A study on promoter characteristics of head-to-head genes in Saccharomyces cerevisiae

A low-temperature-responsive element involved in the regulation of the Arabidopsis thaliana At1g71850/At1g71860 divergent gene pair

The bidirectional promoter of the Arabidopsis thaliana gene pair At1g71850/At1g71860 harbors low-temperature-responsive elements, which participate in anti-correlated transcription regulation of the driving genes in response to environmental low temperature. A divergent gene pair is defined as two adjacent genes organized head to head in opposite orientation, sharing a common promoter region. Divergent gene pairs are mainly coexpressed, but some display opposite regulation. The mechanistic basis of such anti-correlated regulation is not well understood. Here, the regulation of the Arabidopsis thaliana gene pair At1g71850/At1g71860 was investigated. Semi-quantitative RT-PCR and Genevestigator analyses showed that while one of the pair was upregulated by exposure to low temperature, the same treatment downregulated the other. Promoter::GUS fusion transgenes were used to show that this behavior was driven by a bidirectional promoter, which harbored an as-1 motif, associated with the low-temperature response; mutation of this sequence produced a significant decrease in cold-responsive expression. With regard to the as-1 motif in the native orientation repressing the promoter's low-temperature responsiveness, the same as-1 motif introduced in the reverse direction showed a slight enhancement in the promoter's responsiveness to low-temperature exposure, indicating that the orientation of the motif was important for the promoter's activity. These findings provide new insights into the complex transcriptional regulation of bidirectional gene pairs as well as plant stress response.

Regulation of cell-to-cell variability in divergent gene expression

Cell-to-cell variability (noise) is an important feature of gene expression that impacts cell fitness and development. The regulatory mechanism of this variability is not fully understood. Here we investigate the effect on gene expression noise in divergent gene pairs (DGPs). We generated reporters driven by divergent promoters, rearranged their gene order, and probed their expressions using time-lapse fluorescence microscopy and single-molecule fluorescence in situ hybridization (smFISH). We show that two genes in a co-regulated DGP have higher expression covariance compared with the separate, tandem and convergent configurations, and this higher covariance is caused by more synchronized firing of the divergent transcriptions. For differentially regulated DGPs, the regulatory signal of one gene can stochastically ‘leak' to the other, causing increased gene expression noise. We propose that the DGPs' function in limiting or promoting gene expression noise may enhance or compromise cell fitness, providing an explanation for the conservation pattern of DGPs.

Gene expression noise affects cell fitness and development. Here, Yan et al. show that co-regulated divergent gene pairs (DGPs) suppress uncorrelated gene expression noise due to more synchronized transcription firing, and differentially regulated DGPs enhance gene expression noise due to transcription leakage.

Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases

Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments.

Fungal genes in context: genome architecture reflects regulatory complexity and function

Gene context determines gene expression, with local chromosomal environment most influential. Comparative genomic analysis is often limited in scope to conserved or divergent gene and protein families, and fungi are well suited to this approach with low functional redundancy and relatively streamlined genomes. We show here that one aspect of gene context, the amount of potential upstream regulatory sequence maintained through evolution, is highly predictive of both molecular function and biological process in diverse fungi. Orthologs with large upstream intergenic regions (UIRs) are strongly enriched in information processing functions, such as signal transduction and sequence-specific DNA binding, and, in the genus Aspergillus, include the majority of experimentally studied, high-level developmental and metabolic transcriptional regulators. Many uncharacterized genes are also present in this class and, by implication, may be of similar importance. Large intergenic regions also share two novel sequence characteristics, currently of unknown significance: they are enriched for plus-strand polypyrimidine tracts and an information-rich, putative regulatory motif that was present in the last common ancestor of the Pezizomycotina. Systematic consideration of gene UIR in comparative genomics, particularly for poorly characterized species, could help reveal organisms’ regulatory priorities.

PRASA: an integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.

YGA: identifying distinct biological features between yeast gene sets

The advance of high-throughput experimental technologies generates many gene sets with different biological meanings, where many important insights can only be extracted by identifying the biological (regulatory/functional) features that are distinct between different gene sets (e.g. essential vs. non-essential genes, TATA box-containing vs. TATA box-less genes, induced vs. repressed genes under certain biological conditions). Although many servers have been developed to identify enriched features in a gene set, most of them were designed to analyze one gene set at a time but cannot compare two gene sets. Moreover, the features used in existing servers were mainly focused on functional annotations (GO terms), pathways, transcription factor binding sites (TFBSs) and/or protein-protein interactions (PPIs). In yeast, various important regulatory features, including promoter bendability, nucleosome occupancy, 5'-UTR length, and TF-gene regulation evidence, are available but have not been used in any enrichment analysis servers. This motivates us to develop the Yeast Genes Analyzer (YGA), a web server that simultaneously analyzes various biological (regulatory/functional) features of two gene sets and performs statistical tests to identify the distinct features between them. Many well-studied gene sets such as essential, stress-response, TATA box-containing and cell cycle genes were pre-compiled in YGA for users, if they have only one gene set, to compare with. In comparison with the existing enrichment analysis servers, YGA tests more comprehensive regulatory features (e.g. promoter bendability, nucleosome occupancy, 5'-UTR length, experimental evidence of TF-gene binding and TF-gene regulation) and functional features (e.g. PPI, GO terms, pathways and functional groups of genes, including essential/non-essential genes, stress-induced/-repressed genes, TATA box-containing/-less genes, occupied/depleted proximal-nucleosome genes and cell cycle genes). Furthermore, YGA uses various statistical tests to provide objective comparison measures. The two major contributions of YGA, comprehensive features and statistical comparison, help to mine important information that cannot be obtained from other servers. The sophisticated analysis tools of YGA can identify distinct biological features between two gene sets, which help biologists to form new hypotheses about the underlying biological mechanisms responsible for the observed difference between these two gene sets. YGA can be accessed from the following web pages: http://cosbi.ee.ncku.edu.tw/yga/ and http://yga.ee.ncku.edu.tw/.