Nuclear import of Cdk/cyclin complexes: identification of distinct mechanisms for import of Cdk2/cyclin E and Cdc2/cyclin B1.

Reversible phosphorylation of nuclear proteins is required for both DNA replication and entry into mitosis. Consequently, most cyclin-dependent kinase (Cdk)/cyclin complexes are localized to the nucleus when active. Although our understanding of nuclear transport processes has been greatly enhanced by the recent identification of nuclear targeting sequences and soluble nuclear import factors with which they interact, the mechanisms used to target Cdk/cyclin complexes to the nucleus remain obscure; this is in part because these proteins lack obvious nuclear localization sequences. To elucidate the molecular mechanisms responsible for Cdk/cyclin transport, we examined nuclear import of fluorescent Cdk2/cyclin E and Cdc2/cyclin B1 complexes in digitonin-permeabilized mammalian cells and also examined potential physical interactions between these Cdks, cyclins, and soluble import factors. We found that the nuclear import machinery recognizes these Cdk/cyclin complexes through direct interactions with the cyclin component. Surprisingly, cyclins E and B1 are imported into nuclei via distinct mechanisms. Cyclin E behaves like a classical basic nuclear localization sequence-containing protein, binding to the alpha adaptor subunit of the importin-alpha/beta heterodimer. In contrast, cyclin B1 is imported via a direct interaction with a site in the NH2 terminus of importin-beta that is distinct from that used to bind importin-alpha.

Nutritional Control of L1 Arrest and Recovery in Caenorhabditis elegans by Insulin-like Peptides and Signaling

Animals must coordinate development with fluctuating nutrient availability. Nutrient availability governs post-embryonic development in Caenorhabditis elegans: larvae that hatch in the absence of food do not initiate post-embryonic development but enter "L1 arrest" (or "L1 diapause") and can survive starvation for weeks, while rapidly resume normal development once get fed. Insulin-like signaling (IIS) has been shown to be a key regulator of L1 arrest and recovery. However, the C. elegans genome encodes 40 insulin-like peptides (ILPs), and it is unknown which peptides participate in nutritional control of L1 arrest and recovery. Work in other contexts has identified putative receptor agonists and antagonists, but the extent of specificity versus redundancy is unclear beyond this distinction.

We measured mRNA expression dynamics with high temporal resolution for all 40 insulin-like genes during entry into and recovery from L1 arrest. Nutrient availability influences expression of the majority of insulin-like genes, with variable dynamics suggesting complex regulation. We identified 13 candidate agonists and 8 candidate antagonists based on expression in response to nutrient availability. We selected ten candidate agonists (daf-28, ins-3, ins-4, ins-5, ins-6, ins-7, ins-9, ins-26, ins-33 and ins-35) for further characterization in L1 stage larvae. We used destabilized reporter genes to determine spatial expression patterns. Expression of candidate agonists was largely overlapping in L1 stage larvae, suggesting a role of the intestine, chemosensory neurons ASI and ASJ, and the interneuron PVT in systemic control of L1 development. Transcriptional regulation of candidate agonists was most significant in the intestine, as if nutrient uptake was a more important influence on transcription than sensory perception. Scanning in the 5' upstream promoter region of these 40 ILPs, We found that transcription factor PQM-1 and GATA putative binding sites are depleted in the promoter region of antagonists. A novel motif was also found to be over-represented in ILPs.

Phenotypic analysis of single and compound deletion mutants did not reveal effects on L1 recovery/developmental dynamics, though simultaneous disruption of ins-4 and daf-28 extended survival of L1 arrest without enhancing thermal tolerance, while overexpression of ins-4, ins-6 or daf-28 shortened L1 survival. Simultaneous disruption of several ILPs showed a temperature independent, transient dauer phenotype. These results revealed the relative redundancy and specificity among agonistic ILPs.

TGF- β and steroid hormone (SH) signaling have been reported to control the dauer formation along with IIS. Our preliminary results suggest they may also mediate the IIS control of L1 arrest and recovery, as the expression of several key components of TGF-β and SH signaling pathway genes are negatively regulated by DAF-16, and loss-of-function of these genes partially represses daf-16 null phenotype in L1 arrest, and causes a retardation in L1 development.

In summary, my dissertation study focused on the IIS, characterized the dynamics and sites of ILPs expression in response to nutrient availability, revealed the function of specific agonistic ILPs in L1 arrest, and suggested potential cross-regulation among IIS, TGF-β signaling and SH signaling in controlling L1 arrest and recovery. These findings provide insights into how post-embryonic development is governed by insulin-like signaling and nutrient availability.

Conformational kinetics reveals affinities of protein conformational states.

Most biological reactions rely on interplay between binding and changes in both macromolecular structure and dynamics. Practical understanding of this interplay requires detection of critical intermediates and determination of their binding and conformational characteristics. However, many of these species are only transiently present and they have often been overlooked in mechanistic studies of reactions that couple binding to conformational change. We monitored the kinetics of ligand-induced conformational changes in a small protein using six different ligands. We analyzed the kinetic data to simultaneously determine both binding affinities for the conformational states and the rate constants of conformational change. The approach we used is sufficiently robust to determine the affinities of three conformational states and detect even modest differences in the protein's affinities for relatively similar ligands. Ligand binding favors higher-affinity conformational states by increasing forward conformational rate constants and/or decreasing reverse conformational rate constants. The amounts by which forward rate constants increase and reverse rate constants decrease are proportional to the ratio of affinities of the conformational states. We also show that both the affinity ratio and another parameter, which quantifies the changes in conformational rate constants upon ligand binding, are strong determinants of the mechanism (conformational selection and/or induced fit) of molecular recognition. Our results highlight the utility of analyzing the kinetics of conformational changes to determine affinities that cannot be determined from equilibrium experiments. Most importantly, they demonstrate an inextricable link between conformational dynamics and the binding affinities of conformational states.

Suppression of conformational heterogeneity at a protein-protein interface.

Staphylococcal protein A (SpA) is an important virulence factor from Staphylococcus aureus responsible for the bacterium's evasion of the host immune system. SpA includes five small three-helix-bundle domains that can each bind with high affinity to many host proteins such as antibodies. The interaction between a SpA domain and the Fc fragment of IgG was partially elucidated previously in the crystal structure 1FC2. Although informative, the previous structure was not properly folded and left many substantial questions unanswered, such as a detailed description of the tertiary structure of SpA domains in complex with Fc and the structural changes that take place upon binding. Here we report the 2.3-Å structure of a fully folded SpA domain in complex with Fc. Our structure indicates that there are extensive structural rearrangements necessary for binding Fc, including a general reduction in SpA conformational heterogeneity, freezing out of polyrotameric interfacial residues, and displacement of a SpA side chain by an Fc side chain in a molecular-recognition pocket. Such a loss of conformational heterogeneity upon formation of the protein-protein interface may occur when SpA binds its multiple binding partners. Suppression of conformational heterogeneity may be an important structural paradigm in functionally plastic proteins.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

A complex intronic enhancer regulates expression of the CFTR gene by direct interaction with the promoter.

Genes can maintain spatiotemporal expression patterns by long-range interactions between cis-acting elements. The cystic fibrosis transmembrane conductance regulator gene (CFTR) is expressed primarily in epithelial cells. An element located within a DNase I-hypersensitive site (DHS) 10 kb into the first intron was previously shown to augment CFTR promoter activity in a tissue-specific manner. Here, we reveal the mechanism by which this element influences CFTR transcription. We employed a high-resolution method of mapping DHS using tiled microarrays to accurately locate the intron 1 DHS. Transfection of promoter-reporter constructs demonstrated that the element displays classical tissue-specific enhancer properties and can independently recruit factors necessary for transcription initiation. In vitro DNase I footprinting analysis identified a protected region that corresponds to a conserved, predicted binding site for hepatocyte nuclear factor 1 (HNF1). We demonstrate by electromobility shift assays (EMSA) and chromatin immunoprecipitation (ChIP) that HNF1 binds to this element both in vitro and in vivo. Moreover, using chromosome conformation capture (3C) analysis, we show that this element interacts with the CFTR promoter in CFTR-expressing cells. These data provide the first insight into the three- dimensional (3D) structure of the CFTR locus and confirm the contribution of intronic cis-acting elements to the regulation of CFTR gene expression.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi.

Although cell cycle control is an ancient, conserved, and essential process, some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins. Here, we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer. Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F, which is still maintained in many basal fungi. We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F, much like extant viral oncogenes. Consistent with this hypothesis, we show that SBF can regulate promoters with E2F binding sites in budding yeast.