Search

The mammalian circadian system controls various physiology processes and behavior responses by regulating thousands of circadian genes with rhythmic expressions. In this study, we redefined circadian-regulated genes based on published results in the mouse liver and compared them with other gene groups defined relative to circadian regulations, especially the non-circadian-regulated genes expressed in liver at multiple molecular levels from gene position to protein expression based on integrative analyses of different datasets from the literature. Based on the intra-tissue analysis, the liver circadian genes or LCGs show unique features when compared to other gene groups. First, LCGs in general have less neighboring genes and larger in both genomic and 3'-UTR lengths but shorter in CDS (coding sequence) lengths. Second, LCGs have higher mRNA and protein abundance, higher temporal expression variations, and shorter mRNA half-life. Third, more than 60% of LCGs form major co-expression clusters centered in four temporal windows: dawn, day, dusk, and night. In addition, larger and smaller LCGs are found mainly expressed in the day and night temporal windows, respectively, and we believe that LCGs are well-partitioned into the gene expression regulatory network that takes advantage of gene size, expression constraint, and chromosomal architecture. Based on inter-tissue analysis, more than half of LCGs are ubiquitously expressed in multiple tissues but only show rhythmical expression in one or limited number of tissues. LCGs show at least three-fold lower expression variations across the temporal windows than those among different tissues, and this observation suggests that temporal expression variations regulated by the circadian system is relatively subtle as compared with the tissue expression variations formed during development. Taken together, we suggest that the circadian system selects gene parameters in a cost effective way to improve tissue-specific functions by adapting temporal variations from the environment over evolutionary time scales.

Background: Housekeeping (HK) genes are ubiquitously expressed in all tissue/cell types and constitute a basal transcriptome for the maintenance of basic cellular functions. Partitioning transcriptomes into HK and tissue-specific (TS) genes relatively is fundamental for studying gene expression and cellular differentiation. Although many studies have aimed at large-scale and thorough categorization of human HK genes, a meaningful consensus has yet to be reached., Results: We collected two latest gene expression datasets (both EST and microarray data) from public databases and analyzed the gene expression profiles in 18 human tissues that have been well-documented by both two data types. Benchmarked by a manually-curated HK gene collection (HK408), we demonstrated that present data from EST sampling was far from saturated, and the inadequacy has limited the gene detectability and our understanding of TS expressions. Due to a likely over-stringent threshold, microarray data showed higher false negative rate compared with EST data, leading to a significant underestimation of HK genes. Based on EST data, we found that 40.0% of the currently annotated human genes were universally expressed in at least 16 of 18 tissues, as compared to only 5.1% specifically expressed in a single tissue. Our current EST-based estimate on human HK genes ranged from 3,140 to 6,909 in number, a ten-fold increase in comparison with previous microarray-based estimates., Conclusion: We concluded that a significant fraction of human genes, at least in the currently annotated data depositories, was broadly expressed. Our understanding of tissue-specific expression was still preliminary and required much more large-scale and high-quality transcriptomic data in future studies. The new HK gene list categorized in this study will be useful for genome-wide analyses on structural and functional features of HK genes.

Background: Newly-evolved multiplex sequencing technology has been bringing transcriptome sequencing into an unprecedented depth. Millions of transcript tags now can be acquired in a single experiment through parallelization. The significant increase in throughput and reduction in cost required us to address some fundamental questions, such as how many transcript tags do we have to sequence for a given transcriptome? How could we estimate the total number of unique transcripts for different cell types (transcriptome diversity) and the distribution of their copy numbers (transcriptome dynamics)? What is the probability that a transcript with a given expression level to be detected at a certain sampling depth?, Methodology/principal Findings: We developed a statistical model to evaluate these parameters based on transcriptome-sampling data. Three mixture models were exploited for their potentials to model the sampling frequencies. We demonstrated that relative abundances of all transcripts in a transcriptome follow the generalized inverse Gaussian distribution. The widely known beta and gamma distributions failed to fulfill the singular characteristics of relative abundance distribution, i.e., highly skewed toward zero and with a long tail. An estimator of transcriptome diversity and an analytical form of sampling growth curve were proposed in a coherent framework. Experimental data fitted this model very well and Monte Carlo simulations based on this model replicated sampling experiments in a remarkable precision., Conclusions: Taking human embryonic stem cell as a prototype, we demonstrated that sequencing tens of thousands of transcript tags in an ordinary EST/SAGE experiment was far from sufficient. In order to fully characterize a human transcriptome, millions of transcript tags had to be sequenced. This model lays a statistical basis for transcriptome-sampling experiments and in essence can be used in all sampling-based data.

Background. Constitution in traditional Chinese medicine (TCM) plays a key role in the genesis, development, and prognosis of diseases. Phlegm-dampness constitution (PDC) is one of the nine constitutions in TCM, susceptible to metabolic disorders, which is mainly manifested by profuse phlegm, loose abdomen, and greasy face. Epidemiologic, genomic, and epigenetic studies have been carried out in previous works, confirming that PDC represents a distinctive population with microcosmic changes related to metabolic disorders. However, whether long noncoding RNAs (lncRNAs) play a regulatory role in metabolic disease in subjects with PDC remains largely unknown. We aimed to investigate distinct lncRNA and mRNA expression signatures and lncRNA-mRNA regulatory networks in the phlegm-dampness constitution (PDC). Methods. The peripheral blood mononuclear cells (PBMCs) were isolated from the subjects with PDC (n = 13) and balanced constitution (BC) (n = 9). The profiles of lncRNAs and mRNAs in PBMCs were analyzed using microarray and further validated with RT-qPCR. Subsequently, pathway analysis was performed to investigate the function of differentially expressed mRNAs by using Ingenuity Pathway Analysis (IPA). Results. Results suggested that some mRNAs, which were regulated by the differentially expressed lncRNAs, were mainly enriched in lipid metabolism and immune inflammation-related pathways. This was consistent with the molecular characteristics of previous studies, indicating that the clinical characteristics of metabolic disorders in PDC might be regulated by lncRNAs. Furthermore, by making coexpression network construction as well as cis-regulated target gene analysis, several lncRNA-mRNA pairs with potential regulatory relationships were identified by bioinformatic analyses, including RP11-317J10.2-CA3, RP11-809C18.3-PIP4K2A, LINC0069-RFTN1, TTTY15-ARHGEF9, and AC135048.13-ORAI3. Conclusions. This study first revealed that the expression characteristics of lncRNAs/mRNAs may be potential biomarkers, indicating that the distinctive physical and clinical characteristics of PDC might be partially attributed to the specific expression signatures of lncRNAs/mRNAs. [ABSTRACT FROM AUTHOR]