Microarray comparative genomic hybridisation analysis incorporating genomic organisation, and application to enterobacterial plant pathogens

Responses of the bacterial community of tobacco phyllosphere to summer climate and wildfire disease

Both biotic and abiotic factors continually affect the phyllospheric ecology of plants. A better understanding of the drivers of phyllospheric community structure and multitrophic interactions is vital for developing plant protection strategies. In this study, 16S rRNA high-throughput sequencing was applied to study how summer climatic factors and bacterial wildfire disease have affected the composition and assembly of the bacterial community of tobacco (Nicotiana tabacum L.) phyllosphere. Our results indicated that three time series groups (T1, T2 and T3) formed significantly distinct clusters. The neutral community model (NCM) and beta nearest taxon index (betaNTI) demonstrated that the overall bacterial community assembly was predominantly driven by stochastic processes. Variance partitioning analysis (VPA) further showed that the complete set of the morbidity and climatic variables together could explain 35.7% of the variation of bacterial communities. The node numbers of the molecular ecological networks (MENs) showed an overall uptrend from T1 to T3. Besides, Pseudomonas is the keystone taxa in the MENs from T1 to T3. PICRUSt2 predictions revealed significantly more abundant genes of osmoprotectant biosynthesis/transport in T2, and more genes for pathogenicity and metabolizing organic substrate in T3. Together, this study provides insights into spatiotemporal patterns, processes and response mechanisms underlying the phyllospheric bacterial community.

Global Gene Expression Analysis of Cross-Protected Phenotype of Pectobacterium atrosepticum

The ability to adapt to adverse conditions permits many bacterial species to be virtually ubiquitous and survive in a variety of ecological niches. This ability is of particular importance for many plant pathogenic bacteria that should be able to exist, except for their host plants, in different environments e.g. soil, water, insect-vectors etc. Under some of these conditions, bacteria encounter absence of nutrients and persist, acquiring new properties related to resistance to a variety of stress factors (cross-protection). Although many studies describe the phenomenon of cross-protection and several regulatory components that induce the formation of resistant cells were elucidated, the global comparison of the physiology of cross-protected phenotype and growing cells has not been performed. In our study, we took advantage of RNA-Seq technology to gain better insights into the physiology of cross-protected cells on the example of a harmful phytopathogen, Pectobacterium atrosepticum (Pba) that causes crop losses all over the world. The success of this bacterium in plant colonization is related to both its virulence potential and ability to persist effectively under various stress conditions (including nutrient deprivation) retaining the ability to infect plants afterwards. In our previous studies, we showed Pba to be advanced in applying different adaptive strategies that led to manifestation of cell resistance to multiple stress factors. In the present study, we determined the period necessary for the formation of cross-protected Pba phenotype under starvation conditions, and compare the transcriptome profiles of non-adapted growing cells and of adapted cells after the cross-protective effect has reached the maximal level. The obtained data were verified using qRT-PCR. Genes that were expressed differentially (DEGs) in two cell types were classified into functional groups and categories using different approaches. As a result, we portrayed physiological features that distinguish cross-protected phenotype from the growing cells.

Comparison of alternative mixture model methods to analyze bacterial CGH experiments with multi-genome arrays

BACKGROUND

Microarray-based comparative genomic hybridization (aCGH) is used for rapid comparison of genomes of different bacterial strains. The purpose is to evaluate the distribution of genes from sequenced bacterial strains (control) among unsequenced strains (test). We previously compared the use of single strain versus multiple strain control with arrays covering multiple genomes. The conclusion was that a multiple strain control promoted a better separation of signals between present and absent genes.

FINDINGS

We now extend our previous study by applying the Expectation-Maximization (EM) algorithm to fit a mixture model to the signal distribution in order to classify each gene as present or absent and by comparing different methods for analyzing aCGH data, using combinations of different control strain choices, two different statistical mixture models, with or without normalization, with or without logarithm transformation and with test-over-control or inverse signal ratio calculation. We also assessed the impact of replication on classification accuracy. Higher values of accuracy have been achieved using the ratio of control-over-test intensities, without logarithmic transformation and with a strain mix control. Normalization and the type of mixture model fitted by the EM algorithm did not have a significant impact on classification accuracy. Similarly, using the average of replicate arrays to perform the classification does not significantly improve the results.

CONCLUSIONS

Our work provides a guiding benchmark comparison of alternative methods to analyze aCGH results that can impact on the analysis of currently ongoing comparative genomic projects or in the re-analysis of published studies.

Genome-enabled perspectives on the composition, evolution, and expression of virulence determinants in bacterial plant pathogens

Genome sequence analyses of bacterial plant pathogens are revealing important insights into the molecular determinants of pathogenicity and, through transcript characterization, responses to environmental conditions, evidence for small RNAs, and validation of uncharacterized genes. Genome comparison sheds further light on the processes impacting pathogen evolution and differences in gene repertoire among isolates contributing to niche specialization. Information derived from pathogen genome analysis is providing tools for use in diagnosis and interference with host-pathogen interactions for the purpose of disease control. However, the existing information infrastructure fails to adequately integrate the increasing numbers of sequence data sets, bioinformatic analyses, and experimental characterization, as required for effective systems-level analysis. Enhanced standardization of data formats at the point of publication is proposed as a possible solution.

Leptospire genomic diversity revealed by microarray-based comparative genomic hybridization

Comparative genomic hybridization was used to compare genetic diversity of five strains of Leptospira (Leptospira interrogans serovars Bratislava, Canicola, and Hebdomadis and Leptospira kirschneri serovars Cynopteri and Grippotyphosa). The array was designed based on two available sequenced Leptospira reference genomes, those of L. interrogans serovar Copenhageni and L. interrogans serovar Lai. A comparison of genetic contents showed that L. interrogans serovar Bratislava was closest to the reference genomes while L. kirschneri serovar Grippotyphosa had the least similarity to the reference genomes. Cluster analysis indicated that L. interrogans serovars Bratislava and Hebdomadis clustered together first, followed by L. interrogans serovar Canicola, before the two L. kirschneri strains. Confirmed/potential virulence factors identified in previous research were also detected in the tested strains.

The role of secretion systems and small molecules in soft-rot Enterobacteriaceae pathogenicity

Soft-rot Enterobacteriaceae (SRE), which belong to the genera Pectobacterium and Dickeya, consist mainly of broad host-range pathogens that cause wilt, rot, and blackleg diseases on a wide range of plants. They are found in plants, insects, soil, and water in agricultural regions worldwide. SRE encode all six known protein secretion systems present in gram-negative bacteria, and these systems are involved in attacking host plants and competing bacteria. They also produce and detect multiple types of small molecules to coordinate pathogenesis, modify the plant environment, attack competing microbes, and perhaps to attract insect vectors. This review integrates new information about the role protein secretion and detection and production of ions and small molecules play in soft-rot pathogenicity.

Evaluating the performance of oligonucleotide microarrays for bacterial strains with increasing genetic divergence from the reference strain

DNA oligonucleotide microarrays (oligoarrays) are being developed continuously; however, several issues regarding the applicability of these arrays for whole-genome DNA-DNA strain comparisons (genomotyping) have not been investigated. For example, the extent of false negatives (i.e., no hybridization signal is observed when the amino acid sequence is conserved but the nucleotide sequence has diverged to a level that does not allow hybridization) remains speculative. To provide quantitative answers to such questions, we performed competitive DNA-DNA oligoarray (60-mer) hybridizations with several fully sequenced (tester) strains and a reference strain (whose genome was used to design the oligoarray probes) of the genus Burkholderia and compared the experimental results obtained to the results predicted based on bioinformatic modeling of the probe-target pair using the available sequences. Our comparisons revealed that the fraction of the total probes that provided experimental results consistent with the predicted results decreased substantially with increasing divergence of the tester strain from the reference strain. The fractions were 90.8%, 84.3%, and 77.4% for tester strains showing 96% 89%, and 80% genome-aggregate average nucleotide identity (ANI) to the reference strain, respectively. New approaches to determine gene presence or absence based on the hybridization signal, which outperformed previous approaches (e.g., 92.9% accuracy versus 86.0% accuracy) and to normalize across different array experiments are also described. Collectively, our results suggest that the performance of oligoarrays is acceptable for tester strains showing >90% ANI to the reference strain and provide useful guidelines for using oligoarray applications in environmental gene detection and gene expression studies with strains other than the reference strain.