Rian Pierneef

Mainstreams of horizontal gene exchange in enterobacteria : consideration of the outbreak of enterohemorrhagic E. coli O104:H4 in Germany in 2011

Background Escherichia coli O104:H4 caused a severe outbreak in Europe in 2011. The strain TY-2482 sequenced from this outbreak allowed the discovery of its closest relatives but failed to resolve ways in which it originated and evolved. On account of the previous statement, may we expect similar upcoming outbreaks to occur recurrently or spontaneously in the future? The inability to answer these questions shows limitations of the current comparative and evolutionary genomics methods. Principal Findings The study revealed oscillations of gene exchange in enterobacteria, which originated from marine γ-Proteobacteria. These mobile genetic elements have become recombination hotspots and effective ‘vehicles’ ensuring a wide distribution of successful combinations of fitness and virulence genes among enterobacteria. Two remarkable peculiarities of the strain TY-2482 and its relatives were observed: i) retaining the genetic primitiveness by these strains as they somehow avoided the main fluxes of horizontal gene transfer which effectively penetrated other enetrobacteria; ii) acquisition of antibiotic resistance genes in a plasmid genomic island of β-Proteobacteria origin which ontologically is unrelated to the predominant genomic islands of enterobacteria. Conclusions Oscillations of horizontal gene exchange activity were reported which result from a counterbalance between the acquired resistance of bacteria towards existing mobile vectors and the generation of new vectors in the environmental microflora. We hypothesized that TY-2482 may originate from a genetically primitive lineage of E. coli that has evolved in confined geographical areas and brought by human migration or cattle trade onto an intersection of several independent streams of horizontal gene exchange. Development of a system for monitoring the new and most active gene exchange events was proposed.

Pre_GI: a global map of ontological links between horizontally transferred genomic islands in bacterial and archaeal genomes.

Abstract The Predicted Genomic Islands database (Pre_GI) is a comprehensive repository of prokaryotic genomic islands (islands, GIs) freely accessible at http://pregi.bi.up.ac.za/index.php . Pre_GI, Version 2015, catalogues 26u2009744 islands identified in 2407 bacterial/archaeal chromosomes and plasmids. It provides an easy-to-use interface which allows users the ability to query against the database with a variety of fields, parameters and associations. Pre_GI is constructed to be a web-resource for the analysis of ontological roads between islands and cartographic analysis of the global fluxes of mobile genetic elements through bacterial and archaeal taxonomic borders. Comparison of newly identified islands against Pre_GI presents an alternative avenue to identify their ontology, origin and relative time of acquisition. Pre_GI aims to aid research on horizontal transfer events and materials through providing data and tools for holistic investigation of migration of genes through ecological niches and taxonomic boundaries. Database URL: http://pregi.bi.up.ac.za/index.php , Version 2015

A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils

BackgroundSoil bacteria naturally produce antibiotics as a competitive mechanism, with a concomitant evolution, and exchange by horizontal gene transfer, of a range of antibiotic resistance mechanisms. Surveys of bacterial resistance elements in edaphic systems have originated primarily from human-impacted environments, with relatively little information from remote and pristine environments, where the resistome may comprise the ancestral gene diversity.MethodsWe used shotgun metagenomics to assess antibiotic resistance gene (ARG) distribution in 17 pristine and remote Antarctic surface soils within the undisturbed Mackay Glacier region. We also interrogated the phylogenetic placement of ARGs compared to environmental ARG sequences and tested for the presence of horizontal gene transfer elements flanking ARGs.ResultsIn total, 177 naturally occurring ARGs were identified, most of which encoded single or multi-drug efflux pumps. Resistance mechanisms for the inactivation of aminoglycosides, chloramphenicol and β-lactam antibiotics were also common. Gram-negative bacteria harboured most ARGs (71%), with fewer genes from Gram-positive Actinobacteria and Bacilli (Firmicutes) (9%), reflecting the taxonomic composition of the soils. Strikingly, the abundance of ARGs per sample had a strong, negative correlation with species richness (ru2009=u2009−u20090.49, Pu2009<u20090.05). This result, coupled with a lack of mobile genetic elements flanking ARGs, suggests that these genes are ancient acquisitions of horizontal transfer events.ConclusionsARGs in these remote and uncontaminated soils most likely represent functional efficient historical genes that have since been vertically inherited over generations. The historical ARGs in these pristine environments carry a strong phylogenetic signal and form a monophyletic group relative to ARGs from other similar environments.

Optimization and Practical Use of Composition Based Approaches Towards Identification and Collection of Genomic Islands and Their Ontology in Prokaryotes

Abstract Motivation Horizontally transferred genomic islands (islands, GIs) have been referred to as important factors which contribute towards bacterial evolution in general and particularly towards the emergences of pathogens and outbreak instances. The development of tools for identification of such elements and retracing their distribution will help to understand how such cases arise. Sequence composition has been used to identify GIs, infer their phylogeny; and determine their relative time of insertion. Collection of metadata on known GIs will enhance insight into horizontal gene transfer ontology and flow. Results This paper introduces the merger of SeqWord Genomic Islands Sniffer (SWGIS), which utilizes composition based approaches for identification of GIs in bacterial genomic sequences, and the Predicted Genomic Islands (Pre_GI) database, which houses 26,744 islands found in 2,407 bacterial plasmids and chromosomes. SWGIS is a standalone program that detects GIs using a set of optimized parametric measures with estimates of acceptable false positive and false negative rates. Pre_GI is a novel repository that includes island ontology and flux. This study furthermore illustrates the need for parametric optimization towards the prediction of GIs to minimize false negative and false positive predictions. In addition Pre_GI emphasizes the practicality of the compounded knowledge that the database affords in detection and visualization of ontological links between GIs. Availability SWGIS is freely available on the web at http://www.bi.up.ac.za/SeqWord/sniffer/index.html , and Pre_GI is freely accessible at http://pregi.bi.up.ac.za/index.php.

Linguistic Approaches for Annotation, Visualization and Comparison of Prokaryotic Genomes and Environmental Sequences

Sequencing of bacterial genomes has become a common technique of the present day microbiology. Thereafter, data mining in complete genome sequence is an essential step to uncover the uniqueness and evolutionary success of microorganisms. Oligonucleotide usage (OU or k-mer) statistics provides invaluable tools to get insight into genome organization and functionality. The study of genome OU signatures has a long history dating back to early publications by Karlin et al. 1995, 1997, 1998, who focused mainly on dinucleotide compositional biases and their evolutionary implications. Statistical approaches of OU comparison were further advanced by Deschavanne et al., 1999, who applied chaos game algorithms; and by Pride et al., 2003, who extended the analysis to tetranucleotides using Markov Chain Model simulations. Later, a number of practical tools for phylogenetic comparison of bacterial genomes (Coenye & Vandamme, 2004; van Passel et al., 2006); identification of horizontally transferred genomic islands (Mrazek & Karlin, 1999; Pried & Blaser, 2002; Nakamura et al., 2004; Azad & Lawrence, 2005; Dufraign et al., 2005; Becq et al., 2007) and assignment of unknown genomic sequences (Abe et al., 2003; Teeling et al., 2004) based on OU statistics became publicly available. These approaches exploited the notion that genomic OU composition was less variable within genomes rather than between them, regardless of which genomic regions had been taken into consideration (Jernigan & Baran, 2002). A general belief was that if a significant compositional difference was discovered in genomic fragments relative to the core genome, these loci most likely can be assigned to horizontally transferred genetic elements (transposons, prophages or integrated plasmids). This approach was criticized by several researchers (Koski et al., 2001; Wang 2001), who pointed out that codon bias and base composition are poor indicators of horizontal gene transfer. Therefore, there is a need for more informative parameters which also take into account higher order DNA variation. An overview of the current OU statistical methods based on di-, tetraand hexanucleotides has been published recently (Bohlin et al., 2008). The conclusion of the review was that all methods were context dependent and, though being efficient and powerful, none of them were superior in all applications. Thus, the major

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes

BackgroundGenomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes.ResultsSWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za, was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface.ConclusionsSWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

Long Non-coding RNAs in the Human Genome Acquired by Horizontal Gene Transfer

Results: Predicted genomic islands were enriched with long non-coding RNAs and also contributed to the acquisition and modification of proteins associated with the immune system and gonad development, albeit to a lesser extent. The estimated rate of acquisition of these genomic islands in vertebrate genomes was non-linear with regards to species divergence times with an acceleration at the time of vertebrate land invasion and during the transition of prosimians to monkeys soon after the Cretaceous-Paleogene extinction.

Selection of marker genes for genetic barcoding of microorganisms and binning of metagenomic reads by Barcoder software tools

BackgroundMetagenomic approaches have revealed the complexity of environmental microbiomes with the advancement in whole genome sequencing displaying a significant level of genetic heterogeneity on the species level. It has become apparent that patterns of superior bioactivity of bacteria applicable in biotechnology as well as the enhanced virulence of pathogens often requires distinguishing between closely related species or sub-species. Current methods for binning of metagenomic reads usually do not allow for identification below the genus level and generally stops at the family level.ResultsIn this work, an attempt was made to improve metagenomic binning resolution by creating genome specific barcodes based on the core and accessory genomes. This protocol was implemented in novel software tools available for use and download from http://bargene.bi.up.ac.za/. The most abundant barcode genes from the core genomes were found to encode for ribosomal proteins, certain central metabolic genes and ABC transporters. Performance of metabarcode sequences created by this package was evaluated using artificially generated and publically available metagenomic datasets. Furthermore, a program (Barcoding 2.0) was developed to align reads against barcode sequences and thereafter calculate various parameters to score the alignments and the individual barcodes. Taxonomic units were identified in metagenomic samples by comparison of the calculated barcode scores to set cut-off values. In this study, it was found that varying sample sizes, i.e. number of reads in a metagenome and metabarcode lengths, had no significant effect on the sensitivity and specificity of the algorithm. Receiver operating characteristics (ROC) curves were calculated for different taxonomic groups based on the results of identification of the corresponding genomes in artificial metagenomic datasets. The reliability of distinguishing between species of the same genus or family by the program was nearly perfect.ConclusionsThe results showed that the novel online tool BarcodeGenerator (http://bargene.bi.up.ac.za/) is an efficient approach for generating barcode sequences from a set of complete genomes provided by users. Another program, Barcoder 2.0 is available from the same resource to enable an efficient and practical use of metabarcodes for visualization of the distribution of organisms of interest in environmental and clinical samples.

Draft genome sequence of Thermoactinomyces sp. strain AS95 isolated from a Sebkha in Thamelaht, Algeria

The members of the genus Thermoactinomyces are known for their protein degradative capacities. Thermoactinomyces sp. strain AS95 is a Gram-positive filamentous bacterium, isolated from moderately saline water in the Thamelaht region of Algeria. This isolate is a thermophilic aerobic bacterium with the capacity to produce extracellular proteolytic enzymes. This strain exhibits up to 99xa0% similarity with members of the genus Thermoactinomyces, based on 16S rRNA gene sequence similarity. Here we report on the phenotypic features of Thermoactinomyces sp. strain AS95 together with the draft genome sequence and its annotation. The genome of this strain is 2,558,690xa0bp in length (one chromosome, but no plasmid) with an average Gu2009+u2009C content of 47.95xa0%, and contains 2550 protein-coding and 60 RNA genes together with 64 ORFs annotated as proteases.