Predicting Housekeeping Genes Based on Fourier Analysis

Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

The Fission Yeast Homeodomain Protein Yox1p Binds to MBF and Confines MBF-Dependent Cell-Cycle Transcription to G1-S via Negative Feedback

The regulation of the G1- to S-phase transition is critical for cell-cycle progression. This transition is driven by a transient transcriptional wave regulated by transcription factor complexes termed MBF/SBF in yeast and E2F-DP in mammals. Here we apply genomic, genetic, and biochemical approaches to show that the Yox1p homeodomain protein of fission yeast plays a critical role in confining MBF-dependent transcription to the G1/S transition of the cell cycle. The yox1 gene is an MBF target, and Yox1p accumulates and preferentially binds to MBF-regulated promoters, via the MBF components Res2p and Nrm1p, when they are transcriptionally repressed during the cell cycle. Deletion of yox1 results in constitutively high transcription of MBF target genes and loss of their cell cycle–regulated expression, similar to deletion of nrm1. Genome-wide location analyses of Yox1p and the MBF component Cdc10p reveal dozens of genes whose promoters are bound by both factors, including their own genes and histone genes. In addition, Cdc10p shows promiscuous binding to other sites, most notably close to replication origins. This study establishes Yox1p as a new regulatory MBF component in fission yeast, which is transcriptionally induced by MBF and in turn inhibits MBF-dependent transcription. Yox1p may function together with Nrm1p to confine MBF-dependent transcription to the G1/S transition of the cell cycle via negative feedback. Compared to the orthologous budding yeast Yox1p, which indirectly functions in a negative feedback loop for cell-cycle transcription, similarities but also notable differences in the wiring of the regulatory circuits are evident

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

Anchoring skeletal muscle development and disease: the role of ankyrin repeat domain containing proteins in muscle physiology

The ankyrin repeat is a protein module with high affinity for other ankyrin repeats based on strong Van der Waals forces. The resulting dimerization is unusually resistant to both mechanical forces and alkanization, making this module exceedingly useful for meeting the extraordinary demands of muscle physiology. Many aspects of muscle function are controlled by the superfamily ankyrin repeat domain containing proteins, including structural fixation of the contractile apparatus to the muscle membrane by ankyrins, the archetypical member of the family. Additionally, other ankyrin repeat domain containing proteins critically control the various differentiation steps during muscle development, with Notch and developmental stage-specific expression of the members of the Ankyrin repeat and SOCS box (ASB) containing family of proteins controlling compartment size and guiding the various steps of muscle specification. Also, adaptive responses in fully formed muscle require ankyrin repeat containing proteins, with Myotrophin/V-1 ankyrin repeat containing proteins controlling the induction of hypertrophic responses following excessive mechanical load, and muscle ankyrin repeat proteins (MARPs) acting as protective mechanisms of last resort following extreme demands on muscle tissue. Knowledge on mechanisms governing the ordered expression of the various members of superfamily of ankyrin repeat domain containing proteins may prove exceedingly useful for developing novel rational therapy for cardiac disease and muscle dystrophies

Amplitude control of cell-cycle waves by nuclear import

Propagation of waves of biochemical activities through consecutive stages of the cell cycle is essential to execute the steps of cell division in a strict temporal order. Mechanisms that ensure the proper amplitude and timing of these waves are poorly understood. Using a synthetic gene circuit, we show that a transcriptional activator driven by yeast cell-cycle promoters propagates transcriptional oscillations with substantial damping. Although regulated nuclear translocation has been implicated in the timing of oscillatory events, mathematical analysis shows that increasing the rate of nuclear transport is an example of a general regulatory principle, which enhances the fidelity of wave propagation. Indeed, increasing the constitutive import rate of the activator counteracts the damping of waves and concurrently preserves the intensity of the signal. In contrast to the regulatory range of nuclear transport, the range of mRNA turnover considerably limits transcriptional wave propagation. This classification of cellular processes outlines potential regulatory mechanisms that can contribute to faithful transmission of oscillations at different stages of the cell cycle

Polo kinase controls cell-cycle-dependent transcription by targeting a coactivator protein

Polo kinases play crucial conserved roles in controlling the eukaryotic cell cycle through orchestrating several events during mitosis1,2. An essential element of cell cycle control is exerted by altering the expression of key regulators3. Here, we demonstrate an important role for the polo kinase, Cdc5p, in controlling cell cycle-dependent gene expression which is critical for the execution of mitosis in the model eukaryote Saccharomyces cerevisiae. In particular, we find that Cdc5p is temporally recruited to promoters of the cell cycle-regulated CLB2 gene cluster, where it targets the Mcm1p-Fkh2p-Ndd1p transcription factor complex, through direct phosphorylation of the coactivator protein Ndd1p. This phosphorylation event is required for the normal temporal expression of cell cycle-regulated genes such as CLB2 and SWI5 in G2/M phases. Furthermore, severe defects in cell division occur in the absence of Cdc5p-mediated phosphorylation of Ndd1p. Thus, polo kinase is required for the production of key mitotic regulators, in addition to previously defined roles in controlling other mitotic events