RESUMEN
BACKGROUND: The use of molecular HIV cluster analysis to supplement public health contact tracing has shown promise in addressing HIV outbreaks. However, the potential of HIV cluster analysis as an adjunct to daily, person-by-person HIV prevention efforts remains unknown. We documented lessons learned within a unique public health-academic partnership while guiding workaday HIV prevention efforts with near-real-time molecular cluster analysis. SETTING: A public health-academic partnership in the State of Rhode Island, the United States. METHODS: We recorded perceptions of our team of academicians and public health practitioners that were encountered in an 18-month study evaluating the integration of molecular cluster analysis with HIV contact tracing for public health benefit. The focus was on monthly conferences where molecular clustering of each new statewide diagnosis was discussed to facilitate targeted interventions and on attempted reinterviews of all newly HIV-diagnosed persons statewide whose HIV sequences clustered to increase partner naming. RESULTS: Three main themes emerged: First, multidisciplinary conferences are substantially beneficial for gleaning actionable inferences from integrating molecular cluster analysis and public health data. Second, universal reinterviews were perceived to potentially have negative consequences but may be selectively beneficial. Third, the translation of cluster analysis into public health action is hampered by jurisdictional surveillance boundaries and within-jurisdictional data silos, across which data sharing is problematic. CONCLUSIONS: Insights from a statewide public health-academic partnership support integration of molecular HIV cluster analyses with public health efforts, which can guide public health activities to prevent transmission while identifying substantial barriers to integration, informing continued research.
Asunto(s)
Trazado de Contacto , Infecciones por VIH , Humanos , Infecciones por VIH/prevención & control , Infecciones por VIH/epidemiología , Análisis por Conglomerados , Rhode Island/epidemiología , Práctica de Salud Pública , Salud PúblicaRESUMEN
A common transcriptome assembly error is to mistake different transcripts of the same gene as transcripts from multiple closely related genes. This error is difficult to identify during assembly, but in a phylogenetic analysis such errors can be diagnosed from gene phylogenies where they appear as clades of tips from the same species with improbably short branch lengths. treeinform is a method that uses phylogenetic information across species to refine transcriptome assemblies within species. It identifies transcripts of the same gene that were incorrectly assigned to multiple genes and reassign them as transcripts of the same gene. The treeinform method is implemented in Agalma, available at https://bitbucket.org/caseywdunn/agalma, and the general approach is relevant in a variety of other contexts.
Asunto(s)
Transcriptoma , Interfaz Usuario-Computador , Algoritmos , Animales , Análisis por Conglomerados , Cnidarios/clasificación , Cnidarios/genética , Modelos Teóricos , FilogeniaRESUMEN
BACKGROUND: Phylogenetic analyses of HIV sequences are used to detect clusters and inform public health interventions. Conventional approaches summarize within-host HIV diversity with a single consensus sequence per host of the pol gene, obtained from Sanger or next-generation sequencing (NGS). There is growing recognition that this approach discards potentially important information about within-host sequence variation, which can impact phylogenetic inference. However, whether alternative summary methods that incorporate intra-host variation impact phylogenetic inference of transmission network features is unknown. METHODS: We introduce profile sampling, a method to incorporate within-host NGS sequence diversity into phylogenetic HIV cluster inference. We compare this approach to Sanger- and NGS-derived pol and near-whole-genome consensus sequences and evaluate its potential benefits in identifying molecular clusters among all newly-HIV-diagnosed individuals over six months at the largest HIV center in Rhode Island. RESULTS: Profile sampling cluster inference demonstrated that within-host viral diversity impacts phylogenetic inference across individuals, and that consensus sequence approaches can obscure both magnitude and effect of these impacts. Clustering differed between Sanger- and NGS-derived consensus and profile sampling sequences, and across gene regions. DISCUSSION: Profile sampling can incorporate within-host HIV diversity captured by NGS into phylogenetic analyses. This additional information can improve robustness of cluster detection.
RESUMEN
Although antibiotics disturb the structure of the gut microbiota, factors that modulate these perturbations are poorly understood. Bacterial metabolism is an important regulator of susceptibility in vitro and likely plays a large role within the host. We applied a metagenomic and metatranscriptomic approach to link antibiotic-induced taxonomic and transcriptional responses within the murine microbiome. We found that antibiotics significantly alter the expression of key metabolic pathways at the whole-community and single-species levels. Notably, Bacteroides thetaiotaomicron, which blooms in response to amoxicillin, upregulated polysaccharide utilization. In vitro, we found that the sensitivity of this bacterium to amoxicillin was elevated by glucose and reduced by polysaccharides. Accordingly, we observed that dietary composition affected the abundance and expansion of B. thetaiotaomicron, as well as the extent of microbiome disruption with amoxicillin. Our work indicates that the metabolic environment of the microbiome plays a role in the response of this community to antibiotics.
Asunto(s)
Amoxicilina/farmacología , Antibacterianos/farmacología , Bacteroides thetaiotaomicron/efectos de los fármacos , Bacteroides thetaiotaomicron/metabolismo , Farmacorresistencia Bacteriana , Microbioma Gastrointestinal/efectos de los fármacos , Animales , Fibras de la Dieta/metabolismo , Femenino , Glucosa/metabolismo , Ratones , Ratones Endogámicos C57BL , Polisacáridos/metabolismoRESUMEN
Molecular phylogenetics is the study of evolutionary relationships between biological sequences, often to infer the evolutionary relationships of organisms. These studies require many analysis components, including sequence assembly, identification of homologous sequences, gene tree inference, and species tree inference. At present, each component is usually treated as a single step in a linear analysis, where the output of each component is passed as input to the next as a point estimate. Here we outline a generative model that helps clarify assumptions that are implicit to phylogenetic workflows, focusing on the assumption of low relative entropy. This perspective unifies currently disparate advances, and will help investigators evaluate which steps would benefit the most from additional computation and future methods development.