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In 2016, a 68-year-old patient with a disseminated multidrug-resistant Acinetobacter baumannii infection was successfully treated using lytic bacteriophages. Here we report the genomes of the nine phages used for treatment and three strains of A. baumannii isolated prior to and during treatment. The phages used in the initial treatment are related, T4-like myophages. Analysis of 19 A. baumannii isolates collected before and during phage treatment shows that resistance to the T4-like phages appeared two days following the start of treatment. We generate complete genomic sequences for three A. baumannii strains (TP1, TP2 and TP3) collected before and during treatment, supporting a clonal relationship. Furthermore, we use strain TP1 to select for increased resistance to five of the phages in vitro, and identify mutations that are also found in phage-insensitive isolates TP2 and TP3 (which evolved in vivo during phage treatment). These results support that in vitro investigations can produce results that are relevant to the in vivo environment.

Background Functional genome annotation is the process of labelling functional genomic regions with descriptive information. Manual curation can produce higher quality genome annotations than fully automated methods. Manual annotation efforts are time-consuming and complex; however, software can help reduce these drawbacks. Results We created Manual Annotation Studio (MAS) to improve the efficiency of the process of manual functional annotation prokaryotic and viral genomes. MAS allows users to upload unannotated genomes, provides an interface to edit and upload annotations, tracks annotation history and progress, and saves data to a relational database. MAS provides users with pertinent information through a simple point and click interface to execute and visualize results for multiple homology search tools (blastp, rpsblast, and HHsearch) against multiple databases (Swiss-Prot, nr, CDD, PDB, and an internally generated database). MAS was designed to accept connections over the local area network (LAN) of a lab or organization so multiple users can access it simultaneously. MAS can take advantage of high-performance computing (HPC) clusters by interfacing with SGE or SLURM and data can be exported from MAS in a variety of formats (FASTA, GenBank, GFF, and excel). Conclusions MAS streamlines and provides structure to manual functional annotation projects. MAS enhances the ability of users to generate, interpret, and compare results from multiple tools. The structure that MAS provides can improve project organization and reduce annotation errors. MAS is ideal for team-based annotation projects because it facilitates collaboration.

Individuals with methicillin-resistant Staphylococcus aureus (MRSA) skin and soft tissue infection (SSTI) can be simultaneously colonized with MRSA on multiple body sites. Using whole genome sequencing (WGS), the intrahost relatedness of MRSA colonization and infection isolates was investigated. In the context of a prospective case–control study of SSTI, we analyzed colonization and infection isolates from US Army Infantry trainees with purulent infection due to MRSA. At the time of clinical presentation for SSTI, culture swabs were obtained from the infection site, as well as from the patient’s nasal, oral, inguinal, and perianal regions. S. aureus culture and susceptibility was performed by standard methods. DNA from MRSA isolates was extracted and libraries were produced. Sequences were generated on an Illumina MiSeq, sequence reads were assembled, and single nucleotide variant (SNV) data were analyzed. Of 74 trainees with MRSA SSTI, 19 (25.7%) were colonized with MRSA. Ten (52.6%) were colonized on more than one body site. Colonization frequency by anatomic site was as follows: inguinal region (33%), nasal region (30%), perianal region (22%), and oral region (14%). A total of 36 MRSA colonization isolates were characterized. The intrahost median number of SNVs between infection and colonization isolates was 17. Among trainees with recurrent MRSA SSTI, limited intrahost diversity suggests that persistent colonization is a major contributor to recurrence risk. Among military trainees with MRSA SSTI, genomic characterization of infection and colonization isolates revealed a high degree of strain relatedness. Single acquisition events may account for MRSA colonization and infection in this population.

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, 5mCpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5 % of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 86.9 % of all SAMMs were assigned to the correct tissue of origin. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues. Electronic supplementary material The online version of this article (doi:10.1007/s00439-015-1566-1) contains supplementary material, which is available to authorized users.

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, (5m)CpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5% of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 10.4% of the SAMMs had an Undetermined tissue of origin, and 92.2% of the remaining SAMMs were assigned to the correct tissue of origin. [corrected]. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues.