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Genomic sequences are becoming available from both pathogenic and nonpathogenic bacteria. Here we analyze an increasing body of information available on the molecular mechanisms Salmonella typhimurium uses to cause disease, in order to divine clues for identifying sequences that play a role in pathogenesis in other bacterial pathogens.
Genomic islands (GIs) play important roles in microbial evolution, which are acquired by horizontal gene transfer. In this paper, the GIs of six completely sequenced plant pathogens are identified using a windowless method based on Z curve representation of DNA sequences. Consequently, four, eight, four, one, two and four GIs are recognized with the length greater than 20-Kb in plant pathogens Agrobacterium tumefaciens str. C58, Rolstonia solanacearum GMI1000, Xanthomonas axonopodis pv. citri str. 306 (Xac), Xanthomonas campestris pv. campestris str. ATCC33913 (Xcc), Xylella fastidiosa 9a5c and Pseudomonas syringae pv. tomato str. DC3000, respectively. Most of these regions share a set of conserved features of GIs, including an abrupt change in GC content compared with that of the rest of the genome, the existence of integrase genes at the junction, the use of tRNA as the integration sites, the presence of genetic mobility genes, the difference of codon usage, codon preference and amino acid usage, etc. The identification of these GIs will benefit the research for the six important phytopathogens.
A wealth of new data have become available to the scientific community as a result of the sequencing of many pathogen genomes. A recent meeting devoted to functional genomics of pathogenic microorganisms confirmed the notion that bacterial genomes are not static, because large blocks of genes can be acquired or deleted. Less complex environments usually result in reduction in genome size, while genome expansion is usually associated with environmental change and complexity. During the meeting, pathogenicity and evolutionary aspects were illustrated for enteric pathogens, as well as the microevolution of the plague bacillus Yersinia pestis. New clues for evolution and pathogenicity were derived from comparative genomics of Listeria species. The genomic organization of Bartonellae, an emerging human pathogen, was also discussed in an evolutionary context. Population and functional genomics of Anthrax-causing bacteria highlighted current scientific interest in this potential biothreat.
Complete genome sequences are now available for multiple strains of several bacterial pathogens and comparative analysis of these sequences is providing important insights into the evolution of bacterial virulence. Recently, DNA microarray analysis of many strains of several pathogenic species has contributed to our understanding of bacterial diversity, evolution and pathogenesis. Comparative genomics has shown that pathogens such as Escherichia coli, Helicobacter pylori and Staphylococcus aureus contain extensive variation in gene content whereas Mycobacterium tuberculosis nucleotide divergence is very limited. Overall, these approaches are proving to be a powerful means of exploring bacterial diversity, and are providing an important framework for the analysis of the evolution of pathogenesis and the development of novel antimicrobial agents.
The past decade has witnessed a rapid transition from the first positional cloning of an infectious disease susceptibility gene (Slc11a1, also called Nramp1) in the mouse to genome-wide scans in human multicase families and the identification of potential disease-causing genes by simple inspection of the public human genome databases. Pathogen genome projects have facilitated multilocus sequence typing of pathogen isolates and studies of ecological fitness and virulence patterns in disease-causing isolates. Comparative sequence analysis of pathogen strains and functional genomics studies are now underway, hopefully providing new insight into infectious disease susceptibility.
Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.
The general principles of structural and functional organization of genomes in pathogenic bacteria are considered. Main data on the specific features of genomes of Chlamydia trachomatis, Rickettsia prowazekii, Treponema pallidum, Helicobacter pylori, Haemophilus influenzae, Neisseria meningitidis, Vibro cholerae and pathogenic strains of Escherichia coli are summarized. Particular attention is paid to the problems of genetic control of pathogenicity, intraspecies variations in bacterial genomes, to the environmental and evolutionary meaning of horizontal gene transfer. Whether methods for genotyping bacterial strains can be used is discussed.
BACKGROUND: Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs. RESULTS: We present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs. CONCLUSION: Supporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis.
This work reports the results of analyses of three complete mycoplasma genomes, a pathogenic (7448) and a nonpathogenic (J) strain of the swine pathogen Mycoplasma hyopneumoniae and a strain of the avian pathogen Mycoplasma synoviae; the genome sizes of the three strains were 920,079 bp, 897,405 bp, and 799,476 bp, respectively. These genomes were compared with other sequenced mycoplasma genomes reported in the literature to examine several aspects of mycoplasma evolution. Strain-specific regions, including integrative and conjugal elements, and genome rearrangements and alterations in adhesin sequences were observed in the M. hyopneumoniae strains, and all of these were potentially related to pathogenicity. Genomic comparisons revealed that reduction in genome size implied loss of redundant metabolic pathways, with maintenance of alternative routes in different species. Horizontal gene transfer was consistently observed between M. synoviae and Mycoplasma gallisepticum. Our analyses indicated a likely transfer event of hemagglutinin-coding DNA sequences from M. gallisepticum to M. synoviae.
Listeria species are ubiquitous in the environment and often contaminate foods because they grow under conditions used for food preservation. Listeria monocytogenes, the human and animal pathogen, causes Listeriosis, an infection with a high mortality rate in risk groups such as immune-compromised individuals. Furthermore, L.monocytogenes is a model organism for the study of intracellular bacterial pathogens. The publication of its genome sequence and that of the non-pathogenic species Listeria innocua initiated numerous comparative studies and efforts to sequence all species comprising the genus. The Proteome database LEGER (http://leger2.gbf.de/cgi-bin/expLeger.pl) was developed to support functional genome analyses by combining information obtained by applying bioinformatics methods and from public databases to improve the original annotations. LEGER offers three unique key features: (i) it is the first comprehensive information system focusing on the functional assignment of genes and proteins; (ii) integrated visualization tools, KEGG pathway and Genome Viewer, alleviate the functional exploration of complex data; and (iii) LEGER presents results of systematic post-genome studies, thus facilitating analyses combining computational and experimental results. Moreover, LEGER provides an unpublished membrane proteome analysis of L.innocua and in total visualizes experimentally validated information about the subcellular localizations of 789 different listerial proteins.
The availability of hundreds of bacterial genome sequences has altered the study of bacterial pathogenesis, affecting both design of experiments and analysis of results. Comparative genomics and genomic tools have been used to identify virulence factors and genes involved in environmental persistence of pathogens. However, a major stumbling block in the genomics revolution has been the large number of genes with unknown function that have been identified in every organism sequenced to date.
Phylodynamic analyses infer epidemiological parameters from pathogen genome sequences for enhanced genomic surveillance in public health. Pathogen genome sequences and their associated sampling dates are the essential data in every analysis. However, sampling dates are usually associated with hospitalisation or testing and can sometimes be used to identify individual patients, posing a threat to patient confidentiality. To lower this risk, sampling dates are often given with reduced date-resolution to the month or year, which can potentially bias inference. Here, we introduce a practical guideline on when date-rounding biases the inference of epidemiologically important parameters across a diverse range of empirical and simulated datasets. We show that the direction of bias varies for different parameters, datasets, and tree priors, while compounding with lower date-resolution and higher substitution rates. We also find that bias decreases for datasets with longer sampling intervals, implying that our guideline is most applicable to emerging datasets. We conclude by discussing future solutions that prioritise patient confidentiality and propose a method for safer sharing of sampling dates that translates them them uniformly by a random number.
Pathogens have evolved effector proteins to suppress host immunity and facilitate plant infections. RxLR effectors are small, secreted effector proteins with conserved RxLR and dEER amino acid motifs at the N terminus and highly variable C termini and are commonly found in oomycete species. We provide computational approaches to annotate RxLR candidate effector genes in a genome assembly in FASTA format with an available GFF file. Hidden Markov Modeling (HHM) is used in combination with regular expressions to search for RxLR and EER amino acid patterns.
Genomic hybridization on whole genome arrays detects the presence or absence of similar DNA regions in sufficiently related microorganisms, allowing genome-wide comparison of their genetic contents. A whole genome array is based on a sequenced bacterial isolate, and is a collection of DNA probes fixed on a solid support. In a single hybridization experiment, the absence/presence status of all genes of the sequenced microbe in the queried isolate can be examined. The objective of this minireview is to summarize the past usage of DNA microarray technology for microbial strain characterizations, and to estimate its future utilization in epidemiological studies and molecular typing of bacterial pathogens. The studies reviewed here confirm the usefulness of microarray technology for the detection of genetic polymorphisms. However, the construction or purchase of DNA microarrays and the performance of strain to strain hybridization experiments are still prohibitively expensive for routine application. Future use of arrays in epidemiology is likely to depend on the development of more cost-effective protocols, more robust and simplified formats, and the adequate evaluation of their performance (efficacy) and convenience (efficiency) compared with other genotyping methods. It seems more likely that a more focused assay, concentrating on genomic regions of variability previously detected by genome-wide microarrays, will find broad application in routine bacterial epidemiology.
BACKGROUND: ESKAPE pathogens are major causes of hospital-acquired infections and are characterized by extensive antimicrobial resistance (AMR) and diverse virulence mechanisms. Although species-specific pan-genome studies have revealed substantial genomic diversity, the relationships among genome plasticity, resistance burden, and virulence remain incompletely understood across the ESKAPE complex. METHODS: We analyzed 120 high-quality genomes representing six single-species ESKAPE groups (20 genomes per species). Genome quality was assessed using CheckM2. Species-specific pan-genomes were constructed with Roary, AMR genes were identified using AMRFinderPlus, and virulence factors were detected against the VFDB database using DIAMOND. AMR genes were mapped to core and accessory genome compartments through integration of Prokka annotations and Roary outputs. Statistical associations were evaluated using Fisher's exact tests and correlation analyses, with false discovery rate correction applied within each test family. Core-genome maximum-likelihood phylogenies were reconstructed to provide an evolutionary framework. RESULTS: Pan-genome sizes ranged from 4720 to 17,272 genes, with Enterobacter and Pseudomonas possessing the largest accessory genomes. Multidrug resistance (MDR; resistance to ≥3 antimicrobial classes) was detected in 93.3% of strains. After false discovery rate correction, AMR genes remained significantly enriched in the accessory genomes of Enterobacter, Enterococcus, Klebsiella, and Staphylococcus, whereas Acinetobacter and Pseudomonas did not show significant enrichment in either genome compartment. Within-species analyses identified significant positive associations between accessory genome size and AMR class burden in Staphylococcus, Enterococcus, and Enterobacter, whereas the moderate Pearson correlation observed in Pseudomonas was not significant after FDR correction. Virulence factor repertoires varied markedly among species, with Pseudomonas exhibiting the highest burden and Enterococcus the lowest. CONCLUSIONS: ESKAPE pathogens display distinct patterns of resistance and virulence. Accessory genome expansion was associated with higher AMR burden in several species, whereas other species showed no significant association between accessory genome size and AMR burden and no significant enrichment of AMR genes in either genome compartment, highlighting the species-specific nature of AMR evolution.
A pressing problem in studying the evolution of microbial pathogens is to determine the extent to which these genomes recombine. This information is essential for locating pathogenicity loci by using association studies or population genetic approaches. Recombination also complicates the use of phylogenetic approaches to estimate evolutionary parameters such as selection pressures. Reliable methods that detect and estimate the rate of recombination are, therefore, vital. This article reviews the approaches that are available for detecting and estimating recombination in microbial pathogens and how they can be used to understand pathogen evolution and to identify medically relevant loci.
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The filamentous fungal genetics community has enthusiastically embraced the utilization of genomics technologies to resolve long-standing issues in fungal biology. For example, such technologies have been proposed to study the mechanics of tip growth, photoreception, gene silencing, the molecular basis of conidiation, the pathway leading to sexual reproduction, and mechanisms of pathogenesis. These studies have provided a refreshing change of pace in research on filamentous fungi, which has lagged behind that on other eukaryotes in the exploitation of genome-wide methodologies. Despite the late start, several fungal genome sequencing projects are underway. The resulting databases will allow the comprehensive analysis of developmental processes that are characteristic of fungi, including the molecular nature of pathogenicity. DNA databases underpin analyses of the fungal transcriptome, proteome, and metabolome. This combined information will contribute to our basic understanding of not only the mechanics of infection but also the evolution of pathogenicity.