Paul Shannon

Exome sequencing identifies the cause of a mendelian disorder

We demonstrate the first successful application of exome sequencing to discover the gene for a rare mendelian disorder of unknown cause, Miller syndrome (MIM%263750). For four affected individuals in three independent kindreds, we captured and sequenced coding regions to a mean coverage of 40× and sufficient depth to call variants at ∼97% of each targeted exome. Filtering against public SNP databases and eight HapMap exomes for genes with two previously unknown variants in each of the four individuals identified a single candidate gene, DHODH, which encodes a key enzyme in the pyrimidine de novo biosynthesis pathway. Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome. Exome sequencing of a small number of unrelated affected individuals is a powerful, efficient strategy for identifying the genes underlying rare mendelian disorders and will likely transform the genetic analysis of monogenic traits.

Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing

Runs in the Family The power to detect mutations involved in disease by genome sequencing is enhanced when combined with the ability to discover specific mutations that may have arisen between offspring and parents. Roach et al. (p. 636, published online 10 March) present the sequence of a family with two offspring affected with two genetic disorders: Miller syndrome and primary ciliary dyskinesia. Sequence analysis of the children and their parents not only showed that the intergenerational mutation rate was lower than anticipated but also revealed recombination sites and the occurrence of rare polymorphisms. Genomic sequencing of an entire family reveals the rate of spontaneous mutations in humans and identifies disease genes. We analyzed the whole-genome sequences of a family of four, consisting of two siblings and their parents. Family-based sequencing allowed us to delineate recombination sites precisely, identify 70% of the sequencing errors (resulting in > 99.999% accuracy), and identify very rare single-nucleotide polymorphisms. We also directly estimated a human intergeneration mutation rate of ~1.1 × 10−8 per position per haploid genome. Both offspring in this family have two recessive disorders: Miller syndrome, for which the gene was concurrently identified, and primary ciliary dyskinesia, for which causative genes have been previously identified. Family-based genome analysis enabled us to narrow the candidate genes for both of these Mendelian disorders to only four. Our results demonstrate the value of complete genome sequencing in families.

Orchestrating high-throughput genomic analysis with Bioconductor

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacteriums global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among approximately 80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organisms interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

PhosphoPep--a phosphoproteome resource for systems biology research in Drosophila Kc167 cells.

The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high‐confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein–protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

Derivation of genetic interaction networks from quantitative phenotype data

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways.

Coordinate regulation of energy transduction modules in Halobacterium sp. analyzed by a global systems approach

The extremely halophilic archaeon Halobacterium NRC-1 can switch from aerobic energy production (energy from organic compounds) to anaerobic phototrophy (energy from light) by induction of purple membrane biogenesis. The purple membrane is made up of multiple copies of a 1:1 complex of bacterioopsin (Bop) and retinal called bacteriorhodopsin that functions as a light-driven proton pump. A light- and redox-sensing transcription regulator, Bat, regulates critical genes encoding the biogenesis of the purple membrane. To better understand the regulatory network underlying this physiological state, we report a systems approach using global mRNA and protein analyses of four strains of Halobacterium sp.: the wild-type, NRC-1; and three genetically perturbed strains: S9 (bat+), a purple membrane overproducer, and two purple membrane deficient strains, SD23 (a bop knockout) and SD20 (a bat knockout). The integrated DNA microarray and proteomic data reveal the coordinated coregulation of several interconnected biochemical pathways for phototrophy: isoprenoid synthesis, carotenoid synthesis, and bacteriorhodopsin assembly. In phototrophy, the second major biomodule for ATP production, arginine fermentation, is repressed. The primary systems level insight provided by this study is that two major energy production pathways in Halobacterium sp., phototrophy and arginine fermentation, are inversely regulated, presumably to achieve a balance in ATP production under anaerobic conditions.

Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Prostate cancer is initially responsive to androgen ablation therapy and progresses to androgen-unresponsive states that are refractory to treatment. The mechanism of this transition is unknown. A systems approach to disease begins with the quantitative delineation of the informational elements (mRNAs and proteins) in various disease states. We employed two recently developed high-throughput technologies, massively parallel signature sequencing (MPSS) and isotope-coded affinity tag, to gain a comprehensive picture of the changes in mRNA levels and more restricted analysis of protein levels, respectively, during the transition from androgen-dependent LNCaP (model for early-stage prostate cancer) to androgen-independent CL1 cells (model for late-stage prostate cancer). We sequenced >5 million MPSS signatures, obtained >142,000 tandem mass spectra, and built comprehensive MPSS and proteomic databases. The integrated mRNA and protein expression data revealed underlying functional differences between androgen-dependent and androgen-independent prostate cancer cells. The high sensitivity of MPSS enabled us to identify virtually all of the expressed transcripts and to quantify the changes in gene expression between these two cell states, including functionally important low-abundance mRNAs, such as those encoding transcription factors and signal transduction molecules. These data enable us to map the differences onto extant physiologic networks, creating perturbation networks that reflect prostate cancer progression. We found 37 BioCarta and 14 Kyoto Encyclopedia of Genes and Genomes pathways that are up-regulated and 23 BioCarta and 22 Kyoto Encyclopedia of Genes and Genomes pathways that are down-regulated in LNCaP cells versus CL1 cells. Our efforts represent a significant step toward a systems approach to understanding prostate cancer progression.

General transcription factor specified global gene regulation in archaea

Cells responding to dramatic environmental changes or undergoing a developmental switch typically change the expression of numerous genes. In bacteria, σ factors regulate much of this process, whereas in eukaryotes, four RNA polymerases and a multiplicity of generalized transcription factors (GTFs) are required. Here, by using a systems approach, we provide experimental evidence (including protein-coimmunoprecipitation, ChIP-Chip, GTF perturbation and knockout, and measurement of transcriptional changes in these genetically perturbed strains) for how archaea likely accomplish similar large-scale transcriptional segregation and modulation of physiological functions. We are able to associate GTFs to nearly half of all putative promoters and show evidence for at least 7 of the possible 42 functional GTF pairs. This report represents a significant contribution toward closing the gap in our understanding of gene regulation by GTFs for all three domains of life and provides an example for how to use various experimental techniques to rapidly learn significant portions of a global gene regulatory network of organisms for which little has been previously known.