Neptune: a bioinformatics tool for rapid discovery of genomic variation in bacterial populations

Schauerella fraxinea gen. nov., sp. nov., a bacterial species that colonises ash trees tolerant to dieback caused by Hymenoscyphus fraxineus

The tolerance of ash trees against the pathogen Hymenoscyphus fraxineus seems to be associated with the occurrence of specific microbial taxa on leaves. A group of bacterial isolates, primarily identified on tolerant trees, was investigated with regard to their taxonomic classification and their potential to suppress the ash dieback pathogen. Examination of OGRI values revealed a separate species position. A phylogenomic analysis, based on orthologous and marker genes, indicated a separate genus position along with the species Achromobacter aestuarii. Furthermore, analysis of the ratio of average nucleotide identities and genome alignment fractions demonstrated genomic dissimilarities typically observed for inter-genera comparisons within this family. As a result of these investigations, the strains are considered to represent a separate species within a new genus, for which the name Schauerella fraxinea gen. nov., sp. nov. is proposed, with the type strain B3P038T (=LMG 33092 T = DSM 115926 T). Additionally, a reclassification of the species Achromobacter aestuarii as Schauerella aestuarii comb. nov. is proposed. In a co-cultivation assay, the strains were able to inhibit the growth of a H. fraxineus strain. Accordingly, a functional analysis of the genome of S. fraxinea B3P038T revealed genes mediating the production of antifungal substances. This potential, combined with the prevalent presence in the phyllosphere of tolerant ash trees, makes this group interesting for an inoculation experiment with the aim of controlling the pathogen in an integrative approach. For future field trials, a strain-specific qPCR system was developed to establish an efficient method for monitoring the inoculation success.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Identification of conserved genomic signatures specific to Bifidobacterium species colonising the human gut

Bifidobacterium species are known for their ability to inhabit various habitats and are often regarded as the first colonisers of the human gut. In the present work, we have used comparative genomics to identify conserved genomic signatures specific to Bifidobacterium species associated with the human gut. Our approach discovered five genomic signatures with varying lengths and confidence. Among the predicted five signatures, a 1790 bp multi-drug resistance (MDR) signature was found to be remarkably specific to only those species that can colonise the human gut. The signature codes for a membrane transport protein belonging to the major facilitator superfamily (MFS) generally involved in MDR. Phylogenetic analyses of the MDR signature suggest a lineage-specific evolution of the MDR signature in bifidobacteria colonising the human gut. Functional annotation led to the discovery of two conserved domains in the protein; a catalytic MFS domain involved in the efflux of drugs and toxins, and a regulatory cystathionine-β-synthase (CBS) domain that can interact with adenosyl-carriers. Molecular docking simulation performed with the modelled tertiary structure of the MDR signature revealed the putative functional role of the covalently linked domains. The MFS domain displayed a high affinity towards various protein synthesis inhibitor antibiotics and human bile acids, whereas the C-terminally linked CBS domain exhibited favourable binding with molecular structures of ATP and AMP. Therefore, we believe that the predicted signature represents a niche-specific survival trait involved in bile and antibiotic resistance, imparting an adaptive advantage to the Bifidobacterium species colonising the human gut.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03492-4.

Development of reverse-transcriptase, real-time PCR assays to distinguish the Southern African Territories (SAT) serotypes 1 and 3 and topotype VII of SAT2 of Foot-and-Mouth Disease Virus

Foot-and-Mouth Disease Virus (FMDV), the causative agent of Foot-and-Mouth Disease, is a highly feared, economically devastating transboundary pathogen. This is due to the virus' extremely contagious nature and its ability to utilize multiple transmission routes. As such, rapid and accurate diagnostic testing is imperative to the control of FMD. Identification of the FMDV serotype is necessary as it provides the foundation for appropriate vaccine selection and aids in outbreak source tracing. With the vast genetic diversity, there is a desperate need to be able to characterize FMDV without relying on prior knowledge of viral serotypes. In this study, the Neptune bioinformatics tool was used to identify genetic signatures specific to each Southern African Territories (SAT) 1, 2 and 3 genomes but exclusionary to the other circulating FMDV serotypes (A, O, Asia1, and the heterologous SAT1, SAT2 and/or SAT3). Identification of these unique genomic regions allowed the design of TaqMan-based real-time reverse transcriptase PCR (rRT-PCR) primer/probe sets for SAT1, SAT2 and SAT3 viruses. These assays were optimized using prototypic FMDV cell culture isolates using the same reagents and thermocycling conditions as the FMDV pan-serotype 3D rRT-PCR assay. Cross-reactivity was evaluated in tandem with the FMDV pan-serotype 3D rRT-PCR utilizing representative strains from FMDV serotypes A, O, Asia1, SAT1, SAT2 and SAT3. The SAT1, SAT2, and SAT3 primer/probe sets were specific for the homologous serotype and exclusionary to all others. SAT1 and SAT3 primer/probe sets were able to detect several topotypes, whereas the SAT2 assay was revealed to be specific for topotype VII. The SAT2 topotype VII specificity was possibly due to the use of sequence data deposited post-2011to design the rRT-PCR primers and probes. Each assay was tested against a panel of 99 bovine tissue samples from Nigeria, where SAT2 topotype VII viruses were correctly identified and no cross-reactivity was exhibited by the SAT1 and 3 assays. These novel SAT1, SAT3 and SAT2 topotype VII rRT-PCR assays have the potential to detect and differentiate circulating FMD SAT viruses.

Genomic Characterization of Enterococcus hirae From Beef Cattle Feedlots and Associated Environmental Continuum

Enterococci are commensal bacteria of the gastrointestinal tract of humans, animals, and insects. They are also found in soil, water, and plant ecosystems. The presence of enterococci in human, animal, and environmental settings makes these bacteria ideal candidates to study antimicrobial resistance in the One-Health continuum. This study focused on Enterococcus hirae isolates (n = 4,601) predominantly isolated from beef production systems including bovine feces (n = 4,117, 89.5%), catch-basin water (n = 306, 66.5%), stockpiled bovine manure (n = 24, 0.5%), and natural water sources near feedlots (n = 145, 32%), and a few isolates from urban wastewater (n = 9, 0.2%) denoted as human-associated environmental samples. Antimicrobial susceptibility profiling of a subset (n = 1,319) of E. hirae isolates originating from beef production systems (n = 1,308) showed high resistance to tetracycline (65%) and erythromycin (57%) with 50.4% isolates harboring multi-drug resistance, whereas urban wastewater isolates (n = 9) were resistant to nitrofurantoin (44.5%) and tigecycline (44.5%) followed by linezolid (33.3%). Genes for tetracycline (tetL, M, S/M, and O/32/O) and macrolide resistance erm(B) were frequently found in beef production isolates. Antimicrobial resistance profiles of E. hirae isolates recovered from different environmental settings appeared to reflect the kind of antimicrobial usage in beef and human sectors. Comparative genomic analysis of E. hirae isolates showed an open pan-genome that consisted of 1,427 core genes, 358 soft core genes, 1701 shell genes, and 7,969 cloud genes. Across species comparative genomic analysis conducted on E. hirae, Enterococcus faecalis and Enterococcus faecium genomes revealed that E. hirae had unique genes associated with vitamin production, cellulose, and pectin degradation, traits which may support its adaptation to the bovine digestive tract. E. faecium and E. faecalis more frequently harbored virulence genes associated with biofilm formation, iron transport, and cell adhesion, suggesting niche specificity within these species.

Comparative Genomic and Transcriptomic Analysis of Naegleria fowleri Clinical and Environmental Isolates

Out of over 40 species of Naegleria, which are free-living thermophilic amebae found in freshwater and soil worldwide, only Naegleria fowleri infects humans, causing primary amebic meningoencephalitis (PAM), a typically fatal brain disease. To understand the population structure of Naegleria species and the genetic relationships between N. fowleri isolates and to detect pathogenic factors, we characterized 52 novel clinical and environmental N. fowleri genomes and a single Naegleria lovaniensis strain, along with transcriptomic data for a subset of 37 N. fowleri isolates. Whole-genome analysis of 56 isolates from three Naegleria species (N. fowleri, N. lovaniensis, and Naegleria gruberi) identified several genes unique to N. fowleri that have previously been linked to the pathogenicity of N. fowleri, while other unique genes could be associated with novel pathogenicity factors in this highly fatal pathogen. Population structure analysis estimated the presence of 10 populations within the three Naegleria species, of which 7 populations were within N. fowleri. The whole-nuclear-genome (WNG) phylogenetic analysis showed an overall geographical clustering of N. fowleri isolates, with few exceptions, and provided higher resolution in identifying potential clusters of isolates beyond that of the traditional locus typing. There were only 34 genes that showed significant differences in gene expression between the clinical and environmental isolates. Genomic data generated in this study can be used for developing rapid molecular assays and to conduct future population-based global genomic analysis and will also be a valuable addition to genomic reference databases, where shotgun metagenomics data from routine water samples could be searched for the presence of N. fowleri strains. IMPORTANCE N. fowleri, the only known Naegleria species to infect humans, causes fatal brain disease. PAM cases from 1965 to 2016 showed <20 cases per year globally. Out of approximately 150 cases in North America since 1962, only four PAM survivors are known, yielding a >97% case fatality rate, which is critically high. Although the pathogenesis of N. fowleri has been studied for the last 50 years, pathogenetic factors that lead to human infection and breaching the blood-brain barrier remain unknown. In addition, little is known regarding the genomic diversity both within N. fowleri isolates and among Naegleria species. In this study, we generated novel genome sequences and performed comparative genomic and transcriptomic analysis of a set of 52 N. fowleri draft genome sequences from clinical and environmental isolates derived from all over the world in the last 53 years, which will help shape future genome-wide studies and develop sensitive assays for routine surveillance.

Mining Public Metagenomes for Environmental Surveillance of Parasites: A Proof of Principle

Parasites often have complex developmental cycles that account for their presence in a variety of difficult-to-analyze matrices, including feces, water, soil, and food. Detection of parasites in these matrices still involves laborious methods. Untargeted sequencing of nucleic acids extracted from those matrices in metagenomic projects may represent an attractive alternative method for unbiased detection of these pathogens. Here, we show how publicly available metagenomic datasets can be mined to detect parasite specific sequences, and generate data useful for environmental surveillance. We use the protozoan parasite Cryptosporidium parvum as a test organism, and show that detection is influenced by the reference sequence chosen. Indeed, the use of the whole genome yields high sensitivity but low specificity, whereas specificity is improved through the use of signature sequences. In conclusion, querying metagenomic datasets for parasites is feasible and relevant, but requires optimization and validation. Nevertheless, this approach provides access to the large, and rapidly increasing, number of datasets from metagenomic and meta-transcriptomic studies, allowing unlocking hitherto idle signals of parasites in our environments.

Rapid and Accurate Campylobacter jejuni Detection With CRISPR-Cas12b Based on Newly Identified Campylobacter jejuni-Specific and -Conserved Genomic Signatures

Campylobacter jejuni is among the most prevalent foodborne zoonotic pathogens leading to diarrheal diseases. In this study, we developed a CRISPR-Cas12b-based system to rapidly and accurately detect C. jejuni contamination. Identification of C. jejuni-specific and -conserved genomic signatures is a fundamental step in development of the detection system. By comparing C. jejuni genome sequences with those of the closely related Campylobacter coli, followed by comprehensive online BLAST searches, a 20-bp C. jejuni-conserved (identical in 1024 out of 1037 analyzed C. jejuni genome sequences) and -specific (no identical sequence detected in non-C. jejuni strains) sequence was identified and the system was then assembled. In further experiments, strong green fluorescence was observed only when C. jejuni DNA was present in the system, highlighting the specificity of this system. The assay, with a sample-to-answer time of ∼40 min, positively detected chicken samples that were contaminated with a dose of approximately 10 CFU C. jejuni per gram of chicken, which was >10 times more sensitive than the traditional Campylobacter isolation method, suggesting that this method shows promise for onsite C. jejuni detection. This study provides an example of bioinformatics-guided CRISPR-Cas12b-based detection system development for rapid and accurate onsite pathogen detection.

Comparative genomics of multidrug-resistant Enterococcus spp. isolated from wastewater treatment plants

BACKGROUND

Wastewater treatment plants (WWTPs) are considered hotspots for the environmental dissemination of antimicrobial resistance (AMR) determinants. Vancomycin-Resistant Enterococcus (VRE) are candidates for gauging the degree of AMR bacteria in wastewater. Enterococcus faecalis and Enterococcus faecium are recognized indicators of fecal contamination in water. Comparative genomics of enterococci isolated from conventional activated sludge (CAS) and biological aerated filter (BAF) WWTPs was conducted.

RESULTS

VRE isolates, including E. faecalis (n = 24), E. faecium (n = 11), E. casseliflavus (n = 2) and E. gallinarum (n = 2) were selected for sequencing based on WWTP source, species and AMR phenotype. The pangenomes of E. faecium and E. faecalis were both open. The genomic fraction related to the mobilome was positively correlated with genome size in E. faecium (p < 0.001) and E. faecalis (p < 0.001) and with the number of AMR genes in E. faecium (p = 0.005). Genes conferring vancomycin resistance, including vanA and vanM (E. faecium), vanG (E. faecalis), and vanC (E. casseliflavus/E. gallinarum), were detected in 20 genomes. The most prominent functional AMR genes were efflux pumps and transporters. A minimum of 16, 6, 5 and 3 virulence genes were detected in E. faecium, E. faecalis, E. casseliflavus and E. gallinarum, respectively. Virulence genes were more common in E. faecalis and E. faecium, than E. casseliflavus and E. gallinarum. A number of mobile genetic elements were shared among species. Functional CRISPR/Cas arrays were detected in 13 E. faecalis genomes, with all but one also containing a prophage. The lack of a functional CRISPR/Cas arrays was associated with multi-drug resistance in E. faecium. Phylogenetic analysis demonstrated differential clustering of isolates based on original source but not WWTP. Genes related to phage and CRISPR/Cas arrays could potentially serve as environmental biomarkers.

CONCLUSIONS

There was no discernible difference between enterococcal genomes from the CAS and BAF WWTPs. E. faecalis and E. faecium have smaller genomes and harbor more virulence, AMR, and mobile genetic elements than other Enterococcus spp.

Development of the Automated Primer Design Workflow Uniqprimer and Diagnostic Primers for the Broad-Host-Range Plant Pathogen Dickeya dianthicola

Uniqprimer, a software pipeline developed in Python, was deployed as a user-friendly internet tool in Rice Galaxy for comparative genome analyses to design primer sets for PCRassays capable of detecting target bacterial taxa. The pipeline was trialed with Dickeya dianthicola, a destructive broad-host-range bacterial pathogen found in most potato-growing regions. Dickeya is a highly variable genus, and some primers available to detect this genus and species exhibit common diagnostic failures. Upon uploading a selection of target and nontarget genomes, six primer sets were rapidly identified with Uniqprimer, of which two were specific and sensitive when tested with D. dianthicola. The remaining four amplified a minority of the nontarget strains tested. The two promising candidate primer sets were trialed with DNA isolated from 116 field samples from across the United States that were previously submitted for testing. D. dianthicola was detected in 41 samples, demonstrating the applicability of our detection primers and suggesting widespread occurrence of D. dianthicola in North America.

Identification of genetic relationships and subspecies signatures in Xylella fastidiosa

BACKGROUND

The phytopathogenic bacterium Xylella fastidiosa was thought to be restricted to the Americas where it infects and kills numerous hosts. Its detection worldwide has been blooming since 2013 in Europe and Asia. Genetically diverse, this species is divided into six subspecies but genetic traits governing this classification are poorly understood.

RESULTS

SkIf (Specific k-mers Identification) was designed and exploited for comparative genomics on a dataset of 46 X. fastidiosa genomes, including seven newly sequenced individuals. It was helpful to quickly check the synonymy between strains from different collections. SkIf identified specific SNPs within 16S rRNA sequences that can be employed for predicting the distribution of Xylella through data mining. Applied to inter- and intra-subspecies analyses, it identified specific k-mers in genes affiliated to differential gene ontologies. Chemotaxis-related genes more prevalently possess specific k-mers in genomes from subspecies fastidiosa, morus and sandyi taken as a whole group. In the subspecies pauca increased abundance of specific k-mers was found in genes associated with the bacterial cell wall/envelope/plasma membrane. Most often, the k-mer specificity occurred in core genes with non-synonymous SNPs in their sequences in genomes of the other subspecies, suggesting putative impact in the protein functions. The presence of two integrative and conjugative elements (ICEs) was identified, one chromosomic and an entire plasmid in a single strain of X. fastidiosa subsp. pauca. Finally, a revised taxonomy of X. fastidiosa into three major clades defined by the subspecies pauca (clade I), multiplex (clade II) and the combination of fastidiosa, morus and sandyi (clade III) was strongly supported by k-mers specifically associated with these subspecies.

CONCLUSIONS

SkIf is a robust and rapid software, freely available, that can be dedicated to the comparison of sequence datasets and is applicable to any field of research. Applied to X. fastidiosa, an emerging pathogen in Europe, it provided an important resource to mine for identifying genetic markers of subspecies to optimize the strategies attempted to limit the pathogen dissemination in novel areas.

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions

Intraspecific variability of the behavior of most foodborne pathogens is well described and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors (strain origin, serotype, …) explaining these differences are scarce or contradictory between studies. Nowadays, Whole Genome Sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens, based on various recently published bioinformatics tools. The objective of this study is to get a better insight into different existing bioinformatics approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food matrices and environments) and belonging to 17 clonal complexes (CC), were selected to represent large population diversity. Furthermore, the phenotypic variability of growth at low temperature was determined (i.e. qualitative phenotype), and the whole genomes of selected strains were sequenced. The almost exhaustive gene content, as well as the core genome SNPs based phylogenetic reconstruction, were derived from the whole sequenced genomes. A Bayesian inference method was applied to identify the branches on which the phenotype distribution evolves within sub-lineages. Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS) were independently performed in order to link genetic mutations to the phenotype of interest. The genomic analyses presented in this study were successfully applied on the selected dataset. The Bayesian phylogenetic approach emphasized an association with "slow" growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II. Moreover, both gene- and SNP-GWAS approaches displayed significant statistical associations with the tested phenotype. A list of 114 significantly associated genes, including genes already known to be involved in the cold adaption mechanism of L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were highlighted by SNP-GWAS, including SNPs detected in genes which were already likely involved in cold adaption; hypothetical proteins; and intergenic regions where for example promotors and regulators can be located. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes, could contribute to improve prediction of microbial behaviors in food. The implementation of this information in hazard identification and exposure assessment processes will open new possibilities to feed QMRA-models.

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).

A primer on microbial bioinformatics for nonbioinformaticians

BACKGROUND

Presently, the bottleneck in the deployment of high-throughput sequencing technology is the ability to analyse the increasing amount of data produced in a fit-for-purpose manner. The field of microbial bioinformatics is thriving and quickly adapting to technological changes, which creates difficulties for nonbioinformaticians in following the complexity and increasingly obscure jargon of this field.

AIMS

This review is directed towards nonbioinformaticians who wish to gain understanding of the overall microbial bioinformatic processes, from raw data obtained from sequencers to final outputs.

SOURCES

The software and analytical strategies reviewed are based on the personal experience of the authors.

CONTENT

The bioinformatic processes of transforming raw reads to actionable information in a clinical and epidemiologic context is explained. We review the advantages and limitations of two major strategies currently applied: read mapping, which is the comparison with a predefined reference genome, and de novo assembly, which is the unguided assembly of the raw data. Finally, we discuss the main analytical methodologies and the most frequently used freely available software and its application in the context of bacterial infectious disease management.

IMPLICATIONS

High-throughput sequencing technologies are overhauling outbreak investigation and epidemiologic surveillance while creating new challenges due to the amount and complexity of data generated. The continuously evolving field of microbial bioinformatics is required for stakeholders to fully harness the power of these new technologies.