模式蓝细菌Synechocystis PCC 6803基因组中有高达46%的基因编码未注释功能的假定蛋白(HP), 如此大量的功能不明HP成为对蓝细菌进行深入理论研究和应用开发的严重障碍。采用传统实验手段确定HP功能会消耗大量的人力和时间，而新开发的权重基因调控网络分析方法（WGCNA）被证实能更高效地用于基因调控网络构建、功能模块抽提及核心基因鉴定。基于此，本申请提出模式蓝细菌Synechocystis HP基因功能解析的新策略：以常见的高盐胁迫处理蓝细菌，获取蛋白组和转录组数据，利用WGCNA方法构建基因调控网络，筛选出高盐胁迫响应相关HP基因；并结合基因敲除或过表达突变株的表型分析鉴定高盐胁迫响应的核心HP的功能。研究结果有助于阐明Synechocystis PCC 6803适应高盐胁迫的机制，也为其它蓝细菌中众多HP基因的功能解析提供可借鉴的新思路及有效解决途径。

The genome of model cyanobacterium, Synechocystis sp. PCC 6803, encodes 46% hypothetical proteins. The large proportion of unknown genes become one of the great challenges for both cyanobacteria genomic research and further understanding cellular metabolism and protein of microorganisms.This proposal aims to explore the characterization of hypothetical proteins responsive to salt stress in Synechocystis PCC 6803. In the study, preliminary efforts will: i) systematically assess authenticity of the hypothetical proteins in the Synechocystis genome under salt stress condition using a quantitative proteomics approach with isobaric tag for relative and absolute quantification (iTRAQ) technique and liquid chromatography–tandem mass spectrometry (LC–MS/MS); ii) establish gene expression networks of Synechocystis using proteomic and transcriptomic data collected from salt stress condition using a Weighted Gene Co-expression Network Analysis (WGCNA) approach, so that the possible relationship of hypothetical proteins with functionally known proteins and salt stress can be inferred; iii) identify the functions of 3-5 selected core hypothetical proteins with connections with more than 10 genes in salt stress correlated functional modules detected in WGCNA,using high throughput gene knockout and konckin techniques previously developed in our lab, which may represent important core stress response in Synechocystis; The analysis will result in functional assignments to stress-responsive core hypothetical proteins in the Synechocystis genome. The study will provide an improved annotation for Synechocystis PCC 6803, and also demonstrat that high-throughput methodologies, such as mass spectrometry-based proteomics and transcriptional networks using transcriptome datasets, could be good supplementations in deciphering function of hypothetical proteins.