Lac repressor, the first discovered transcriptional regulator, is a homodimer protein and presumably would bind to a perfectly symmetric palindromic operator site. However, it was shown that lac repressor binds to its wild-type operator O1 in an intrinsic asymmetric fashion, depending on its central spacer configuration, which makes it behave like a "heterodimer" protein. In this issue, Zuo et al. used Spec-seq approach coupled with site-directed m[Detail] ...
The determination of cell fate is one of the key questions of developmental biology. Recent experiments showed that feedforward regulation is a novel feature of regulatory networks that controls reversible cellular transitions. However, the underlying mechanism of feedforward regulation-mediated cell fate decision is still unclear. Therefore, using experimental data, we develop a full mathematical model of the molecular network responsible for cell fate selection in budding yeast. To validate our theoretical model, we first investigate the dynamical behaviors of key proteins at the Start transition point and the G1/S transition point; a crucial three-node motif consisting of cyclin (Cln1/2), Substrate/Subunit Inhibitor of cyclin-dependent protein kinase (Sic1) and cyclin B (Clb5/6) is considered at these points. The rapid switches of these important components between high and low levels at two transition check points are demonstrated reasonably by our model. Many experimental observations about cell fate decision and cell size control are also theoretically reproduced. Interestingly, the feedforward regulation provides a reliable separation between different cell fates. Next, our model reveals that the threshold for the amount of WHIskey (Whi5) removed from the nucleus is higher at the Reentry point in pheromone-arrested cells compared with that at the Start point in cycling cells. Furthermore, we analyze the hysteresis in the cell cycle kinetics in response to changes in pheromone concentration, showing that Cln3 is the primary driver of reentry and Cln1/2 is the secondary driver of reentry. In particular, we demonstrate that the inhibition of Cln1/2 due to the accumulation of Factor ARrest (Far1) directly reinforces arrest. Finally, theoretical work verifies that the three-node coherent feedforward motif created by cell FUSion (Fus3), Far1 and STErile (Ste12) ensures the rapid arrest and reversibility of a cellular state. The combination of our theoretical model and the previous experimental data contributes to the understanding of the molecular mechanisms of the cell fate decision at the G1 phase in budding yeast and will stimulate further biological experiments in future.
Lac repressor, the first discovered transcriptional regulator, has been shown to confer multiple modes of binding to its operator sites depending on the central spacer length. Other homolog members in the LacI/GalR family (PurR and YcjW) cannot bind their operator sites with similar structural flexibility. To decipher the underlying mechanism for this unique property, we used Spec-seq approach combined with site-directed mutagenesis to quantify the DNA binding specificity of multiple hybrids of lacI and PurR. We find that lac repressor’s recognition di-residues YQ and its hinge helix loop regions are both critical for its structural flexibility. Also, specificity profiling of the whole lac operator suggests that a simple additive model from single variants suffice to predict other multivariant sites’ energy reasonably well, and the genome occupancy model based on this specificity data correlates well with in vivo lac repressor binding profile.
Over the last decade the 3C-based (Chromosome Conformation Capture, 3C) approaches have been developed to describe the frequency of chromatin interaction. The invention of Hi-C allows us to obtain genome-wide chromatin interaction map. However, it is challenging to develop efficient and robust analytical tools to interpret the Hi-C data. Here we present a new method called Clustering based Hi-C Domain Finder (CHDF), which is based on the difference of interaction intensity inside/outside domains, to identify Hi-C domains. We also compared CHDF with existing methods including Direction Index (DI) and HiCseg. CHDF can define more chromatin domains validated by higher resolution local chromatin structure data (Chromosome Conformation Capture Carbon Copy (5C) data). Using Hi-C data of lower sequencing depth, chromatin structure identified by CHDF is closer to that discovered by data of higher sequencing depth. Furthermore, the implement of CHDF is faster than the other two. Using CHDF, we are potentially able to discover more hints and clues about chromatin structural elements at domain level.
Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and kidney renal clear cell carcinoma (KIRC) studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.
Hepatitis B is a well-known risk factor for the development of liver cancer and is closely associated with patient morbidity and mortality. Viral mutants and variants have the potential to evade immune response and prolong infection, and thus it is crucial to develop a methodology for the rapid identification of multi-strain hepatitis infections in patients. Here we describe a method based on selective region amplification of viral genome and deep sequencing, which may be used for rapid identification of multi-strain hepatitis B virus (HBV) infection in patients. The method works even with significantly low amounts of patients’ serum samples, where the wet-lab procedures take about 1.5 days, followed by a quick bioinformatic analysis to reveal the final results. Our method can potentially be applied to the rapid and reliable identification of multi-strain HBV infection and help improve treatment regiments.