Genome mapping and characterization of the Anopheles gambiae heterochromatin

<p>Abstract</p> <p>Background</p> <p>Heterochromatin plays an important role in chromosome function and gene regulation. Despite the availability of polytene chromosomes and genome sequence, the heterochromatin of the major malaria vector <it>Anopheles gambiae </it>has not been mapped and characterized.</p> <p>Results</p> <p>To determine the extent of heterochromatin within the <it>An. gambiae </it>genome, genes were physically mapped to the euchromatin-heterochromatin transition zone of polytene chromosomes. The study found that a minimum of 232 genes reside in 16.6 Mb of mapped heterochromatin. Gene ontology analysis revealed that heterochromatin is enriched in genes with DNA-binding and regulatory activities. Immunostaining of the <it>An. gambiae </it>chromosomes with antibodies against <it>Drosophila melanogaster </it>heterochromatin protein 1 (HP1) and the nuclear envelope protein lamin Dm₀identified the major invariable sites of the proteins' localization in all regions of pericentric heterochromatin, diffuse intercalary heterochromatin, and euchromatic region 9C of the 2R arm, but not in the compact intercalary heterochromatin. To better understand the molecular differences among chromatin types, novel Bayesian statistical models were developed to analyze genome features. The study found that heterochromatin and euchromatin differ in gene density and the coverage of retroelements and segmental duplications. The pericentric heterochromatin had the highest coverage of retroelements and tandem repeats, while intercalary heterochromatin was enriched with segmental duplications. We also provide evidence that the diffuse intercalary heterochromatin has a higher coverage of DNA transposable elements, minisatellites, and satellites than does the compact intercalary heterochromatin. The investigation of 42-Mb assembly of unmapped genomic scaffolds showed that it has molecular characteristics similar to cytologically mapped heterochromatin.</p> <p>Conclusions</p> <p>Our results demonstrate that <it>Anopheles </it>polytene chromosomes and whole-genome shotgun assembly render the mapping and characterization of a significant part of heterochromatic scaffolds a possibility. These results reveal the strong association between characteristics of the genome features and morphological types of chromatin. Initial analysis of the <it>An. gambiae </it>heterochromatin provides a framework for its functional characterization and comparative genomic analyses with other organisms.</p

Arm-specific dynamics of chromosome evolution in malaria mosquitoes

<p>Abstract</p> <p>Background</p> <p>The malaria mosquito species of subgenus <it>Cellia </it>have rich inversion polymorphisms that correlate with environmental variables. Polymorphic inversions tend to cluster on the chromosomal arms 2R and 2L but not on X, 3R and 3L in <it>Anopheles gambiae </it>and homologous arms in other species. However, it is unknown whether polymorphic inversions on homologous chromosomal arms of distantly related species from subgenus <it>Cellia </it>nonrandomly share similar sets of genes. It is also unclear if the evolutionary breakage of inversion-poor chromosomal arms is under constraints.</p> <p>Results</p> <p>To gain a better understanding of the arm-specific differences in the rates of genome rearrangements, we compared gene orders and established syntenic relationships among <it>Anopheles gambiae, Anopheles funestus</it>, and <it>Anopheles stephensi</it>. We provided evidence that polymorphic inversions on the 2R arms in these three species nonrandomly captured similar sets of genes. This nonrandom distribution of genes was not only a result of preservation of ancestral gene order but also an outcome of extensive reshuffling of gene orders that created new combinations of homologous genes within independently originated polymorphic inversions. The statistical analysis of distribution of conserved gene orders demonstrated that the autosomal arms differ in their tolerance to generating evolutionary breakpoints. The fastest evolving 2R autosomal arm was enriched with gene blocks conserved between only a pair of species. In contrast, all identified syntenic blocks were preserved on the slowly evolving 3R arm of <it>An. gambiae </it>and on the homologous arms of <it>An. funestus </it>and <it>An. stephensi</it>.</p> <p>Conclusions</p> <p>Our results suggest that natural selection favors specific gene combinations within polymorphic inversions when distant species are exposed to similar environmental pressures. This knowledge could be useful for the discovery of genes responsible for an association of inversion polymorphisms with phenotypic variations in multiple species. Our data support the chromosomal arm specificity in rates of gene order disruption during mosquito evolution. We conclude that the distribution of breakpoint regions is evolutionary conserved on slowly evolving arms and tends to be lineage-specific on rapidly evolving arms.</p

The Plant Pathogen Pseudomonas syringae pv. tomato Is Genetically Monomorphic and under Strong Selection to Evade Tomato Immunity

Recently, genome sequencing of many isolates of genetically monomorphic bacterial human pathogens has given new insights into pathogen microevolution and phylogeography. Here, we report a genome-based micro-evolutionary study of a bacterial plant pathogen, Pseudomonas syringae pv. tomato. Only 267 mutations were identified between five sequenced isolates in 3,543,009 nt of analyzed genome sequence, which suggests a recent evolutionary origin of this pathogen. Further analysis with genome-derived markers of 89 world-wide isolates showed that several genotypes exist in North America and in Europe indicating frequent pathogen movement between these world regions. Genome-derived markers and molecular analyses of key pathogen loci important for virulence and motility both suggest ongoing adaptation to the tomato host. A mutational hotspot was found in the type III-secreted effector gene hopM1. These mutations abolish the cell death triggering activity of the full-length protein indicating strong selection for loss of function of this effector, which was previously considered a virulence factor. Two non-synonymous mutations in the flagellin-encoding gene fliC allowed identifying a new microbe associated molecular pattern (MAMP) in a region distinct from the known MAMP flg22. Interestingly, the ancestral allele of this MAMP induces a stronger tomato immune response than the derived alleles. The ancestral allele has largely disappeared from today's Pto populations suggesting that flagellin-triggered immunity limits pathogen fitness even in highly virulent pathogens. An additional non-synonymous mutation was identified in flg22 in South American isolates. Therefore, MAMPs are more variable than expected differing even between otherwise almost identical isolates of the same pathogen strain

Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study

Modeling the Spread of Infectious Disease Using Genetic Information Within a Marked Branching Process

Accurate assessment of disease dynamics requires a quantification of many unknown parameters governing disease transmission processes. While infection control strategies within hospital settings are stringent, some disease will be propagated due to human interactions (patient-to-patient or patient-to- caregiver-topatient). In order to understand infectious transmission rates within the hospital, it is necessary to isolate the amount of disease that is endemic to the outside environment. While discerning the origins of disease is difficult when using ordinary spatio-temporal data (locations and time of disease detection), genotypes that are common to pathogens, with common sources, aid in distinguishing nosocomial infections from independent arrivals of the disease. The purpose of this study was to demonstrate a Bayesian modeling procedure for identifying nosocomial infections, and quantify the rate of these transmissions. We will demonstrate our method using a 10-year history of Morexella catarhallis. Results will show the degree to which pathogen-specific, genotypic information impacts inferences about the nosocomial rate of infection

Descriptions of Abstracts 20, 22, 32 and 39 in Fig 4.

<p>Descriptions of Abstracts 20, 22, 32 and 39 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129122#pone.0129122.g004" target="_blank">Fig 4</a>.</p

This table lists the Top 10 keywords that either differentiate clusters A, B, C, and D or are shared among all of the clusters in Fig 4.

<p>This table lists the Top 10 keywords that either differentiate clusters A, B, C, and D or are shared among all of the clusters in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129122#pone.0129122.g004" target="_blank">Fig 4</a>.</p

The progression of V2PI-GTM.

<p>Fig a is a GTM display (in latent dimensions <i>q</i>₁, <i>q</i>₂) of the simulated dataset when <i>K</i> = 16, <i>J</i> = 400. The data points are labeled according to their cluster numbers. The arrows show how a user may interact. A user may move one point from location A to location B and another point from location C to location D. Figs b, c, and d show respectively how the observations respond (or do not respond) to the move when stages 1, 2, and 3 of V2PI-GTM are in place.</p

A visual description of GTM.

<p>This exemplifies how the latent space constructed by <b><i>r</i></b> (denoted by ⋆ on the left) and the manifold constructed by <b><i>y</i></b> (denoted by ⋆ on the right) in a three-dimensional data space relate. Raw data points <b><i>x</i></b> are denoted by •.</p

V2PI-GTM with NIH data.

<p>We provide a GTM display of the NIH abstracts (labeled by their identification numbers) before and after user interaction in Figs a and b, respectively. The interaction is portrayed by the pink arrow in Fig a; Abstract 7 was moved to a location near cluster D. In addition, to labeling and learning about four clusters in the data (marked by A, B, C, and D), we also tagged the latent GTM space. After the interaction, we see that the clusters grouped differently and the meaning of the latent space changed. Also, the manifold changed dramatically.</p