Comparative genomics: the key to understanding the Human Genome Project

The sequencing of the human genome is well underway. Technology has advanced, such that the total genomic sequence is possible, along with an extensive catalogue of genes via comprehensive cDNA libraries. With the recent completion of the Saccharomyces cerevisiae sequencing project and the imminent completion of that of Caenorhabditis elegans, the most frequently asked question is how much can sequence data alone tell us? The answer is that that a DNA sequence taken in isolation from a single organism reveals very little. The vast majority of DNA in most organisms is noncoding. Protein coding sequences or genes cannot function as isolated units without interaction with noncoding DNA and neighboring genes. This genomic environment is specific to each organism. In order to understand this we need to look at similar genes in different organisms, to determine how function and position has changed over the course of evolution. By understanding evolutionary processes we can gain a greater insight into what makes a gene and the wider processes of genetics and inheritance. Comparative genomics (with model organisms), once the poor relation of the human genome project, is starting to provide the key to unlock the DNA code.     

Gene Regulatory Network Reconstruction Using Conditional Mutual Information

The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes. For this purpose, we propose a conditional mutual information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables. We provide experimental results that demonstrate that the proposed regulatory network inference algorithm can provide better performance when the target network contains coregulated and interactively regulated genes.     

EpoDB: a prototype database for the analysis of genes expressed during vertebrate erythropoiesis.

EpoDB is a database of genes expressed in vertebrate red blood cells. It is also a prototype for the creation of cell and tissue-specific databases from multiple external sources. The information in EpoDB obtained from GenBank, SWISS-PROT, Transfac, TRRD and GERD is curated to provide high quality data for sequence analysis aimed at understanding gene regulation during erythropoiesis. New protocols have been developed for data integration and updating entries. Using a BLAST-based algorithm, we have grouped GenBank entries representing the same gene together. This sequence similarity protocol was also used to identify new entries to be included in EpoDB. We have recently implemented our database in Sybase (relational tables) in addition to SICStus Prolog to provide us with greater flexibility in asking complex queries that utilize information from multiple sources. New additions to the public web site (http://www.cbil.upenn.edu/epodb) for accessing EpoDB are the ability to retrieve groups of entries representing different variants of the same gene and to retrieve gene expression data. The BLAST query has been enhanced by incorporating BLASTView, an interactive and graphical display of BLAST results. We have also enhanced the queries for retrieving sequence from specified genes by the addition of MEME, a motif discovery tool, to the integrated analysis tools which include CLUSTALW and TESS.     

Genomes for jeans: cotton genomics for engineering superior fiber

Twenty years ago, scientists predicted that better understanding of fiber development would lead to novel ways to engineer superior cotton fiber. Advances in genetic resources, DNA markers, DNA sequence information, and gene expression data have indeed provided new insights into fiber initiation, elongation and maturation. Many exciting applications of this knowledge offer the potential to select better cotton genotypes more effectively in mainstream breeding programs or engineer genotypes with improved agronomic and/or quality traits. Here, we discuss recent progress in understanding genes involved in fiber development, and their regulation and manipulation to engineer improved fibers. Better understanding of quantitative trait loci/gene interactions that influence fiber quality and yield may help to tailor superior cotton genotypes to diverse environments.     

Use of the complete genome sequence information of Haemophilus influenzae strain Rd to investigate lipopolysaccharide biosynthesis

The availability of the complete 1.83-megabase-pair sequence of the Haemophilus influenzae strain Rd genome has facilitated significant progress in investigating the biology of H. influenzae lipopolysaccharide (LPS), a major virulence determinant of this human pathogen. By searching the H. influenzae genomic database, with sequences of known LPS biosynthetic genes from other organisms, we identified and then cloned 25 candidate LPS genes. Construction of mutant strains and characterization of the LPS by reactivity with monoclonal antibodies, PAGE fractionation patterns and electrospray mass spectrometry comparative analysis have confirmed a potential role in LPS biosynthesis for the majority of these candidate genes. Virulence studies in the infant rat have allowed us to estimate the minimal LPS structure required for intravascular dissemination. This study is one of the first to demonstrate the rapidity, economy and completeness with which novel biological information can be accessed once the complete genome sequence of an organism is available.     

Mutations affecting the development of the peripheral nervous system in Drosophila: a molecular screen for novel proteins

In our quest for novel genes required for the development of the embryonic peripheral nervous system (PNS), we have performed three genetic screens using MAb 22C10 as a marker of terminally differentiated neurons. A total of 66 essential genes required for normal PNS development were identified, including 49 novel genes. To obtain information about the molecular nature of these genes, we decided to complement our genetic screens with a molecular screen. From transposon-tagged mutations identified on the basis of their phenotype in the PNS we selected 31 P-element strains representing 26 complementation groups on the second and third chromosomes to clone and sequence the corresponding genes. We used plasmid rescue to isolate and sequence 51 genomic fragments flanking the sites of these P-element insertions. Database searches using sequences derived from the ends of plasmid rescues allowed us to assign genes to one of four classes: (1) previously characterized genes (11), (2) first mutations in cloned genes (1), (3) P-element insertions in genes that were identified, but not characterized molecularly (1), and (4) novel genes (13). Here, we report the cloning, sequence, Northern analysis, and the embryonic expression pattern of candidate cDNAs for 10 genes: astray, chrowded, dalmatian, gluon, hoi-polloi, melted, pebble, skittles, sticky ch1, and vegetable. This study allows us to draw conclusions about the identity of proteins required for the development of the nervous system in Drosophila and provides an example of a molecular approach to characterize en masse transposon-tagged mutations identified in genetic screens.     

Beyond Mendel: an evolving view of human genetic disease transmission

Methodological and conceptual advances in human genetics have led to the identification of an impressive number of human disease genes. This wealth of information has also revealed that the traditional distinction between Mendelian and complex disorders might sometimes be blurred. Genetic and mutational data on an increasing number of disorders have illustrated how phenotypic effects can result from the combined action of alleles in many genes. In this review, we discuss how an improved understanding of the genetic basis of multilocus inheritance is catalysing the transition from a segmented view of human genetic disease to a conceptual continuum between Mendelian and complex traits.     

The ciliopathies: a transitional model into systems biology of human genetic disease

The last decade has witnessed an explosion in the identification of genes, mutations in which appear sufficient to cause clinical phenotypes in humans. This is especially true for disorders of ciliary dysfunction in which an excess of 50 causal loci are now known; this discovery was driven partly by an improved understanding of the protein composition of the cilium and the co-occurrence of clinical phenotypes associated with ciliary dysfunction. Despite this progress, the fundamental challenge of predicting phenotype and or clinical progression based on single locus information remains unsolved. Here, we explore how the combinatorial knowledge of allele quality and quantity, an improved understanding of the biological composition of the primary cilium, and the expanded appreciation of the subcellular roles of this organelle can be synthesized to generate improved models that can explain both causality but also variable penetrance and expressivity.     

Sequence analysis of the ly49 cluster in C57BL/6 mice: a rapidly evolving multigene family in the immune system

The cytotoxic activity of murine natural killer cells is controlled in part through the action of genes belonging to the Ly49 family. Members of this multigene family are found in a region on mouse chromosome 6 termed the natural killer gene complex. Using data available through public databases, we performed sequence analysis of a 620-kb region in C57Bl/6 (B6) mice that contains the Ly49 genes. The contiguous genomic sequence has allowed us to describe the complete B6 Ly49 gene repertoire, which includes two recently described genes as well as three partial genes. We have shown that the genes in the cluster have evolved through a series of large duplication events involving units of one or more genes and we have attempted to characterize the nature of the duplication end points. Finally, we have used information regarding gene sequence relationships and insertion of repetitive elements to construct a model for the evolution of the gene cluster. Our study illustrates that the Ly49 cluster represents an example of a rapidly evolving gene family, and continued analysis of this region in other strains will undoubtedly provide further insight into mechanisms for generating genomic diversity.     

The influence of genetics on future crop production strategies: from traits to genes, and genes to traits

This paper discusses how a genetical approach to plant physiology can contribute to research underpinning the production of new crop varieties. It highlights the interactions between genetics and plant breeding and how the current advances in genetics and the new science of genomics can contribute to our understanding of the genetical control of key agronomic traits ‐ the process of ‘translating’ traits to identified and mapped genes. Advances in genomics, such as the sequencing of whole genomes and expressed sequence tags, are producing information on genes and gene structures, but without knowing their function. A great deal more biology will be necessary to translate gene structure to function ‐ the process of translating genes to traits. Combining these ‘forward’ and ‘reverse’ genetic approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical and molecular levels, so that the ‘circuitry’ of our crop plants can be elucidated. This will enable plant breeders to manipulate crop phenotype using marker‐assisted breeding or genetic engineering approaches with a precision not previously possible.     

Seven genes of mitochondrial genome enabling differentiation of honeybee subspecies <Emphasis Type="Italic">Apis mellifera</Emphasis>

On the basis of comparative sequence analysis of 12 honeybee mitochondrial genes, seven genes enabling us to differentiate honeybees subspecies of the A, M, C, O evolutionary lineages were found. Applying comparative sequence analysis of ND2 gene mtDNA as an example on a statistically valid sample size, we showed a high level of differentiating ability of this gene and assumed that each of these seven genes probably can be used for differentiation of the subspecies within four evolutionary lineages.     

Understanding quantitative genetic variation

Until recently, it was impracticable to identify the genes that are responsible for variation in continuous traits, or to directly observe the effects of their different alleles. Now, the abundance of genetic markers has made it possible to identify quantitative trait loci (QTL)--the regions of a chromosome or, ideally, individual sequence variants that are responsible for trait variation. What kind of QTL do we expect to find and what can our observations of QTL tell us about how organisms evolve? The key to understanding the evolutionary significance of QTL is to understand the nature of inherited variation, not in the immediate mechanistic sense of how genes influence phenotype, but, rather, to know what evolutionary forces maintain genetic variability.     

Transcriptome profiling in crustaceans as a tool for ecotoxicogenomics

Chemicals released into the environment have the potential to affect various species and it is important to evaluate such chemical effect on ecosystems, including aquatic organisms. Among aquatic organisms, Daphnia magna has been used extensively for acute toxicity or reproductive toxicity tests. Although these types of tests can provide information on hazardous concentrations of chemicals, they provide no information on their mode of action. Recent advances in toxicogenomics, the integration of genomics with toxicology, have the potential to afford a better understanding of the responses of aquatic organisms to pollutants. In a previous study, we developed an oligonucleotide-based DNA microarray with high reproducibility using a Daphnia expressed sequence tag (EST) database. In this study, we increased the number of genes on the array and used it for a careful ecotoxicogenomic assessment of Daphnia magna. The DNA microarray was used to evaluate gene expression profiles of neonate daphnids exposed to beta-naphthoflavone (bNF). Exposure to this chemical resulted in a characteristic gene expression pattern. As the number of the genes on an array was increased, the number of genes that were found to respond to the chemicals was also increased, which made the classification of the toxic chemicals easier and more accurate. This newly developed DNA microarray can be useful for a obtaining a better mechanistic understanding of chemical toxicity effects on a common freshwater organism.     

Comparative genomics of Bacillus thuringiensis phage 0305φ8-36: defining patterns of descent in a novel ancient phage lineage

Background

Members of the familyIridoviridae can cause severe diseases resulting in significant economic and environmental losses. Very little is known about how iridoviruses cause disease in their host. In the present study, we describe the re-analysis of theIridoviridae family of complex DNA viruses using a variety of comparative genomic tools to yield a greater consensus among the annotated sequences of its members.

Results

A series of genomic sequence comparisons were made among, and between theRanavirus andMegalocytivirus genera in order to identify novel conserved ORFs. Of these two genera, theMegalocytivirus genomes required the greatest number of altered annotations. Prior to our re-analysis, the Megalocytivirus species orange-spotted grouper iridovirus and rock bream iridovirus shared 99% sequence identity, but only 82 out of 118 potential ORFs were annotated; in contrast, we predict that these species share an identical complement of genes. These annotation changes allowed the redefinition of the group of core genes shared by all iridoviruses. Seven new core genes were identified, bringing the total number to 26.

Conclusion

Our re-analysis of genomes within theIridoviridae family provides a unifying framework to understand the biology of these viruses. Further re-defining the core set of iridovirus genes will continue to lead us to a better understanding of the phylogenetic relationships between individual iridoviruses as well as giving us a much deeper understanding of iridovirus replication. In addition, this analysis will provide a better framework for characterizing and annotating currently unclassified iridoviruses.     

Identifying causal genes and dysregulated pathways in complex diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.