Lynn M. Schriml

Dating the Origin of the CCR5-Δ32 AIDS-Resistance Allele by the Coalescence of Haplotypes

The CCR5-Delta32 deletion obliterates the CCR5 chemokine and the human immunodeficiency virus (HIV)-1 coreceptor on lymphoid cells, leading to strong resistance against HIV-1 infection and AIDS. A genotype survey of 4,166 individuals revealed a cline of CCR5-Delta32 allele frequencies of 0%-14% across Eurasia, whereas the variant is absent among native African, American Indian, and East Asian ethnic groups. Haplotype analysis of 192 Caucasian chromosomes revealed strong linkage disequilibrium between CCR5 and two microsatellite loci. By use of coalescence theory to interpret modern haplotype genealogy, we estimate the origin of the CCR5-Delta32-containing ancestral haplotype to be approximately 700 years ago, with an estimated range of 275-1,875 years. The geographic cline of CCR5-Delta32 frequencies and its recent emergence are consistent with a historic strong selective event (e.g. , an epidemic of a pathogen that, like HIV-1, utilizes CCR5), driving its frequency upward in ancestral Caucasian populations.

Disease Ontology: a backbone for disease semantic integration

The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCIs thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DOs extended relations and logical definition representation along with these biomedical resource cross-mappings.

Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBIs Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DOs current asserted view and multiple inferred views through reasoning.

The human phenotype ontology: semantic unification of common and rare disease

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.

MIxS-BE: a MIxS extension defining a minimum information standard for sequence data from the built environment.

MIxS-BE: a MIxS extension defining a minimum information standard for sequence data from the built environment

Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea

We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.

The Disease Ontology: fostering interoperability between biological and clinical human disease-related data

The Disease Ontology (DO) enables cross-domain data integration through a common standard of human disease terms and their etiological descriptions. Standardized disease descriptors that are integrated across mammalian genomic resources provide a human-readable, machine-interpretable, community-driven disease corpus that unifies the representation of human common and rare diseases. The DO is populated by consensus-driven disease data descriptors that incorporate disease terms utilized by genomic and genetic projects and resources engaged in studies to understand the genetics of human disease through the study of model organisms. The DO project serves multiple roles for the model organism community by providing: (1) a structured “backbone” of disease concepts represented among the model organism databases; (2) authoritative disease curation services to researchers and resource providers; and (3) development of subsets of the DO representative of human diseases annotated to animal models curated within the model organism databases.

An ATP-binding cassette gene (ABCG3) closely related to the multidrug transporter ABCG2 (MXR/ABCP) has an unusual ATP-binding domain

The recent identification of multiple ABC transporters with potential roles in drug resistance has offered hope for the treatment of cancer (Borst 1999; Sandor et al. 1998). ATP binding cassette (ABC) proteins bind and hydrolyze ATP providing energy for the transport of an array of substrates (Dean and Allikmets 1995; Higgins 1992). MDR1/P-glycoprotein (ABCB1) was the first transporter described in association with drug resistance, and inhibitors of P-glycoprotein-mediated transport are undergoing clinical trials for reversal of drug resistance. However, numerous laboratory models have been described with non-ABCB1-mediated drug resistance. Eukaryotic ABC transporters are either full size, as are ABCB1, and the MRPs, with 12 transmembrane domains and two ATP binding sites on each molecule; or they are half-size with six transmembrane domains and one ATP binding site (Dean and Allikmets 1995). The half-transporters dimerize to form a functional transporter (Ewart and Howells 1998; Shani and Valle 1998). Dimerization could allow for diversification in the function of ABC transporters by increasing the number of possible combinations. Recently a half-transporter molecule, MXR/ABCP/ ABCG2, was identified in cell lines with non-ABCB1-mediated multidrug resistance characterized by high levels of resistance to mitoxantrone, anthracyclines, and to the camptothecin analogs, topotecan, SN38, and 9AC (Allikmets et al. 1998; Brangi et al. 1999; Doyle et al. 1998; Maliepaard et al. 1999; Miyake et al. 1999; Litman et al. 2000). This protein is a half-transporter with six transmembrane domains and one ATP binding site, and bears striking homology to theDrosophilawhite genes. Computer searches of the EST databases with the BLAST program led to the identification of several mouse and rat sequences that had high homology to ABCG2 but that appeared to encode a unique gene. RACE was used to amplify and clone the entire coding region of the gene by using mouse spleen RNA, and the sequence revealed a single open reading frame encoding a 650-AA protein, designated Abcg3. Primers to the 3 8 untranslated region were used to isolate a BAC clone from a mouse 129SV library. Each exon of the gene was sequenced from the BAC clone to identify the splice junction sites. The Abcg3gene has 16 exons, including one 58 non-coding exon. Figure 1 displays an alignment of Abcg3 with the amino acid sequence of the other white family genes. Considerable identity is seen in the ATP-binding domain, but clear homology is seen throughout the coding region. Abcg3 is most closely related to ABCG2 with 54% amino acid identity overall, 64% in the NBF and 50% in the TM region. The alignment was used to generate a phylogenetic tree of the genes, and this analysis confirms that Abcg3 and ABCG2 are closely related (data not shown). Surprisingly, Abcg3 contains several unusual residues in the Walker A and signature (C) domains. The Walker A consensus is GAGKST, and the Abcg3 sequence is DGSRSL; the C region consensus is LSGG, and the Abcg3 sequence in this region is RSKE. Many of these residues are absolutely conserved in all ABC genes, and mutations in these regions typically lead to non-functional proteins. This suggests that Abcg3 may not bind and/or hydrolyze ATP. The Abcg3BAC clone was used for in situ hybridization on both mouse and human chromosomes. The gene localized to a single region on mouse Chr 5, band E3-4. The murine BAC clone gave a single signal on human Chr 8p12 (data not shown). However, degenerate PCR and database searches have failed to reveal a human Abcg2-related gene. The mouse Abcg3 gene was also mapped by radiation hybrid analysis to mouse Chr 5, 59 cM from the centromere, a position consistent with the in situ hybridization data (Schriml and Dean 2000). Using a quantitative PCR assay previously developed for evaluating MDR1 (multidrug resistance-1) expression, we examined expression of Abcg2andAbcg3in normal murine tissues. The levels of expression in the various tissues were quantitated as previously described for MDR1 and normalized to the level found in muscle, which was arbitrarily assigned a value of 10. Muscle expression ofAbcg2andAbcg3was at low but detectable levels. Figure 2A presents a bar graph depicting the PCR expression data. The highest levels of expression for Abcg2 were found in the kidney, lung, and small intestine. For Abcg3,the highest levels of expression were found in thymus and spleen. Except for the relative higher levels of expression in the small intestine for both Abcg2andAbcg3,there was no concordance in expression pattern. To confirm the pattern of Abcg3expression, Northern analysis was performed and is shown in Fig. 2B. Although the tissues with the lower levels of expression are below the level of detection by Northern blot analysis, the high levels found in the thymus and spleen are still detectable; with faint signals also detected in lung and small intestine. It should be noted that the quantitative values were calculated from PCR product generated in the exponential range of amplification. The ABCG2 gene is amplified and/or overexpressed in several multidrug resistant tumor cell lines derived from breast, colon, stomach, and leukemic cells (Doyle et al. 1998; Miyake et al., 1999). The cells overexpressing ABCG2 are resistant to mitoxantrone, bisantrene, anthracyclines, and the camptothecin analogs * Present address: National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20892, USA

Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium

Summary The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community.

Metagenomes and metatranscriptomes from the L4 long-term coastal monitoring station in the Western English Channel

Both metagenomic data and metatranscriptomic data were collected from surface water (0–2m) of the L4 sampling station (50.2518 N, 4.2089 W), which is part of the Western Channel Observatory long-term coastal-marine monitoring station. We previously generated from this area a six-year time series of 16S rRNA V6 data, which demonstrated robust seasonal structure for the bacterial community, with diversity correlated with day length. Here we describe the features of these metagenomes and metatranscriptomes. We generated 8 metagenomes (4.5 million sequences, 1.9 Gbp, average read-length 350 bp) and 7 metatranscriptomes (392,632 putative mRNA-derived sequences, 159 Mbp, average read-length 272 bp) for eight time-points sampled in 2008. These time points represent three seasons (winter, spring, and summer) and include both day and night samples. These data demonstrate the major differences between genetic potential and actuality, whereby genomes follow general seasonal trends yet with surprisingly little change in the functional potential over time; transcripts tended to be far more structured by changes occurring between day and night.