Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily.

MOTIVATION: Kinases of the eukaryotic protein kinase superfamily are key regulators of most aspects eukaryotic cellular behavior and have provided several drug targets including kinases dysregulated in cancers. The rapid increase in the number of genomic sequences has created an acute need to identify and classify members of this important class of enzymes efficiently and accurately. RESULTS: Kinannote produces a draft kinome and comparative analyses for a predicted proteome using a single line command, and it is currently the only tool that automatically classifies protein kinases using the controlled vocabulary of Hanks and Hunter [Hanks and Hunter (1995)]. A hidden Markov model in combination with a position-specific scoring matrix is used by Kinannote to identify kinases, which are subsequently classified using a BLAST comparison with a local version of KinBase, the curated protein kinase dataset from www.kinase.com. Kinannote was tested on the predicted proteomes from four divergent species. The average sensitivity and precision for kinome retrieval from the test species are 94.4 and 96.8%. The ability of Kinannote to classify identified kinases was also evaluated, and the average sensitivity and precision for full classification of conserved kinases are 71.5 and 82.5%, respectively. Kinannote has had a significant impact on eukaryotic genome annotation, providing protein kinase annotations for 36 genomes made public by the Broad Institute in the period spanning 2009 to the present. AVAILABILITY: Kinannote is freely available at http://sourceforge.net/projects/kinannote.

Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal.

BACKGROUND: As public health interventions drive parasite populations to elimination, genetic epidemiology models that incorporate population genomics can be powerful tools for evaluating the effectiveness of continued intervention. However, current genetic epidemiology models may not accurately simulate the population genetic profile of parasite populations, particularly with regard to polygenomic (multi-strain) infections. Current epidemiology models simulate polygenomic infections via superinfection (multiple mosquito bites), despite growing evidence that cotransmission (a single mosquito bite) may contribute to polygenomic infections. METHODS: Here, we quantified the relatedness of strains within 31 polygenomic infections collected from patients in Thiès, Senegal using a hidden Markov model to measure the proportion of the genome that is inferred to be identical by descent. RESULTS: We found that polygenomic infections can be composed of highly related parasites and that superinfection models drastically underestimate the relatedness of strains within polygenomic infections. CONCLUSIONS: Our findings suggest that cotransmission is a major contributor to polygenomic infections in Thiès, Senegal. The incorporation of cotransmission into existing genetic epidemiology models may enhance our ability to characterize and predict changes in population structure associated with reduced transmission intensities and the emergence of important phenotypes like drug resistance that threaten to undermine malaria elimination activities.

Evolution of invasion in a diverse set of Fusobacterium species.

UNLABELLED: The diverse Fusobacterium genus contains species implicated in multiple clinical pathologies, including periodontal disease, preterm birth, and colorectal cancer. The lack of genetic tools for manipulating these organisms leaves us with little understanding of the genes responsible for adherence to and invasion of host cells. Actively invading Fusobacterium species can enter host cells independently, whereas passively invading species need additional factors, such as compromise of mucosal integrity or coinfection with other microbes. We applied whole-genome sequencing and comparative analysis to study the evolution of active and passive invasion strategies and to infer factors associated with active forms of host cell invasion. The evolution of active invasion appears to have followed an adaptive radiation in which two of the three fusobacterial lineages acquired new genes and underwent expansions of ancestral genes that enable active forms of host cell invasion. Compared to passive invaders, active invaders have much larger genomes, encode FadA-related adhesins, and possess twice as many genes encoding membrane-related proteins, including a large expansion of surface-associated proteins containing the MORN2 domain of unknown function. We predict a role for proteins containing MORN2 domains in adhesion and active invasion. In the largest and most comprehensive comparison of sequenced Fusobacterium species to date, we have generated a testable model for the molecular pathogenesis of Fusobacterium infection and illuminate new therapeutic or diagnostic strategies. IMPORTANCE: Fusobacterium species have recently been implicated in a broad spectrum of human pathologies, including Crohn's disease, ulcerative colitis, preterm birth, and colorectal cancer. Largely due to the genetic intractability of member species, the mechanisms by which Fusobacterium causes these pathologies are not well understood, although adherence to and active invasion of host cells appear important. We examined whole-genome sequence data from a diverse set of Fusobacterium species to identify genetic determinants of active forms of host cell invasion. Our analyses revealed that actively invading Fusobacterium species have larger genomes than passively invading species and possess a specific complement of genes-including a class of genes of unknown function that we predict evolved to enable host cell adherence and invasion. This study provides an important framework for future studies on the role of Fusobacterium in pathologies such as colorectal cancer.

Evolutionary dynamics of the accessory genome of Listeria monocytogenes.

Listeria monocytogenes, a foodborne bacterial pathogen, is comprised of four phylogenetic lineages that vary with regard to their serotypes and distribution among sources. In order to characterize lineage-specific genomic diversity within L. monocytogenes, we sequenced the genomes of eight strains from several lineages and serotypes, and characterized the accessory genome, which was hypothesized to contribute to phenotypic differences across lineages. The eight L. monocytogenes genomes sequenced range in size from 2.85-3.14 Mb, encode 2,822-3,187 genes, and include the first publicly available sequenced representatives of serotypes 1/2c, 3a and 4c. Mapping of the distribution of accessory genes revealed two distinct regions of the L. monocytogenes chromosome: an accessory-rich region in the first 65° adjacent to the origin of replication and a more stable region in the remaining 295°. This pattern of genome organization is distinct from that of related bacteria Staphylococcus aureus and Bacillus cereus. The accessory genome of all lineages is enriched for cell surface-related genes and phosphotransferase systems, and transcriptional regulators, highlighting the selective pressures faced by contemporary strains from their hosts, other microbes, and their environment. Phylogenetic analysis of O-antigen genes and gene clusters predicts that serotype 4 was ancestral in L. monocytogenes and serotype 1/2 associated gene clusters were putatively introduced through horizontal gene transfer in the ancestral population of L. monocytogenes lineage I and II.