Natalia Maltsev

The BioPAX community standard for pathway data sharing

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction

The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) system has been designed to support comparative analysis of sequenced genomes and to generate metabolic reconstructions based on chromosomal sequences and metabolic modules from the EMP/MPW family of databases. This system contains data derived from about 40 completed or nearly completed genomes. Sequence homologies, various ORF-clustering algorithms, relative gene positions on the chromosome and placement of gene products in metabolic pathways (metabolic reconstruction) can be used for the assignment of gene functions and for development of overviews of genomes within WIT. The integration of a large number of phylogenetically diverse genomes in WIT facilitates the understanding of the physiology of different organisms.

A reconstruction of the metabolism of Methanococcus jannaschii from sequence data.

The interpretation of the Methanococcus jannaschii genome will inevitably require many years of effort. This initial attempt to connect the sequence data to aspects of known biochemistry and to provide an overview of what is already apparent from the sequence data will be refined. Numerous issues remain that can be resolved only by direct biochemical analysis. Let us draw the readers attention to just a few that might be considered central: (1) We are still missing key enzymes from the glycolytic pathway, and the conjecture is that this is due to ADP-dependency. The existence of glycolytic activity in the cell-free extract should be tested. (2) The issue of whether the Calvin cycle is present needs to be examined. (3) We need to determine whether the 2-oxoglutarate synthase (ferredoxin-dependent) (EC 1.2.7.3) activity is present. (4) The issue of whether cyclic 2,3-bisphosphate is detectable in the cell-free extracts needs to be checked. If it is, this result would confirm our assertion of the two pathways controlling synthesis and degradation of cyclic 2,3-bisphosphate.

The metabolic pathway collection from EMP: the enzymes and metabolic pathways database.

The Enzymes and Metabolic Pathways database (EMP) is an encoding of the contents of over 10 000 original publications on the topics of enzymology and metabolism. This large body of information has been transformed into a queryable database. An extraction of over 1800 pictorial representations of metabolic pathways from this collection is freely available on the World Wide Web. We believe that this collection will play an important role in the interpretation of genetic sequence data, as well as offering a meaningful framework for the integration of many other forms of biological data.

Identification of Francisella tularensis Himar1-Based Transposon Mutants Defective for Replication in Macrophages

ABSTRACT Francisella tularensis, the etiologic agent of tularemia in humans, is a potential biological threat due to its low infectious dose and multiple routes of entry. F. tularensis replicates within several cell types, eventually causing cell death by inducing apoptosis. In this study, a modified Himar1 transposon (HimarFT) was used to mutagenize F. tularensis LVS. Approximately 7,000 Kmr clones were screened using J774A.1 macrophages for reduction in cytopathogenicity based on retention of the cell monolayer. A total of 441 candidates with significant host cell retention compared to the parent were identified following screening in a high-throughput format. Retesting at a defined multiplicity of infection followed by in vitro growth analyses resulted in identification of approximately 70 candidates representing 26 unique loci involved in macrophage replication and/or cytotoxicity. Mutants carrying insertions in seven hypothetical genes were screened in a mouse model of infection, and all strains tested appeared to be attenuated, which validated the initial in vitro results obtained with cultured macrophages. Complementation and reverse transcription-PCR experiments suggested that the expression of genes adjacent to the HimarFT insertion may be affected depending on the orientation of the constitutive groEL promoter region used to ensure transcription of the selective marker in the transposon. A hypothetical gene, FTL_0706, postulated to be important for lipopolysaccharide biosynthesis, was confirmed to be a gene involved in O-antigen expression in F. tularensis LVS and Schu S4. These and other studies demonstrate that therapeutic targets, vaccine candidates, or virulence-related genes may be discovered utilizing classical genetic approaches in Francisella.

Copy number variants and infantile spasms: evidence for abnormalities in ventral forebrain development and pathways of synaptic function

Infantile spasms (ISS) are an epilepsy disorder frequently associated with severe developmental outcome and have diverse genetic etiologies. We ascertained 11 subjects with ISS and novel copy number variants (CNVs) and combined these with a new cohort with deletion 1p36 and ISS, and additional published patients with ISS and other chromosomal abnormalities. Using bioinformatics tools, we analyzed the gene content of these CNVs for enrichment in pathways of pathogenesis. Several important findings emerged. First, the gene content was enriched for the gene regulatory network involved in ventral forebrain development. Second, genes in pathways of synaptic function were overrepresented, significantly those involved in synaptic vesicle transport. Evidence also suggested roles for GABAergic synapses and the postsynaptic density. Third, we confirm the association of ISS with duplication of 14q12 and maternally inherited duplication of 15q11q13, and report the association with duplication of 21q21. We also present a patient with ISS and deletion 7q11.3 not involving MAGI2. Finally, we provide evidence that ISS in deletion 1p36 may be associated with deletion of KLHL17 and expand the epilepsy phenotype in that syndrome to include early infantile epileptic encephalopathy. Several of the identified pathways share functional links, and abnormalities of forebrain synaptic growth and function may form a common biologic mechanism underlying both ISS and autism. This study demonstrates a novel approach to the study of gene content in subjects with ISS and copy number variation, and contributes further evidence to support specific pathways of pathogenesis.

PUMA2—grid-based high-throughput analysis of genomes and metabolic pathways

The PUMA2 system (available at ) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.

Microvesicles and intercellular communication in the context of parasitism

There is a rapidly growing body of evidence that production of microvesicles (MVs) is a universal feature of cellular life. MVs can incorporate microRNA (miRNA), mRNA, mtDNA, DNA and retrotransposons, camouflage viruses/viral components from immune surveillance, and transfer cargo between cells. These properties make MVs an essential player in intercellular communication. Increasing evidence supports the notion that MVs can also act as long-distance vehicles for RNA molecules and participate in metabolic synchronization and reprogramming eukaryotic cells including stem and germinal cells. MV ability to carry on DNA and their general distribution makes them attractive candidates for horizontal gene transfer, particularly between multi-cellular organisms and their parasites; this suggests important implications for the co-evolution of parasites and their hosts. In this review, we provide current understanding of the roles played by MVs in intracellular pathogens and parasitic infections. We also discuss the possible role of MVs in co-infection and host shifting.

The metabolic pathway collection: an update

The Metabolic Pathway Collection from EMP is an extraction of data from the larger Enzymes and Metabolic Pathways database (EMP). This extraction has been made publicly available in the hope that others will find it useful for a variety of purposes. The original release in October 1995 contained 1814 distinct pathways. The current collection contains 2180. Metabolic reconstructions for the first completely sequenced organisms-Haemophilus influenzae,Mycoplasma genitalium,Saccharomyces cerevisiaeandMethanococcus janaschii-are all included in the current release. All of the pathways in the collections are available as ASCII files in the form generated by the main curator, Evgeni Selkov. In addition, we are offering a more structured encoding of a subset of the collection; our initial release of this subcollection includes all of the pathways inMycoplasma genitalium, and we ultimately intend to offer the entire collection in this form as well.

Representation of function : the next step.

The research community now has access to a number of w x complete genomes 1–3 , and several more have already been completed or are nearing completion. The incredible successes of the past eighteen months have set the stage for one of the most exciting scientific quests of the twentieth century: to characterize the functions of all the genes in these phylogenetically diverse microbial genomes, and then to characterize the dynamic behavior of these complex systems in terms of these basic components. This task certainly will not be completed by the turn of the century, but it will be well advanced. The analysis growing out of these microbial genomes ultimately will be seen as the foundation used to understand the more complex issues presented by more complex organisms. Our grasp of many of the fundamental mechanisms of life will emerge from the wealth of data produced by sequencing a phylogenetically diverse set of microbes. The community is now trying to organize the newly available data to support the characterization of function. Clearly, the detailed experiments required to identify the specific functions of the biological components will be extensive. What general strategies should be employed, and what data processing and computational support could be used to reduce the effort? A basic step in documenting the function of systems is the hierarchical decomposition of components. The overall system is decomposed into a set of interconnected components, these components are further decomposed into sets of interconnected subcomponents, and so forth. The actual breakdown of a functional unit into interconnecting subcomponents cannot always be done either uniquely or precisely. The reality of the situation often involves thousands of entities with complex interconnections and dependencies. The hierarchical decomposition, however, is of critical importance in comprehending complex systems. We believe that a hierarchical decomposition into interconnected subcomponents is a good abstraction for working with genomic sequence data for organisms. At the lowest level, enzymes and other protein complexes are formed by aggregating several polypeptides. At a somewhat higher level, enzymes group conceptually into a pathway that transforms a set of inputs to a set of outputs. Pathways may themselves be aggregated into larger subsystems. We acknowledge, however, that a hierarchical decomposition — while conveying real insights — also leaves out essential aspects. It is a means for classifying the parts. It does not convey the dynamic interplay of the parts as the whole system moves from one state into other states with completely different operating characteristics. The hierarchical decomposition we are discussing attempts to classify components based on functional groupings. Specifically, parts with quite different characteristics are grouped together if they participate in a single complex that plays a coherent functional role. Other meaningful hierarchical clusterings also exist. The most significant is the hierarchical clustering based on evolutionary history of the components. Thus, understanding the relationships of existing genes that encode polypeptides in the context of Ž ‘‘protein families’’ that is, in terms of their evolution . from common ancestral sequences must certainly be one of our strongest tools for obtaining an accurate picture of biological systems. The hierarchy based on homology complements the hierarchy based on functional systems: each reveals key features of the system, and each plays an important role. One possible, oversimplified view of ‘‘functional characterization of genes’’ is to fill out the two hierarchical representations — one hierarchy decomposing an organism into increasingly lower-level subcomponents, and a second hierarchy capturing the evolution of thousands of distinct components and functional roles from a relatively small ancestral set. Certainly, access to complete and accurate versions of both hierarchies would offer a wonderful starting point for comprehension of biological systems. Indeed, we argue for precisely this notion as a reasonable short-range objective. However, it would be