Diagnostic Sequences That Distinguish M. avium Subspecies Strains

Over a decade ago Mycobacterium avium subspecies paratuberculosis (Map) specific genes were initially identified in a whole genome context by comparing draft genome sequences of Map strain K-10 with Mycobacterium avium subspecies hominissuis (Mah) strain 104. This resulted in identification of 32 Map specific genes, not including repetitive elements, based on the two-genome comparison. The goal of this study was to define a more complete catalog of M. avium subspecies-specific genes. This is important for obtaining additional diagnostic targets for Johne's disease detection and for understanding the unique biology, evolution and niche adaptation of these organisms. There are now over 28 complete genome sequences representing three M. avium subspecies, including avium (Maa), Mah, and Map. We have conducted a comprehensive comparison of these genomes using two independent pan genomic comparison tools, PanOCT and Roary. This has led to the identification of more than 250 subspecies defining genes common to both analyses. The majority of these genes are arranged in clusters called genomic islands. We further reduced the number of diagnostic targets by excluding sequences having high BLAST similarity to other mycobacterial species recently added to the National Center for Biotechnology Information database. Genes identified as diagnostic following these bioinformatic approaches were further tested by DNA amplification PCR on an additional 20 M. avium subspecies strains. This combined approach confirmed 86 genes as Map-specific, seven as Maa-specific and three as Mah-specific. A single-tube PCR reaction was conducted as a proof of concept method to quickly distinguish M. avium subspecies strains. With these novel data, researchers can classify isolates in their freezers, quickly characterize clinical samples, and functionally analyze these unique genes.

Genetic Diversity Among Mycobacterium avium Subspecies Revealed by Analysis of Complete Genome Sequences

Mycobacterium avium comprises four subspecies that contain both human and veterinary pathogens. At the inception of this study, twenty-eight M. avium genomes had been annotated as RefSeq genomes, facilitating direct comparisons. These genomes represent strains from around the world and provided a unique opportunity to examine genome dynamics in this species. Each genome was confirmed to be classified correctly based on SNP genotyping, nucleotide identity and presence/absence of repetitive elements or other typing methods. The Mycobacterium avium subspecies paratuberculosis (Map) genome size and organization was remarkably consistent, averaging 4.8 Mb with a variance of only 29.6 kb among the 13 strains. Comparing recombination events along with the larger genome size and variance observed among Mycobacterium avium subspecies avium (Maa) and Mycobacterium avium subspecies hominissuis (Mah) strains (collectively termed non-Map) suggests horizontal gene transfer occurs in non-Map, but not in Map strains. Overall, M. avium subspecies could be divided into two major sub-divisions, with the Map type II (bovine strains) clustering tightly on one end of a phylogenetic spectrum and Mah strains clustering more loosely together on the other end. The most evolutionarily distinct Map strain was an ovine strain, designated Telford, which had >1,000 SNPs and showed large rearrangements compared to the bovine type II strains. The Telford strain clustered with Maa strains as an intermediate between Map type II and Mah. SNP analysis and genome organization analyses repeatedly demonstrated the conserved nature of Map versus the mosaic nature of non-Map M. avium strains. Finally, core and pangenomes were developed for Map and non-Map strains. A total of 80% Map genes belonged to the Map core genome, while only 40% of non-Map genes belonged to the non-Map core genome. These genomes provide a more complete and detailed comparison of these subspecies strains as well as a blueprint for how genetic diversity originated.

A New Genome-to-Genome Comparison Approach for Large-Scale Revisiting of Current Microbial Taxonomy

Microbial diversity has always presented taxonomic challenges. With the popularity of next-generation sequencing technology, more unculturable bacteria have been sequenced, facilitating the discovery of additional new species and complicated current microbial classification. The major challenge is to assign appropriate taxonomic names. Hence, assessing the consistency between taxonomy and genomic relatedness is critical. We proposed and applied a genome comparison approach to a large-scale survey to investigate the distribution of genomic differences among microorganisms. The approach applies a genome-wide criterion, homologous coverage ratio (HCR), for describing the homology between species. The survey included 7861 microbial genomes that excluded plasmids, and 1220 pairs of genera exhibited ambiguous classification. In this study, we also compared the performance of HCR and average nucleotide identity (ANI). The results indicated that HCR and ANI analyses yield comparable results, but a few examples suggested that HCR has a superior clustering effect. In addition, we used the Genome Taxonomy Database (GTDB), the gold standard for taxonomy, to validate our analysis. The GTDB offers 120 ubiquitous single-copy proteins as marker genes for species classification. We determined that the analysis of the GTDB still results in classification boundary blur between some genera and that the marker gene-based approach has limitations. Although the choice of marker genes has been quite rigorous, the bias of marker gene selection remains unavoidable. Therefore, methods based on genomic alignment should be considered for use for species classification in order to avoid the bias of marker gene selection. On the basis of our observations of microbial diversity, microbial classification should be re-examined using genome-wide comparisons.

Assembly and comparison of two closely related Brassica napus genomes

As an increasing number of plant genome sequences become available, it is clear that gene content varies between individuals, and the challenge arises to predict the gene content of a species. However, genome comparison is often confounded by variation in assembly and annotation. Differentiating between true gene absence and variation in assembly or annotation is essential for the accurate identification of conserved and variable genes in a species. Here, we present the de novo assembly of the B. napus cultivar Tapidor and comparison with an improved assembly of the Brassica napus cultivar Darmor-bzh. Both cultivars were annotated using the same method to allow comparison of gene content. We identified genes unique to each cultivar and differentiate these from artefacts due to variation in the assembly and annotation. We demonstrate that using a common annotation pipeline can result in different gene predictions, even for closely related cultivars, and repeat regions which collapse during assembly impact whole genome comparison. After accounting for differences in assembly and annotation, we demonstrate that the genome of Darmor-bzh contains a greater number of genes than the genome of Tapidor. Our results are the first step towards comparison of the true differences between B. napus genomes and highlight the potential sources of error in future production of a B. napus pangenome.

The rice blast pathosystem as a case study for the development of new tools and raw materials for genome analysis of fungal plant pathogens

Fungi have an astounding and diverse impact on this planet. While they are agents of human diseases and the cause of allergic reactions, factories for the conversion of carbon in environmental and industrially adapted systems, and potential biological weapons, their importance as plant pathogens is unparalleled. In plants alone, fungi cause tens of thousands of different diseases and are responsible for massive losses of food, fiber and forestry at an estimated annual cost of hundreds of billions of dollars. These losses are not only realized in the incomes of individual farmers and state economies, but contribute significantly to world hunger problems and issues relating to safeguarding a global food supply. Our collective understanding of how fungi, particularly plant pathogens, grow, reproduce, identify a host and cause disease is still at a formative stage. There is an equal lack of detailed knowledge about how a plant recognizes that it is being attacked and then mounts an adequate defense response. The advent of genomic technologies has given researchers an unprecedented opportunity to address these mysteries in a powerful and more holistic manner. Where the genetic revolution of only a few years ago allowed for the characterization of single genes, today's genomic technologies are facilitating the evaluation of the entire complement of genes in an organism and the discovery of the suites of genes that act during any one time or particular condition. This review will describe the recent development of tools for whole or partial genome analysis and multigenome comparisons. Th discussion focuses on the rice blast pathosystem as a case study.