High genetic diversity and different type VI secretion systems in Enterobacter species revealed by comparative genomics analysis

The human-pathogenic Enterobacter species are widely distributed in diverse environmental conditions, however, the understanding of the virulence factors and genetic variations within the genus is very limited. In this study, we performed comparative genomics analysis of 49 strains originated from diverse niches and belonged to eight Enterobacter species, in order to further understand the mechanism of adaption to the environment in Enterobacter. The results showed that they had an open pan-genome and high genomic diversity which allowed adaptation to distinctive ecological niches. We found the number of secretion systems was the highest among various virulence factors in these Enterobacter strains. Three types of T6SS gene clusters including T6SS-A, T6SS-B and T6SS-C were detected in most Enterobacter strains. T6SS-A and T6SS-B shared 13 specific core genes, but they had different gene structures, suggesting they probably have different biological functions. Notably, T6SS-C was restricted to E. cancerogenus. We detected a T6SS gene cluster, highly similar to T6SS-C (91.2%), in the remote related Citrobacter rodenitum, suggesting that this unique gene cluster was probably acquired by horizontal gene transfer. The genomes of Enterobacter strains possess high genetic diversity, limited number of conserved core genes, and multiple copies of T6SS gene clusters with differentiated structures, suggesting that the origins of T6SS were not by duplication instead by independent acquisition. These findings provide valuable information for better understanding of the functional features of Enterobacter species and their evolutionary relationships.

Mining therapeutic targets from the antibiotic-resistant Campylobacter coli and virtual screening of natural product inhibitors against its riboflavin synthase

Campylobacter coli resides in the intestine of several commonly consumed animals, as well as water and soil. It leads to campylobacteriosis when humans eat raw/undercooked meat or come into contact with infected animals. A common manifestation of the infection is fever, nausea, headache, and diarrhea. Increasing antibiotic resistance is being observed in this pathogen. The increased incidence of C. coli infection, and post-infection complications like Guillain-Barré syndrome, make it an important pathogen. It is essential to find novel therapeutic targets and drugs against it, especially with the emergence of antibiotic-resistant strains. In the current study, genomes of 89 antibiotic-resistant strains of C. coli were downloaded from the PATRIC database. Potent drug targets (n = 36) were prioritized from the core genome (n = 1,337 genes) of this species. Riboflavin synthase was selected as a drug target and pharmacophore-based virtual screening was performed to predict its inhibitors from the NPASS (n =  ~ 30,000 compounds) natural product library. The top three docked compounds (NPC115144, NPC307895, and NPC470462) were selected for dynamics simulation (for 50 ns) and ADMET profiling. These identified compounds appear safe for targeting this pathogen and can be further validated by experimental analysis before clinical trials.

An in silico hierarchal approach for drug candidate mining and validation of natural product inhibitors against pyrimidine biosynthesis enzyme in the antibiotic-resistant Shigella flexneri

Shigella flexneri is the main causative agent of the communicable diarrheal disease, shigellosis. It is estimated that about 80-165 million cases and > 1 million deaths occur every year due to this disease. S. flexneri causes dysentery mostly in young children, elderly and immunocompromised patients, all over the globe. Recently, due to the emergence of S. flexneri antibiotic resistance strains, it is a dire need to predict novel therapeutic drug targets in the bacterium and screen natural products against it, which could eliminate the curse of antibiotic resistance. Therefore, in current study, available antibiotic-resistant genomes (n = 179) of S. flexneri were downloaded from PATRIC database and a pan-genome and resistome analysis was conducted. Around 5059 genes made up the accessory, 2469 genes made up the core, and 1558 genes made up the unique genome fraction, with 44, 34, and 13 antibiotic-resistant genes in each fraction, respectively. Core genome fraction (27% of the pan-genome), which was common to all strains, was used for subtractive genomics and resulted in 384 non-homologous, and 85 druggable targets. Dihydroorotase was chosen for further analysis and docked with natural product libraries (Ayurvedic and Streptomycin compounds), while the control was orotic acid or vitamin B13 (which is a natural binder of this protein). Dynamics simulation of 50 ns was carried out to validate findings for top-scored inhibitors. The current study proposed dihydroorotase as a significant drug target in S. flexneri and 4-tritriacontanone & patupilone compounds as potent drugs against shigellosis. Further experiments are required to ascertain validity of our findings.

Pan-genomic analyses of 47 complete genomes of the Rickettsia genus and prediction of new vaccine targets and virulence factors of the species

The genus Rickettsia belongs to the Proteobacteria phylum and these bacteria infect animals and humans causing a range of diseases worldwide. The genus is divided into 4 groups and despite the public health threat and the knowledge accumulated so far, the mandatory intracellular bacteria behaviour and limitation for in vitro culture makes it difficult to create new vaccines and drug targets to these bacteria. In an attempt to overcome these limitations, pan-genomic approaches has used 47 genomes of the genus Rickettsia, in order to describe species similarities and genomics islands. Moreover, we conducted reverse vaccinology and docking analysis aiming the identification of proteins that have great potential to become vaccine and drug targets. We found out that the bacteria of the four Rickettsia groups have a high similarity with each other, with about 90 to 100% of identity. A pathogenicity island and a resistance island were predicted. In addition, 8 proteins were also predicted as strong candidates for vaccine and 9 as candidates for drug targets. The prediction of the proteins leads us to believe in a possibility of prospecting potential drugs or creating a polyvalent vaccine, which could reach most strains of this large group of bacteria.Communicated by Ramaswamy H. Sarma.

Assessing graph-based read mappers against a baseline approach highlights strengths and weaknesses of current methods

BACKGROUND

Graph-based reference genomes have become popular as they allow read mapping and follow-up analyses in settings where the exact haplotypes underlying a high-throughput sequencing experiment are not precisely known. Two recent papers show that mapping to graph-based reference genomes can improve accuracy as compared to methods using linear references. Both of these methods index the sequences for most paths up to a certain length in the graph in order to enable direct mapping of reads containing common variants. However, the combinatorial explosion of possible paths through nearby variants also leads to a huge search space and an increased chance of false positive alignments to highly variable regions.

RESULTS

We here assess three prominent graph-based read mappers against a hybrid baseline approach that combines an initial path determination with a tuned linear read mapping method. We show, using a previously proposed benchmark, that this simple approach is able to improve overall accuracy of read-mapping to graph-based reference genomes.

CONCLUSIONS

Our method is implemented in a tool Two-step Graph Mapper, which is available at https://github.com/uio-bmi/two_step_graph_mapperalong with data and scripts for reproducing the experiments. Our method highlights characteristics of the current generation of graph-based read mappers and shows potential for improvement for future graph-based read mappers.

PGAP-X: extension on pan-genome analysis pipeline

Background

Since PGAP (pan-genome analysis pipeline) was published in 2012, it has been widely employed in bacterial genomics research. Though PGAP has integrated several modules for pan-genomics analysis, how to properly and effectively interpret and visualize the results data is still a challenge.

Result

To well present bacterial genomic characteristics, a novel cross-platform software was developed, named PGAP-X. Four kinds of data analysis modules were developed and integrated: whole genome sequences alignment, orthologous genes clustering, pan-genome profile analysis, and genetic variants analysis. The results from these analyses can be directly visualized in PGAP-X. The modules for data visualization in PGAP-X include: comparison of genome structure, gene distribution by conservation, pan-genome profile curve and variation on genic and genomic region. Meanwhile, result data produced by other programs with similar function can be imported to be further analyzed and visualized in PGAP-X. To test the performance of PGAP-X, we comprehensively analyzed 14 Streptococcus pneumonia strains and 14 Chlamydia trachomatis. The results show that, S. pneumonia strains have higher diversity on genome structure and gene contents than C. trachomatis strains. In addition, S. pneumonia strains might have suffered many evolutionary events, such genomic rearrangements, frequent horizontal gene transfer, homologous recombination, and other evolutionary process.

Conclusion

Briefly, PGAP-X directly presents the characteristics of bacterial genomic diversity with different visualization methods, which could help us to intuitively understand dynamics and evolution in bacterial genomes. The source code and the pre-complied executable programs are freely available from http://pgapx.ybzhao.com.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12864-017-4337-7) contains supplementary material, which is available to authorized users.

Pan-Genome Storage and Analysis Techniques

Computational pan-genome analysis has emerged from the rapid increase of available genome sequencing data. Starting from a microbial pan-genome, the concept has spread to a variety of species, such as plants or viruses. Characterizing a pan-genome provides insights into intra-species evolution, functions, and diversity. However, researchers face challenges such as processing and maintaining large datasets while providing accurate and efficient analysis approaches. Comparative genomics methods are required for detecting conserved and unique regions between a set of genomes. This chapter gives an overview of tools available for indexing pan-genomes, identifying the sub-regions of a pan-genome and offering a variety of downstream analysis methods. These tools are categorized into two groups, gene-based and sequence-based, according to the pan-genome identification method. We highlight the differences, advantages, and disadvantages between the tools, and provide information about the general workflow, methodology of pan-genome identification, covered functionalities, usability and availability of the tools.