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Introduction: Due to the radiation-sparing effects on salivary gland acini, changes in the composition of the oral microbiome may be a driver for improved outcomes in patients receiving proton radiation with potentially worse outcomes in patients exposed to photon radiation therapy. To date, a head-to-head comparison of oral microbiome changes at a metagenomic level with longitudinal sampling has yet to be performed in these patient cohorts. Methods and Materials: To comparatively analyze oral microbiome shifts during head and neck radiation therapy, a prospective pilot cohort study was performed at the Maryland Proton Treatment Center (MPTC) and the University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center. A longitudinal metagenomic comparative analysis of oral microbiome shifts was performed at three-time points (pre-radiation, during radiation, and immediately post-radiation). Head and neck cancer patients receiving proton radiation (n=4) were compared to photon radiation (n=4). Additional control groups included healthy age- and sex-matched controls (n=5), head and neck cancer patients who never received radiation therapy (n=8), and patients with oral inflammatory disease (n=3). Results: Photon therapy patients presented with a lower microbial alpha diversity at all timepoints and there was a trend towards reduced species richness as compared with proton therapy. Healthy controls and proton patients exhibited overall higher and similar diversity. A more dysbiotic state was observed in patients receiving photon therapy as compared to proton therapy in which oral microbial homeostasis was maintained. Mucositis was observed in 3/4 photon patients and was not observed in any proton patients during radiation therapy. Bacterial de novo pyrimidine biosynthesis pathway and nitrate reduction V pathway were comparatively higher following photon exposure. These functional changes in bacterial metabolism may suggest that photon exposure produces a more permissive environment for proliferation of pathogenic bacteria. Conclusion: Oral microbiome dysbiosis in patients receiving photon radiation may be associated with increased mucositis occurrence. Proton radiation therapy for head and neck cancer demonstrates a safer side effect profile in terms of oral complications, oral microbiome dysbiosis, and functional metabolic status.

To be able to use infectious disease next generation sequencing as a diagnostic tool, appropriate reference datasets are required. Here, Sichtig et al. describe FDA-ARGOS, a reference database for high-quality microbial reference genomes, and demonstrate its utility on the example of two use cases.

Theileria parva is an economically important, intracellular, tick-transmitted parasite of cattle. A live vaccine against the parasite is effective against challenge from cattle-transmissible T. parva but not against genotypes originating from the African Cape buffalo, a major wildlife reservoir, prompting the need to characterize genome-wide variation within and between cattle- and buffalo-associated T. parva populations. Here, we describe a capture-based target enrichment approach that enables, for the first time, de novo assembly of nearly complete T. parva genomes derived from infected host cell lines. This approach has exceptionally high specificity and sensitivity and is successful for both cattle- and buffalo-derived T. parva parasites. De novo genome assemblies generated for cattle genotypes differ from the reference by ~54K single nucleotide polymorphisms (SNPs) throughout the 8.31 Mb genome, an average of 6.5 SNPs/kb. We report the first buffalo-derived T. parva genome, which is ~20 kb larger than the genome from the reference, cattle-derived, Muguga strain, and contains 25 new potential genes. The average non-synonymous nucleotide diversity (πN) per gene, between buffalo-derived T. parva and the Muguga strain, was 1.3%. This remarkably high level of genetic divergence is supported by an average Wright's fixation index (FST), genome-wide, of 0.44, reflecting a degree of genetic differentiation between cattle- and buffalo-derived T. parva parasites more commonly seen between, rather than within, species. These findings present clear implications for vaccine development, further demonstrated by the ability to assemble nearly all known antigens in the buffalo-derived strain, which will be critical in design of next generation vaccines. The DNA capture approach used provides a clear advantage in specificity over alternative T. parva DNA enrichment methods used previously, such as those that utilize schizont purification, is less labor intensive, and enables in-depth comparative genomics in this apicomplexan parasite.

High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), informed by interactions with numerous collaborating scientists. It includes mapping to terms from other data standards initiatives, including the Genomic Standards Consortium's minimal information (MIxS) and NCBI's BioSample/BioProjects checklists and the Ontology for Biomedical Investigations (OBI). The standard includes data fields about characteristics of the organism or environmental source of the specimen, spatial-temporal information about the specimen isolation event, phenotypic characteristics of the pathogen/vector isolated, and project leadership and support. By modeling metadata fields into an ontology-based semantic framework and reusing existing ontologies and minimum information checklists, the application standard can be extended to support additional project-specific data fields and integrated with other data represented with comparable standards. The use of this metadata standard by all ongoing and future GSCID sequencing projects will provide a consistent representation of these data in the BRC resources and other repositories that leverage these data, allowing investigators to identify relevant genomic sequences and perform comparative genomics analyses that are both statistically meaningful and biologically relevant.