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511800.8768Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132
511710.8730Metagenomic sequencing of mpox virus clade Ib lesions identifies possible bacterial and viral co-infections in hospitalized patients in eastern DRC. Mpox is an emerging zoonotic disease that caused two public health emergencies of international concern within two years. Less is known about the interplay of microbial organisms in mpox lesions which could result in superinfections that exacerbate outcomes or delay recovery. We utilized a unified metagenomic sequencing approach involving slow-speed centrifugation and differential lysis on 19 mpox lesion swabs of hospitalized patients in South Kivu province (eastern DRC) to characterize bacteria, antimicrobial resistance genes, mpox virus (MPXV), and viral co-infections. High-quality MPXV whole-genome sequences were obtained until a Ct value of 27. Furthermore, co-infections with other clinically relevant viruses, such as varicella zoster virus and herpes simplex virus-2, were detected and confirmed by real-time PCR. In addition, metagenomic sequence analysis of the bacterial content showed the presence of bacteria associated with skin and soft tissue infection in 10 of the 19 samples analyzed. These bacteria had a high abundance of resistance genes, with possible implications for antimicrobial treatment based on the predicted antimicrobial resistance. In conclusion, we report the presence of bacterial and viral pathogens in mpox lesions and detection of widespread resistance genes to the standard antibiotic treatment. The possibility of a co-infection, including antimicrobial resistance, should be considered when discussing treatment options, along with the determination of the case-fatality ratio.IMPORTANCEThe mpox virus clade Ib lineage emerged in the eastern Democratic Republic of the Congo owing to continuous human-to-human transmission in a vulnerable patient population. A major challenge of this ongoing outbreak is its occurrence in regions with severely limited healthcare infrastructure. As a result, less is known about co-infections in affected patients. Identifying and characterizing pathogens, including their antimicrobial resistance, is crucial for reducing infection-related complications and improving antimicrobial stewardship. In this study, we applied a unified metagenomics approach to detect and characterize bacterial and viral co-infections in mpox lesions of hospitalized mpox patients in the eastern DRC.202540445195
519420.8713Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis. We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.201930429253
506930.8713MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples. Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla(NDM) and bla(KPC), colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10(2) CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.202337047757
908340.8703ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.202438725076
635350.8698Diversity of silver resistance genes in IncH incompatibility group plasmids. Silver compounds are used as antimicrobial agents in medicine and bacteria that develop resistance to silver cations (Ag(+)) pose problems similar to those of antibiotic-resistant bacteria. The first set of Ag(+) resistance genes (sil) was from plasmid pMG101, now assigned to the IncHI incompatibility group. Questions of whether sil genes are unique to pMG101 or are more widely found, and whether they are associated with a specific incompatibility group or occur in many plasmid groups and on bacterial chromosomes were addressed. sil genes were identified in five IncH plasmids, but not in plasmids of the IncP incompatibility group. Three sil genes (silP, silR and silE) from these plasmids were PCR-amplified, cloned, sequenced and compared to those of pMG101. Differences of 0-50 nt per kb of sequence were found. Predicted gene products were 0-6% different in amino acid sequence, but the differences did not alter residues thought to be involved in protein function (see supplementary data at http://mic.sgmjournals.org or http://www.uic.edu/depts/mcmi/individual/gupta/index.htm). For representative IncH plasmid R476b and pMG101 the effects of Ag(+) exposure on resistance levels were measured by growth. The inducibility of silC, silR and silE gene expression after Ag(+) exposure was studied by reverse transcriptase (RT)-PCR. Silver resistance increased after Ag(+) exposure for strains carrying plasmid R476b. silC and silE expression from R476b was inducible after Ag(+) exposure and was constitutive and high from pMG101. The mRNA levels for the regulatory gene silR was constitutive for both pMG101 and R476b. Close homologues for silABC(ORF96)RS from pMG101 are clustered on the chromosomes of Escherichia coli strains K-12 and O157:H7, without contiguous silP and silE homologues. Insertion deletions of the E. coli K-12 chromosomal homologues for silA and silP gave Ag(+) hypersensitivity for growth. The silA homologue knockout was complemented back to wild-type resistance by the same gene cloned on a plasmid. Homologues of sil genes have also been identified on other enterobacterial genomes.200111739772
999760.8697RNAi screen of DAF-16/FOXO target genes in C. elegans links pathogenesis and dauer formation. The DAF-16/FOXO transcription factor is the major downstream output of the insulin/IGF1R signaling pathway controlling C. elegans dauer larva development and aging. To identify novel downstream genes affecting dauer formation, we used RNAi to screen candidate genes previously identified to be regulated by DAF-16. We used a sensitized genetic background [eri-1(mg366); sdf-9(m708)], which enhances both RNAi efficiency and constitutive dauer formation (Daf-c). Among 513 RNAi clones screened, 21 displayed a synthetic Daf-c (SynDaf) phenotype with sdf-9. One of these genes, srh-100, was previously identified to be SynDaf, but twenty have not previously been associated with dauer formation. Two of the latter genes, lys-1 and cpr-1, are known to participate in innate immunity and six more are predicted to do so, suggesting that the immune response may contribute to the dauer decision. Indeed, we show that two of these genes, lys-1 and clc-1, are required for normal resistance to Staphylococcus aureus. clc-1 is predicted to function in epithelial cohesion. Dauer formation exhibited by daf-8(m85), sdf-9(m708), and the wild-type N2 (at 27°C) were all enhanced by exposure to pathogenic bacteria, while not enhanced in a daf-22(m130) background. We conclude that knockdown of the genes required for proper pathogen resistance increases pathogenic infection, leading to increased dauer formation in our screen. We propose that dauer larva formation is a behavioral response to pathogens mediated by increased dauer pheromone production.201021209831
511970.8691ROCker models for reliable detection and typing of short-read sequences carrying mcr, erm, mph, and lnu antibiotic resistance genes. Quantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short reads, we present novel ROCker models for mcr, mph, erm, and lnu ARG families, as well as models for variants of special public health concern within these families, including mcr-1, mphA, ermB, lnuF, lnuB, and lnuG genes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker versus common static filtering approaches, such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10× higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp reads compared to alternative methods. The ROCker models and all related reference materials and data are freely available through http://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection previously developed for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.IMPORTANCEAntimicrobial resistance gene families encoding erm and mph genes confer resistance to the macrolide class of antimicrobials, which are used to treat a wide range of infections. Similarly, the mcr gene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and the lnu gene family confers resistance to lincomycin, which is reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spread to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, thus contributing toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance.202541143534
616180.8684Unraveling radiation resistance strategies in two bacterial strains from the high background radiation area of Chavara-Neendakara: A comprehensive whole genome analysis. This paper reports the results of gamma irradiation experiments and whole genome sequencing (WGS) performed on vegetative cells of two radiation resistant bacterial strains, Metabacillus halosaccharovorans (VITHBRA001) and Bacillus paralicheniformis (VITHBRA024) (D10 values 2.32 kGy and 1.42 kGy, respectively), inhabiting the top-ranking high background radiation area (HBRA) of Chavara-Neendakara placer deposit (Kerala, India). The present investigation has been carried out in the context that information on strategies of bacteria having mid-range resistance for gamma radiation is inadequate. WGS, annotation, COG and KEGG analyses and manual curation of genes helped us address the possible pathways involved in the major domains of radiation resistance, involving recombination repair, base excision repair, nucleotide excision repair and mismatch repair, and the antioxidant genes, which the candidate could activate to survive under ionizing radiation. Additionally, with the help of these data, we could compare the candidate strains with that of the extremely radiation resistant model bacterium Deinococccus radiodurans, so as to find the commonalities existing in their strategies of resistance on the one hand, and also the rationale behind the difference in D10, on the other. Genomic analysis of VITHBRA001 and VITHBRA024 has further helped us ascertain the difference in capability of radiation resistance between the two strains. Significantly, the genes such as uvsE (NER), frnE (protein protection), ppk1 and ppx (non-enzymatic metabolite production) and those for carotenoid biosynthesis, are endogenous to VITHBRA001, but absent in VITHBRA024, which could explain the former's better radiation resistance. Further, this is the first-time study performed on any bacterial population inhabiting an HBRA. This study also brings forward the two species whose radiation resistance has not been reported thus far, and add to the knowledge on radiation resistant capabilities of the phylum Firmicutes which are abundantly observed in extreme environment.202438857267
511690.8681Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. BACKGROUND: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND FINDINGS: We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. CONCLUSION: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.202032528441
9086100.8680Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.201829746491
5098110.8680Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
5163120.8680Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. BACKGROUND: The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control. METHODS: Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content. RESULTS: The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep. CONCLUSIONS: The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.202438429820
8740130.8680Nitrite reductase activity of sulphate-reducing bacteria prevents their inhibition by nitrate-reducing, sulphide-oxidizing bacteria. Sulphate-reducing bacteria (SRB) can be inhibited by nitrate-reducing, sulphide-oxidizing bacteria (NR-SOB), despite the fact that these two groups are interdependent in many anaerobic environments. Practical applications of this inhibition include the reduction of sulphide concentrations in oil fields by nitrate injection. The NR-SOB Thiomicrospira sp. strain CVO was found to oxidize up to 15 mM sulphide, considerably more than three other NR-SOB strains that were tested. Sulphide oxidation increased the environmental redox potential (Eh) from -400 to +100 mV and gave 0.6 nitrite per nitrate reduced. Within the genus Desulfovibrio, strains Lac3 and Lac6 were inhibited by strain CVO and nitrate for the duration of the experiment, whereas inhibition of strains Lac15 and D. vulgaris Hildenborough was transient. The latter had very high nitrite reductase (Nrf) activity. Southern blotting with D. vulgaris nrf genes as a probe indicated the absence of homologous nrf genes from strains Lac3 and Lac6 and their presence in strain Lac15. With respect to SRB from other genera, inhibition of the known nitrite reducer Desulfobulbus propionicus by strain CVO and nitrate was transient, whereas inhibition of Desulfobacterium autotrophicum and Desulfobacter postgatei was long-lasting. The results indicate that inhibition of SRB by NR-SOB is caused by nitrite production. Nrf-containing SRB can overcome this inhibition by further reducing nitrite to ammonia, preventing a stalling of the favourable metabolic interactions between these two bacterial groups. Nrf, which is widely distributed in SRB, can thus be regarded as a resistance factor that prevents the inhibition of dissimilatory sulphate reduction by nitrite.200312823193
5171140.8676Adaptive laboratory-evolved MRSA with PPEF manifests cross-susceptibility to oxacillin and hypersensitivity to ciprofloxacin. Emerging resistance to current antibiotics is a global threat to human health. Therefore, comprehending the mechanism behind antibiotic resistance holds paramount importance. In the pursuit of finding new antibacterial agents, our group has developed a small molecule, PPEF (2'-(4-ethoxyphenyl)-5-(4-propylpiperazin-1-yl)-1H,1'H-2,5'-bibenzo(d)imidazole), having bisbenzimidazole as a pharmacophore, targeting bacterial type IA topoisomerase, a novel drug target in bacteria. We examined the emergence of mutations leading to PPEF resistance in laboratory-evolved Staphylococcus aureus strains. The growth curve revealed that S. aureus 25923 PPEF-resistant (SA-PR) and methicillin-resistant S. aureus 43300 PPEF-resistant (MRSA-PR) attained stationary phase earlier than their respective reference strains. RNA sequencing analysis revealed that atpD (ATP synthase gene) was downregulated by 2 log(2)-fold in both SA-PR and MRSA-PR strains, whereas there was 10 to 13 log(2)-fold downregulation of mecR1 (methicillin resistance-inducing gene), ble (bleomycin resistance-inducing gene), blaZ (beta-lactamase), pbp (penicillin-binding protein gene), ermA (rRNA adenine methyltransferase gene), and kdpB (potassium-transporting ATPase) in the MRSA-PR strain. Quantitative reverse-transcriptase PCR data confirmed these results. Additionally, MRSA-PR showed a 5 log(2)-fold upregulation of comG and a 9 log(2)-fold downregulation of topB, indicating increased genomic variability and stress adaptation contributing to resistance. Genomic sequencing revealed deletions of resistance genes, including aac(6')-aph(2''), aadD, mecA, and blaZ in MRSA-PR, resulting in a gain in resistance and a diminishing returns epistasis pattern in PPEF-evolved S. aureus strains. This led to the development of an evolved MRSA-PR strain susceptible to oxacillin, ciprofloxacin, gentamicin, and imipenem. Our findings indicate that adaptation to PPEF has increased antibiotic susceptibility, thereby changing the clinical outcomes of infections.IMPORTANCEThis study investigates how Staphylococcus aureus bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) strain, develop resistance to a new candidate antibacterial compound, PPEF (2'-(4-ethoxyphenyl)-5-(4-propylpiperazin-1-yl)-1H,1'H-2,5'-bibenzo(d)imidazole). The research found that resistant strains grew slower and showed significant changes in the activity of genes related to antibiotic resistance. Some resistance genes were deleted in the resistant MRSA strain, making it more sensitive to other antibiotics like oxacillin and ciprofloxacin. These findings highlight how resistance to PPEF leads to increased sensitivity to conventional antibiotics. This suggests that developing combination therapies of PPEF with other antibiotics could optimize treatment regimens and slow resistance evolution. This study also indicates that the antibiotic regimens could be designed to force resistant bacteria into evolutionary trade-offs, where they lose resistance to widely used antibiotics while gaining resistance to a new compound like PPEF.202540662666
5494150.8673Molecular characterization of antimicrobial resistance in Brachyspira species isolated from UK chickens: Identification of novel variants of pleuromutilin and beta-lactam resistance genes. Brachyspira species are Gram negative, anaerobic bacteria that colonise the gut of many animals, including poultry. In poultry, Brachyspira species can be commensal (B. innocens, B. murdochii, 'B. pulli') or pathogenic (B. pilosicoli, B. intermedia, B. alvinipulli or rarely B. hyodysenteriae), the latter causing avian intestinal spirochaetosis (AIS). Antimicrobial therapy options for treatment is limited, frequently involving administration of the pleuromutilin, tiamulin, in water. In this study 38 Brachyspira isolates from chickens in the UK, representing both commensal and pathogenic species, were whole genome sequenced to identify antimicrobial resistance (AMR) mechanisms and the minimum inhibitory concentration (MIC) to a number of antimicrobials was also determined. We identified several new variants of bla(OXA) in B. pilosicoli and B. pulli isolates, and variations in tva which led to two new tva variants in B.murdochii and B.pulli. A number of isolates also harboured mutations known to encode AMR in the 16S and 23S rRNA genes. The percentage of isolates that were genotypically multi-drug resistance (MDR) was 16%, with the most common resistance profile being: tetracycline, pleuromutilin and beta-lactam, which were found in three 'B. pulli' and one B. pilosicoli. There was good correlation with the genotype and the corresponding antibiotic MIC phenotypes: pleuromutilins (tiamulin and valnemulin), macrolides (tylosin and tylvalosin), lincomycin and doxycycline. The occurrence of resistance determinants identified in this study in pathogenic Brachyspira, especially those which were MDR, is likely to impact treatment of AIS and clearance of infections on farm.202438306769
6350160.8672Characterization and genomic analysis of chromate resistant and reducing Bacillus cereus strain SJ1. BACKGROUND: Chromium is a toxic heavy metal, which primarily exists in two inorganic forms, Cr(VI) and Cr(III). Chromate [Cr(VI)] is carcinogenic, mutational, and teratogenic due to its strong oxidizing nature. Biotransformation of Cr(VI) to less-toxic Cr(III) by chromate-resistant and reducing bacteria has offered an ecological and economical option for chromate detoxification and bioremediation. However, knowledge of the genetic determinants for chromate resistance and reduction has been limited so far. Our main aim was to investigate chromate resistance and reduction by Bacillus cereus SJ1, and to further study the underlying mechanisms at the molecular level using the obtained genome sequence. RESULTS: Bacillus cereus SJ1 isolated from chromium-contaminated wastewater of a metal electroplating factory displayed high Cr(VI) resistance with a minimal inhibitory concentration (MIC) of 30 mM when induced with Cr(VI). A complete bacterial reduction of 1 mM Cr(VI) was achieved within 57 h. By genome sequence analysis, a putative chromate transport operon, chrIA1, and two additional chrA genes encoding putative chromate transporters that likely confer chromate resistance were identified. Furthermore, we also found an azoreductase gene azoR and four nitroreductase genes nitR possibly involved in chromate reduction. Using reverse transcription PCR (RT-PCR) technology, it was shown that expression of adjacent genes chrA1 and chrI was induced in response to Cr(VI) but expression of the other two chromate transporter genes chrA2 and chrA3 was constitutive. In contrast, chromate reduction was constitutive in both phenotypic and gene expression analyses. The presence of a resolvase gene upstream of chrIA1, an arsenic resistance operon and a gene encoding Tn7-like transposition proteins ABBCCCD downstream of chrIA1 in B. cereus SJ1 implied the possibility of recent horizontal gene transfer. CONCLUSION: Our results indicate that expression of the chromate transporter gene chrA1 was inducible by Cr(VI) and most likely regulated by the putative transcriptional regulator ChrI. The bacterial Cr(VI)-resistant level was also inducible. The presence of an adjacent arsenic resistance gene cluster nearby the chrIA1 suggested that strong selective pressure by chromium and arsenic could cause bacterial horizontal gene transfer. Such events may favor the survival and increase the resistance level of B. cereus SJ1.201020723231
2522170.8672Identification and specificity validation of unique and antimicrobial resistance genes to trace suspected pathogenic AMR bacteria and to monitor the development of AMR in non-AMR strains in the environment and clinical settings. The detection of developing antimicrobial resistance (AMR) has become a global issue. The detection of developing antimicrobial resistance has become a global issue. The growing number of AMR bacteria poses a new threat to public health. Therefore, a less laborious and quick confirmatory test becomes important for further investigations into developing AMR in the environment and in clinical settings. This study aims to present a comprehensive analysis and validation of unique and antimicrobial-resistant strains from the WHO priority list of antimicrobial-resistant bacteria and previously reported AMR strains such as Acinetobacter baumannii, Aeromonas spp., Anaeromonas frigoriresistens, Anaeromonas gelatinfytica, Bacillus spp., Campylobacter jejuni subsp. jejuni, Enterococcus faecalis, Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Klebsiella pneumonia subsp. pneumoniae, Pseudomonas aeruginosa, Salmonella enterica subsp. enterica serovar Typhimurium, Thermanaeromonas toyohensis, and Vibrio proteolyticus. Using in-house designed gene-specific primers, 18 different antibiotic resistance genes (algJ, alpB, AQU-1, CEPH-A3, ciaB, CMY-1-MOX-7, CMY-1-MOX-9, CMY-1/MOX, cphA2, cphA5, cphA7, ebpA, ECP_4655, fliC, OXA-51, RfbU, ThiU2, and tolB) from 46 strains were selected and validated. Hence, this study provides insight into the identification of strain-specific, unique antimicrobial resistance genes. Targeted amplification and verification using selected unique marker genes have been reported. Thus, the present detection and validation use a robust method for the entire experiment. Results also highlight the presence of another set of 18 antibiotic-resistant and unique genes (Aqu1, cphA2, cphA3, cphA5, cphA7, cmy1/mox7, cmy1/mox9, asaI, ascV, asoB, oxa-12, acr-2, pepA, uo65, pliI, dr0274, tapY2, and cpeT). Of these sets of genes, 15 were found to be suitable for the detection of pathogenic strains belonging to the genera Aeromonas, Pseudomonas, Helicobacter, Campylobacter, Enterococcus, Klebsiella, Acinetobacter, Salmonella, Haemophilus, and Bacillus. Thus, we have detected and verified sets of unique and antimicrobial resistance genes in bacteria on the WHO Priority List and from published reports on AMR bacteria. This study offers advantages for confirming antimicrobial resistance in all suspected AMR bacteria and monitoring the development of AMR in non-AMR bacteria, in the environment, and in clinical settings.202338058762
3764180.8671Evidence for diversifying selection in a set of Mycobacterium tuberculosis genes in response to antibiotic- and nonantibiotic-related pressure. Tuberculosis (TB) is a global health problem estimated to kill 1.4 million people per year. Recent advances in the genomics of the causative agents of TB, bacteria known as the Mycobacterium tuberculosis complex (MTBC), have allowed a better comprehension of its population structure and provided the foundation for molecular evolution analyses. These studies are crucial for a better understanding of TB, including the variation of vaccine efficacy and disease outcome, together with the emergence of drug resistance. Starting from the analysis of 73 publicly available genomes from all the main MTBC lineages, we have screened for evidences of positive selection, a set of 576 genes previously associated with drug resistance or encoding membrane proteins. As expected, because antibiotics constitute strong selective pressure, some of the codons identified correspond to the position of confirmed drug-resistance-associated substitutions in the genes embB, rpoB, and katG. Furthermore, we identified diversifying selection in specific codons of the genes Rv0176 and Rv1872c coding for MCE1-associated transmembrane protein and a putative l-lactate dehydrogenase, respectively. Amino acid sequence analyses showed that in Rv0176, sites undergoing diversifying selection were in a predicted antigen region that varies between "modern" lineages and "ancient" MTBC/BCG strains. In Rv1872c, some of the sites under selection are predicted to impact protein function and thus might result from metabolic adaptation. These results illustrate that diversifying selection in MTBC is happening as a consequence of both antibiotic treatment and other evolutionary pressures.201323449927
5802190.8670Dissecting vancomycin-intermediate resistance in staphylococcus aureus using genome-wide association. Vancomycin-intermediate Staphylococcus aureus (VISA) is currently defined as having minimal inhibitory concentration (MIC) of 4-8 µg/ml. VISA evolves through changes in multiple genetic loci with at least 16 candidate genes identified in clinical and in vitro-selected VISA strains. We report a whole-genome comparative analysis of 49 vancomycin-sensitive S. aureus and 26 VISA strains. Resistance to vancomycin was determined by broth microdilution, Etest, and population analysis profile-area under the curve (PAP-AUC). Genome-wide association studies (GWAS) of 55,977 single-nucleotide polymorphisms identified in one or more strains found one highly significant association (P = 8.78 E-08) between a nonsynonymous mutation at codon 481 (H481) of the rpoB gene and increased vancomycin MIC. Additionally, we used a database of public S. aureus genome sequences to identify rare mutations in candidate genes associated with VISA. On the basis of these data, we proposed a preliminary model called ECM+RMCG for the VISA phenotype as a benchmark for future efforts. The model predicted VISA based on the presence of a rare mutation in a set of candidate genes (walKR, vraSR, graSR, and agrA) and/or three previously experimentally verified mutations (including the rpoB H481 locus) with an accuracy of 81% and a sensitivity of 73%. Further, the level of resistance measured by both Etest and PAP-AUC regressed positively with the number of mutations present in a strain. This study demonstrated 1) the power of GWAS for identifying common genetic variants associated with antibiotic resistance in bacteria and 2) that rare mutations in candidate gene, identified using large genomic data sets, can also be associated with resistance phenotypes.201424787619