preparative changes in rat intestine and lung for birth

Transcript profiling using microarray analysis was performed using lung and intestine RNA from a single animal from each of 10 litters at a given gestational age. Array experiments were performed by the Genomics CoreLab, Cambridge Comprehensive Biomedical Research Centre. Briefly, the Affymetrix Rat Genome 230.2 GeneChip was employed. Total RNA was processed using the standard Affymetrix one-cycle target labeling and hybridization protocols. Data were pre-normalized using robust multiarray averaging and normalization was achieved using the LIMMA software package. Normalized transcript abundance data were compared between E16 and E20 using two independent methods: the Cyber-T algorithm and Rank Product Analysis. Transcripts that were significantly regulated in both Cyber-T (Bayes p value < 0.001, posterior probability of differential expression, ppde > 0.99) and Rank Product Analysis (p<0.0001) and showed an absolute fold change of over 5 were defined as differentially expressed. Microarray data were annotated using the NetAffx Analysis Center (Affymetrix). To generate the list of up-or down-regulated genes only Entrez genes or Unigene clusters were considered if at least one probe set gave an unambiguous match.本研究采用微阵列分析技术开展转录组谱研究，实验材料取自10窝孕鼠各1只在特定胎龄时的肺与肠组织RNA。微阵列实验由剑桥综合生物医学研究中心基因组核心实验室（Genomics CoreLab, Cambridge Comprehensive Biomedical Research Centre）完成。简言之，本实验采用Affymetrix公司的大鼠基因组230.2基因芯片（Affymetrix Rat Genome 230.2 GeneChip）。总RNA的处理严格遵循Affymetrix标准的单循环靶标标记与杂交实验流程。数据先通过稳健多阵列平均法（robust multiarray averaging）进行预归一化，随后借助LIMMA软件包完成最终归一化。将E16与E20胎龄的标准化转录本丰度数据采用两种独立方法进行差异分析：Cyber-T算法（Cyber-T algorithm）与秩积分析法（Rank Product Analysis）。同时满足以下条件的转录本被定义为差异表达转录本：在Cyber-T分析中贝叶斯p值（Bayes p value）小于0.001且差异表达后验概率（posterior probability of differential expression, ppde）大于0.99，在秩积分析中p值小于0.0001，且绝对倍数变化超过5。微阵列数据的注释工作通过Affymetrix公司的NetAffx分析中心（NetAffx Analysis Center）完成。为构建上调或下调基因列表，仅保留那些至少有一个探针组可实现明确匹配的Entrez基因（Entrez genes）或Unigene基因簇（Unigene clusters）。