Bioinformatics in the post-genome era

Recent years saw a dramatic increase in genomic and proteomic data in public archives. Now with the complete genome sequences of human and other species in hand, detailed analyses of the genome sequences will undoubtedly improve our understanding of biological systems and at the same time require sophisticated bioinformatic tools. Here we review what computational challenges are ahead and what are the new exciting developments in this exciting field.     

Functional genomics in the post-genome era

As the biomedical research community enters the post-genome era, studying gene expression patterns and phenotypes in model organisms will be an important part of analyzing the role of genes in human health and disease. New technologies involving DNA chips will improve the ability to evaluate the differential expression of a large number of genes simultaneously. Also, new approaches for generating mutations in mice will significantly decrease the cost and increase the rate of generating mutant lines that model human disease.     

Arabidopsis map-based cloning in the post-genome era

Map-based cloning is an iterative approach that identifies the underlying genetic cause of a mutant phenotype. The major strength of this approach is the ability to tap into a nearly unlimited resource of natural and induced genetic variation without prior assumptions or knowledge of specific genes. One begins with an interesting mutant and allows plant biology to reveal what gene or genes are involved. Three major advances in the past 2 years have made map-based cloning in Arabidopsis fairly routine: sequencing of the Arabidopsis genome, the availability of more than 50,000 markers in the Cereon Arabidopsis Polymorphism Collection, and improvements in the methods used for detecting DNA polymorphisms. Here, we describe the Cereon Collection and show how it can be used in a generic approach to mutation mapping in Arabidopsis. We present the map-based cloning of the VTC2 gene as a specific example of this approach.     

Needs for new plant-derived pharmaceuticals in the post-genome era: an industrial view in drug research and development

Plant metabolites have been the successful source of drugs and provided considerable value not only to the pharmaceutical industry but also to human health problems. Although pharmaceutical companies significantly decreased their activities in natural product discovery during the past few decades, various multidisciplinary approaches have been made to create new opportunities for finding innovative plant derived pharmaceuticals in post-genome era. Strategies to integrate the knowledge on medicinal plants into rational drug screening, the unique biodiversity of plant metabolites into random drug screening, and the chemical diversity of plant metabolites into combinatorial chemistry have been reviewed with concrete examples. Innovative biotechnologies in plant cell and tissue cultures, and the latest achievements in metabolic engineering and genetic modification should significantly improve the production sustainability and efficiency of plant-derived pharmaceuticals.     

后基因组时代的结核新疫苗研究

利用蛋白质组技术、生物信息学的DNA芯片等技术筛选适当的保护性抗原,重点在亚单位疫苗的DNA疫苗方面构建预防性和治疗性的疫苗,并配合使用亚单位疫苗,DNA疫苗和减毒活疫苗是值得重视的方向。     

A protein interaction map of the mitotic spindle

The mitotic spindle consists of a complex network of proteins that segregates chromosomes in eukaryotes. To strengthen our understanding of the molecular composition, organization, and regulation of the mitotic spindle, we performed a system-wide two-hybrid screen on 94 proteins implicated in spindle function in Saccharomyces cerevisiae. We report 604 predominantly novel interactions that were detected in multiple screens, involving 303 distinct prey proteins. We uncovered a pattern of extensive interactions between spindle proteins reflecting the intricate organization of the spindle. Furthermore, we observed novel connections between kinetochore complexes and chromatin-modifying proteins and used phosphorylation site mutants of NDC80/TID3 to gain insights into possible phospho-regulation mechanisms. We also present analyses of She1p, a novel spindle protein that interacts with the Dam1 kinetochore/spindle complex. The wealth of protein interactions presented here highlights the extent to which mitotic spindle protein functions and regulation are integrated with each other and with other cellular activities.     

Mathematical modelling in the post-genome era: understanding genome expression and regulation--a system theoretic approach

This paper introduces a mathematical framework for modelling genome expression and regulation. Starting with a philosophical foundation, causation is identified as the principle of explanation of change in the realm of matter. Causation is, therefore, a relationship, not between components, but between changes of states of a system. We subsequently view genome expression (formerly known as 'gene expression') as a dynamic process and model aspects of it as dynamic systems using methodologies developed within the areas of systems and control theory. We begin with the possibly most abstract but general formulation in the setting of category theory. The class of models realised are state-space models, input--output models, autoregressive models or automata. We find that a number of proposed 'gene network' models are, therefore, included in the framework presented here. The conceptual framework that integrates all of these models defines a dynamic system as a family of expression profiles. It becomes apparent that the concept of a 'gene' is less appropriate when considering mathematical models of genome expression and regulation. The main claim of this paper is that we should treat (model) the organisation and regulation of genetic pathways as what they are: dynamic systems. Microarray technology allows us to generate large sets of time series data and is, therefore, discussed with regard to its use in mathematical modelling of gene expression and regulation.     

A proteome-wide protein interaction map for Campylobacter jejuni

Background

Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide.

Results

Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms.

Conclusion

The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.     

A genomic approach of the hepatitis C virus generates a protein interaction map

The hepatitis C virus (HCV) causes severe liver disease, including liver cancer. A vaccine preventing HCV infection has not yet been developed, and, given the increasing number of infected people, this virus is now considered a major public-health problem. The HCV genome is a plus-stranded RNA that encodes a single polyprotein processed into at least 10 mature polypeptides. So far, only the interaction between the protease NS3 and its cofactor, NS4A, which is involved in the processing of the non-structural region, has been extensively studied. Our work was aimed at constructing a protein interaction map of HCV. A classical two-hybrid system failed to detect any interactions between mature HCV polypeptides, suggesting incorrect folding, expression or targetting of these proteins. We therefore developed a two-hybrid strategy, based on exhaustive screens of a random genomic HCV library. Using this method, we found known interactions, such as the capsid homodimer and the protease dimer, NS3-NS4A, as well as several novel interactions such as NS4A-NS2. Thus, our results are consistent with the idea that the use of a random genomic HCV library allows the selection of correctly folded viral protein fragments. Interacting domains of the viral polyprotein are identified, opening the possibility of developing specific anti-viral agents, based on their ability to modulate these interactions.