抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

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Network biology: understanding the cell's functional organization

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.

A General Framework for Weighted Gene Co-Expression Network Analysis

A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

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Global analysis of protein localization in budding yeast

The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

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Global analysis of protein expression in yeast

Differences in gene expression underlie many of the phenotypic variations between related organisms, yet approaches to characterize such differences on a genome-wide scale are not well developed. Here, we introduce the “differential clustering algorithm” for revealing conserved and diverged co-expression patterns. Our approach is applied at different levels of organization, ranging from pair-wise correlations within specific groups of functionally linked genes, to higher-order correlations between such groups. Using the differential clustering algorithm, we systematically compared the transcription program of the fungal pathogen Candida albicans with that of the model organism Saccharomyces cerevisiae. Many of the identified differences are related to the differential requirement for mitochondrial function in the two yeasts. Distinct regulation patterns of cell cycle genes and of amino acid metabolic genes were also revealed and, in some cases, could be linked to the differential appearance of cis-regulatory elements in the gene promoter regions. Our study provides a comprehensive framework for comparative gene expression analysis and a rich source of hypotheses for uncharacterized open reading frames and putative cis-regulatory elements in C. albicans.

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Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program

Cells must adjust their gene expression in order to compete in a constantly changing environment. Two alternative strategies could in principle ensure optimal coordination of gene expression with physiological requirements. First, characters of the internal physiological state, such as growth rate, metabolite levels, or energy availability, could be feedback to tune gene expression. Second, internal needs could be inferred from the external environment, using evolutionary-tuned signaling pathways. Coordination of ribosomal biogenesis with the requirement for protein synthesis is of particular importance, since cells devote a large fraction of their biosynthetic capacity for ribosomal biogenesis. To define the relative contribution of internal vs. external sensing to the regulation of ribosomal biogenesis gene expression in yeast, we subjected S. cerevisiae cells to conditions which decoupled the actual vs. environmentally-expected growth rate. Gene expression followed the environmental signal according to the expected, but not the actual, growth rate. Simultaneous monitoring of gene expression and growth rate in continuous cultures further confirmed that ribosome biogenesis genes responded rapidly to changes in the environments but were oblivious to longer-term changes in growth rate. Our results suggest that the capacity to anticipate and prepare for environmentally-mediated changes in cell growth presented a major selection force during yeast evolution.

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Strategy of Transcription Regulation in the Budding Yeast

Large heterogeneous expression data comprising a variety of cellular conditions hold the promise of a global view of transcriptional regulation. While standard analysis methods have been successfully applied to smaller data sets, large-scale data pose specific challenges that have prompted the development of new and more sophisticated approaches. This paper focuses on one such approach (the Signature Algorithm) and discusses the central challenges in the analysis of large data sets, and how they might be overcome. Biological questions that have been addressed using the Signature Algorithm are highlighted and a summary of other important methods from the literature is provided.

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Challenges and prospects in the analysis of large-scale gene expression data

A method for analyzing and identifying functional linkages between biological processes which are regulated in a coordinate manner. An especially preferred example of a biological process to which the method of the present invention may be applied is the analysis of groups of coordinately regulated genes. More preferably, the present invention analyzes large set of genome-wide expression data or transcriptional data in order to discover coordinately regulated genes, which can be termed 'transcriptional modules'. Optionally and most preferably, the groups of genes are identified with their corresponding cis-regulatory elements. Thus, if the cis-regulatory elements for the transcriptional module are not known, they can optionally be identified with the method of the present invention, for example if functional linkages between genes are known and the associated cis-regulatory element is also available, this information can be used to identify the corresponding transcriptional module.

"Recurrent signature" identifies transcriptional modules

In addition to providing an inventory of genes and their regulatory regions, whole-genome sequencing has paved the way for high-throughput technologies that take snapshots of the regulatory programs governing the expression of these genes. Analyzing many different C. albicans transcription profile experiments revealed global patterns of gene expression that would have been difficult to glean from individual studies. A next step is to compare these global patterns between organisms; comparison to S. cerevisiae global expression patterns is particularly useful because of the large datasets available. The Differential Clustering Algorithm (DCA) is a flexible algorithm that systematically analyzes differences, as well as similarities, in gene expression patterns. It was used to identify important aspects of the transcriptional networks that have been rewired through the evolution of S. cerevisiae and C. albicans. Specific examples of changes in the patterns of expression of cell cycle genes and amino acid biosynthesis genes are detailed. Analysis of the evolution of a promoter sequence motif associated with rapid growth metabolism is also considered.

Jan Ihmels

Papers

Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program

Strategy of Transcription Regulation in the Budding Yeast

Challenges and prospects in the analysis of large-scale gene expression data

"Recurrent signature" identifies transcriptional modules

Global transcription profiles of C. albicans and their comparison with other yeast species.