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Background: While overall rates of meningococcal disease have been declining in the United States for the past several decades, New York City (NYC) has experienced two serogroup C meningococcal disease outbreaks in 2005–2006 and in 2010–2013. The outbreaks were centered within drug use and sexual networks, were difficult to control, and required vaccine campaigns. Methods: Whole Genome Sequencing (WGS) was used to analyze preserved meningococcal isolates collected before and during the two outbreaks. We integrated and analyzed epidemiologic, geographic, and genomic data to better understand transmission networks among patients. Betweenness centrality was used as a metric to understand the most important geographic nodes in the transmission networks. Comparative genomics was used to identify genes associated with the outbreaks. Results: Neisseria meningitidis serogroup C (ST11/ET-37) was responsible for both outbreaks with each outbreak having distinct phylogenetic clusters. WGS did identify some misclassifications of isolates that were more distant from the outbreak strains, as well as those that should have been included based on high genomic similarity. Genomes for the second outbreak were more similar than the first and no polymorphism was found to either be unique or specific to either outbreak lineage. Betweenness centrality as applied to transmission networks based on phylogenetic analysis demonstrated that the outbreaks were transmitted within focal communities in NYC with few transmission events to other locations. Conclusions: Neisseria meningitidis is an ever changing pathogen and comparative genomic analyses can help elucidate how it spreads geographically to facilitate targeted interventions to interrupt transmission. [ABSTRACT FROM AUTHOR]

Emerging infectious diseases are critical issues of public health and the economic and social stability of nations. As demonstrated by the international response to the severe acute respiratory syndrome (SARS) and influenza A, rapid genomic sequencing is a crucial tool to understand diseases that occur at the interface of human and animal populations. However, our ability to make sense of sequence data lags behind our ability to acquire the data. The potential of sequence data on pathogens is not fully realized until raw data are translated into public health intelligence. Sequencing technologies have become highly mechanized. If the political will for data sharing remains strong, the frontier for progress in emerging infectious diseases will be in analysis of sequence data and translation of results into better public health science and policy. For example, applying analytical tools such as Supramap (http:// supramap.osu.edu) to genomic data for pathogens, public health scientists can track specific mutations in pathogens that confer the ability to infect humans or resist drugs. The results produced by the Supramap application are compelling visualizations of pathogen lineages and features mapped into geographic information systems that can be used to test hypotheses and to follow the spread of diseases across geography and hosts and communicate the results to a wide audience. [ABSTRACT FROM AUTHOR]

Abstract Malaria is a major public health concern in Ethiopia. With the increase in malaria cases in the Somali Region of Ethiopia, understanding the distribution and identifying the species of malaria vectors is vital to public health. Here we report the first detection of Anopheles stephensi in Ethiopia, a malaria vector typically found in the Middle East, the Indian subcontinent, and China, but recently found in Djibouti. An entomological investigation was conducted during November to December 2016 in Kebri Dehar town of the Ethiopian Somali Regional State as ancillary work for Anopheles spp. surveillance. Mosquito larvae were collected from water reservoirs. Larvae were reared in the laboratory to the adult stage and identified morphologically. PCR and sequencing of cytochrome oxidase 1 (COI) and internal transcribed spacer 2 (ITS2) loci were performed. Basic Local Alignment Search Tool (BLAST) was used to compare sample sequences to sequences in the NCBI nucleotide database for species identification. To further analyze the relationship between the specimen we collected in Kebri Dehar and other Anopheles samples available in Genbank, phylogenetic analysis was performed using a maximum likelihood approach. Molecular and morphological results confirm specimens were An. stephensi. The closest high scoring hit was for all specimens was for the An. stephensi sequence. Independent phylogenetic analyses of COI and ITS2 sequences revealed in both cases strong bootstrap (100) support for our sequence forming a clade with other An. stephensi sequences to the exclusion of any other species of Anopheles. In conclusion, Anopheles stephensi is present in Kebri Dehar town in Ethiopia. These findings highlight the need for additional research to examine the role of An. stephensi in malaria transmission in Ethiopia. [ABSTRACT FROM AUTHOR]

Background: Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts. However, common ancestry can lead to apparent associations between biologically unrelated features. The novel method Cladograms with Path to Event (ClaPTE) detects associations between character-pairs (either a pair of mutations or a mutation paired with a phenotype) while adjusting for common ancestry, using phylogenetic trees. Methods: ClaPTE tests for character-pairs changing close together on the phylogenetic tree, consistent with an associated character-pair. ClaPTE is compared to three existing methods (independent contrasts, mixed model, and likelihood ratio) to detect character-pair associations adjusted for common ancestry. Comparisons utilize simulations on gene trees for: HIV Env, HIV promoter, and bacterial DnaJ and GuaB; and case studies for Oseltamavir resistance in Influenza, and for DnaJ and GuaB. Simulated data include both true-positive/ associated character-pairs, and true-negative/not-associated character-pairs, used to assess type I (frequency of p-values in true-negatives) and type II (sensitivity to true-positives) error control. Results and conclusions: ClaPTE has competitive sensitivity and better type I error control than existing methods. In the Influenza/Oseltamavir case study, ClaPTE reports no new permissive mutations but detects associations between adjacent (in primary sequence) amino acid positions which other methods miss. In the DnaJ and GuaB case study, ClaPTE reports more frequent associations between positions both from the same protein family than between positions from different families, in contrast to other methods. In both case studies, the results from ClaPTE are biologically plausible. [ABSTRACT FROM AUTHOR]