MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Development of a new barcode-based, multiplex-PCR, next-generation-sequencing assay and data processing and analytical pipeline for multiplicity of infection detection of Plasmodium falciparum.

BACKGROUND: Simultaneous infection with multiple malaria parasite strains is common in high transmission areas. Quantifying the number of strains per host, or the multiplicity of infection (MOI), provides additional parasite indices for assessing transmission levels but it is challenging to measure accurately with current tools. This paper presents new laboratory and analytical methods for estimating the MOI of Plasmodium falciparum. METHODS: Based on 24 single nucleotide polymorphisms (SNPs) previously identified as stable, unlinked targets across 12 of the 14 chromosomes within P. falciparum genome, three multiplex PCRs of short target regions and subsequent next generation sequencing (NGS) of the amplicons were developed. A bioinformatics pipeline including B4Screening pathway removed spurious amplicons to ensure consistent frequency calls at each SNP location, compiled amplicons by SNP site diversity, and performed algorithmic haplotype and strain reconstruction. The pipeline was validated by 108 samples generated from cultured-laboratory strain mixtures in different proportions and concentrations, with and without pre-amplification, and using whole blood and dried blood spots (DBS). The pipeline was applied to 273 smear-positive samples from surveys conducted in western Kenya, then providing results into StrainRecon Thresholding for Infection Multiplicity (STIM), a novel MOI estimator. RESULTS: The 24 barcode SNPs were successfully identified uniformly across the 12 chromosomes of P. falciparum in a sample using the pipeline. Pre-amplification and parasite concentration, while non-linearly associated with SNP read depth, did not influence the SNP frequency calls. Based on consistent SNP frequency calls at targeted locations, the algorithmic strain reconstruction for each laboratory-mixed sample had 98.5% accuracy in dominant strains. STIM detected up to 5 strains in field samples from western Kenya and showed declining MOI over time (q < 0.02), from 4.32 strains per infected person in 1996 to 4.01, 3.56 and 3.35 in 2001, 2007 and 2012, and a reduction in the proportion of samples with 5 strains from 57% in 1996 to 18% in 2012. CONCLUSION: The combined approach of new multiplex PCRs and NGS, the unique bioinformatics pipeline and STIM could identify 24 barcode SNPs of P. falciparum correctly and consistently. The methodology could be applied to field samples to reliably measure temporal changes in MOI.

Entropy of mitochondrial DNA circulating in blood is associated with hepatocellular carcinoma.

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 < 0.05) and 1.77% of the sites having lower mean entropy (p < 0.05) as compared to NC. The entropy profile of each sample was used to further explore the association between mtDNA heterogeneity and HCC by means of a Random Forest (RF) classifier The RF-classifier separated 232 HCC and 232 NC patients with accuracy of up to 99.78% and average accuracy of 92.23% in the 10-fold cross-validation. The classifier accurately separated 93.08% of HCC (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.

First Full Draft Genome Sequence of Plasmodium brasilianum.

Plasmodium malariae is a protozoan parasite that can cause human malaria. The simian parasite Plasmodium brasilianum infects New World monkeys from Latin America and is morphologically indistinguishable from P. malariae Here, we report the first full draft genome sequence for P. brasilianum.

Draft Genome Sequences from Cyclospora cayetanensis Oocysts Purified from a Human Stool Sample.

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.

Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study.

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamine's predicted effect on suppression of cell division and metabolism in the immune system.

FungiDB: an integrated functional genomics database for fungi.

FungiDB (http://FungiDB.org) is a functional genomic resource for pan-fungal genomes that was developed in partnership with the Eukaryotic Pathogen Bioinformatic resource center (http://EuPathDB.org). FungiDB uses the same infrastructure and user interface as EuPathDB, which allows for sophisticated and integrated searches to be performed using an intuitive graphical system. The current release of FungiDB contains genome sequence and annotation from 18 species spanning several fungal classes, including the Ascomycota classes, Eurotiomycetes, Sordariomycetes, Saccharomycetes and the Basidiomycota orders, Pucciniomycetes and Tremellomycetes, and the basal 'Zygomycete' lineage Mucormycotina. Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yeast two hybrid interaction data. The underlying genomic sequence and annotation combined with functional data, additional data from the FungiDB standard analysis pipeline and the ability to leverage orthology provides a powerful resource for in silico experimentation.

AmoebaDB and MicrosporidiaDB: functional genomic resources for Amoebozoa and Microsporidia species.

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: a portal to eukaryotic pathogen databases.

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.

TriTrypDB: a functional genomic resource for the Trypanosomatidae.

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.

GiardiaDB and TrichDB: integrated genomic resources for the eukaryotic protist pathogens Giardia lamblia and Trichomonas vaginalis.

GiardiaDB (http://GiardiaDB.org) and TrichDB (http://TrichDB.org) house the genome databases for Giardia lamblia and Trichomonas vaginalis, respectively, and represent the latest additions to the EuPathDB (http://EuPathDB.org) family of functional genomic databases. GiardiaDB and TrichDB employ the same framework as other EuPathDB sites (CryptoDB, PlasmoDB and ToxoDB), supporting fully integrated and searchable databases. Genomic-scale data available via these resources may be queried based on BLAST searches, annotation keywords and gene ID searches, GO terms, sequence motifs and other protein characteristics. Functional queries may also be formulated, based on transcript and protein expression data from a variety of platforms. Phylogenetic relationships may also be interrogated. The ability to combine the results from independent queries, and to store queries and query results for future use facilitates complex, genome-wide mining of functional genomic data.

PlasmoDB: a functional genomic database for malaria parasites.

PlasmoDB (http://PlasmoDB.org) is a functional genomic database for Plasmodium spp. that provides a resource for data analysis and visualization in a gene-by-gene or genome-wide scale. PlasmoDB belongs to a family of genomic resources that are housed under the EuPathDB (http://EuPathDB.org) Bioinformatics Resource Center (BRC) umbrella. The latest release, PlasmoDB 5.5, contains numerous new data types from several broad categories--annotated genomes, evidence of transcription, proteomics evidence, protein function evidence, population biology and evolution. Data in PlasmoDB can be queried by selecting the data of interest from a query grid or drop down menus. Various results can then be combined with each other on the query history page. Search results can be downloaded with associated functional data and registered users can store their query history for future retrieval or analysis.

The Cancer Biomedical Informatics Grid (caBIGTM).

The Cancer Biomedical Informatics Grid (caBIGTM) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data and the use of common analytical tools. The emergence of genomics and proteomics high-throughput technologies are creating a paradigm shift in biomedical research from small independent labs to large teams of researchers exploring entire genomes and proteomes and how they relate to disease. caBIGTMis developing new software and modifying existing software within Clinical Trials Management Systems, Tissue Banks and Pathology Tools and Integrated Cancer Research tools to manage the huge volume of data being generated and to facilitate collaboration across the broad spectrum of cancer research.