A machine learning-based service for estimating quality of genomes using PATRIC

Imhoff J. Genome of Lamprobacter modestohalophilus ShNLb02, a moderate halophilic photosynthetic purple bacterium of the Chromatiaceae family

The genome sequence of the moderately halophilic Lamprobacter modestohalophilus ShNLb02 was compared to those of other Lamprobacter and Halochromatium species. It revealed an average nucleotide identity of 94% to Lpb. modestohalophilus DSM 25653 and of 89.7% to Halochromatium roseum DSM 18859, underscoring their close phylogenetic relationship.

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.

Pangenome Analysis of Helicobacter pylori Isolates from Selected Areas of Africa Indicated Diverse Antibiotic Resistance and Virulence Genes

The challenge facing Helicobacter pylori (H. pylori) infection management in some parts of Africa is the evolution of drug-resistant species, the lack of gold standard in diagnostic methods, and the ineffectiveness of current vaccines against the bacteria. It is being established that even though clinical consequences linked to the bacteria vary geographically, there is rather a generic approach to treatment. This situation has remained problematic in the successful fight against the bacteria in parts of Africa. As a result, this study compared the genomes of selected H. pylori isolates from selected areas of Africa and evaluated their virulence and antibiotic drug resistance, those that are highly pathogenic and are associated with specific clinical outcomes and those that are less virulent and rarely associated with clinical outcomes. 146 genomes of H. pylori isolated from selected locations of Africa were sampled, and bioinformatic tools such as Abricate, CARD RGI, MLST, Prokka, Roary, Phandango, Google Sheets, and iTOLS were used to compare the isolates and their antibiotic resistance or susceptibility. Over 20 k virulence and AMR genes were observed. About 95% of the isolates were genetically diverse, 90% of the isolates harbored shell genes, and 50% harbored cloud and core genes. Some isolates did not retain the cagA and vacA genes. Clarithromycin, metronidazole, amoxicillin, and tinidazole were resistant to most AMR genes (vacA, cagA, oip, and bab). Conclusion. This study found both virulence and AMR genes in all H. pylori strains in all the selected geographies around Africa with differing quantities. MLST, Pangenome, and ORF analyses showed disparities among the isolates. This in general could imply diversities in terms of genetics, evolution, and protein production. Therefore, generic administration of antibiotics such as clarithromycin, amoxicillin, and erythromycin as treatment methods in the African subregion could be contributing to the spread of the bacterium's antibiotic resistance.

Comparative Genomic Analysis of Bacterial Data in BV-BRC: An Example Exploring Antimicrobial Resistance

As genomic and related data continue to expand, research biologists are often hampered by the computational hurdles required to analyze their data. The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Centers (BRC) to assist researchers with their analysis of genome sequence and other omics-related data. Recently, the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD), and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs merged to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) at https://www.bv-brc.org/ . The combined BV-BRC leverages the functionality of the original resources for bacterial and viral research communities with a unified data model, enhanced web-based visualization and analysis tools, and bioinformatics services. Here we demonstrate how antimicrobial resistance data can be analyzed in the new resource.

A Plasmid-Borne Gene Cluster Flanked by Two Restriction-Modification Systems Enables an Arctic Strain of Psychrobacter sp. to Decompose SDS

The cold-adapted Psychrobacter sp. strain DAB_AL62B, isolated from ornithogenic deposits on the Arctic island of Spitsbergen, harbors a 34.5 kb plasmid, pP62BP1, which carries a genetic SLF module predicted to enable the host bacterium to metabolize alkyl sulfates including sodium dodecyl sulfate (SDS), a common anionic surfactant. In this work, we experimentally confirmed that the pP62BP1-harboring strain is capable of SDS degradation. The slfCHSL genes were shown to form an operon whose main promoter, PslfC, is negatively regulated by the product of the slfR gene in the absence of potential substrates. We showed that lauryl aldehyde acts as an inducer of the operon. The analysis of the draft genome sequence of the DAB_AL62B strain revealed that the crucial enzyme of the SDS degradation pathway-an alkyl sulfatase-is encoded only within the plasmid. The SLF module is flanked by two restriction-modification systems, which were shown to exhibit the same sequence specificity. We hypothesize that the maintenance of pP62BP1 may be dependent on this unique genetic organization.

Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Illuminating the Genomic Landscape of Lactiplantibacillus plantarum PU3-A Novel Probiotic Strain Isolated from Human Breast Milk, Explored through Nanopore Sequencing

Lactiplantibacillus plantarum stands out as a remarkably diverse species of lactic acid bacteria, occupying a myriad of ecological niches. Particularly noteworthy is its presence in human breast milk, which can serve as a reservoir of probiotic bacteria, contributing significantly to the establishment and constitution of infant gut microbiota. In light of this, our study attempted to conduct an initial investigation encompassing both genomic and phenotypic aspects of the L. plantarum PU3 strain, that holds potential as a probiotic agent. By employing the cutting-edge third-generation Nanopore sequencing technology, L. plantarum PU3 revealed a circular chromosome of 3,180,940 bp and nine plasmids of various lengths. The L. plantarum PU3 genome has a total of 2962 protein-coding and non-coding genes. Our in-depth investigations revealed more than 150 probiotic gene markers that unfold the genetic determinants for acid tolerance, bile resistance, adhesion, and oxidative and osmotic stress. The in vivo analysis showed the strain's proficiency in utilizing various carbohydrates as growth substrates, complementing the in silico analysis of the genes involved in metabolic pathways. Notably, the strain demonstrated a pronounced affinity for D-sorbitol, D-mannitol, and D-Gluconic acid, among other carbohydrate sources. The in vitro experimental verification of acid, osmotic and bile tolerance validated the robustness of the strain in challenging environments. Encouragingly, no virulence factors were detected in the genome of PU3, suggesting its safety profile. In search of beneficial properties, we found potential bacteriocin biosynthesis clusters, suggesting its capability for antimicrobial activity. The characteristics exhibited by L. plantarum PU3 pave the way for promising strain potential, warranting further investigations to unlock its full capacity and contributions to probiotic and therapeutic avenues.

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.

New Bacteriophages Members of the Ackermannviridae Family Specific for Klebsiella pneumoniae ST258

Background

Carbapenem-resistant Klebsiella pneumoniae, particularly isolates classified as sequence-type 258 (ST258), are multidrug-resistant strains that are strongly associated with poor-prognosis nosocomial infections, as current therapeutic options are limited and ineffective. In recent years, phage therapy has emerged as a promising treatment option for these scenarios.

Methodology and Results

We report the isolation and characterization of three new phages against Klebsiella pneumoniae ST258 strains recovered from Machángara river wastewater. These new members of the Ackermannviridae family showed stability over a wide temperature and pH range and burst sizes ranging from 6 to 44 plaque-forming units per bacteria. Their genomes were about 157 kilobases, with an average guanine-cytosine content of 46.4% and showed presence of several transfer RNAs, which also allowed us to predict in silico a lytic replicative cycle due to the presence of endolysins and lysozymes.

Conclusion

Three lytic phages of Ackermannviridae family were recovered against Klebsiella pneumoniae ST258 strains from sewage; however, further characterization is needed for future consideration as therapeutic alternatives.

Cave Thiovulum (Candidatus Thiovulum stygium) differs metabolically and genomically from marine species

Thiovulum spp. (Campylobacterota) are large sulfur bacteria that form veil-like structures in aquatic environments. The sulfidic Movile Cave (Romania), sealed from the atmosphere for ~5 million years, has several aqueous chambers, some with low atmospheric O₂ (~7%). The cave's surface-water microbial community is dominated by bacteria we identified as Thiovulum. We show that this strain, and others from subsurface environments, are phylogenetically distinct from marine Thiovulum. We assembled a closed genome of the Movile strain and confirmed its metabolism using RNAseq. We compared the genome of this strain and one we assembled from public data from the sulfidic Frasassi caves to four marine genomes, including Candidatus Thiovulum karukerense and Ca. T. imperiosus, whose genomes we sequenced. Despite great spatial and temporal separation, the genomes of the Movile and Frasassi Thiovulum were highly similar, differing greatly from the very diverse marine strains. We concluded that cave Thiovulum represent a new species, named here Candidatus Thiovulum stygium. Based on their genomes, cave Thiovulum can switch between aerobic and anaerobic sulfide oxidation using O₂ and NO₃^- as electron acceptors, the latter likely via dissimilatory nitrate reduction to ammonia. Thus, Thiovulum is likely important to both S and N cycles in sulfidic caves. Electron microscopy analysis suggests that at least some of the short peritrichous structures typical of Thiovulum are type IV pili, for which genes were found in all strains. These pili may play a role in veil formation, by connecting adjacent cells, and in the motility of these exceptionally fast swimmers.

Comprehensive analysis of the metabolic and genomic features of tannin transforming Lactiplantibacillus plantarum strains

Extracellular tannase Lactiplantibacillus plantarum-producing strains (TanA+) release bioactive metabolites from dietary tannins. However, there is a paucity of knowledge of TanA+ strains and their hydrolyzing capacities. This study aimed to shed light on the metabolic and genomic features of TanA+ L. plantarum strains and to develop a screening technique. The established spectrophotometric was validated by UPLC-UV-QToF. Eight of 115 screened strains harbored the tanA gene, and six presented TanA activity (PROBI S126, PROBI S204, RKG 1-473, RKG 1-500, RKG 2-219, and RKG 2-690). When cultured with tannic acid (a gallotannin), TanA+ strains released 3.2-11 times more gallic acid than a lacking strain (WCFS1) (p < 0.05). TanA+ strains with gallate decarboxylase (n = 5) transformed this latter metabolite, producing 2.2-4.8 times more pyrogallol than the TanA lacking strain (p < 0.05). However, TanA+ strains could not transform punicalagin (an ellagitannin). Genomic analysis revealed high similarity between TanA+ strains, as only two variable regions of phage and polysaccharide synthesis were distinguished. A phylogenetic analysis of 149 additional genome sequences showed that tanA harboring strains form a cluster and present two bacteriocin coding sequences profile. In conclusion, TanA+ L. plantarum strains are closely related and possess the ability to resist and transform gallotannins. TanA can be screened by the method proposed herein.

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer-including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements-were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

Background Filtering of Clinical Metagenomic Sequencing with a Library Concentration-Normalized Model

Metagenomic next-generation sequencing (mNGS) can accurately detect pathogens in clinical samples. However, wet-lab contamination constrains mNGS analysis and may result in erroneous interpretation of results. Many existing methods rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS tests. By generation of a pretrained profile of common laboratory contaminants, we developed an mNGS noise-filtering model based on the inverse linear relationship between microbial sequencing reads and sample library concentration, named the background elimination and correction by library concentration-normalized (BECLEAN) model. Its efficacy was evaluated with bacteria- and yeast-spiked samples and 28 cerebrospinal fluid (CSF) specimens. The diagnostic accuracy, precision, sensitivity, and specificity of BECLEAN with reference to conventional methods and diagnosis were 92.9%, 86.7%, 100%, and 86.7%, respectively. BECLEAN led to a dramatic reduction of background noise without affecting the true-positive rate and thus can provide a time-saving and convenient tool in various clinical settings. IMPORTANCE Most of the existing methods to remove wet-lab contamination rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS testing in individual cases. In clinical settings, only a handful of samples might be sequenced in a run. The lab-specific microbiome can complicate existing statistical approaches for removing contamination from small-scale clinical metagenomic sequencing data sets; thus, use of a preliminary lab-specific training set is necessary. Our study provides a rapid and accurate background-filtering tool for clinical metagenomic sequencing by generation of a pretrained profile of common laboratory contaminants. Notably, our work demonstrates that the inverse linear relationship between microbial sequencing reads and library concentration can serve to identify true contaminants and evaluate the relative abundance of a taxon in samples by comparing the observed microbial reads to the model-predicted value. Our findings extend the previously published research and demonstrate confirmatory results in clinical settings.

Putative Pathogenic Genes of Leptospira interrogans and Leptospira weilii Isolated from Patients with Acute Febrile Illness

Leptospirosis is an important worldwide tropical disease caused by pathogenic Leptospira spp. The determination of virulence genes is important, as it influences patients' clinical manifestations and clinical outcomes. This case report focused on detecting the pathogenic genes of Leptospira in association with the clinical manifestations of patients at the Hospital Universiti Sains Malaysia, Malaysia, who presented with acute febrile illness. Two cases were found and, to the best of our knowledge, these were the first two cases in Malaysia in which patients presented with febrile illness were associated with successful Leptospira isolation from clinical samples. Both clinical isolates were identified by 16S rRNA sequencing as Leptospira weilii and Leptospira interrogans, respectively, and they were classified as pathogenic Leptospira by the presence of different pathogenic genes, based on a polymerase chain reaction (PCR) amplification of targeted genes. This report emphasizes that different infecting Leptospira species and the presence of different virulence factors cause a slight difference in clinical manifestations and laboratory findings of leptospirosis. Genomic sequencing and annotation revealed the detection of classical leptospiral virulence factor genes that were otherwise missed using PCR for detection of Leptospira weilii genome B208.

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.

Accurate prediction of metagenome-assembled genome completeness by MAGISTA, a random forest model built on alignment-free intra-bin statistics

BACKGROUND

Although the total number of microbial taxa on Earth is under debate, it is clear that only a small fraction of these has been cultivated and validly named. Evidently, the inability to culture most bacteria outside of very specific conditions severely limits their characterization and further studies. In the last decade, a major part of the solution to this problem has been the use of metagenome sequencing, whereby the DNA of an entire microbial community is sequenced, followed by the in silico reconstruction of genomes of its novel component species. The large discrepancy between the number of sequenced type strain genomes (around 12,000) and total microbial diversity (10⁶-10¹² species) directs these efforts to de novo assembly and binning. Unfortunately, these steps are error-prone and as such, the results have to be intensely scrutinized to avoid publishing incomplete and low-quality genomes.

RESULTS

We developed MAGISTA (metagenome-assembled genome intra-bin statistics assessment), a novel approach to assess metagenome-assembled genome quality that tackles some of the often-neglected drawbacks of current reference gene-based methods. MAGISTA is based on alignment-free distance distributions between contig fragments within metagenomic bins, rather than a set of reference genes. For proper training, a highly complex genomic DNA mock community was needed and constructed by pooling genomic DNA of 227 bacterial strains, specifically selected to obtain a wide variety representing the major phylogenetic lineages of cultivable bacteria.

CONCLUSIONS

MAGISTA achieved a 20% reduction in root-mean-square error in comparison to the marker gene approach when tested on publicly available mock metagenomes. Furthermore, our highly complex genomic DNA mock community is a very valuable tool for benchmarking (new) metagenome analysis methods.

Comparative Genome Analysis and Spore Heat Resistance Assay Reveal a New Component to Population Structure and Genome Epidemiology Within Clostridium perfringens Enterotoxin-Carrying Isolates

Clostridium perfringens causes a variety of human and animal enteric diseases including food poisoning, antibiotic-associated diarrhea, and necrotic enteritis. Yet, the reservoirs of enteropathogenic enterotoxin-producing strains remain unknown. We conducted a genomic comparison of 290 strains and a heat resistance phenotyping of 30 C. perfringens strains to elucidate the population structure and ecology of this pathogen. C. perfringens genomes shared a conserved genetic backbone with more than half of the genes of an average genome conserved in >95% of strains. The cpe-carrying isolates were found to share genetic context: the cpe-carrying plasmids had different distribution patterns within the genetic lineages and the estimated pan genome of cpe-carrying isolates had a larger core genome and a smaller accessory genome compared to that of 290 strains. We characterize cpe-negative strains related to chromosomal cpe-carrying strains elucidating the origin of these strains and disclose two distinct groups of chromosomal cpe-carrying strains with different virulence characteristics, spore heat resistance properties, and, presumably, ecological niche. Finally, an antibiotic-associated diarrhea isolate carrying two copies of the enterotoxin cpe gene and the associated genetic lineage with the potential for the emergence of similar strains are outlined. With C. perfringens as an example, implications of input genome quality for pan genome analysis are discussed. Our study furthers the understanding of genome epidemiology and population structure of enteropathogenic C. perfringens and brings new insight into this important pathogen and its reservoirs.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

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.

Desulfovibrio diazotrophicus sp. nov., a sulfate-reducing bacterium from the human gut capable of nitrogen fixation

Sulfate-reducing bacteria (SRB) are widespread in human guts, yet their expansion has been linked to colonic diseases. We report the isolation, sequencing and physiological characterization of strain QI0027^T , a novel SRB species belonging to the class Desulfovibrionia. Metagenomic sequencing of stool samples from 45 Chinese individuals, and comparison with 1690 Desulfovibrionaceae metagenome-assembled genomes recovered from humans of diverse geographic locations, revealed the presence of QI0027^T in 22 further individuals. QI0027^T encoded nitrogen fixation genes and based on the acetylene reduction assay, actively fixed nitrogen. Transcriptomics revealed that QI0027^T overexpressed 42 genes in nitrogen-limiting conditions compared to cultures supplemented with ammonia, including genes encoding nitrogenases, a urea uptake system and the urease complex. Reanalyses of 835 public stool metatranscriptomes showed that nitrogenase genes from Desulfovibrio bacteria were expressed in six samples suggesting that nitrogen fixation might be active in the gut environment. Although frequently thought of as a nutrient-rich environment, nitrogen fixation can occur in the human gut. Animals are often nitrogen limited and have evolved diverse strategies to capture biologically active nitrogen, ranging from amino acid transporters to stable associations with beneficial microbes that provide fixed nitrogen. QI0027^T is the first Desulfovibrio human isolate for which nitrogen fixation has been demonstrated, suggesting that some sulfate-reducing bacteria could also play a role in the availability of nitrogen in the gut.

Supervised extraction of near-complete genomes from metagenomic samples: A new service in PATRIC

Large amounts of metagenomically-derived data are submitted to PATRIC for analysis. In the future, we expect even more jobs submitted to PATRIC will use metagenomic data. One in-demand use case is the extraction of near-complete draft genomes from assembled contigs of metagenomic origin. The PATRIC metagenome binning service utilizes the PATRIC database to furnish a large, diverse set of reference genomes. We provide a new service for supervised extraction and annotation of high-quality, near-complete genomes from metagenomically-derived contigs. Reference genomes are assigned to putative draft genome bins based on the presence of single-copy universal marker roles in the sample, and contigs are sorted into these bins by their similarity to reference genomes in PATRIC. Each set of binned contigs represents a draft genome that will be annotated by RASTtk in PATRIC. A structured-language binning report is provided containing quality measurements and taxonomic information about the contig bins. The PATRIC metagenome binning service emphasizes extraction of high-quality genomes for downstream analysis using other PATRIC tools and services. Due to its supervised nature, the binning service is not appropriate for mining novel or extremely low-coverage genomes from metagenomic samples.

Paenibacillus glycinis sp. nov., an Endophytic Bacterium Isolated from the Nodules of Soybean (Glycine max (L.) Merr)

A Gram-negative-staining, endospore-forming, rod-shaped bacterium, designated T1T, was isolated from root nodules of soybean (Glycine max (L.) Merr) in Heilongjiang Province of China. The isolate was identified as a member of the genus Paenibacillus based on phenotypic and phylogenetic characteristics. The 16S rRNA sequence was closely related to that of Paenibacillus sacheonensis SY01T with a similarity of 98.4%. Average nucleotide identity and in silico DNA-DNA hybridization values between strain T1T and P. sacheonensis DSM 23054 T were 81.4% and 25.4%, respectively. The DNA G + C content of strain T1T was 58.2 mol%. meso-Diaminopimelic acid was detected in the cell-wall peptidoglycan. The major cellular fatty acids were anteiso-C15:0, iso-C16:0 and iso-C15:0. The predominant respiratory quinone was MK-7. The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, five unidentified phospholipids, four unidentified aminophospholipids, an unidentified glycolipid and an unidentified lipid. Based on these results, T1T is considered to represent a novel species of the genus Paenibacillus, for which the name Paenibacillus glycinis sp. nov. is proposed. The type strain is T1T (= CGMCC 1.18563 = KCTC43227).

Elevation of Lactococcus lactis subsp. cremoris to the species level as Lactococcus cremoris sp. nov. and transfer of Lactococcus lactis subsp. tructae to Lactococcus cremoris as Lactococcus cremoris subsp. tructae comb. nov

Currently, Lactococcus lactis contains four subspecies: L. lactis subsp. lactis, L. lactis subsp. hordniae, L. lactis subsp. cremoris and L. lactis subsp. tructae. In the study of Pérez et al., these four subspecies could be clearly divided into two groups based on recA sequence analysis: L. lactis subsp. lactis and L. lactis subsp. hordniae; L. lactis subsp. cremoris and L. lactis subsp. tructae. The two groups had a relatively low DNA-DNA hybridization value (about 60 %). In the present study, the taxonomic position of L. lactis subsp. cremoris and L. lactis subsp. tructae was re-examined based on sequence analyses of 16S rRNA, rpoB, recA and pheS genes, average nucleotide identity (ANI) values and digital DNA-DNA hybridization (dDDH) values. The result of 16S rRNA gene sequence analysis indicated that L. lactis subsp. cremoris NCDO 607^T and L. lactis subsp. tructae L105^T were phylogenetically related to the type strains of L. lactis subsp. hordniae, L. lactis subsp. lactis, Lactococcus taiwanensis, Lactococcus kimchii, Lactococcus allomyrinae, Lactococcus protaetiae, Lactococcus hircilactis, Lactococcus fujiensis and Lactococcus nasutitermitis. The 16S rRNA gene, rpoB, recA, pheS and concatenated rpoB, recA and pheS sequence similarities, ANI values, and dDDH values between the type strains of L. lactis subsp. cremoris, L. lactis subsp. tructae and phylogenetically related species were 93.5-99.4 %, 83.3-97.6 %, 80.6-92.4 %, 79.7-92.7 %, 83.5-94.3 %, 72.4-86.9 % and 21.4-32.5 %, respectively. Lower than 95-96 % ANI values and lower than 70 % dDDH values confirmed that the type strains of L. lactis subsp. cremoris and L. lactis subsp. tructae represent a novel species in the genus Lactococcus. Because L. lactis subsp. cremoris was proposed and validated before L. lactis subsp. tructae, L. lactis subsp. cremoris is elevated to the species level as Lactococcus cremoris sp. nov. and L. lactis subsp. tructae is transferred to L. cremoris as L. cremoris subsp. tructae comb. nov. The type strain of L. cremoris sp. nov. is NCDO 607^T (=ATCC 19257^T=DSM 20069^T=JCM 16167^T=LMG 6897^T=NBRC 100676^T). The type strain of L. cremoris subsp. tructae comb. nov. is L105^T (=NBRC 110453^T=DSM 21502^T=JCM 31125^T=LMG 24662^T).

cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large, bacterial whole genome sequencing datasets

Background

The quantity of genomic data is expanding at an increasing rate. Tools for phylogenetic analysis which scale to the quantity of available data are required. To address this need, we present cognac, a user-friendly software package to rapidly generate concatenated gene alignments for phylogenetic analysis.

Results

We illustrate that cognac is able to rapidly identify phylogenetic marker genes using a data driven approach and efficiently generate concatenated gene alignments for very large genomic datasets. To benchmark our tool, we generated core gene alignments for eight unique genera of bacteria, including a dataset of over 11,000 genomes from the genus Escherichia producing an alignment with 1353 genes, which was constructed in less than 17 h.

Conclusions

We demonstrate that cognac presents an efficient method for generating concatenated gene alignments for phylogenetic analysis. We have released cognac as an R package (https://github.com/rdcrawford/cognac) with customizable parameters for adaptation to diverse applications.

Two Nearly Complete Nosocomial Pathogen Genome Sequences Reconstructed from Early-Middle 20th-Century Dental Calculus

Acinetobacter baumannii and Stenotrophomonas maltophilia genomes were reconstructed from early-middle 20th-century human skeletal remains, maintained in natural history museums, using a metagenomic binning approach.

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.