Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs ("vFams") to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam).

Papillomaviruses have been implicated in a variety of human diseases ranging from common warts to invasive carcinoma of the anogenital mucosa. Existing assays for genotyping human papillomavirus are restricted to a small number of types. Here, we present a comprehensive, accurate microarray strategy for detection and genotyping of 102 human papillomavirus types and validate its use in a panel of 91 anal swabs. This array has equal performance to traditional dot blot analysis with the benefits of added genotype coverage and the ability to calibrate readout over a range of sensitivity or specificity values.

Dengue virus is an emerging infectious agent that infects an estimated 50-100 million people annually worldwide, yet current diagnostic practices cannot detect an etiologic pathogen in ∼40% of dengue-like illnesses. Metagenomic approaches to pathogen detection, such as viral microarrays and deep sequencing, are promising tools to address emerging and non-diagnosable disease challenges. In this study, we used the Virochip microarray and deep sequencing to characterize the spectrum of viruses present in human sera from 123 Nicaraguan patients presenting with dengue-like symptoms but testing negative for dengue virus. We utilized a barcoding strategy to simultaneously deep sequence multiple serum specimens, generating on average over 1 million reads per sample. We then implemented a stepwise bioinformatic filtering pipeline to remove the majority of human and low-quality sequences to improve the speed and accuracy of subsequent unbiased database searches. By deep sequencing, we were able to detect virus sequence in 37% (45/123) of previously negative cases. These included 13 cases with Human Herpesvirus 6 sequences. Other samples contained sequences with similarity to sequences from viruses in the Herpesviridae, Flaviviridae, Circoviridae, Anelloviridae, Asfarviridae, and Parvoviridae families. In some cases, the putative viral sequences were virtually identical to known viruses, and in others they diverged, suggesting that they may derive from novel viruses. These results demonstrate the utility of unbiased metagenomic approaches in the detection of known and divergent viruses in the study of tropical febrile illness.

Guadinomines are a recently discovered family of anti-infective compounds produced by Streptomyces sp. K01-0509 with a novel mode of action. With an IC(50) of 14 nM, guadinomine B is the most potent known inhibitor of the type III secretion system (TTSS) of Gram-negative bacteria. TTSS activity is required for the virulence of many pathogenic Gram-negative bacteria including Escherichia coli , Salmonella spp., Yersinia spp., Chlamydia spp., Vibrio spp., and Pseudomonas spp. The guadinomine (gdn) biosynthetic gene cluster has been cloned and sequenced and includes 26 open reading frames spanning 51.2 kb. It encodes a chimeric multimodular polyketide synthase, a nonribosomal peptide synthetase, along with enzymes responsible for the biosynthesis of the unusual aminomalonyl-acyl carrier protein extender unit and the signature carbamoylated cyclic guanidine. Its identity was established by targeted disruption of the gene cluster as well as by heterologous expression and analysis of key enzymes in the biosynthetic pathway. Identifying the guadinomine gene cluster provides critical insight into the biosynthesis of these scarce but potentially important natural products.