Using the nucleotide substitution rate matrix to detect horizontal gene transfer

Blue Light Sensing BlsA-Mediated Modulation of Meropenem Resistance and Biofilm Formation in Acinetobacter baumannii

The presence or absence of BlsA, a protein with a blue light-sensing flavin domain in the genomes of Acinetobacter species has aroused curiosity about its roles in the regulation of bacterial lifestyle under light. Genomic and transcriptomic analyses revealed the loss of BlsA in several multidrug-resistant (MDR) A. baumannii strains as well as the light-mediated induction of blsA, along with a possible BlsA-interacting partner BipA. Their direct in vivo interactions were verified using a bacterial two-hybrid system. The results demonstrated that the C-terminal region of BipA could bind to the C-terminal residues of BlsA under blue light at 23°C but not at 37°C. Genetic manipulations of blsA and bipA revealed that the coexistence of BlsA and BipA was required to induce the light-dependent expression of ompA in A. baumannii ATCC 17978 at 23°C. The same phenomenon occurred in the BlsA-deficient MDR strain in our functional complementation assay; however, the underlying molecular mechanism remains poorly understood. BlsA-modulated amounts of OmpA, the most abundant porin, in the outer membrane affected the membrane integrity and permeability of small molecules. Dark conditions or the deletion of ompA made the membrane more permeable to lipophilic ethidium bromide (EtBr) but not to meropenem. Interestingly, light illumination and low temperature conditions made the cells more sensitive to meropenem; however, this bactericidal effect was not noted in the blsA mutant or in the BlsA-deficient MDR strains. Light-mediated cell death and the reduction of biofilm formation at 23°C were abolished in the blsA mutant strain, suggesting multifaceted roles of BlsA in A. baumannii strains. IMPORTANCE Little is known about the functional roles of BlsA and its interacting partners in Acinetobacter species. Intriguingly, no BlsA homolog was found in several clinical isolates, suggesting that BlsA was not required inside the host because of the lack of blue light and the warm temperature conditions. As many chromophore-harboring proteins interact with various partners to control light-dependent cellular behaviors, the maintenance of blsA in the genomes of many Acinetobacter species during their evolution may be beneficial when fluctuations occur in two important environmental factors: light and temperature. Our study is the first to report the novel protein partner of BlsA, namely, BipA, and its contribution to multiple phenotypic changes, including meropenem resistance and biofilm formation. Rapid physiological acclimation to changing light or temperature conditions may be possible in the presence of the light-sensing BlsA protein, which may have more interacting partners than expected.

Occurrence of antibiotic resistance genes and multidrug-resistant bacteria during wastewater treatment processes

Wastewater treatment plants (WWTPs) constantly receive a wide variety of contaminants, including pharmaceuticals, and are potential reservoirs of antibiotic resistance genes (ARGs). This favors the development of multidrug-resistant bacteria (MRB) through horizontal gene transfer. Samples from five different WWTP processes were collected in September 2020 and January 2021 to monitor ARG resistomes and culturable MRB in the presence of eight different antibiotics. Nanopore-based ARG abundance and bacterial community analyses suggested that ARG accumulation favors the generation of MRB. Activated and mixed sludges tended to have lower bacterial diversity and ARG abundance because of selective forces that favored the growth of specific microorganisms during aeration processes. Escherichia strains enriched in WWTPs (up to 71%) were dominant in all the samples, whereas Cloacamonas species were highly abundant only in anaerobically digested sludge samples (60%-79%). Two ARG types [sulfonamide resistance genes (sul1) and aminoglycoside resistance genes (aadA1, aadA13, and aadA2)] were prevalent in all the processes. The total counts of culturable MRB, such as Niabella, Enterococcus, Bacillus, and Chryseobacterium species, gradually increased during aerobic WWTP processes. Genomic analyses of all MRB isolated from the samples revealed that the resistome of Enterococcus species harbored the highest number of ARGs (7-18 ARGs), commonly encoding ant(6)-la, lnu(B), erm(B), and tet(S/M). On the other hand, Niablella strains possibly had intrinsic resistant phenotypes without ARGs. All MRB possessed ARGs originating from the same mobile genetic elements, suggesting that WWTPs are hotspots for the migration of ARGs and emergence of MRB.

The nucleotide composition of microbial genomes indicates differential patterns of selection on core and accessory genomes

Background

The core genome consists of genes shared by the vast majority of a species and is therefore assumed to have been subjected to substantially stronger purifying selection than the more mobile elements of the genome, also known as the accessory genome. Here we examine intragenic base composition differences in core genomes and corresponding accessory genomes in 36 species, represented by the genomes of 731 bacterial strains, to assess the impact of selective forces on base composition in microbes. We also explore, in turn, how these results compare with findings for whole genome intragenic regions.

Results

We found that GC content in coding regions is significantly higher in core genomes than accessory genomes and whole genomes. Likewise, GC content variation within coding regions was significantly lower in core genomes than in accessory genomes and whole genomes. Relative entropy in coding regions, measured as the difference between observed and expected trinucleotide frequencies estimated from mononucleotide frequencies, was significantly higher in the core genomes than in accessory and whole genomes. Relative entropy was positively associated with coding region GC content within the accessory genomes, but not within the corresponding coding regions of core or whole genomes.

Conclusion

The higher intragenic GC content and relative entropy, as well as the lower GC content variation, observed in the core genomes is most likely associated with selective constraints. It is unclear whether the positive association between GC content and relative entropy in the more mobile accessory genomes constitutes signatures of selection or selective neutral processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3543-7) contains supplementary material, which is available to authorized users.

Antibiotic-free selection in biotherapeutics: now and forever

The continuously improving sophistication of molecular engineering techniques gives access to novel classes of bio-therapeutics and new challenges for their production in full respect of the strengthening regulations. Among these biologic agents are DNA based vaccines or gene therapy products and to a lesser extent genetically engineered live vaccines or delivery vehicles. The use of antibiotic-based selection, frequently associated with genetic manipulation of microorganism is currently undergoing a profound metamorphosis with the implementation and diversification of alternative selection means. This short review will present examples of alternatives to antibiotic selection and their context of application to highlight their ineluctable invasion of the bio-therapeutic world.

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning

Background

A wealth of genome sequences has provided thousands of genes of unknown function, but identification of functions for the large numbers of hypothetical genes in phytopathogens remains a challenge that impacts all research on plant-microbe interactions. Decades of research on the molecular basis of pathogenesis focused on a limited number of factors associated with long-known host-microbe interaction systems, providing limited direction into this challenge. Computational approaches to identify virulence genes often rely on two strategies: searching for sequence similarity to known host-microbe interaction factors from other organisms, and identifying islands of genes that discriminate between pathogens of one type and closely related non-pathogens or pathogens of a different type. The former is limited to known genes, excluding vast collections of genes of unknown function found in every genome. The latter lacks specificity, since many genes in genomic islands have little to do with host-interaction.

Result

In this study, we developed a supervised machine learning approach that was designed to recognize patterns from large and disparate data types, in order to identify candidate host-microbe interaction factors. The soft rot Enterobacteriaceae strains Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 were used for development of this tool, because these pathogens are important on multiple high value crops in agriculture worldwide and more genomic and functional data is available for the Enterobacteriaceae than any other microbial family. Our approach achieved greater than 90% precision and a recall rate over 80% in 10-fold cross validation tests.

Conclusion

Application of the learning scheme to the complete genome of these two organisms generated a list of roughly 200 candidates, many of which were previously not implicated in plant-microbe interaction and many of which are of completely unknown function. These lists provide new targets for experimental validation and further characterization, and our approach presents a promising pattern-learning scheme that can be generalized to create a resource to study host-microbe interactions in other bacterial phytopathogens.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-508) contains supplementary material, which is available to authorized users.

Towards a better detection of horizontally transferred genes by combining unusual properties effectively

Background

Horizontal gene transfer (HGT) is one of the major mechanisms contributing to microbial genome diversification. A number of computational methods for finding horizontally transferred genes have been proposed in the past decades; however none of them has provided a reliable detector yet. In existing parametric approaches, only one single compositional property can participate in the detection process, or the results obtained through each single property are just simply combined. It’s known that different properties may mean different information, so the single property can’t sufficiently contain the information encoded by gene sequences. In addition, the class imbalance problem in the datasets, which also results in great errors for the gene detection, hasn’t been considered by the published methods. Here we developed an effective classifier system (Hgtident) that used support vector machine (SVM) by combining unusual properties effectively for HGT detection.

Results

Our approach Hgtident includes the introduction of more representative datasets, optimization of SVM model, feature selection, handling of imbalance problem in the datasets and extensive performance evaluation via systematic cross-validation methods. Through feature selection, we found that JS-DN and JS-CB have higher discriminating power for HGT detection, while GC1–GC3 and k-mer (k = 1, 2, …, 7) make the least contribution. Extensive experiments indicated the new classifier could reduce Mean error dramatically, and also improve Recall by a certain level. For the testing genomes, compared with the existing popular multiple-threshold approach, on average, our Recall and Mean error was respectively improved by 2.81% and reduced by 26.32%, which means that numerous false positives were identified correctly.

Conclusions

Hgtident introduced here is an effective approach for better detecting HGT. Combining multiple features of HGT is also essential for a wider range of HGT events detection.

[Analysis of horizontal transfer gene of Bombyx mori NPV]

For research on genetic characters and evolutionary origin of the genome of baculoviruses, a comprehensive homology search and phylogenetic analysis of the complete genomes of Bombyx mori NPV and Bombyx mori were used. Three horizontally transferred genes (inhibitor of apoptosis, chitinase, and UDP-glucosyltransferase) were identified, and there was evidence that all of these genes were derived from the insect host. The results of analysis showed lots of differences between the features of horizontal transferred genes and the ones of whole genomic genes, such as nucleotide composition, codon usagebias and selection pressure. These results reconfirmed that the horizontally transferred genes are exogenous. The analysis of gene function suggested that horizontally transferred genes acquired from an ancestral host insect can increase the efficiency of baculoviruses transmission.

A benchmark of parametric methods for horizontal transfers detection

Horizontal gene transfer (HGT) has appeared to be of importance for prokaryotic species evolution. As a consequence numerous parametric methods, using only the information embedded in the genomes, have been designed to detect HGTs. Numerous reports of incongruencies in results of the different methods applied to the same genomes were published. The use of artificial genomes in which all HGT parameters are controlled allows testing different methods in the same conditions. The results of this benchmark concerning 16 representative parametric methods showed a great variety of efficiencies. Some methods work very poorly whatever the type of HGTs and some depend on the conditions or on the metrics used. The best methods in terms of total errors were those using tetranucleotides as criterion for the window methods or those using codon usage for gene based methods and the Kullback-Leibler divergence metric. Window methods are very sensitive but less specific and detect badly lone isolated gene. On the other hand gene based methods are often very specific but lack of sensitivity. We propose using two methods in combination to get the best of each category, a gene based one for specificity and a window based one for sensitivity.

Insights into bacterial genome composition through variable target GC content profiling

This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate significant regions without prior knowledge of the sequence, nor the features being sought. The use of MRS has provided novel insights into bacterial genome composition. Key findings include those that are related to the core composition of bacterial genomes, to the identification of large genomic islands (in Enterobacterial genomes), and to the identification of surface protein determinants in human pathogenic organisms (e.g., Staphylococcus genomes). We observed that bacterial surface binding proteins maintain abnormal GC content, potentially pointing to a viral origin. This study has demonstrated that GC content holds a high informational worth and hints at many underlying evolutionary processes. For online Supplementary Material, see www.liebertonline.com .

Detection and quantitative assessment of horizontal gene transfer

This chapter discusses the pros and cons of the existing computational methods for the detection of horizontal (or lateral) gene transfer and highlights the genome-wide studies utilizing these methods. The impact of horizontal gene transfer (HGT) on prokaryote genome evolution is discussed.

Comparison of methods for estimating the nucleotide substitution matrix

Background

The nucleotide substitution rate matrix is a key parameter of molecular evolution. Several methods for inferring this parameter have been proposed, with different mathematical bases. These methods include counting sequence differences and taking the log of the resulting probability matrices, methods based on Markov triples, and maximum likelihood methods that infer the substitution probabilities that lead to the most likely model of evolution. However, the speed and accuracy of these methods has not been compared.

Results

Different methods differ in performance by orders of magnitude (ranging from 1 ms to 10 s per matrix), but differences in accuracy of rate matrix reconstruction appear to be relatively small. Encouragingly, relatively simple and fast methods can provide results at least as accurate as far more complex and computationally intensive methods, especially when the sequences to be compared are relatively short.

Conclusion

Based on the conditions tested, we recommend the use of method of Gojobori et al. (1982) for long sequences (> 600 nucleotides), and the method of Goldman et al. (1996) for shorter sequences (< 600 nucleotides). The method of Barry and Hartigan (1987) can provide somewhat more accuracy, measured as the Euclidean distance between the true and inferred matrices, on long sequences (> 2000 nucleotides) at the expense of substantially longer computation time. The availability of methods that are both fast and accurate will allow us to gain a global picture of change in the nucleotide substitution rate matrix on a genomewide scale across the tree of life.