, gene frequency, and the aforementioned heterogeneity of intra-class gene diversity. It is understandable that a unified AMR ontology is difficult to achieve, yet an effort of the community to create common, standardized protocols for benchmarking and comparison is warranted.It is worth mentioning that comparing different AMR tools can be challenging because not all use the same ontology or provide classifications at the same annotation level. For instance, Meta-MARC is trained on a self-determined similarity-based clustering of AMR genes, yet it is able to provide predictions at the mechanism/class/group level according to MEGARes ontology, matching the outputs of AMRPlusPlus 2.0 and AMR-meta. Instead, DeepARG uses a unique set of AMR categories derived from the CARD and ARDB. At this point, comparison of tools requires making an arbitrary choice on the AMR ontology to be used, and on the annotation level , potentially penalizing one approach over another, as we show in our semi-synthetic PATRIC datasets PSSOne limitation of our approach is in the sample resistance/susceptibility annotation for validation and benchmark datasets. First, we label most of the bacterial genes that do not match to MEGARes as drug-susceptible, while in reality these sequences might contain new, undiscovered AMR genes. Second, there might be inconsistencies with antibiogram results in PATRIC.k-mer approaches are not very sensitive to mutations, while mutant genes can still carry resistance.Other limitations include the fact that we try only 1 metafeature approach—matrix factorization—while other methods could be tested, e.g., sparse binary principal/independent component analysis. Finally, it is known that k-mer modeling, the LASSO module of AMR-meta could also be efficiently implemented as stand-alone AMR classifier to process data from portable sequencers in real time using mobile devices [Future development for AMR-meta including new strategies to select positive/negative labeled examples (and mutant genes) can further improve the classification performance. As another perspective, given the availability of efficient data structures for  devices .Project name: AMR-metahttps://github.com/smarini/AMR-metaProject home page: RRID:SCR_022026biotoolsID: biotools:amr-metaOperating system: LinuxProgramming language: Bash, R, C++Other requirements: R packages Matrix, stringr, glmnetLicense: MITncbi.nlm.nih.gov/refseq; MEGARes, megares.meglab.org; NCBI BioProject PRJNA244044, ncbi.nlm.nih.gov/bioproject/244044; NCBI BioProject PRJNA215106, ncbi.nlm.nih.gov/bioproject/215106; and PATRIC, patricbrc.org. The AMR-meta algorithm, including a containerized version via Singularity, is available at github.com/smarini/AMR-meta. Snapshots of our code and other data further supporting this work are openly available in the GigaScience respository, GigaDB [As stated in the Methods, the datasets supporting the results of this article are obtainable from public sources, specifically Refseq, Supplementary Figure S1: Cumulative FP ratio over PSScla subsets. Negative short reads from the set genomes have been collated according to the growing similarity of BLAST matches, i.e., 70–75%, >75–80%, >80–85%, and >85–90%. FP ratio has been calculated for each subset. The figure reports the cumulative FP ratio for the different thresholds. Sets not represented in the figure did not provide genomes with similarities within the corresponding column ranges.giac029_GIGA-D-21-00267_Original_SubmissionClick here for additional data file.giac029_GIGA-D-21-00267_Revision_1Click here for additional data file.giac029_GIGA-D-21-00267_Revision_2Click here for additional data file.giac029_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac029_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac029_Reviewer_1_Report_Original_SubmissionJacob Luber -- 10/13/2021 ReviewedClick here for additional data file.giac029_Reviewer_2_Report_Original_SubmissionKamil Khanipov -- 10/24/2021 ReviewedClick here for additional data file.giac029_Supplemental_FileClick here for additional data file.AMR: antimicrobial resistance; ARDB: Antibiotic Resistance Genes Database; BLAST: Basic Local Alignment Search Tool; BWA: Burrows-Wheeler Aligner; CARD: Comprehensive Antibiotic Resistance Database; DIAMOND: double index alignment of next-generation sequencing data; LASSO: least absolute shrinkage and selection operator; PATRIC: Pathosystems Resource Integration Center; IQR: interquartile range; HMM: hidden Markov model; UNIPROT: Universal Protein Resource.The authors declare that they have no competing interests.This work was supported by the National Institutes of Health NIAID R01AI141810; the National Science Foundation SCH 2013998; and the United States Department of Agriculture AFRI 2019-67017-29110.S.M., M.P., C.B., N.R.N., and T.K. conceived the idea and wrote the manuscript. S.M., M.O., I.B.S., and R.A.D. prepared the data and performed the experiments. All authors read and approved the final manuscript.


Idiopathic pulmonary fibrosis (IPF) is a progressive, chronic, interstitial lung disease with a poor prognosis. Although specific anti-fibrotic medications are now available, the median survival time following diagnosis remains very low, and new therapies are urgently needed. To uncover novel therapeutic targets, we examined how biochemical properties of the fibrotic lung are different from the healthy lung. Previous work identified lactate as a metabolite that is upregulated in IPF lung tissue. Importantly, inhibition of the enzyme responsible for lactate production prevents fibrosis in vivo. Further studies revealed that fibrotic lesions of the lung experience a significant decline in tissue pH, likely due to the overproduction of lactate. It is not entirely clear how cells in the lung respond to changes in extracellular pH, but a family of proton sensing G-protein coupled receptors has been shown to be activated by reductions in extracellular pH. This work examines the expression profiles of proton sensing GPCRs in non-fibrotic and IPF-derived primary human lung fibroblasts. We identify TDAG8 as a proton sensing GPCR that is upregulated in IPF fibroblasts and that knockdown of TDAG8 dampens myofibroblast differentiation. To our surprise, BTB, a proposed positive allosteric modulator of TDAG8, inhibits myofibroblast differentiation. Our data suggest that BTB does not require TDAG8 to inhibit myofibroblast differentiation, but rather inhibits myofibroblast differentiation through suppression of RhoA mediated signaling. Our work highlights the therapeutic potential of BTB as an anti-fibrotic treatment and expands upon the importance of RhoA-mediated signaling pathways in the context of myofibroblast differentiation. Furthermore, this works also suggests that TDAG8 inhibition may have therapeutic relevance in the treatment of IPF. Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease affecting approximately 3 million people worldwide . AlthougMyofibroblasts exhibit altered pro-fibrotic behaviors and experience metabolic reprogramming which accompany differentiation ,9,10. MyAlthough lactate induces myofibroblast differentiation, this effect is abolished when media containing lactate is pH adjusted to neutral . Our priIn the early 2000s, a group of proton sensing GPCRs was discovered ,22,23. ATo illuminate the role of TDAG8 activation in fibroblasts, we utilized BTB. As BTB was described as a positive allosteric modulator (PAM) for TDAG8, we anticipated that BTB would activate TDAG8 and thereby induce myofibroblast differentiation. Contrary to our hypothesis, we discovered that BTB potently inhibits myofibroblast differentiation, matrix production and fibroblast proliferation. Subsequently, we identified that BTB did not inhibit myofibroblast differentiation via activation of TDAG8. Thus, although BTB may be a PAM of TDAG8, the off-target effects of BTB predominate. Our data suggest that the anti-fibrotic effects of BTB are related the inhibition of RhoA mediated signaling pathways, including the downstream mediators ROCK1 and mDia.This work highlights the novel small molecule BTB, as an inhibitor of myofibroblast differentiation. Furthermore, this work suggests that the development of TDAG8 inhibitors may have significant therapeutic potential. This manuscript also highlights the importance of diverse RhoA signaling in myofibroblast differentiation. Overall, the work builds towards the development of pH targeted therapeutics which may prove efficacious in the treatment of IPF and other progressive fibrotic diseases.Cell Culture and Reagents: Primary human lung fibroblasts were isolated from biopsy as previously described . The diaTransfections: For siRNA transfections, fibroblasts were seeded at 70% confluency and given 24 h to adhere. Subsequently, fibroblasts were transfected with 50 nM non-targeting siRNA to siRNA specific to TDAG8 . The transfection reagent and siRNA constructs were incubated for 20 min in basal media before the mixture was applied to cells. The media was replaced 18 h after transfection, and protein was harvested 96 h after transfection.Western blotting and Slot blotting: Cell lysates were run on an SDS-PAGE gel and transferred using the Trans-Blot Turbo Transfer System . Western blots were blocked using EveryBlot Blocking Buffer (Bio-Rad) and imaged using the Chemidoc MP fluorescent imager (Bio-Rad). Western blots were quantified utilizing Image Lab Software 6.1 (Bio-Rad). For slot blots, supernatants were collected 72 h after treatment. For each sample, 5 µL supernatant was diluted into 195 µL PBS. These samples were run through a slot blot apparatus (Bio-Rad) with a PVDF membrane and two pieces of filter paper for 5 min, followed by three washes with 1X PBS. The membrane was then blocked with 5% milk in TBST for one hour and probed like a Western blot. All primary antibodies used are listed in Proliferation Assays: Proliferation was assessed directly and indirectly. Proliferation was directly assessed by trypan blue counts as previously described . As an aCytotoxicity Assay: Cytotoxicity was measured by LDH release using the LDH-Glo Cytotoxicity Kit . Supernatants were harvested from cells and stored in an assay compatible storage buffer  until the time course was complete. As a positive control, cells were treated with 10% Triton-X for 5–10 min. Complete cell death was confirmed visually to ensure all cells had detached. The samples were incubated with a luciferase reagent mixture for one hour. Upon reduction of luciferase, a luminescent signal was released, which is proportional to the concentration of LDH released. Luminescence was then measured with a Tecan Infinite F-Plex plate reader.Caspase Activation Assay: The Caspase-Glo assay purchased from Promega contains a pro-luminescent caspase substrate (caspase-3/7 DEVD-aminoluciferin substrate) and a thermostable luciferase. Cells were plated in a 96-well plate format and treated with BTB. Seventy-two hours after treatment, the caspase substrate and reagent were added to induced cell lysis. The cell mixture was incubated for 45 min and intermittently mixed. When caspases are activated, the caspase 3/7 aminoluciferin substrate is cleaved and a luminescent signal is produced. Luminescence was then measured with a Tecan Infinite F-Plex plate reader.RhoA Activity Assay: RhoA activity was assessed utilizing a Rho Activation Assay Kit from Cytoskeleton . This assay exploits the Rho binding domain (RBD) of the effector protein rhotekin. The RBD of rhotekin has a high affinity for the binding domain of GTP-bound RhoA, allowing for affinity-based precipitation. The complete assay was performed at 4 °C to slow the rate of hydrolysis of GTP. Briefly, cells were stimulated with 1 ng/mL of TGFβ to induce activation. Lysates were harvested 15 min after treatment and immediately snap frozen. Ten percent of each lysate was set aside to measure protein concentration. The remaining 90% of lysates were diluted to equal concentrations. Lysates were incubated with RBD-rhotekin coated beads to precipitate active RhoA. Lysates were then run on a Western blot and normalized to total RhoA protein. Positive and negative controls were obtained by incubating lysates with GTPγS (non-hydrolysable GTP) and GDP, respectively. Notably, a high concentration (5 mM) of GDP was required to neutralize basal GTP activity in the negative control.t-test or one-way analysis of variance (ANOVA) where indicated. GraphPad Prism was used for all data analysis. Values of p < 0.05 were considered statistically significant.Statistical Analyses: All data are presented as mean +/− SEM. All experiments were performed in triplicate. Data were analyzed by an unpaired Student’s The proton sensing G-protein coupled receptor family includes TDAG8 (GPR65), OGR1 (GPR68), G2A (GPR132), and GPR4. To identify receptors of interest, we examined the protein expression profiles of TDAG8, OGR1, G2A, and GPR4 in primary human lung fibroblasts isolated from patients with or without a diagnosis of Idiopathic Pulmonary Fibrosis (IPF). We hypothesized that receptors that were upregulated in the IPF fibroblasts may be pro-fibrotic and would be prime candidates for drug development. We found that TDAG8 expression was upregulated in IPF fibroblasts A while OWe hypothesized that proton sensing receptors that were more robustly expressed in IPF fibroblasts may be pro-fibrotic. As TDAG8 was the only receptor exhibiting increased expression in IPF fibroblast, knockdown of TDAG8 was performed to assess if the loss of TDAG8 would be associated with a reduction in pro-fibrotic protein expression A. KnockdThere are currently no known inhibitors of TDAG8. However, the novel compound BTB was specifically designed to activate TDAG8 . Given oIn addition to the expression of αSMA, myofibroblasts are also distinguished by their ability to produce increased matrix proteins. Fibronectin and collagen 1 are two of the most abundant matrix proteins in the IPF lung, the majority of which is produced by myofibroblasts. BTB inhibits the production of fibronectin A–C collaIn IPF, there is a marked expansion of the fibroblast population, likely as a result of TGFβ mediated proliferation. BTB inhibits the proliferation of both non-fibrotic and IPF-derived fibroblasts in a dose dependent manner B. AdditiBTB is a novel ligand designed to bind and activate TDAG8. To verify BTB was working via TDAG8 in fibroblasts, TDAG8 was knocked down and then the cells were subsequently challenged with BTB and/or TGFβ. Despite a 70% reduction in TDAG8 expression A, BTB waTo identify the mechanism(s) by which BTB inhibits the pro-fibrotic behavior of fibroblasts, several signaling pathways were interrogated. Due to the ability of BTB to inhibit the effects of TGFβ, canonical TGFβ signaling was examined. However, BTB did not inhibit Smad2 phosphorylation . FollowiWe next identified the ROCK/Rho signaling axis as a promising mechanism given that this pathway has been identified to induce matrix production ,30,31, iROCK1 and mDia are both regulated by the GTPase RhoA . Given tIPF is a devastating disease with limited treatment options. Central to the pathogenesis of IPF is myofibroblast differentiation. A number of cellular changes accompany myofibroblast differentiation including increased proliferation, matrix production, and metabolic reprogramming. Our previous studies have centered around how myofibroblasts upregulate glycolytic enzyme expression and produce increased lactate, ultimately inducing myofibroblast differentiation. Lactate is the main byproduct of glycolysis and the primary source of extracellular acidification. As tissue pH is decreased in fibrotic areas of the lung in animal models of pulmonary fibrosis, we began to examine if pH sensing mechanisms could be exploited for therapeutic benefit.As tissue acidification is associated with fibrosis, we hypothesized that pH-sensing receptors would be upregulated in IPF-derived fibroblasts and that inhibition of these receptors could be anti-fibrotic. We examined the expression profiles of the family of proton sensing GPCRs, identifying TDAG8 as the only proton sensing receptor upregulated in IPF fibroblasts . When TDGiven these data, we wanted to assess how pharmacologic manipulation of TDAG8 would affect fibroblast behavior. Although there are no known inhibitors of TDAG8 at this time, a positive allosteric modulator has been identified (BTB09089). As this was the only tool available to manipulate TDAG8 activity, we decided to utilize BTB to illuminate the function of TDAG8 activation in fibroblasts. Given our data, we hypothesized that BTB would activate TDAG8 and thus exacerbate myofibroblast differentiation, but, much to our surprise, we discovered that BTB inhibits myofibroblast differentiation. BTB potently inhibits myofibroblast differentiation, matrix production, and proliferation in primary lung fibroblasts isolated from non-fibrotic and IPF donors .Given the unanticipated finding that BTB inhibits myofibroblast differentiation, we set out to confirm whether BTB was indeed inhibiting myofibroblast differentiation through activation of TDAG8. To accomplish this, we knocked down TDAG8 and re-introduced BTB. When TDAG8 was knocked down, BTB was even more efficacious than when TDAG8 expression was normal. This indicates that BTB does not require TDAG8 expression to inhibit myofibroblast differentiation . There aTo better understand the mechanism(s) of action of BTB, we examined if BTB inhibited canonical and/or non-canonical TGFβ signaling. Although BTB did not alter Smad2 phosphorylation , it did This manuscript, in conjunction with other work, highlights several interesting future directions. TDAG8 is a prime candidate for study, both in basic science and clinical research. First, the novel finding that TDAG8 knockdown is anti-fibrotic in fibroblasts warrants further investigation. Published work on TDAG8 has largely focused on inflammation in the epithelium, which suggests that TDAG8 activation is anti-inflammatory while knockdown of TDAG8 is pro-inflammatory ,53,54. TIn regard to clinical research, TDAG8 is a genetically diverse receptor with several single nucleotide polymorphisms (SNPs). In the context of IBD, specific TDAG8 genotypes correlated with disease severity . ExaminaIn recent years, publicly available databases containing wide-scale sequencing information in IPF cohorts have greatly expanded our ability to interrogate associations between pathobiology and genetics in a cell-type specific fashion ,57,58,59BTB represents a prime candidate for future therapeutic development. This paper identifies how BTB interferes with RhoA mediated signaling; however, more work is needed to determine its precise mechanism of action. Additionally, other pro-fibrotic pathways, such as Wnt, converge with RhoA signaling  and may From this study, we conclude that BTB has strong therapeutic potential as it potently inhibits myofibroblast differentiation. Future work will examine if BTB is an effective treatment for pulmonary fibrosis in vivo by utilizing a TGFβ-adenoviral model and a bleomycin induced model of fibrosis. This work also indicates that TDAG8 knockdown has anti-fibrotic potential. Future studies will focus on how TDAG8 may regulate the balance between inflammation and wound healing as it relates to pathogenesis of fibrosis.


In fact, Antarctica is often perceived as the last pristine continent on Earth. Such remote regions, which are assumed to have very low levels of AMR due to limited human activity, represent potential model environments to understand the mechanisms and interactions underpinning the early stages of evolution, de novo development, acquisition and transmission of AMR. Antarctica, with its defined zones of human colonisation (centred around scientific research stations) and large populations of migratory birds and animals, also has great potential with regard to mapping and understanding the spread of early-stage zoonotic interactions. However, to date, studies of AMR in Antarctica are limited. Here, we survey the current literature focussing on the following:There is clear evidence for greater concentrations of AMR around research stations, and although data show reverse zoonosis of the characteristic human gut bacteria to endemic wildlife, AMR within birds and seals appears to be very low, albeit on limited samplings. Furthermore, areas where there is little, to no, human activity still appear to be free from anthropogenically introduced AMR. However, a comprehensive assessment of AMR levels in Antarctica is virtually impossible on current data due to the wide variation in reporting standards and methodologies used and poor geographical coverage. Thus, future studies should engage directly with policymakers to promote the implementation of continent-wide AMR reporting standards. The development of such standards alongside a centralised reporting system would provide baseline data to feedback directly into wastewater treatment policies for the Antarctic Treaty Area to help preserve this relatively pristine environment.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01250-x. Antimicrobial resistance (AMR) is a major global health problem . The higOne global region where surveillance is particularly poor is Antarctica. This remote, extreme environment is not a known AMR hotspot, yet relatively pristine regions, where levels of human settlement are low, can act as simpler model systems to study the factors that propagate AMR. Investigating AMR in such regions can help determine the extent to which human activity has promoted the spread of antimicrobial resistance genes and antibiotic-resistant bacteria . In addiAntarctica is known as the last pristine continent, effectively isolated from the rest of the world by both oceanic and atmospheric circulations , 13. AltHistorically, the rapid rise of human presence in Antarctica only started in the build-up to the International Geophysical Year in 1957/1958. Before that, there was relatively little human activity apart from a couple of whaling stations and Argentinian and UK research stations . The International Geophysical Year (IGY) of 1957-1958 sponsored the building of several research stations in Antarctica to support scientific exploration. This resulted in the core of the present-day network, which currently comprises 30 nations operating 76 permanent research stations , 22. Thehttps://www.ats.aq). The Treaty which designated Antarctica ‘as a natural reserve, devoted to peace and science’, includes the Protocol on Environmental Protection to the Antarctic Treaty (1991) and puts in place strict regulations for environmental protection, including waste management, although the requirements for wastewater treatment are limited (see https://www.ats.aq/e/protocol.html). To date, there have been no rigorous evaluations of AMR in Antarctica, which clearly has health implications for personnel working in such a remote environment, although this issue has been noted by the Council of Managers of National Antarctic Programs [Uniquely, Antarctica, more particularly all regions from 60° S latitude to the South Pole, is governed through consensus by the 29 Consultative Parties to the Antarctic Treaty  ice core. Sulphonamides are synthetic drugs, but an antibiotic-resistant gene cluster in such an ancient sample supports the hypothesis that most antibiotic-resistant genes active against synthetic antibiotics originated from environmental bacteria [Staphylococcus edaphicus sp. that encoded a high number of antibiotic-resistant genes including β-lactamase and an alternative penicillin-binding MecC protein [Single-strain genome sequencing has identified a high diversity of antibiotic-resistant genes present in Antarctic bacteria. For example, numerous multi-drug efflux pumps were identified in the genome of hypolith . Interesbacteria . Hence, bacteria  and phylbacteria . Both of protein . However protein . Further protein , 39. A mThe examples above relate to terrestrial samples (hypoliths and soil), but antibiotic-resistant genes have also been found in freshwater lake and marine samples, albeit at much lower levels than soil samples , 42. In Escherichia coli strains from a range of environmental samples for the presence of AMR at Davis Station  [E. coli carrying AMR determinants were isolated from water, sediments and the filter-feeding bivalve, Laternula elliptica. Interestingly, no resistance was found in the E. coli in the burrowing urchin, Abatus nimrodi. This led to the hypothesis that antibiotic-resistant gene spread was being mediated via the water column, which would impact the filter-feeding bivalve more as it actively pumps water across its gills, whereas the urchin feeds on sediment deposits. Clearly, more studies are needed to validate this hypothesis. In a slightly different approach, cultivatable bacteria isolated from three very different areas were screened for antibiotic sensitivity [E. coli, and resistance to synthetic antibiotics have generally been used as proxies for anthropogenic-introduced AMR. This correlative approach is validated by the identification of E. coli gene cassettes in Antarctic samples, which are identical to those found in clinical contexts [Globally, the emergence of AMR as a serious health concern is connected to human activity. The increases in human activity on the Antarctic continent have provoked a question of whether humans may be introducing antibiotic-resistant genes into this unique region . However winter) , 50. E. sitivity . The firsitivity . Similarsitivity . Thus, icontexts .E. coli into the ecosystem, albeit at very low levels given the number of stations and occupancy rates in the initial ‘colonisation’ of land south of 60° S. However, regulation of waste disposal and treatment changed dramatically at the end of the twentieth century with the implementation of the Protocol on Environmental Protection to the Antarctic Treaty . The Protocol was originally signed in 1991 and formally adopted by the signatory Parties in 1998. Annex III to the Environmental Protocol provides strict regulations for minimising and disposing of waste on the continent, including the banning of the introduction of certain products like polychlorinated biphenyls (https://www.ats.aq/e/protocol.html), but its treatment requirements with regard to wastewater disposal are limited to maceration, albeit some stations operate wastewater treatment plants that deliver much higher treatment standards [https://documents.ats.aq/recatt/Att667_e.pdf) that contained practical guidelines for member states to meet their obligations under Article 1(5) of Annex III to the Protocol.When research stations were built in Antarctica prior to the 1980s, waste regulation was, at most, minimal. Even the earliest station residents had access to synthetic antibiotics  in a bacterium isolated from the Antarctic environment [E. coli [E. coli were present in the water samples tested for antibiotics, the researchers were unable to directly correlate the observed resistance with the compounds identified in the same samples [E. coli collected from various sources around Davis Station also revealed AMR for aminoglycosides and sulphonamides and the presence of mobile genetic element intl1 amplicons in gene cassettes. These gene cassettes were identical to those found in clinical contexts and thus are almost certainly of human origin [The introduction of antibiotics and antibiotic-resistant genes into the wider ecosystem is well known via wastewater treatment plants in other global regions , 55. Thiironment , 58. Theironment . In this[E. coli . Eight a samples . Similarn origin .E. coli in very cold waters. These studies highlight the need for more extensive research to determine whether certain types of wastewater treatment plants perform better in such an extreme environment, or if they can be modified to work more efficiently in very cold conditions [These five isolated studies clearly show incomplete degradation of antibiotics via wastewater treatment plants and their introduction into the Antarctic environment. They also catalogue the persistence of human-derived nditions , 57, 59.nditions . A recennditions . This evnditions .https://www.comnap.aq/). Nine studies concentrated on King George Island and identified both phenotypic and genotypic resistance to a range of natural, semi-synthetic and synthetic antibiotics   with antibiotic-resistant genes. Antibiotic-resistant genes, such as pikR2, were associated with the mobile genetic element Intl1, whilst StrB, SulA and qacedelta1-01 were associated with the transposon tnpA05 and SulA, qacedelta1-01 and cmla-01 with transposon tnpA2 [Intl1 with the sulphonamide- and quinolone-resistant genes sul1, sul2 and qnrS, which was not reproduced in the later study. This may not be surprising as the approaches in the two studies were completely different with PCR of antibiotic-resistant genes directly from soil DNA in one [In terms of how those antibiotic-resistant genes are propagated, the results of the more molecular-centred analyses on King George Island failed to present a clear picture. In general, levels of plasmids and mobile genetic elements were lower in Antarctic bacteria compared with those from other more impacted regions  . Howeveron tnpA2 . InteresA in one  and idenA in one .E. coli strains [Unfortunately, due to the wide variety of methods employed in these studies, it is difficult to develop an overview of AMR incidence on King George Island. For example, five studies surveyed cultivatable bacteria , 61, 63  strains –58. With strains , 51, whi strains   and Weddell seals (Leptonychotes weddellii)) [Salmonella and Campylobacter species in Antarctic wildlife, which likely originated from humans or domestic animals . These studies provided evidence of reverse zoonosis, with birds such as brown skuas (Stercorarius antarcticus) particularly likely to pick up human pathogens as they actively scavenge around areas of human activity [Salmonella isolates were susceptible [E. coli strains found in wastewater outfalls from research stations, low levels of human extra-intestinal E. coli strains (ST95 and ST131) were found in seawater, sediments, the clam Laternula elliptica and Weddell seals [Arctocephalus gazella) and southern elephant seals (Mirounga leonina) identified the presence of E. coli strains ST73, ST95, ST141 and ST131 in several populations [E. coli strain is a significant cause of avian disease [A major gap in our knowledge is the influence of the charismatic megafauna on AMR in the region. Data concerning the role of wildlife as vectors of AMR in Antarctica are very limited, even though it is known that animals and humans may work synergistically to promote the spread of antibiotic-resistant genes . In thisdellii)) . Researcactivity , 65. In ceptible , 69. In ll seals , 46. Furulations . The tra disease , 50 and  disease . Thus, ftet(M) resistance was reported in cloacal swabs from Adélie penguins (Pygoscelis adeliae) colonising Hukuro Cove, Langhovde, on the coast of eastern Antarctica [tet(M) genes were present in the same Adélie population [Aptendytes forsteri) and almost 450 gentoo penguins (Pygoscelis papua), failed to identify antibiotic-resistant bacteria in the faecal samples, with the exception of a single gentoo penguin sample that contained bacterial resistant to chloramphenicol [E. coli strains collected from cloacal swabs or faecal samples from gentoo penguins, kelp gulls (Larus dominicanus) and snowy sheathbills  showed only intermediate level resistance [Salmonella enterica serovar Enteriditis was isolated from gentoo penguins close to the Bernardo O’Higgins Research Station on the Antarctic Peninsula in 2014. However, this strain was only found in 2% of penguins sampled and was not found in samples collected in the two subsequent years (2015 and 2016) [In 2008, the first identification of tarctica . The respulation . Subsequphenicol , 73, 74.sistance . Three ond 2016) . The resnd 2016)  and a sind 2016) . A furthnd 2016) .Overall data on AMR in Antarctic wildlife are very limited, with more recent results largely dominated by penguin-derived samples. It is clear that AMR may reside in endemic wildlife, but the extent and transmissibility are virtually unknown. Where instances of AMR in wildlife have been identified, the significance of the findings have been difficult to verify, particularly when they are either not repeatable, or sample numbers are very low. Clearly, more extensive and systematic analyses are required, especially in view of the current COVID-19 pandemic, which has exacerbated concerns about animal-human transmission (and vice versa) of novel viruses and diseases , 76.This review carried out an extensive literature search of articles investigating AMR in Antarctic environments. Analysis in the relevant publications is based on the premise that resistance to synthetic antibiotics is derived from human activity and is largely correlative. There is also the issue of whether culture-based tests for antibiotic activity in the laboratory  are suitable for AMR detection in this environment, especially as cultivation success levels are low and there may be as yet undefined specific triggers for expression. Overall, the isolated and uncoordinated nature of the studies limits our ability to interpret the results further and estimate the true prevalence of AMR levels on the continent. The results published by many studies describe a few isolates that had been extracted from a limited number of sampling sites, with a variety of different experimental approaches used to describe AMR in terms of antibiotic-resistant bacteria (identified by assaying cultivatable bacteria with antibiotics) or molecular analyses of antibiotic-resistant genes , 46, 49.Culture-based approaches for investigating antibiotic resistance were employed by most studies. Whilst these are useful for associating resistance with a particular bacterium, they do not provide data on the molecular resistance mechanisms unless whole-genome sequencing is employed. Very few antibiotic-producing bacteria are currently cultivatable in the laboratory, which restricts the capacity to fully capture antibiotic-resistant gene diversity or abundance, unless deep sequencing metagenomics approaches are employed , 39. In It is clear from the research carried out to date that more extensive and detailed research is needed to obtain even a reasonable understanding of the extent of AMR in Antarctica and the influence of humans in this regard. Given the chronic and generally accumulative nature of the AMR problem, any short-term evaluations need to be conducted alongside long-term monitoring of AMR in designated regions, specifically at/near research stations. More extensive surveys are also required of the AMR status of endemic wildlife to determine how they may act as either reservoirs of AMR and/or vectors of AMR  and the occurrence of reverse zoonosis . In paraGreater emphasis needs to be placed on reporting metadata, with minimal reporting standards introduced, including logging the exact sampling date and GPS co-ordinates for each sampling event. Studies which combine both culture-based and metagenomics and genome sequencing approaches are to be encouraged, as only then can real links be made between AMR, the underlying mechanisms and how antibiotic resistance genes are propagated in this relatively pristine environment . Similarly, sequencing approaches should enable better differentiation between natural/intrinsic and anthropogenically introduced/acquired AMR and also shed light on the role of animals in AMR interactions. With regard to culture-based methodologies, there is a requirement for an equivalent to EUCAST/CLSI breakpoints for environmental bacteria. Currently, there are no standardised breakpoints to validate disc diffusion assays with non-clinical bacterial isolates. In this regard, minimal inhibitory concentration (MIC) can be used to study phenotypic resistance. However, with many samples and multiple antimicrobials to test, this is a costly and laborious task. These recommendations should form the basis for discussion and the development of an AMR protocol and checklist by Antarctic Treaty Consultative Parties, as discussed further below.https://www.scar.org/policy/scar-codes-of-conduct/), which are endorsed by the ATCM, that include recommendations to reduce contamination of terrestrial and geothermal environments by microorganisms originating from human sources and other environments. However, much still needs to be done with the issue of AMR having been little discussed within the Antarctic Treaty System [E. coli and antibiotic-resistant bacteria into the marine and/or ice environment [Due to the extreme environmental conditions, many Antarctic terrestrial environments are dominated by microbial species, and these comparatively simple communities provide excellent opportunities for research that would be more difficult elsewhere. For some time, researchers have recognised the importance of reducing the release of human-associated microorganisms into marine and terrestrial environments to prevent ‘genetic pollution’ of the continent , 81, 82.y System . With maAlthough traditionally viewed as a relatively pristine environment, antibiotic-resistant genes and antibiotic-resistant determinants were found in studies all across the continent. Initial evaluations suggest that AMR in most of Antarctica may closely represent a ‘pre-antibiotic state’  and can Additional file 1. Extract from MV John Biscoe medical stores list written by Dr William Sladen in 1949, when he was the doctor on call between postings at Hope Bay (Antarctic Peninsula) and Signy Island (South Orkney Islands), showing tablets and bottles of sulphathiazole.Additional file 2. Initial screening criteria for literature review. Parameters for choice of studies investigating AMR on the Antarctic continent.Additional file 3. Referenced GPS locations of studies of AMR in Antarctica.Additional file 4. Summarised data of studies on King George Island.Additional file 5. Locations of studies that found resistance markers for β-lactams and aminoglycosides in Antarctica.Additional file 6. Natural, synthetic and semi-synthetic antibiotics and their drug classes. Examples of antibiotics, the drug classes they belong to, their targets and nature . The list is not exhaustive of all antibiotics or drug classes.