Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.     

Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior Knowledge

Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partial data sets are also disregarded due to their nonstationarity. This paper proposes a novel algorithm to identify cyclic-process-involved genes by integrating the prior knowledge with the gene expression analysis. The proposed algorithm is applied on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, respectively. Biological evidences are found to validate the roles of the discovered genes in cell cycle and circadian rhythm. Dendrograms are presented to cluster the identified genes and to reveal expression patterns. It is corroborated that the proposed novel identification scheme provides a valuable technique for unveiling pathways related to cyclic processes.     

Transplanted Drosophila excretory tubules maintain circadian clock cycling out of phase with the host

Circadian rhythms in behaviors and physiological processes are driven by conserved molecular mechanisms involving the rhythmic expression of clock genes in the brains of animals [1]. The persistence of similar molecular rhythms in peripheral tissues in vitro [2] [3] suggests that these tissues contain self-sustained circadian clocks that may be linked to rhythmic physiological functions. It is not known how brain and peripheral clocks are organized into a synchronized timing system; however, it has been assumed that peripheral clocks submit to a master clock in the brain. To address this matter we examined the expression of two clock genes, period (per) and timeless (tim), in host and transplanted abdominal organs of Drosophila. We found that excretory organs in tissue culture display free-running, light-sensitive oscillations in per and tim gene activity indicating that they house self-sustained circadian clocks. To test for humoral factors, we monitored cycling of the TIM protein in excretory tubules transplanted into host flies entrained to an opposite light-dark cycle. We show that the clock protein in the donor tubules cycled out of phase with that in the host tubules, indicating that different organs may cycle independently, despite sharing the same hormonal milieu. We suggest that one way to achieve circadian coordination of physiological sub-systems in higher animals may be through the direct entrainment of light-sensitive clocks by environmental signals.     

Genome-Wide Methylation and Gene Expression Changes in Newborn Rats following Maternal Protein Restriction and Reversal by Folic Acid

A large body of evidence from human and animal studies demonstrates that the maternal diet during pregnancy can programme physiological and metabolic functions in the developing fetus, effectively determining susceptibility to later disease. The mechanistic basis of such programming is unclear but may involve resetting of epigenetic marks and fetal gene expression. The aim of this study was to evaluate genome-wide DNA methylation and gene expression in the livers of newborn rats exposed to maternal protein restriction. On day one postnatally, there were 618 differentially expressed genes and 1183 differentially methylated regions (FDR 5%). The functional analysis of differentially expressed genes indicated a significant effect on DNA repair/cycle/maintenance functions and of lipid, amino acid metabolism and circadian functions. Enrichment for known biological functions was found to be associated with differentially methylated regions. Moreover, these epigenetically altered regions overlapped genetic loci associated with metabolic and cardiovascular diseases. Both expression changes and DNA methylation changes were largely reversed by supplementing the protein restricted diet with folic acid. Although the epigenetic and gene expression signatures appeared to underpin largely different biological processes, the gene expression profile of DNA methyl transferases was altered, providing a potential link between the two molecular signatures. The data showed that maternal protein restriction is associated with widespread differential gene expression and DNA methylation across the genome, and that folic acid is able to reset both molecular signatures.     

Identification of CDK- and cyclin-like proteins in the eye of Bulla gouldiana

The ocular circadian rhythm in the eye of Bulla gouldiana is generated by a rhythm in membrane potential of retinal neurons that is driven by alterations in potassium conductance. Since potassium conductance may be modulated by the phosphorylation of potassium channels, the circadian rhythm may reflect rhythmic changes in protein kinase activity. Furthermore, the circadian rhythm recorded from the Bulla eye can be phase shifted by agents that affect protein synthesis and protein phosphorylation on tyrosine residues. Interestingly, the eukaryotic cell division residues. Interestingly, the eukaryotic cell division cycle is generated by similar processes. Rhythmic cell division is regulated by periodic synthesis and degradation of a protein, cyclin, and periodic tyrosine phosphorylation of a cyclin-dependent kinase (cdk), p34^cdc2. The interaction between these two proteins results in rhythmic kinase activity of p34^cdc2. Both cyclin and p34^cdc2 are pat of two diverse gene families, some of whose members have been localized to postmitotic cell types with no function yet determined. In the current work, we identify proteins similar to the cdks and cyclin in the eye of Bulla. Neither of these ocular proteins are found in mitotic cells in Bulla, and the cdk-like protein (p40) is specific to the eye. Furthermore, the concentration of the cyclin-like protein (p66) is affected by treatments that phase shift the circadain rhythm. The identification of cdk and cyclin-like proteins in the Bulla eye is consistent with the hypothesis that the biochemical mechanism responsible for generating the ocular circadian rhythm in Bulla is related to the biochemical mechnism that regulates the eukaryotic cell division cycle. 1994 John Wiley & Sons, Inc.     

基于基因差异表达的微弱信号识别疾病相关功能

当两组样本间基因表达的差异程度较低或样本量较少时,采用通常的错误发现率(falsediscovery rate,FDR)控制水平(如5%或10%),可能无法识别足够多的差异表达基因以进行后续的功能富集分析。然而,功能富集分析对差异表达基因中的错误发现具有一定的稳健性。所以,采用较低的FDR控制水平(即允许较高的FDR)识别差异表达基因,可能可以可靠地发现疾病相关功能。本文分析了5套研究乳腺癌转移的基因表达谱,通过其中差异表达信号较强的3套数据,论证了即使差异表达基因的FDR达到25%,功能富集分析的结果仍具有较高的稳健性。然后,在另外2套差异表达信号微弱的数据中,采用25%的FDR控制水平筛选差异表达基因来进行功能富集分析,并与前述3套数据的功能富集结果做比较。结果显示,采用较低的FDR控制水平筛选差异表达基因,仍然可以可靠地识别乳腺癌转移相关功能。分析结果也提示,在乳腺癌转移过程中,一些功能较为宽泛的生物学过程(如细胞分裂、细胞周期和DNA复制等)整体受到了扰动,反映出乳腺癌转移是一种涉及广泛基因表达改变的系统性疾病。     

Differing responses of G2-related genes during differentiation of HL60 cells induced by TPA or DMSO

Differentiation leads to the cessation of cellular proliferation, but little is known about the molecular mechanisms of growth arrest. We compared the effect of two differentiation inducers, 12-o-tetradecanoyl 13-acetate (TPA) and dimethyl sulfoxide (DMSO) on both the cell-cycle and the modulation of G2-related genes in synchronized HL60 cells. TPA treatment of HL60 cells resulted in G1 arrest within 24 h. In contrast, the cell cycling of DMSO-treated cells was initially accelerated and they progressed to the second cycle before accumulating in the G1 phase. Expression of cyclin B, cdc25, wee1 and cdc2 was studied during cell cycle arrest by Northern blot hybridization. Expression of cyclin B, cdc25 and cdc2 fluctuated in association with cell cycle progression towards the G2/M phase, while wee1 expression remained constant in untreated cells. These four genes were highly expressed in TPA-treated cells for the first 12 h, but drastic down-regulation was seen at 18 h and expression became undetectable after 24 h. In contrast, no remarked changes of gene expression were seen in DMSO-treated cells. These findings suggest that cell cycle progression along with the initial process of differentiation in response to TPA differs from the response to DMSO and that the down-regulation of cdc2 expression by TPA-treated HL60 cells contributes to endorsement of G1 arrest.     

从microarray时序表达数据识别周期表达基因

根据周期表达基因的周期性和峰值特点,提出了一种将microarray时序表达数据划分为若干个基因表达周期,并对周期内的峰值特点进行评估以识别周期表达基因的方法,能有效减小microarray实验时的噪声干扰。选取了三组广泛使用的时序表达数据和一组可靠的周期表达基因集合对该方法的效果进行了测试,并与三种典型的周期表达基因识别方法的效果进行了比较。该方法能有效地从各种microarray时序表达数据中识别周期表达基因。