High-throughput proteomics: protein expression and purification in the postgenomic world.

Proteomics has become a major focus as researchers attempt to understand the vast amount of genomic information. Protein complexity makes identifying and understanding gene function inherently difficult. The challenge of studying proteins in a global way is driving the development of new technologies for systematic and comprehensive analysis of protein structure and function. Protein expression and purification are key processes in these studies, but have typically only been applied on a case-by-case basis to proteins of interest. Researchers are addressing the challenge of parallel expression and purification of large numbers of gene products through the principles of high-throughput screening technologies commonly used in pharmaceutical development. Some of the issues relevant to these approaches are discussed here.     

Inference of biological pathway from gene expression profiles by time delay boolean networks

One great challenge of genomic research is to efficiently and accurately identify complex gene regulatory networks. The development of high-throughput technologies provides numerous experimental data such as DNA sequences, protein sequence, and RNA expression profiles makes it possible to study interactions and regulations among genes or other substance in an organism. However, it is crucial to make inference of genetic regulatory networks from gene expression profiles and protein interaction data for systems biology. This study will develop a new approach to reconstruct time delay Boolean networks as a tool for exploring biological pathways. In the inference strategy, we will compare all pairs of input genes in those basic relationships by their corresponding [Formula: see text]-scores for every output gene. Then, we will combine those consistent relationships to reveal the most probable relationship and reconstruct the genetic network. Specifically, we will prove that [Formula: see text] state transition pairs are sufficient and necessary to reconstruct the time delay Boolean network of [Formula: see text] nodes with high accuracy if the number of input genes to each gene is bounded. We also have implemented this method on simulated and empirical yeast gene expression data sets. The test results show that this proposed method is extensible for realistic networks.     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

Genomics and proteomics in process development: opportunities and challenges

Global gene expression profiling by genomic and proteomic analyses has changed the face of drug discovery and biological research in the past few years. The benefit of these technologies in the area of process development for recombinant protein production has been increasingly realized. This review discusses the application of genome-wide expression profiling tools in the design and optimization of bioprocesses, with the emphasis on the effect on process development of mammalian cell culture. Despite the lack of genome sequence information for most of the relevant mammalian cell lines used, these technologies can be applied during various process development steps. Although there are only a few examples in the literature that present a major improvement in productivity based on genomics and proteomics, further advances in analytical tools and genome sequencing technologies will greatly increase our knowledge at the molecular level and will drive the design of future bioprocesses.     

遗传与基因表达数据的整合—— eQTL的方法及应用

高通量的基因型分析和芯片技术的发展使人们能够进一步研究哪些遗传差异最终影响基因的表达。通过表达数量性状座位(eQTL)作图方法可对基因表达水平的遗传基础进行解析。与传统的QTL分析方法一样, eQTL的主要目标是鉴别表达性状座位所在的染色体区域。但由于表达谱数据成千上万, 而传统的QTL分析方法最多分析几十个性状, 因此需要考虑这类实验设计的特点以及统计分析方法。本文详细介绍了eQTL定位过程及其研究方法, 重点从个体选择、基因芯片实验设计、基因表达数据的获得与标准化、作图方法及结果分析等方面进行了综述, 指出了当前eQTL研究存在的问题和局限性。最后介绍了eQTL研究在估计基因表达遗传率、挖掘候选基因、构建基因调控网络、理解基因间及基因与环境的互作的应用进展。     

Plasmodium berghei: cloning of the circumsporozoite protein gene

A DNA fragment encoding the carboxy terminal 80% of the Plasmodium berghei circumsporozoite protein was selected from a genomic DNA expression library. Sequencing revealed that the P. berghei circumsporozoite protein was similar in overall structure to circumsporozoite proteins from other malaria species, although the central repeat region was unique in comprising two different blocks of tandem peptide repeats: 11 eight amino acid repeats with predominant sequence DPAPPNAN were followed by 16 two amino repeats, predominantly PQ. The P. berghei circumsporozoite protein exhibited limited, but about equal amino acid homology to circumsporozoite proteins from P. knowlesi, P. vivax, and P. falciparum, indicating that P. berghei is not closely related to any of these other malaria species. Cloning of the P. berghei circumsporozoite protein gene will allow direct testing of sporozoite vaccines in mice.     

Mass spectrometry at the interface of proteomics and genomics

With the onset of modern DNA sequencing technologies, genomics is experiencing a revolution in terms of quantity and quality of sequencing data. Rapidly growing numbers of sequenced genomes and metagenomes present a tremendous challenge for bioinformatics tools that predict protein-coding regions. Experimental evidence of expressed genomic regions, both at the RNA and protein level, is becoming invaluable for genome annotation and training of gene prediction algorithms. Evidence of gene expression at the protein level using mass spectrometry-based proteomics is increasingly used in refinement of raw genome sequencing data. In a typical "proteogenomics" experiment, the whole proteome of an organism is extracted, digested into peptides and measured by a mass spectrometer. The peptide fragmentation spectra are identified by searching against a six-frame translation of the raw genomic assembly, thus enabling the identification of hitherto unpredicted protein-coding genomic regions. Application of mass spectrometry to genome annotation presents a range of challenges to the standard workflows in proteomics, especially in terms of proteome coverage and database search strategies. Here we provide an overview of the field and argue that the latest mass spectrometry technologies that enable high mass accuracy at high acquisition rates will prove to be especially well suited for proteogenomics applications.     

Gene manipulation in parasitic helminths

In light of recent growth in available DNA sequence information for a number of parasitic helminths, it is crucial that suitable gene manipulation technologies are developed to facilitate functional genomic studies in these organisms. In this review we discuss recent progress in the development of these technologies in nematode and platyhelminth parasites of medical and veterinary importance. Specifically, the current status of transient transfection, double-stranded RNA interference and antisense RNA as viable techniques for the manipulation of parasitic helminth gene expression is presented. In addition, the potential for the development of stable, or germ-line, transformation methods in these organisms is also discussed.     

The triad of success in personalised medicine: pharmacogenomics, biotechnology and regulatory issues from a Central European perspective

The population of the world has recently passed the 7 billion milestone and as the cost of human genome sequencing is rapidly declining, sequence data of billions of people should be accessible much sooner than anyone would have predicted 10years ago. This will form the basis of personalised medicine. However it is still not clear, even in principle, whether these data, combined with data of the expression of one's genome in various cells and tissues relevant to different diseases, could be used effectively in clinical medicine and healthcare, or in predicting responses to different therapies. Therefore this is an important issue which needs to be addressed before more resources are wasted on less than informative studies and surveys simply because technologies exist. As a typical example, we have selected and summarise here key studies from the biomedical literature that focus on gene expression profiling of the response to biologic therapies in peripheral blood and biopsy samples in autoimmune diseases such as rheumatoid arthritis, spondylarthropathy, inflammatory bowel diseases and psoriasis. We also present the state of the biotechnology market from a European perspective, discuss how spin-offs leverage the power of genomic technologies and describe how they might contribute to personalised medicine. As ethical, legal and social issues are essential in the area of genomics, we analysed these aspects and present here the European situation with a special focus on Hungary. We propose that the synergy of these three issues: pharmacogenomics, biotechnology and regulatory issues should be considered a triad necessary to succeed in personalised medicine.