Xiang Qin

The complete genome of an individual by massively parallel DNA sequencing.

The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of ‘genomic medicine’. However, the formidable size of the diploid human genome, approximately 6u2009gigabases, has prevented the routine application of sequencing methods to deciphering complete individual human genomes. To realize the full potential of genomics for human health, this limitation must be overcome. Here we report the DNA sequence of a diploid genome of a single individual, James D. Watson, sequenced to 7.4-fold redundancy in two months using massively parallel sequencing in picolitre-size reaction vessels. This sequence was completed in two months at approximately one-hundredth of the cost of traditional capillary electrophoresis methods. Comparison of the sequence to the reference genome led to the identification of 3.3u2009million single nucleotide polymorphisms, of which 10,654 cause amino-acid substitution within the coding sequence. In addition, we accurately identified small-scale (2–40,000 base pair (bp)) insertion and deletion polymorphism as well as copy number variation resulting in the large-scale gain and loss of chromosomal segments ranging from 26,000 to 1.5u2009million base pairs. Overall, these results agree well with recent results of sequencing of a single individual by traditional methods. However, in addition to being faster and significantly less expensive, this sequencing technology avoids the arbitrary loss of genomic sequences inherent in random shotgun sequencing by bacterial cloning because it amplifies DNA in a cell-free system. As a result, we further demonstrate the acquisition of novel human sequence, including novel genes not previously identified by traditional genomic sequencing. This is the first genome sequenced by next-generation technologies. Therefore it is a pilot for the future challenges of ‘personalized genome sequencing’.

Complete Genome Sequence of Rickettsia typhi and Comparison with Sequences of Other Rickettsiae

Rickettsia typhi, the causative agent of murine typhus, is an obligate intracellular bacterium with a life cycle involving both vertebrate and invertebrate hosts. Here we present the complete genome sequence of R. typhi (1,111,496 bp) and compare it to the two published rickettsial genome sequences: R. prowazekii and R. conorii. We identified 877 genes in R. typhi encoding 3 rRNAs, 33 tRNAs, 3 noncoding RNAs, and 838 proteins, 3 of which are frameshifts. In addition, we discovered more than 40 pseudogenes, including the entire cytochrome c oxidase system. The three rickettsial genomes share 775 genes: 23 are found only in R. prowazekii and R. typhi, 15 are found only in R. conorii and R. typhi, and 24 are unique to R. typhi. Although most of the genes are colinear, there is a 35-kb inversion in gene order, which is close to the replication terminus, in R. typhi, compared to R. prowazekii and R. conorii. In addition, we found a 124-kb R. typhi-specific inversion, starting 19 kb from the origin of replication, compared to R. prowazekii and R. conorii. Inversions in this region are also seen in the unpublished genome sequences of R. sibirica and R. rickettsii, indicating that this region is a hot spot for rearrangements. Genome comparisons also revealed a 12-kb insertion in the R. prowazekii genome, relative to R. typhi and R. conorii, which appears to have occurred after the typhus (R. prowazekii and R. typhi) and spotted fever (R. conorii) groups diverged. The three-way comparison allowed further in silico analysis of the SpoT split genes, leading us to propose that the stringent response system is still functional in these rickettsiae.

Convergent evolution of the genomes of marine mammals

Marine mammals from different mammalian orders share several phenotypic traits adapted to the aquatic environment and therefore represent a classic example of convergent evolution. To investigate convergent evolution at the genomic level, we sequenced and performed de novo assembly of the genomes of three species of marine mammals (the killer whale, walrus and manatee) from three mammalian orders that share independently evolved phenotypic adaptations to a marine existence. Our comparative genomic analyses found that convergent amino acid substitutions were widespread throughout the genome and that a subset of these substitutions were in genes evolving under positive selection and putatively associated with a marine phenotype. However, we found higher levels of convergent amino acid substitutions in a control set of terrestrial sister taxa to the marine mammals. Our results suggest that, whereas convergent molecular evolution is relatively common, adaptive molecular convergence linked to phenotypic convergence is comparatively rare.

Identification of genes and pathways involved in kidney renal clear cell carcinoma

BackgroundKidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.ResultsFollowed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.ConclusionsOur study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Phylogenomics and the Dynamic Genome Evolution of the Genus Streptococcus

The genus Streptococcus comprises important pathogens that have a severe impact on human health and are responsible for substantial economic losses to agriculture. Here, we utilize 46 Streptococcus genome sequences (44 species), including eight species sequenced here, to provide the first genomic level insight into the evolutionary history and genetic basis underlying the functional diversity of all major groups of this genus. Gene gain/loss analysis revealed a dynamic pattern of genome evolution characterized by an initial period of gene gain followed by a period of loss, as the major groups within the genus diversified. This was followed by a period of genome expansion associated with the origins of the present extant species. The pattern is concordant with an emerging view that genomes evolve through a dynamic process of expansion and streamlining. A large proportion of the pan-genome has experienced lateral gene transfer (LGT) with causative factors, such as relatedness and shared environment, operating over different evolutionary scales. Multiple gene ontology terms were significantly enriched for each group, and mapping terms onto the phylogeny showed that those corresponding to genes born on branches leading to the major groups represented approximately one-fifth of those enriched. Furthermore, despite the extensive LGT, several biochemical characteristics have been retained since group formation, suggesting genomic cohesiveness through time, and that these characteristics may be fundamental to each group. For example, proteolysis: mitis group; urea metabolism: salivarius group; carbohydrate metabolism: pyogenic group; and transcription regulation: bovis group.

PGRNseq: a targeted capture sequencing panel for pharmacogenetic research and implementation.

Objectives Although the costs associated with whole-genome and whole-exome next-generation sequencing continue to decline, they remain prohibitively expensive for large-scale studies of genetic variation. As an alternative, custom-target sequencing has become a common methodology on the basis of its favorable balance between cost, throughput, and deep coverage. Methods We have developed PGRNseq, a custom-capture panel of 84 genes with associations to pharmacogenetic phenotypes, as a tool to explore the relationship between drug response and genetic variation, both common and rare. We utilized a set of 32 diverse HapMap trios and two clinical cohorts to assess platform performance, accuracy, and ability to discover novel variation. Results We found that PGRNseq generates ultra-deep coverage data (mean=496x) that are over 99.8% concordant with orthogonal datasets. In addition, in our testing sets, PGRNseq identified many novel, rare variants of interest, underscoring its value in both research and clinical settings. Conclusion PGRNseq is an ideal platform for carrying out sequencing-based analyses of pharmacogenetic variation in large cohorts. In addition, the high accuracy associated with genotypes from PGRNseq highlight its utility as a clinical test.

Extreme Sensory Complexity Encoded in the 10-Megabase Draft Genome Sequence of the Chromatically Acclimating Cyanobacterium Tolypothrix sp. PCC 7601

ABSTRACT Tolypothrix sp. PCC 7601 is a freshwater filamentous cyanobacterium with complex responses to environmental conditions. Here, we present its 9.96-Mbp draft genome sequence, containing 10,065 putative protein-coding sequences, including 305 predicted two-component system proteins and 27 putative phytochrome-class photoreceptors, the most such proteins in any sequenced genome.

Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines

Variation in the expression level and activity of genes involved in drug disposition and action (‘pharmacogenes’) can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20–45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.

Generation and Characterization of Antibodies against Asian Elephant (Elephas maximus) IgG, IgM, and IgA

Asian elephant (Elephas maximus) immunity is poorly characterized and understood. This gap in knowledge is particularly concerning as Asian elephants are an endangered species threatened by a newly discovered herpesvirus known as elephant endotheliotropic herpesvirus (EEHV), which is the leading cause of death for captive Asian elephants born after 1980 in North America. While reliable diagnostic assays have been developed to detect EEHV DNA, serological assays to evaluate elephant anti-EEHV antibody responses are lacking and will be needed for surveillance and epidemiological studies and also for evaluating potential treatments or vaccines against lethal EEHV infection. Previous studies have shown that Asian elephants produce IgG in serum, but they failed to detect IgM and IgA, further hampering development of informative serological assays for this species. To begin to address this issue, we determined the constant region genomic sequence of Asian elephant IgM and obtained some limited protein sequence information for putative serum IgA. The information was used to generate or identify specific commercial antisera reactive against IgM and IgA isotypes. In addition, we generated a monoclonal antibody against Asian elephant IgG. These three reagents were used to demonstrate that all three immunoglobulin isotypes are found in Asian elephant serum and milk and to detect antibody responses following tetanus toxoid booster vaccination or antibodies against a putative EEHV structural protein. The results indicate that these new reagents will be useful for developing sensitive and specific assays to detect and characterize elephant antibody responses for any pathogen or vaccine, including EEHV.

Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.