PATRIC as a unique resource for studying antimicrobial resistance

Unraveling the molecular dynamics of Pseudomonas aeruginosa biofilms at the air-liquid interface

Aim: The aim of this study was to probe the dynamics of Pseudomonas aeruginosa PA14 air-liquid interface (ALI) biofilms over time through global proteomic analysis. Materials & methods: P. aeruginosa PA14 ALI biofilm samples, collected over 48-144 h, underwent differential expression analysis to identify varying proteins at each time point. Results: A consistent set of 778 proteins was identified, with variable expression over time. Upregulated proteins were mainly linked to 'amino acid transport and metabolism'. Biofilm-related pathways, including cAMP/Vfr and QS, underwent significant changes. Flagella were more influential than pili, especially in early biofilm development. Proteins associated with virulence, transporters and iron showed differential expression throughout. Conclusion: The findings enhance our understanding of ALI biofilm development.

Characterization of Solitalea canadensis α-mannosidase with specific activity towards α1,3-Mannosidic linkages

A recombinant exo-α-mannosidase from Solitalea canadensis (Sc3Man) has been characterized to exhibit strict specificity for hydrolyzing α1,3-mannosidic linkages located at the non-reducing end of glycans containing α-mannose. Enzymatic characterization revealed that Sc3Man operates optimally at a pH of 5.0 and at a temperature of 37 °C. The enzymatic activity was notably enhanced twofold in the presence of Ca2+ ions, emphasizing its potential dependency on this metal ion, while Cu2+ and Zn2+ ions notably impaired enzyme function. Sc3Man was able to efficiently cleave the terminal α1,3 mannose residue from various high-mannose N-glycan structures and from the model glycoprotein RNase B. This work not only expands the categorical scope of bacterial α-mannosidases, but also offers new insight into the glycan metabolism of S. canadensis, highlighting the enzyme's utility for glycan analysis and potential biotechnological applications.

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa

BACKGROUND

Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

RESULTS

Model performance varied across antibiotics. The Support Vector Machine excelled in predicting ciprofloxacin resistance (87% accuracy, F1 Score: 0.57), Light Gradient Boosting Machine for cefotaxime (92% accuracy, F1 Score: 0.42), and Gradient Boosting for ampicillin (58% accuracy, F1 Score: 0.66). In validation with data from Africa, Logistic Regression showed high accuracy for ampicillin (94%, F1 Score: 0.97), while Random Forest and Light Gradient Boosting Machine were effective for ciprofloxacin (50% accuracy, F1 Score: 0.56) and cefotaxime (45% accuracy, F1 Score:0.54), respectively. Key mutations associated with AMR were identified for these antibiotics.

CONCLUSION

As the threat of AMR continues to rise, the successful application of these models, particularly on genomic datasets from LMICs, signals a promising avenue for improving AMR prediction to support large AMR surveillance programs. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against AMR.

Comparative genomics and characterization of a multidrug-resistant Acinetobacter baumannii VRL-M19 isolated from a crowded setting in India

A crowded vegetable market serves as a mass gathering, posing a potential risk for infection transmission. In this study, we isolated a multidrug-resistant Acinetobacter baumannii strain, VRL-M19, from the air of such a market and conducted comparative genomics and phenotypic characterization. Antimicrobial susceptibility testing, genome sequencing using Illumina HiSeq X10, and pan-genome analysis with 788 clinical isolates identified core, accessory, and unique drug-resistant determinants. Mutational analysis of drug-resistance genes, virulence factor annotation, in vitro pathogenicity assessment, subsystem analysis, Multilocus sequence typing, and whole genome phylogenetic analysis were performed. VRL-M19 exhibited multidrug resistance with 69 determinants, and analysis across 788 clinical isolates and 350 Indian isolates revealed more accessory genes (52 out of 69) in the Indian isolates. Multiple mutations were observed in drug target modification genes, and the strain was identified as a moderate biofilm-former with 55 virulence factors. Whole genome phylogenetics indicated a close relationship between VRL-M19 and clinical A. baumannii strains. In conclusion, our comprehensive study suggests that VRL-M19 is a multidrug-resistant, potential pathogen with biofilm-forming capabilities, closely associated with clinical A. baumannii strains.

Resistome Diversity in Escherichia coli Isolates of Global Wastewaters

Antimicrobial resistance (AMR) is a global health threat requiring urgent attention and effective strategies for containment. AMR is fueled by wastewater mismanagement and global mobility, disseminating multidrug-resistant (MDR) strains worldwide. While global estimates of AMR burden have been informative, community-level understanding has received little attention despite reports of high AMR prevalence in healthy communities. We assessed the "invasion" of antibiotic resistance genes (ARGs) into the normal human flora by characterizing AMR Escherichia coli in local wastewaters contributed by a healthy youth population. This study estimated 26% (out of 300 isolates) resistant and 59% plasmid-bearing E. coli in local wastewater. Of the 78 AMR isolates, the frequency of mono-resistance was higher against tetracycline (32%), followed by kanamycin (17%) and chloramphenicol (9%). Five isolates were potentially MDR. We further sequenced four MDRs and four sensitive strains to comprehend the genome and resistome diversity in comparison to the global wastewater E. coli (genomes from the PATRIC database). The whole-genome analysis revealed extensive genome similarity among global isolates, suggesting global dissemination and colonization of E. coli. Global wastewater resistome majorly comprised ARGs against aminoglycosides (26%), beta-lactam (17%), sulfonamide (11%), and trimethoprim (8%). Resistance to colistin, a last-resort antibiotic, was prevalent in MDRs of European and South Asian isolates. A systems approach is required to address the AMR crisis on a global scale, reduce antibiotic usage, and increase the efficiency of wastewater management and disinfection.

Phenotypic and resistome analysis of antibiotic and heavy metal resistance in the Antarctic bacterium Pseudomonas sp. AU10

Resistance to antibiotics and heavy metals in Antarctic bacteria has been investigated due to anthropogenic impact on the continent. However, there is still much to learn about the genetic determinants of resistance in native bacteria. In this study, we investigated antibiotic, heavy metal, and metalloid resistance in Pseudomonas sp. AU10, isolated from King George Island (Antarctica), and analyzed its genome to look for all the associated genetic determinants (resistome). We found that AU10 displayed resistance to Cr(VI), Cu(II), Mn(II), Fe(II), and As(V), and produced an exopolysaccharide with high Cr(VI)-biosorption capacity. Additionaly, the strain showed resistance to aminopenicillins, cefotaxime, aztreonam, azithromycin, and intermediate resistance to chloramphenicol. Regarding the resistome, we did not find resistance genes in AU10's natural plasmid or in a prophage context. Only a copper resistance cluster indicated possible horizontal acquisition. The mechanisms of resistance found were mostly efflux systems, several sequestering proteins, and a few enzymes, such as an AmpC β-lactamase or a chromate reductase, which would account for the observed phenotypic profile. In contrast, the presence of a few gene clusters, including the terZABCDE operon for tellurite resistance, did not correlate with the expected phenotype. Despite the observed resistance to multiple antibiotics and heavy metals, the lack of resistance genes within evident mobile genetic elements is suggestive of the preserved nature of AU10's Antarctic habitat. As Pseudomonas species are good bioindicators of human impact in Antarctic environments, we consider that our results could help refine surveillance studies based on monitoring resistances and associated resistomes in these populations.

An Eco-evolutionary Model on Surviving Lysogeny Through Grounding and Accumulation of Prophages

Temperate phages integrate into the bacterial genomes propagating along with the bacterial genomes. Multiple phage elements, representing diverse prophages, are present in most bacterial genomes. The evolutionary events and the ecological dynamics underlying the accumulation of prophage elements in bacterial genomes have yet to be understood. Here, we show that the local wastewater had 7% of lysogens (hosting mitomycin C-inducible prophages), and they showed resistance to superinfection by their corresponding lysates. Genomic analysis of four lysogens and four non-lysogens revealed the presence of multiple prophages (belonging to Myoviridae and Siphoviridae) in both lysogens and non-lysogens. For large-scale comparison, 2180 Escherichia coli genomes isolated from various sources across the globe and 523 genomes specifically isolated from diverse wastewaters were analyzed. A total of 15,279 prophages were predicted among 2180 E. coli genomes and 2802 prophages among 523 global wastewater isolates, with a mean of ~ 5 prophages per genome. These observations indicate that most putative prophages are relics of past bacteria-phage conflicts; they are "grounded" prophages that cannot excise from the bacterial genome. Prophage distribution analysis based on the sequence homology suggested the random distribution of E. coli prophages within and between E. coli clades. The independent occurrence pattern of these prophages indicates extensive horizontal transfers across the genomes. We modeled the eco-evolutionary dynamics to reconstruct the events that could have resulted in the prophage accumulation accounting for infection, superinfection immunity, and grounding. In bacteria-phage conflicts, the bacteria win by grounding the prophage, which could confer superinfection immunity.

The hologenome of Daphnia magna reveals possible DNA methylation and microbiome-mediated evolution of the host genome

Properties that make organisms ideal laboratory models in developmental and medical research are often the ones that also make them less representative of wild relatives. The waterflea Daphnia magna is an exception, by both sharing many properties with established laboratory models and being a keystone species, a sentinel species for assessing water quality, an indicator of environmental change and an established ecotoxicology model. Yet, Daphnia's full potential has not been fully exploited because of the challenges associated with assembling and annotating its gene-rich genome. Here, we present the first hologenome of Daphnia magna, consisting of a chromosomal-level assembly of the D. magna genome and the draft assembly of its metagenome. By sequencing and mapping transcriptomes from exposures to environmental conditions and from developmental morphological landmarks, we expand the previously annotates gene set for this species. We also provide evidence for the potential role of gene-body DNA-methylation as a mutagen mediating genome evolution. For the first time, our study shows that the gut microbes provide resistance to commonly used antibiotics and virulence factors, potentially mediating Daphnia's environmental-driven rapid evolution. Key findings in this study improve our understanding of the contribution of DNA methylation and gut microbiota to genome evolution in response to rapidly changing environments.

Mining the nanotube-forming Bacillus amyloliquefaciens MR14M3 genome for determining anti-Candida auris and anti-Candida albicans potential by pathogenicity and comparative genomics analysis

There is a global health concern associated with the emergence of the multidrug-resistant (MDR) fungus Candida auris, which has significant mortality rates. Finding innovative and distinctive anti-Candida compounds is essential for treating infections caused by MDR C. auris. A bacterial strain with anti-Candida activity was isolated and identified using 16 S rRNA gene sequencing. The whole genome was sequenced to identify biosynthesis-related gene clusters. The pathogenicity and cytotoxicity of the isolate were analyzed in Candida and HFF-1 cell lines, respectively. This study set out to show that whole-genome sequencing, cytotoxicity testing, and pathogenicity analysis combined with genome mining and comparative genomics can successfully identify biosynthesis-related gene clusters in native bacterial isolates that encode antifungal natural compounds active against Candida albicans and C. auris. The native isolate MR14M3 has the ability to inhibit C. auris (zone of inhibition 25 mm) and C. albicans (zone of inhibition 25 mm). The 16 S rRNA gene sequence of MR14M3 aligned with Bacillus amyloliquefaciens with similarity (100%). Bacillus amyloliquefaciens MR14M3 establishes bridges of intercellular nanotubes (L 258.56 ± 35.83 nm; W 25.32 ± 6.09 nm) connecting neighboring cells. Candida cell size was reduced significantly, and crushed phenotypes were observed upon treatment with the defused metabolites of B. amyloliquefaciens MR14M3. Furthermore, the pathogenicity of B. amyloliquefaciens MR14M3 on Candida cells was observed through cell membrane disruption and lysed yeast cells. The whole-genome alignment of the MR14M3 genome (3981,643 bp) using 100 genes confirmed its affiliation with Bacillus amyloliquefaciens. Genome mining analysis revealed that MR14M3-coded secondary metabolites are involved in the biosynthesis of polyketides (PKs) and nonribosomal peptide synthases (NRPSs), including 11 biosynthesis-related gene clusters with one hundred percent similarity. Highly conserved biosynthesis-related gene clusters with anti-C. albicans and anti-C. auris potentials and cytotoxic-free activity of B. amyloliquefaciens MR14M3 proposes the utilization of Bacillus amyloliquefaciens MR14M3 as a biofactory for an anti-Candida auris and anti-C. albicans compound synthesizer.

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

Characterization of Antibiotic-Resistant Stenotrophomonas Isolates from Painted Turtles Living in the Wild

Stenotrophomonas maltophilia is a ubiquitous multidrug-resistant opportunistic pathogen commonly associated with nosocomial infections. The purpose of this study was to isolate and characterize extended-spectrum beta-lactamase (ESBL) producing bacteria from painted turtles (Chrysemys picta) living in the wild and captured in southeastern Wisconsin. Fecal samples from ten turtles were examined for ESBL producing bacteria after incubation on HardyCHROM™ ESBL agar. Two isolates were cultivated and identified by 16S rRNA gene sequencing and whole genome sequencing (WGS) as Stenotrophomonas sp. 9A and S. maltophilia 15A. They were multidrug-resistant, as determined by antibiotic susceptibility testing. Stenotrophomonas sp. 9A was found to produce an extended spectrum beta-lactamase (ESBL) and both isolates were found to be carbapenem-resistant. EDTA-modified carbapenem inactivation method (eCIM) and the modified carbapenem inactivation method (mCIM) tests were used to examine the carbapenemase production and the test results were negative. Through WGS several antimicrobial resistance genes were identified in S. maltophilia 15A. For example a chromosomal L1 β-lactamase gene, which is known to hydrolyze carbapenems, a L2 β-lactamase gene, genes for the efflux systems smeABC and smeDEF and the aminoglycosides resistance genes aac(6')-lz and aph(3')-llc were found. An L2 β-lactamase gene in Stenotrophomonas sp. 9A was identified through WGS.

A Cross-Validated Feature Selection (CVFS) approach for extracting the most parsimonious feature sets and discovering potential antimicrobial resistance (AMR) biomarkers

Understanding genes and their underlying mechanisms is critical in deciphering how antimicrobial-resistant (AMR) bacteria withstand detrimental effects of antibiotic drugs. At the same time the genes related to AMR phenotypes may also serve as biomarkers for predicting whether a microbial strain is resistant to certain antibiotic drugs. We developed a Cross-Validated Feature Selection (CVFS) approach for robustly selecting the most parsimonious gene sets for predicting AMR activities from bacterial pan-genomes. The core idea behind the CVFS approach is interrogating features among non-overlapping sub-parts of the datasets to ensure the representativeness of the features. By randomly splitting the dataset into disjoint sub-parts, conducting feature selection within each sub-part, and intersecting the features shared by all sub-parts, the CVFS approach is able to achieve the goal of extracting the most representative features for yielding satisfactory AMR activity prediction accuracy. By testing this idea on bacterial pan-genome datasets, we showed that this approach was able to extract the most succinct feature sets that predicted AMR activities very well, indicating the potential of these genes as AMR biomarkers. The functional analysis demonstrated that the CVFS approach was able to extract both known AMR genes and novel ones, suggesting the capabilities of the algorithm in selecting relevant features and highlighting the potential of the novel genes in expanding the antimicrobial resistance gene databases.

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning

Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

In vitro and in silico β-lactamase inhibitory properties and phytochemical profile of Ocimum basilicum cultivated in central delta of Egypt

CONTEXT

Some studies reported the chemical content and antimicrobial properties of Ocimum basilicum L. (Lamiaceae), relevant to the ecological variations in some areas of Egypt and other countries, yet no research was conducted on the plant cultivated in the central delta region of Egypt. Also, no previous data reported on inhibition of β-lactamases by O. basilicum.

OBJECTIVE

To assess β-lactamases inhibition by O. basilicum extracts and the individual constituents.

MATERIALS AND METHODS

Dried aerial parts of O. basilicum were extracted by hydrodistillation for preparation of essential oil and by methanol for non-volatile constituents. Essential oil content and the methanol extract were analysed by GC-MS and UPLC-PDA-MS/MS, respectively. Methyl cinnamate was isolated and analysed by NMR. Broth microdilution method was used to investigate the antimicrobial against resistant clinical isolates of Escherichia coli identified by double disc synergy, combination disc tests and PCR. The most active oil content was further tested with a nitrocefin kit for β-lactamase inhibition and investigated by docking.

RESULTS

O. basilicum was found to contain methyl cinnamate as the major content of the essential oil. More interestingly, methyl cinnamate inhibited ESBL β-lactamases of the type CTX-M. The in vitro IC₅₀ using nitrocefin kit was 11.6 µg/mL vs. 8.1 µg/mL for clavulanic acid as a standard β-lactamase inhibitor.

DISCUSSION AND CONCLUSIONS

This is the first study to report the inhibitory activity of O. basilicum oil and methyl cinnamate against β-lactamase-producing bacteria. The results indicate that methyl cinnamate could be a potential alternative for β-lactamase inhibition.

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

Concentration-Dependent Global Quantitative Proteome Response of Staphylococcus epidermidis RP62A Biofilms to Subinhibitory Tigecycline

Staphylococcus epidermidis is a leading cause of biofilm-associated infections on implanted medical devices. During the treatment of an infection, bacterial cells inside biofilms may be exposed to sublethal concentrations of the antimicrobial agents. In the present study, the effect of subinhibitory concentrations of tigecycline (TC) on biofilms formed by S. epidermidis strain RP62A was investigated using a quantitative global proteomic technique. Sublethal concentrations of TC [1/8 (T1) and 1/4 minimum inhibitory concentration (MIC) (T2)] promoted biofilm production in strain RP62A, but 1/2 MIC TC (T3) significantly inhibited biofilm production. Overall, 413, 429, and 518 proteins were differentially expressed in biofilms grown with 1/8 (T1), 1/4 (T2), and 1/2 (T3) MIC of TC, respectively. As the TC concentration increased, the number of induced proteins in each Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway increased. The TC concentration dependence of the proteome response highlights the diverse mechanisms of adaptive responses in strain RP62A biofilms. In both COG and KEGG functional analyses, most upregulated proteins belong to the metabolism pathway, suggesting that it may play an important role in the defense of strain RP62A biofilm cells against TC stress. Sub-MIC TC treatment of strain RP62A biofilms led to significant changes of protein expression related to biofilm formation, antimicrobial resistance, virulence, quorum sensing, ABC transporters, protein export, purine/pyrimidine biosynthesis, ribosomes, and essential proteins. Interestingly, in addition to tetracycline resistance, proteins involved in resistance of various antibiotics, including aminoglycosides, antimicrobial peptides, β-lactams, erythromycin, fluoroquinolones, fusidic acid, glycopeptides, lipopeptides, mupirocin, rifampicin and trimethoprim were differentially expressed. Our study demonstrates that global protein expression profiling of biofilm cells to antibiotic pressure may improve our understanding of the mechanisms of antibiotic resistance in biofilms.

Characteristics of the plasmid-mediated colistin-resistance gene mcr-1 in Escherichia coli isolated from a veterinary hospital in Shanghai

The mobile colistin-resistance (mcr)-1 gene is primarily detected in Enterobacteriaceae species, such as Escherichia coli and Salmonella enterica, and represents a significant public health threat. Herein, we investigated the prevalence and characteristics of mcr-1-positive E. coli (MCRPEC) in hospitalized companion animals in a pet hospital in Shanghai, China, from May 2021 to July 2021. Seventy-nine non-duplicate samples were collected from the feces (n = 52) and wounds (n = 20) of cats and dogs and the surrounding hospital environment (n = 7). Seven MCRPEC strains, identified using screening assays and polymerase chain reaction, exhibited multidrug-resistant phenotypes in broth-microdilution and agar-dilution assays. Based in whole-genome sequencing and bioinformatics analyses, all seven isolates were determined to belong to sequence type (ST) 117. Moreover, the Incl2 plasmid was prevalent in these MCRPEC isolates, and the genetic environment of the seven E. coli strains was highly similar to that of E. coli SZ02 isolated from human blood. The isolates also harbored the β-lactamase gene bla CTX-M-65, and florfenicol resistance gene floR, among other resistance genes. Given that horizontal transfer occurred in all seven strains, E. coli plasmid transferability may accelerate the emergence of multidrug-resistant bacteria and may be transmitted from companion animals to humans. Therefore, the surveillance of MCRPEC isolates among companion animals should be strengthened.

Novel Megaplasmid Driving NDM-1-Mediated Carbapenem Resistance in Klebsiella pneumoniae ST1588 in South America

Carbapenem-resistant Enterobacterales (CRE) is a critical public health problem in South America, where the prevalence of NDM metallo-betalactamases has increased substantially in recent years. In this study, we used whole genome sequencing to characterize a multidrug-resistant (MDR) Klebsiella pneumoniae (UCO-361 strain) clinical isolate from a teaching hospital in Chile. Using long-read (Nanopore) and short-read (Illumina) sequence data, we identified a novel un-typeable megaplasmid (314,976 kb, pNDM-1_UCO-361) carrying the bla_NDM-1 carbapenem resistance gene within a Tn3000 transposon. Strikingly, conjugal transfer of pNDM-1_UCO-361 plasmid only occurs at low temperatures with a high frequency of 4.3 × 10⁻⁶ transconjugants/receptors at 27 °C. UCO-361 belonged to the ST1588 clone, previously identified in Latin America, and harbored aminoglycoside, extended-spectrum β-lactamases (ESBLs), carbapenem, and quinolone-resistance determinants. These findings suggest that bla_NDM-1-bearing megaplasmids can be adapted to carriage by some K. pneumoniae lineages, whereas its conjugation at low temperatures could contribute to rapid dissemination at the human–environmental interface.

Genomic evaluation of novel Kenyan virulent phage isolates infecting carbapenemase-producing Klebsiella pneumoniae and safety determination of their lysates in Balb/c mice

This study aimed to evaluate the genomic features of novel Kenyan virulent phage isolates infecting carbapenemase-producing Klebsiella pneumoniae and to determine the safety of their lysates using mice model in a preclinical study. The genomics showed that the Klebsiella phages vB_KpM_CPRSA and vB_KpM_CPRSB belonged to the genus Slopekvirus with a similarity index of less than 92% compared to the most closest relative species. Their genomes did not contain antimicrobial resistance and toxin genes. Then endotoxin levels in the Klebsiella phage lysates were statistically significant (p value ˃ 0.05). The serum activities of aspartate aminotransferase, alanine aminotransferase and urea in the group of balb/c mice injected with bacteriophage lysates through the intravenous route were higher compared to that of the intranasal route. Unexpectedly, there was mild congestion of the central veins of kidneys and liver without damage to renal tubules and hepatocytes and a lack of physical discomfort and pain in the mice. Our study isolated and characterised Klebsiella phages against carbapenem-resistant K. pneumoniae, which are promising therapeutic agents for the treatment of respiratory tract infections using the topical mode of administration as the preferred route of bacteriophage delivery.

Insights into the Antimicrobial Resistance Profile of a Next Generation Probiotic Akkermansia muciniphila DSM 22959

Akkermansia muciniphila is a Gram-negative intestinal anaerobic bacterium recently proposed as a novel probiotic candidate to be incorporated in food and pharmaceutical forms. Despite its multiple health benefits, the data addressing its antimicrobial susceptibility profile remain scarce. However, the absence of acquired resistance in probiotic strains is a compulsory criterion for its approval in the qualified presumption of safety list. This study aimed at characterizing the A. muciniphila DSM 22959 strain’s antimicrobial susceptibility profile using phenotypic and in silico approaches. To establish the phenotypic antimicrobial susceptibility profile of this strain, minimum inhibitory concentrations of eight antimicrobials were determined using broth microdilution and E-test methods. Additionally, the A. muciniphila DSM 22959 genome was screened using available databases and bioinformatics tools to identify putative antimicrobial resistance genes (ARG), virulence factors (VF), genomic islands (GI), and mobile genetic elements (MGE). The same categorization was obtained for both phenotypic methods. Resistance phenotype was observed for gentamicin, kanamycin, streptomycin, and ciprofloxacin, which was supported by the genomic context. No evidence was found of horizontal acquisition or potential transferability of the identified ARG and VF. Thus, this study provides new insights regarding the phenotypic and genotypic antimicrobial susceptibility profiles of the probiotic candidate A. muciniphila DSM 22959.

Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

Pangenome analysis of Enterobacteria reveals richness of secondary metabolite gene clusters and their associated gene sets

In silico genome mining provides easy access to secondary metabolite biosynthetic gene clusters (BGCs) encoding the biosynthesis of many bioactive compounds, which are the basis for many important drugs used in human medicine. However, the association between BGCs and other functions encoded in the genomes of producers have remained elusive. Here, we present a systems biology workflow that integrates genome mining with a detailed pangenome analysis for detecting genes associated with a particular BGC. We analyzed 3,889 enterobacterial genomes and found 13,266 BGCs, represented by 252 distinct BGC families and 347 additional singletons. A pangenome analysis revealed 88 genes putatively associated with a specific BGC coding for the colon cancer-related colibactin that code for diverse metabolic and regulatory functions. The presented workflow opens up the possibility to discover novel secondary metabolites, better understand their physiological roles, and provides a guide to identify and analyze BGC associated gene sets.

Review and Comparison of Antimicrobial Resistance Gene Databases

As the prevalence of antimicrobial resistance genes is increasing in microbes, we are facing the return of the pre-antibiotic era. Consecutively, the number of studies concerning antibiotic resistance and its spread in the environment is rapidly growing. Next generation sequencing technologies are widespread used in many areas of biological research and antibiotic resistance is no exception. For the rapid annotation of whole genome sequencing and metagenomic results considering antibiotic resistance, several tools and data resources were developed. These databases, however, can differ fundamentally in the number and type of genes and resistance determinants they comprise. Furthermore, the annotation structure and metadata stored in these resources can also contribute to their differences. Several previous reviews were published on the tools and databases of resistance gene annotation; however, to our knowledge, no previous review focused solely and in depth on the differences in the databases. In this review, we compare the most well-known and widely used antibiotic resistance gene databases based on their structure and content. We believe that this knowledge is fundamental for selecting the most appropriate database for a research question and for the development of new tools and resources of resistance gene annotation.

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

Revealing antimicrobial resistance profile of the novel probiotic candidate Faecalibacterium prausnitzii DSM 17677

Faecalibacterium prausnitzii, a resident anaerobic bacterium commonly found in healthy gut microbiota, has been proposed as a next generation probiotic with high potential for application in food matrices and pharmaceutical formulations. Despite its recognized health benefits, detailed information regarding its antimicrobial susceptibility profile is still lacking. However, this information is crucial to determine its safety, since the absence of acquired antimicrobial resistance is required to qualify a probiotic candidate as safe for human and animal consumption. Herein, the antimicrobial susceptibility profile of F. prausnitzii DSM 17677 strain was evaluated by integrating both phenotypic and in silico data. Phenotypic antimicrobial susceptibility was evaluated by determining minimum inhibitory concentrations of 9 antimicrobials using broth microdilution and E-test® methods. Also, the whole genome of F. prausnitzii DSM 17677 was analysed, using several databases and bioinformatics tools, to identify possible antibiotic resistance genes (ARG), genomic islands (GI) and mobile genetic elements (MGE). With exception of erythromycin, the same classification (susceptible or resistant) was obtained in both broth microdilution and E-test® methods. Phenotypic resistance to ampicillin, gentamycin, kanamycin and streptomycin were detected, which was supported by the genomic context. Other ARG were also identified but they seem not to be expressed under the tested conditions. F. prausnitzii DSM 17677 genome contains 24 annotated genes putatively involved in resistance against the following classes of antimicrobials: aminoglycosides (such as gentamycin, kanamycin and streptomycin), macrolides (such as erythromycin), tetracyclines and lincosamides. The presence of putative ARG conferring resistance to β-lactams could only be detected using a broader homology search. The majority of these genes are not encoded within GI or MGE and no plasmids were reported for this strain. Despite the fact that most genes are related with general resistance mechanisms, a streptomycin-specific ARG poses the only potential concern identified. This specific ARG is encoded within a GI and a MGE, meaning that it could have been laterally acquired and might be transferred to other bacteria present in the same environment. Thus, our findings provide relevant insights regarding the phenotypic and genotypic antimicrobial resistance profiles of the probiotic candidate F. prausnitzii DSM 17677.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

Draft genome sequence of a prodigiosin-hyperproducing Serratia marcescens strain isolated from Cairo, Egypt

Serratia marcescens is a Gram-negative bacterium with both environmental and host-associated strains. Pigmentation is reportedly inversely correlated with infection frequency, and prodigiosin is one of Serratia pigments that has medical and industrial applications. Here, we report the draft genome sequence of prodigiosin-hyperproducing Serratia marcescens strain N2, isolated from Cairo, Egypt. The sequence is assembled into 142 contigs, with a combined size of 5,570,793 bp. The assembled genome carries typical S. marcescens genes, with potential prodigiosin-biosynthesizing genes detected.

Comparative genomic insights into culturable symbiotic cyanobacteria from the water fern Azolla

Species of the floating, freshwater fern Azolla form a well-characterized symbiotic association with the non-culturable cyanobacterium Nostoc azollae, which fixes nitrogen for the plant. However, several cyanobacterial strains have over the years been isolated and cultured from Azolla from all over the world. The genomes of 10 of these strains were sequenced and compared with each other, with other symbiotic cyanobacterial strains, and with similar strains that were not isolated from a symbiotic association. The 10 strains fell into three distinct groups: six strains were nearly identical to the non-symbiotic strain, Nostoc (Anabaena) variabilis ATCC 29413; three were similar to the symbiotic strain, Nostoc punctiforme, and one, Nostoc sp. 2RC, was most similar to non-symbiotic strains of Nostoc linckia. However, Nostoc sp. 2RC was unusual because it has three sets of nitrogenase genes; it has complete gene clusters for two distinct Mo-nitrogenases and an alternative V-nitrogenase. Genes for Mo-nitrogenase, sugar transport, chemotaxis and pili characterized all the symbiotic strains. Several of the strains infected the liverwort Blasia, including N. variabilis ATCC 29413, which did not originate from Azolla but rather from a sewage pond. However, only Nostoc sp. 2RC, which produced highly motile hormogonia, was capable of high-frequency infection of Blasia. Thus, some of these strains, which grow readily in the laboratory, may be useful in establishing novel symbiotic associations with other plants.

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review

Background

Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance.

Methods

We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates.

Results

CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%).

Conclusions

We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions.

IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

Genome and Pangenome Analysis of Lactobacillus hilgardii FLUB-A New Strain Isolated from Mead

The production of mead holds great value for the Polish liquor industry, which is why the bacterium that spoils mead has become an object of concern and scientific interest. This article describes, for the first time, Lactobacillus hilgardii FLUB newly isolated from mead, as a mead spoilage bacteria. Whole genome sequencing of L. hilgardii FLUB revealed a 3 Mbp chromosome and five plasmids, which is the largest reported genome of this species. An extensive phylogenetic analysis and digital DNA-DNA hybridization confirmed the membership of the strain in the L. hilgardii species. The genome of L. hilgardii FLUB encodes 3043 genes, 2871 of which are protein coding sequences, 79 code for RNA, and 93 are pseudogenes. L. hilgardii FLUB possesses three clustered regularly interspaced short palindromic repeats (CRISPR), eight genomic islands (44,155 bp to 6345 bp), and three (two intact and one incomplete) prophage regions. For the first time, the characteristics of the genome of this species were described and a pangenomic analysis was performed. The concept of the pangenome was used not only to establish the genetic repertoire of this species, but primarily to highlight the unique characteristics of L. hilgardii FLUB. The core of the genome of L. hilgardii is centered around genes related to the storage and processing of genetic information, as well as to carbohydrate and amino acid metabolism. Strains with such a genetic constitution can effectively adapt to environmental changes. L. hilgardii FLUB is distinguished by an extensive cluster of metabolic genes, arsenic detoxification genes, and unique surface layer proteins. Variants of MRS broth with ethanol (10-20%), glucose (2-25%), and fructose (2-24%) were prepared to test the strain's growth preferences using Bioscreen C and the PYTHON script. L. hilgardii FLUB was found to be more resistant than a reference strain to high concentrations of alcohol (18%) and sugars (25%). It exhibited greater preference for fructose than glucose, which suggests it has a fructophilic nature. Comparative genomic analysis supported by experimental research imitating the conditions of alcoholic beverages confirmed the niche specialization of L. hilgardii FLUB to the mead environment.

The extent of carbapenemase-encoding genes in public genome sequences

Genome sequences provide information on the genetic elements present in an organism, and currently there are databases containing hundreds of thousands of bacterial genome sequences. These repositories allow for mining patterns concerning antibiotic resistance gene occurrence in both pathogenic and non-pathogenic bacteria in e.g. natural or animal environments, and link these to relevant metadata such as bacterial host species, country and year of isolation, and co-occurrence with other resistance genes. In addition, the advances in the prediction of mobile genetic elements, and discerning chromosomal from plasmid DNA, broadens our view on the mechanism mediating dissemination. In this study we utilize the vast amount of data in the public database PATRIC to investigate the dissemination of carbapenemase-encoding genes (CEGs), the emergence and spread of which is considered a grave public health concern. Based on publicly available genome sequences from PATRIC and manually curated CEG sequences from the beta lactam database, we found 7,964 bacterial genomes, belonging to at least 70 distinct species, that carry in total 9,892 CEGs, amongst which bla _NDM, bla _OXA, bla _VIM, bla _IMP and bla _KPC. We were able to distinguish between chromosomally located resistance genes (4,137; 42%) and plasmid-located resistance genes (5,753; 58%). We found that a large proportion of the identified CEGs were identical, i.e. displayed 100% nucleotide similarity in multiple bacterial species (8,361 out of 9,892 genes; 85%). For example, the New Delhi metallo-beta-lactamase NDM-1 was found in 42 distinct bacterial species, and present in seven different environments. Our data show the extent of carbapenem-resistance far beyond the canonical species Acetinobacter baumannii, Klebsiella pneumoniae or Pseudomonas aeruginosa. These types of data complement previous systematic reviews, in which carbapenem-resistant Enterobacteriaceae were found in wildlife, livestock and companion animals. Considering the widespread distribution of CEGs, we see a need for comprehensive surveillance and transmission studies covering more host species and environments, akin to previous extensive surveys that focused on extended spectrum beta-lactamases. This may help to fully appreciate the spread of CEGs and improve the understanding of mechanisms underlying transmission, which could lead to interventions minimizing transmission to humans.

A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors

PURPOSE

While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.

EXPERIMENTAL DESIGN

We have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (n = 303 unique patients) using a uniform computational approach.

RESULTS

Herein, we identify novel bacterial signals associated with clinical responders (R) or nonresponders (NR) and develop an integrated microbiome prediction index. Unexpectedly, the NR-associated integrated index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (P < 0.01). We subsequently tested the integrated index using validation cohorts across three distinct and diverse cancers (n = 105).

CONCLUSIONS

Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

Carbapenems drive the collateral resistance to ceftaroline in cystic fibrosis patients with MRSA

Chronic airways infection with methicillin-resistant Staphylococcus aureus (MRSA) is associated with worse respiratory disease cystic fibrosis (CF) patients. Ceftaroline is a cephalosporin that inhibits the penicillin-binding protein (PBP2a) uniquely produced by MRSA. We analyzed 335 S. aureus isolates from CF sputum samples collected at three US centers between 2015-2018. Molecular relationships demonstrated that high-level resistance of preceding isolates to carbapenems were associated with subsequent isolation of ceftaroline resistant CF MRSA. In vitro evolution experiments showed that pre-exposure of CF MRSA to meropenem with further selection with ceftaroline implied mutations in mecA and additional mutations in pbp1 and pbp2, targets of carbapenems; no effects were achieved by other β-lactams. An in vivo pneumonia mouse model showed the potential therapeutic efficacy of ceftaroline/meropenem combination against ceftaroline-resistant CF MRSA infections. Thus, the present findings highlight risk factors and potential therapeutic strategies offering an opportunity to both prevent and address antibiotic resistance in this patient population.

Predicting antimicrobial resistance using conserved genes

A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features derived from comprehensive sets of AMR genes or whole genome sequences and may not be suitable for use when genomes are incomplete. In this study, we explore the possibility of predicting AMR phenotypes using incomplete genome sequence data. Models were built from small sets of randomly-selected core genes after removing the AMR genes. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in cases where the primary AMR mechanisms result from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes, we show that F1 scores and error rates are stable and have little variance between replicates. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes.

Experimental approaches to tracking mobile genetic elements in microbial communities

Horizontal gene transfer is an important mechanism of microbial evolution and is often driven by the movement of mobile genetic elements between cells. Due to the fact that microbes live within communities, various mechanisms of horizontal gene transfer and types of mobile elements can co-occur. However, the ways in which horizontal gene transfer impacts and is impacted by communities containing diverse mobile elements has been challenging to address. Thus, the field would benefit from incorporating community-level information and novel approaches alongside existing methods. Emerging technologies for tracking mobile elements and assigning them to host organisms provide promise for understanding the web of potential DNA transfers in diverse microbial communities more comprehensively. Compared to existing experimental approaches, chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts. We also briefly discuss how fermented food microbiomes, given their experimental tractability and moderate species complexity, make ideal models to which to apply the techniques discussed herein and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities.

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes

BACKGROUND

The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work.

RESULTS

Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings.

CONCLUSION

We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

sraX: A Novel Comprehensive Resistome Analysis Tool

The accurate identification of the assortment of antibiotic resistance genes within a collection of genomes enables the discernment of intricate antimicrobial resistance (AMR) patterns while depicting the diversity of resistome profiles of the analyzed samples. The availability of large amount of sequence data, owing to the advancement of novel sequencing technologies, have conceded exciting possibilities for developing suitable AMR exploration tools. However, the level of complexity of bioinformatic analyses has raised as well, since the achievement of desired results involves executing several challenging steps. Here, sraX is proposed as a fully automated analytical pipeline for performing a precise resistome analysis. Our nominated tool is capable of scrutinizing hundreds of bacterial genomes in-parallel for detecting and annotating putative resistant determinants. Particularly, sraX presents unique features: genomic context analysis, validation of known mutations conferring resistance, illustration of drug classes and type of mutated loci proportions and integration of results into a single hyperlinked navigable HTML-formatted file. Furthermore, sraX also exhibits relevant operational features since the complete analysis is accomplished by executing a single-command step. The capacity and efficacy of sraX was demonstrated by re-analyzing 197 strains belonging to Enterococcus spp., from which we confirmed 99.15% of all detection events that were reported in the original study. sraX can be downloaded from https://github.com/lgpdevtools/srax.

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high-density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for protein variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species that contained AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

Antimicrobial resistance (AMR) is a global health threat. The current method to determine AMR is inefficient and complete understanding of the mechanisms of AMR is lacking. With the increased feasibility of sequencing bacterial genomes, it is now easier, faster and cheaper to have genomic insights into AMR. In this manuscript, we propose a novel bioinformatic tool for variant mapping and prediction of antibiotic resistance (VAMPr). We curated 3,393 bacterial genomes from 9 bacterial species that contained AMR phenotypes for 29 antibiotics. We used protein orthology and detected 14,615 variants. Combined with AMR phenotypes, we built 93 association and prediction models. The association model confirms known genetic AMR mechanisms, and the prediction models achieved high accuracies. Together, our work will be valuable for AMR research for basic scientists with the potential for clinical applicability.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Genomic diversity of prevalent Staphylococcus epidermidis multidrug-resistant strains isolated from a Children's Hospital in México City in an eight-years survey

Staphylococcus epidermidis is a human commensal and pathogen worldwide distributed. In this work, we surveyed for multi-resistant S. epidermidis strains in eight years at a children's health-care unit in México City. Multidrug-resistant S. epidermidis were present in all years of the study, including resistance to methicillin, beta-lactams, fluoroquinolones, and macrolides. To understand the genetic basis of antibiotic resistance and its association with virulence and gene exchange, we sequenced the genomes of 17 S. epidermidis isolates. Whole-genome nucleotide identities between all the pairs of S. epidermidis strains were about 97% to 99%. We inferred a clonal structure and eight Multilocus Sequence Types (MLSTs) in the S. epidermidis sequenced collection. The profile of virulence includes genes involved in biofilm formation and phenol-soluble modulins (PSMs). Half of the S. epidermidis analyzed lacked the ica operon for biofilm formation. Likely, they are commensal S. epidermidis strains but multi-antibiotic resistant. Uneven distribution of insertion sequences, phages, and CRISPR-Cas immunity phage systems suggest frequent horizontal gene transfer. Rates of recombination between S. epidermidis strains were more prevalent than the mutation rate and affected the whole genome. Therefore, the multidrug resistance, independently of the pathogenic traits, might explain the persistence of specific highly adapted S. epidermidis clonal lineages in nosocomial settings.

Isolation and characterisation of carbapenem-resistant Xanthomonas citri pv. mangiferaeindicae-like strain gir from the faecal material of giraffes

The purpose of this study was to determine if giraffes (Giraffa camelopardalis) living in captivity at the Jacksonville Zoo and Gardens, Jacksonville, FL were colonised with carbapenem-resistant bacteria and, if found, to identify underlying genetic mechanisms contributing to a carbapenem-resistant phenotype. Faecal samples from seven giraffes were examined for carbapenem-resistant bacteria. Only one isolate (a Xanthomondaceae) was found to be carbapenem-resistant by antibiotic susceptibility testing. This isolate was selected for additional characterization, including whole genome sequencing (WGS). Based on average nucleotide identity, the bacterium was identified as Xanthomonas citri pv. mangiferaeindicae-like strain gir. Phenotypic carbapenemase tests and PCR for the most common carbapenemase genes produced negative results, suggesting that carbapenem resistance was mediated by another mechanism. Resistance gene profile analysis of WGS results confirmed these results. Among identified resistance genes, a chromosomal class A beta-lactamase with 71% identity to the penP beta-lactamase gene from Xanthomonas citri ssp. citri was identified, which could contribute to a carbapenem-resistant phenotype.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.