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EVOLUTION OF METABOLISM: MICROBIAL GENOMES

EVOLUTION OF METABOLISM: MICROBIAL GENOMES
新陈代谢的进化:微生物基因组
批准号:
6295239
负责人:
CHRISTIAN FORST
金额:
$9.25万
依托单位国家:
美国
项目类别:
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-02-01 至 1999-07-31

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中文摘要
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英文摘要
The abundance of genomic information by completely sequenced microbial genomes provides a starting point for new insights in the multi-level organization of organisms and their evolution*. We take the next step, away from annotating genes by functions of expressed proteins, towards a more comprehensive description of metabolic pathways of proteins acting as enzymes processing substrates [64]. By comparing pathogenic microbes with free living organisms the pathogenicity can be related to functions which are missing in autotrophs. H. influencae and E. coli are a well known example for such relationships. Similar results can be reported for free living B. subtilis and pathogenic Streptococci. This study aids in drug design and disease treatment by interfering with host-interaction factors and pathways of pathogens. We also pursue the development of methods to construct phylogenies between organisms on the level of metabolic pathways. By a graph-theoretical approach combined with sequence information, pathways of different organisms are related to each other. First results have been obtained by applying this method on pathways related to electron transfer. This suggests the chimeric origin of archaea and thus an early evolution of such pathways [65]. On sequence level and gene cluster organization a closer relationship to bacteria than to eucarya can be reported. These accomplishments open the door for reconstructing the minimal set of genes, regulatory and metabolic pathways of the universal ancestor of all three kingdoms, archaea, bacteria and eucarya.
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Deciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems Approach
Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections
Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections
EVOLUTION OF METABOLISM
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