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Tick-borne diseases, due to a diversity of bacterial pathogens, represent a significant and increasing public health threat throughout the Northern Hemisphere. A high-throughput 16S V1-V2 rRNA gene-based metagenomics assay was developed and evaluated using13,000 residual samples from patients suspected of having tick-borne illness and1,000 controls. Taxonomic predictions for tick-borne bacteria were exceptionally accurate, as independently validated by secondary testing. Overall, 881 specimens were positive for bacterial tick-borne agents. Twelve tick-borne bacterial species were detected, including two novel pathogens, representing a 100% increase in the number of tick-borne bacteria identified compared to what was possible by initial PCR testing. In three blood specimens, two tick-borne bacteria were simultaneously detected. Seven bacteria, not known to be tick transmitted, were also confirmed to be unique to samples from persons suspected of having tick-borne illness. These results indicate that 16S V1-V2 metagenomics can greatly simplify diagnosis and accelerate the discovery of bacterial tick-borne pathogens.

Background Cyclospora cayetanensis is a food-borne intestinal human parasite that causes outbreaks of diarrhea. There is a need for efficient laboratory methods for strain-level characterization to assist in outbreak investigations. By using next generation sequencing, genomic sequences can be obtained and compared to identify potential genotyping markers. However, there is no method available to propagate this parasite in the laboratory. Therefore, genomic DNA must be extracted from oocysts purified from human stool. The objective of this study was to apply optimized methods to purify C. cayetanensis oocysts and extract DNA in order to obtain high-quality whole genome sequences with minimum contamination of DNA from other organisms. Results Oocysts from 21 human stool specimens were separated from other stool components using discontinuous density gradient centrifugation and purified further by flow cytometry. Genomic DNA was used to construct Ovation Ultralow libraries for Illumina sequencing. MiSeq sequencing reads were taxonomically profiled for contamination, de novo assembled, and mapped to a draft genome available in GenBank to assess the quality of the resulting genomic sequences. Following all purification steps, the majority (81–99%) of sequencing reads were from C. cayetanensis. They could be assembled into draft genomes of around 45 MB in length with GC-content of 52%. Conclusions Density gradients performed in the presence of a detergent followed by flow cytometry sorting of oocysts yielded sufficient genomic DNA largely free from contamination and suitable for whole genome sequencing of C. cayetanensis. The methods described here will facilitate the accumulation of genomic sequences from various samples, which is a prerequisite for the development of typing tools to aid in outbreak investigations.

Background Ultra-Deep Sequencing (UDS) enabled identification of specific changes in human genome occurring in malignant tumors, with current approaches calling for the detection of specific mutations associated with certain cancers. However, such associations are frequently idiosyncratic and cannot be generalized for diagnostics. Mitochondrial DNA (mtDNA) has been shown to be functionally associated with several cancer types. Here, we study the association of intra-host mtDNA diversity with Hepatocellular Carcinoma (HCC). Results UDS mtDNA exome data from blood of patients with HCC (n = 293) and non-cancer controls (NC, n = 391) were used to: (i) measure the genetic heterogeneity of nucleotide sites from the entire population of intra-host mtDNA variants rather than to detect specific mutations, and (ii) apply machine learning algorithms to develop a classifier for HCC detection. Average total entropy of HCC mtDNA is 1.24-times lower than of NC mtDNA (p = 2.84E-47). Among all polymorphic sites, 2.09% had a significantly different mean entropy between HCC and NC, with 0.32% of the HCC mtDNA sites having greater (p p n = 61) and NC (n = 159) patients in a validation dataset that was not used for the RF parameter optimization. Conclusions Polymorphic sites contributing most to the mtDNA association with HCC are scattered along the mitochondrial genome, affecting all mitochondrial genes. The findings suggest that application of heterogeneity profiles of intra-host mtDNA variants from blood may help overcome barriers associated with the complex association of specific mutations with cancer, enabling the development of accurate, rapid, inexpensive and minimally invasive diagnostic detection of cancer.

Hepatitis E virus (HEV) genotype 1 (gt1) and gt3 infections have distinct epidemiologic characteristics and genotype-specific molecular mechanisms of pathogenesis are not well characterized. Previously, we showed differences in immune response-related gene expression profiles of HEV gt1 and gt3 infections using qPCR. We hypothesize that HEV gt1 and gt3 infections induce transcriptome modifications contributing to disease pathogenesis. RNAseq analysis was performed using liver biopsy samples of naïve (baseline), HEV gt1, or gt3-infected rhesus macaques, and nine anti-HEV positive rhesus macaques re-inoculated with HEV gt1. All 10 primary HEV gt1/gt3 infected animals exhibited the typical course of acute viral hepatitis and cleared the infection between 27 to 67 days after inoculation. Viremic stages of HEV infection were defined as early, peak, and decline based on HEV RNA titers in daily stool specimens. During early, peak, and decline phases of infection, HEV gt1 induced 415, 417, and 1769 differentially expressed genes, respectively, and 310, 678, and 388 genes were differentially expressed by HEV gt3, respectively (fold change ≥ 2.0, p-value ≤ 0.05). In the HEV gt1 infection, genes related to metabolic pathways were differentially expressed during the three phases of infection. In contrast, oxidative reduction (early phase), immune responses (peak phase), and T cell cytokine production (decline phase) were found to be regulated during HEV gt3 infection. In addition, FoxO and MAPK signaling pathways were differentially regulated in re-infected and protected animals against HEV gt1 reinfection, respectively. Significant differences of hepatic gene regulation exist between HEV gt1 and gt3 infections. These findings reveal a new link between molecular pathogenesis and epidemiological characteristics seen in HEV gt1 and gt3 infections.

BACKGROUND Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe respiratory illness in humans. Fundamental questions about circulating viruses and transmission routes remain. METHODS We assessed routinely collected epidemiologic data for MERS-CoV cases reported in Saudi Arabia during 1 January-30 June 2015 and conducted a more detailed investigation of cases reported during February 2015. Available respiratory specimens were obtained for sequencing. RESULTS During the study period, 216 MERS-CoV cases were reported. Full genome (n = 17) or spike gene sequences (n = 82) were obtained from 99 individuals. Most sequences (72 of 99 [73%]) formed a discrete, novel recombinant subclade (NRC-2015), which was detected in 6 regions and became predominant by June 2015. No clinical differences were noted between clades. Among 87 cases reported during February 2015, 13 had no recognized risks for secondary acquisition; 12 of these 13 also denied camel contact. Most viruses (8 of 9) from these 13 individuals belonged to NRC-2015. DISCUSSIONS Our findings document the spread and eventual predominance of NRC-2015 in humans in Saudi Arabia during the first half of 2015. Our identification of cases without recognized risk factors but with similar virus sequences indicates the need for better understanding of risk factors for MERS-CoV transmission.

EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNA-seq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010.

Burkholderia pseudomallei strain Bp1651, a human isolate, is resistant to all clinically relevant antibiotics. We report here on the finished genome sequence assembly and annotation of the two chromosomes of this strain. This genome sequence may assist in understanding the mechanisms of antimicrobial resistance for this pathogenic species.

The parasite Cyclospora cayetanensis causes foodborne diarrheal illness. Here, we report draft genome sequences obtained from C. cayetanensis oocysts purified from a human stool sample. The genome assembly consists of 865 contigs with a total length of 44,563,857 bases. These sequences can facilitate the development of subtyping tools to aid outbreak investigations.

AmoebaDB (http://AmoebaDB.org) and MicrosporidiaDB (http://MicrosporidiaDB.org) are new functional genomic databases serving the amoebozoa and microsporidia research communities, respectively. AmoebaDB contains the genomes of three Entamoeba species (E. dispar, E. invadens and E. histolityca) and microarray expression data for E. histolytica. MicrosporidiaDB contains the genomes of Encephalitozoon cuniculi, E. intestinalis and E. bieneusi. The databases belong to the National Institute of Allergy and Infectious Diseases (NIAID) funded EuPathDB (http://EuPathDB.org) Bioinformatics Resource Center family of integrated databases and assume the same architectural and graphical design as other EuPathDB resources such as PlasmoDB and TriTrypDB. Importantly they utilize the graphical strategy builder that affords a database user the ability to ask complex multi-data-type questions with relative ease and versatility. Genomic scale data can be queried based on BLAST searches, annotation keywords and gene ID searches, GO terms, sequence motifs, protein characteristics, phylogenetic relationships and functional data such as transcript (microarray and EST evidence) and protein expression data. Search strategies can be saved within a user's profile for future retrieval and may also be shared with other researchers using a unique strategy web address.

EuPathDB (http://EuPathDB.org; formerly ApiDB) is an integrated database covering the eukaryotic pathogens of the genera Cryptosporidium, Giardia, Leishmania, Neospora, Plasmodium, Toxoplasma, Trichomonas and Trypanosoma. While each of these groups is supported by a taxon-specific database built upon the same infrastructure, the EuPathDB portal offers an entry point to all these resources, and the opportunity to leverage orthology for searches across genera. The most recent release of EuPathDB includes updates and changes affecting data content, infrastructure and the user interface, improving data access and enhancing the user experience. EuPathDB currently supports more than 80 searches and the recently-implemented ‘search strategy’ system enables users to construct complex multi-step searches via a graphical interface. Search results are dynamically displayed as the strategy is constructed or modified, and can be downloaded, saved, revised, or shared with other database users.

Chlamydia psittaci is an obligate intracellular bacterium that can cause significant disease among a broad range of hosts. In humans, this organism may cause psittacosis, a respiratory disease that can spread to involve multiple organs, and in rare untreated cases may be fatal. There are ten known genotypes based on sequencing the major outer-membrane protein gene, ompA, of C. psittaci. Each genotype has overlapping host preferences and virulence characteristics. Recent studies have compared C. psittaci among other members of the Chlamydiaceae family and showed that this species frequently switches hosts and has undergone multiple genomic rearrangements. In this study, we sequenced five genomes of C. psittaci strains representing four genotypes, A, B, D and E. Due to the known association of the type III secretion system (T3SS) and polymorphic outer-membrane proteins (Pmps) with host tropism and virulence potential, we performed a comparative analysis of these elements among these five strains along with a representative genome from each of the remaining six genotypes previously sequenced. We found significant genetic variation in the Pmps and tbl3SS genes that may partially explain differences noted in C. psittaci host infection and disease.

Background: Recent studies in Southeast Asia have demonstrated substantial zoonotic transmission of Plasmodium knowlesi to humans. Microscopically, P. knowlesi exhibits several stage-dependent morphological similarities to P. malariae and P. falciparum. These similarities often lead to misdiagnosis of P. knowlesi as either P. malariae or P. falciparum and PCRbased molecular diagnostic tests are required to accurately detect P. knowlesi in humans. The most commonly used PCR test has been found to give false positive results, especially with a proportion of P. vivax isolates. To address the need for more sensitive and specific diagnostic tests for the accurate diagnosis of P. knowlesi, we report development of a new single-step PCR assay that uses novel genomic targets to accurately detect this infection. Methodology and Significant Findings: We have developed a bioinformatics approach to search the available malaria parasite genome database for the identification of suitable DNA sequences relevant for molecular diagnostic tests. Using this approach, we have identified multi-copy DNA sequences distributed in the P. knowlesi genome. We designed and tested several novel primers specific to new target sequences in a single-tube, non-nested PCR assay and identified one set of primers that accurately detects P. knowlesi. We show that this primer set has 100% specificity for the detection of P. knowlesi using three different strains (Nuri, H, and Hackeri), and one human case of malaria caused by P. knowlesi. This test did not show cross reactivity with any of the four human malaria parasite species including 11 different strains of P. vivax as well as 5 additional species of simian malaria parasites. Conclusions: The new PCR assay based on novel P. knowlesi genomic sequence targets was able to accurately detect P. knowlesi. Additional laboratory and field-based testing of this assay will be necessary to further validate its utility for clinical diagnosis of P. knowlesi.

Accurate and rapid diagnosis of malaria infections is crucial for implementing species-appropriate treatment and saving lives. Molecular diagnostic tools are the most accurate and sensitive method of detecting Plasmodium , differentiating between Plasmodium species, and detecting subclinical infections. Despite available whole-genome sequence data for Plasmodium falciparum and P. vivax , the majority of PCR-based methods still rely on the 18S rRNA gene targets. Historically, this gene has served as the best target for diagnostic assays. However, it is limited in its ability to detect mixed infections in multiplex assay platforms without the use of nested PCR. New diagnostic targets are needed. Ideal targets will be species specific, highly sensitive, and amenable to both single-step and multiplex PCRs. We have mined the genomes of P. falciparum and P. vivax to identify species-specific, repetitive sequences that serve as new PCR targets for the detection of malaria. We show that these targets (Pvr47 and Pfr364) exist in 14 to 41 copies and are more sensitive than 18S rRNA when utilized in a single-step PCR. Parasites are routinely detected at levels of 1 to 10 parasites/μl. The reaction can be multiplexed to detect both species in a single reaction. We have examined 7 P. falciparum strains and 91 P. falciparum clinical isolates from Tanzania and 10 P. vivax strains and 96 P. vivax clinical isolates from Venezuela, and we have verified a sensitivity and specificity of ∼100% for both targets compared with a nested 18S rRNA approach. We show that bioinformatics approaches can be successfully applied to identify novel diagnostic targets and improve molecular methods for pathogen detection. These novel targets provide a powerful alternative molecular diagnostic method for the detection of P. falciparum and P. vivax in conventional or multiplex PCR platforms.

TriTrypDB (http://tritrypdb.org) is an integrated database providing access to genome-scale datasets for kinetoplastid parasites, and supporting a variety of complex queries driven by research and development needs. TriTrypDB is a collaborative project, utilizing the GUS/WDK computational infrastructure developed by the Eukaryotic Pathogen Bioinformatics Resource Center (EuPathDB.org) to integrate genome annotation and analyses from GeneDB and elsewhere with a wide variety of functional genomics datasets made available by members of the global research community, often pre-publication. Currently, TriTrypDB integrates datasets from Leishmania braziliensis, L. infantum, L. major, L. tarentolae, Trypanosoma brucei and T. cruzi. Users may examine individual genes or chromosomal spans in their genomic context, including syntenic alignments with other kinetoplastid organisms. Data within TriTrypDB can be interrogated utilizing a sophisticated search strategy system that enables a user to construct complex queries combining multiple data types. All search strategies are stored, allowing future access and integrated searches. 'User Comments' may be added to any gene page, enhancing available annotation; such comments become immediately searchable via the text search, and are forwarded to curators for incorporation into the reference annotation when appropriate.

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.

Ratiometric fluorescent indicators are becoming increasingly prevalent in many areas of biology. They are used for making quantitative measurements of intracellular free calcium both in vitro and in vivo, as well as measuring membrane potentials, pH, and other important physiological variables of interest to researchers in many subfields. Often, functional changes in the fluorescent yield of ratiometric indicators are small, and the signal-to-noise ratio (SNR) is of order unity or less. In particular, variability in the denominator of the ratio can lead to very poor ratio estimates. We present a statistical optimization method for objectively detecting and estimating ratiometric signals in dual-wavelength measurements of fluorescent, ratiometric indicators that improves on standard methods. With the use of an appropriate statistical model for ratiometric signals and by taking the pixel-pixel covariance of an imaging dataset into account, we are able to extract user-independent spatiotemporal information that retains high resolution in both space and time.