Glioma-specific alternative RNA splicing: A role for polypyrimidine tract binding protein-1

Alternative RNA splicing plays an integral role in cell fate determination and function, especially in the cells of the brain. Errors in RNA processing contribute to diseases such as cancer, where it leads to the production of oncogenic proteins or the loss of tumor suppressors. In silica mining suggests that hundreds of splice isoforms are misexpressed in the glial cell-derived glioma. However, there is little experimental evidence of the prevalence and contribution of these changes and whether they contribute to the formation and progression of this devastating malignancy. To determine the frequency of these aberrant events, global profiling of alternative RNA splice patterns in glioma and nontumor brain was conducted using an exon array. Most splicing changes were less than 5-fold in magnitude and 14 cassette exon events were validated, including 7 previously published events. To determine the possible causes of missplicing, the differential expression levels of splicing factors in these two tissues were also analyzed. Six RNA splicing factors had greater than 2-fold changes in expression. The highest differentially expressed factor was polypyrimidine tract binding protein-1 (PTB). Evaluation by immunohistochemistry determined that this factor was elevated in both early and late stages of glioma. Glial cell-specific PTB expression in the adult brain led me to examine the role of PTB in gliomagenesis. Downregulation of PTB slowed glioma cell proliferation and migration and enhanced cell adhesion to fibronectin and vitronectin. To determine whether PTB was affecting these processes through splicing, genome-wide exon expression levels were correlated with PTB levels. Surprisingly, previously reported PTB target transcripts were insensitive to changes in PTB levels in both patient samples and PTB-depleted glioma cells. Only one validated glioma-specific splice target, RTN4/Nogo, had a significant PTB-mediated splicing change. Downregulation of PTB enhanced inclusion of its alternative exon 3, which encodes an auxiliary domain within a neurite inhibitor protein. Overexpression of this splice isoform in glioma cells slowed proliferation in a manner similar to that observed in PTB knockdown cells. In summary, aberrant expression of splicing factors such as PTB in glioma may elicit changes in splicing patterns that enhance tumorigenesis. ^

Antigen-specific proteolytic antibodies

The antigen recognition site of antibodies is composed of residues contributed by the variable domains of the heavy and light chain subunits (VL and VH domains). VL domains can catalyze peptide bond hydrolysis independent of VH domains (Mei S et al. J Biol Chem. 1991 Aug 25;266(24):15571-4). VH domains can bind antigens noncovalently independent of V L domains (Ward et al. Nature. 1989 Oct 12;341(6242):544-6). This dissertation describe the specific hydrolysis of fusion proteins containing the hepatitis C virus coat protein E2 by recombinant hybrid Abs composed of the heavy chain of a high affinity anti-E2 IgG1 paired with light chains expressing promiscuous catalytic activity. The proteolytic activity was evident from electrophoresis assays using recombinant E2 substrates containing glutathione S-transferase (E2-GST) or FLAG peptide (E2-FLAG) tags. The proteolytic reaction proceeded more rapidly in the presence of the hybrid IgG1 compared to the unpaired light chain, consistent with accelerated peptide bond hydrolysis due to noncovalent VH domain-E2 recognition. An active site-directed inhibitor of serine proteases inhibited the proteolytic activity of the hybrid IgG, indicating a serine protease mechanism. Binding studies confirmed that the hybrid IgG retained detectable noncovalent E2 recognition capability, although at a level smaller than the wildtype anti-E2 IgG. Immunoblotting of E2-FLAG treated with the hybrid IgG suggested a scissile bond within E2 located ∼11 kD from the N terminus of the protein. E2-GST was hydrolyzed by the hybrid IgG at peptide bonds located in the GST tag. The differing cleavage pattern of E2-FLAG and E2-GST can be explained by the split-site model of catalysis, in which conformational differences in the E2 fusion protein substrates position alternate peptide bonds in register with the antibody catalytic subsite despite a common noncovalent binding mechanism. This is the first proof-of principle that the catalytic activity of a light chain can be rendered antigen-specific by pairing with a noncovalently binding heavy chain subunit. ^

Identification of a conserved cluster in the RH domain of GRK critical for activation by GPCRs

One of the most critical aspects of G Protein Coupled Receptors (GPCRs) regulation is their rapid and acute desensitization following agonist stimulation. Phosphorylation of these receptors by GPCR kinases (GRK) is a major mechanism of desensitization. Considerable evidence from studies of rhodopsin kinase and GRK2 suggests there is an allosteric docking site for the receptor distinct from the GRK catalytic site. While the agonist-activated GPCR appears crucial for GRK activation, the molecular details of this interaction remain unclear. Recent studies suggested an important role for the N- and C-termini and domains in the small lobe of the kinase domain in allosteric activation; however, neither the mechanism of action of that site nor the RH domain contributions have been elucidated. To search for the allosteric site, we first indentified evolutionarily conserved sites within the RH and kinase domains presumably deterministic of protein function employing evolutionary trace (ET) methodology and crystal structures of GRK6. Focusing on a conserved cluster centered on helices 3, 9, and 10 in the RH domain, key residues of GRK5 and 6 were targeted for mutagenesis and functional assays. We found that a number of double mutations within helices 3, 9, and 10 and the N-terminus markedly reduced (50–90%) the constitutive phosphorylation of the β-2 Adrenergic Receptor (β2AR) in intact cells and phosphorylation of light-activated rhodopsin (Rho*) in vitro as compared to wild type (WT) GRK5 or 6. Based on these results, we designed peptide mimetics of GRK5 helix 9 both computationally and through chemical modifications with the goal of both confirming the importance of helix 9 and developing a useful inhibitor to disrupt the GPCR-GRK interaction. Several peptides were found to block Rho* phosphorylation by GRK5 including the native helix 9 sequence, Peptide Builder designed-peptide preserving only the key ET residues, and chemically locked helices. Most peptidomimetics showed inhibition of GRK5 activity greater than 80 % with an IC50 of ∼ 30 µM. Alanine scanning of helix 9 has further revealed both essential and non-essential residues for inhibition. Importantly, substitution of Arg 169 by an alanine in the native helix 9-based peptide gave an almost complete inhibition at 30 µM with an IC50 of ∼ 10 µM. In summary we report a previously unrecognized crucial role for the RH domain of GRK5 and 6, and the subsequent identification of a lead peptide inhibitor of protein-protein interaction with potential for specific blockade of GPCR desensitization. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

TRIM24-REGULATED ESTROGEN RESPONSE IS DEPENDENT ON SPECIFIC HISTONE MODIFICATIONS IN BREAST CANCER CELLS

In this dissertation, I discovered that function of TRIM24 as a co-activator of ERα-mediated transcriptional activation is dependent on specific histone modifications in tumorigenic human breast cancer-derived MCF7 cells. In the first part, I proved that TRIM24-PHD finger domain, which recognizes unmethylated histone H3 lysine K4 (H3K4me0), is critical for ERα-regulated transcription. Therefore, when LSD1-mediated demethylation of H3K4 is inhibited, activation of TRIM24-regulated ERα target genes is greatly impaired. Importantly, I demonstrated that TRIM24 and LSD1 are cyclically recruited to estrogen responsive elements (EREs) in a time-dependent manner upon estrogen induction, and depletion of their expression exert corresponding time-dependent effect on target gene activation. I also identified that phosphorylation of histone H3 threonine T6 disrupts TRIM24 from binding to the chromatin and from activating ERα-regulated targets. In the second part, I revealed that TRIM24 depletion has additive effect to LSD1 inhibitor- and Tamoxifen-mediated reduction in survival and proliferation in breast cancer cells.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

CONFORMATIONAL DYNAMICS OF K-RAS AND H-RAS PROTEINS: IS THERE FUNCTIONAL SPECIFICITY AT THE CATALYTIC DOMAIN?

Ras proteins serve as crucial signaling modulators in cell proliferation through their ability to hydrolyze GTP and exist in a GTP “on” state and GTP “off” state. There are three different human Ras isoforms: H-ras, N-ras and K-ras (4A and 4B). Although their sequence identity is very high at the catalytic domain, these isoforms differ in their ability to activate different effectors and hence different signaling pathways. Much of the previous work on this topic has attributed this difference to the hyper variable region of Ras proteins, which contains most of the sequence variance among the isoforms and encodes specificity for differential distribution in the membrane. However, we hypothesize that sequence variation on lobe II of Ras catalytic domain alters dynamics and leads to differential preference for different effectors or modulators. In this work, we used all atom molecular dynamics to analyze the dynamics in the catalytic domain of H-ras and K-ras. We have also analyzed the dynamics of a transforming mutant of H-ras and K-ras and further studied the dynamics of an effectorselective mutant of H-ras. Collectively we have determined that wild type K-ras is more dynamic than H-ras and that the structure of the effector binding loop more closely resembles that of the T35S Raf-selective mutant, possibly giving us a new view and insight into the v mode of effector specificity. Furthermore we have determined that specific mutations at the same location perturb the conformational equilibrium differently in H-ras and K-ras and that an enhanced oncogenic potential may arise from different structural perturbations for each point mutation of a specific isoform.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

The transcriptional regulation of the {\it neu\/} gene: Examples of activation, repression, and cell-specific expression

The neu oncogene encodes a growth factor receptor-like protein, p185, with an intrinsic tyrosine kinase activity. A single point mutation, an A to T transversion resulting in an amino acid substitution from valine to glutamic acid, in the transmembrane domain of the rat neu gene was found to be responsible for the transforming and tumorigenic phenotype of the cells that carry it. In contrast, the human proto-neu oncogene is frequently amplified in tumors and cell lines derived from tumors and the human neu gene overexpression/amplification in breast and ovarian cancers is known to correlate with poor patient prognosis. Examples of the human neu gene overexpression in the absence of gene amplification have been observed, which may suggest the significant role of the transcriptional and/or post-transcriptional control of the neu gene in the oncogenic process. However, little is known about the transcriptional mechanisms which regulate the neu gene expression. In this study, three examples are presented to demonstrate the positive and negative control of the neu gene expression.^ First, by using band shift assays and methylation interference analyses, I have identified a specific protein-binding sequence, AAGATAAAACC ($-$466 to $-$456), that binds a specific trans-acting factor termed RVF (for EcoRV factor on the neu promoter). The RVF-binding site is required for maximum transcriptional activity of the rat neu promoter. This same sequence is also found in the corresponding regions of both human and mouse neu promoters. Furthermore, this sequence can enhance the CAT activity driven by a minimum promoter of the thymidine kinase gene in an orientation-independent manner, and thus it behaves as an enhancer. In addition, Southwestern (DNA-protein) blot analysis using the RVF-binding site as a probe points to a 60-kDa polypeptide as a potential candidate for RVF.^ Second, it has been reported that the E3 region of adenovirus 5 induces down-regulation of epidermal growth factor (EGF) receptor through endocytosis. I found that the human neu gene product, p185, (an EGF receptor-related protein) is also down-regulated by adenovirus 5, but via a different mechanism. I demonstrate that the adenovirus E1a gene is responsible for the repression of the human neu gene at the transcriptional level.^ Third, a differential expression of the neu gene has been found in two cell model systems: between the mouse fibroblast Swiss-Webster 3T3 (SW3T3) and its variant NR-6 cells; and between the mouse liver tumor cell line, Hep1-a, and the mouse pancreas tumor cell line, 266-6. Both NR-6 and 266-6 cell lines are not able to express the neu gene product, p185. I demonstrate that, in both cases, the transcriptional repression of the neu gene may account for the lack of the p185 expression in these two cell lines. ^

Molecular interactions of the central conserved domain of p53

p53 mutations are the most commonly observed genetic alterations in human cancers to date. A majority of these point mutations cluster in four evolutionarily conserved domains spanning amino acids 100-300. This region of p53 has been called its central conserved, or conformational domain. This domain of p53 is also targeted by the SV40 T antigen. Mutation, as well as interaction with SV40 T antigen results in inactivation of p53. We hypothesized that mutations and SV40 T antigen disrupt p53 function by interfering with the molecular interactions of the central conserved domain. Using a chimeric protein consisting of the central conserved domain of wild-type p53 (amino acids 115-295) and a protein A affinity tail, we isolated several cellular proteins that interact specifically with this domain of p53. These proteins range in size from 30K to 90K M$\rm\sb{r}.$ We also employed the p53 fusion protein to demonstrate that the central conserved domain of p53 possesses sequence-specific DNA-binding activity. Interestingly, the cellular proteins binding to the central conserved domain of p53 enhance the sequence-specific DNA-binding activity of full length p53. Partial purification of the individual proteins binding to the conformational domain of p53 by utilizing a sodium chloride step-gradient enabled further characterization of two proteins: (1) a 42K M$\rm\sb{r}$ protein that eluted at 0.5M NaCl, and bound DNA nonspecifically, and (2) a 35K M$\rm\sb{r}$ protein eluting into the 1.0M NaCl fraction, capable of enhancing the sequence-specific DNA-binding activity of p53. In order to determine the physiologic relevance of the molecular interactions of the conformational domain of p53, we examined the biochemical processes underlying the TNF-$\alpha$ mediated growth suppression of the NSCLC cell line H460. While growth suppression was accompanied by enhanced sequence-specific p53-DNA binding activity in TNF-$\alpha$ treated H460 nuclei, there was no increase in p53 protein levels. Furthermore, p35 was upregulated in TNF-$\alpha$ treated H460 cells, suggesting that the enhanced p53-DNA binding seen in these cells may be mediated by p35. Our studies define two novel interactions involving the central conserved domain of p53 that appear to be functionally relevant: (1) sequence-specific DNA-binding, and (2) interaction with other cellular proteins. ^

Regulation of p53 antiproliferative function by transactivation domain phosphorylation at threonine 18 and serine 20

We investigated the induction and physiological role of Thr18 and Ser20 phosphorylation of p53 in response to DNA damage caused by treatment with ionizing (IR) or ultraviolet (UV) radiation. Polyclonal antibodies specifically recognizing phospho-Thr18 and phospho-Ser20 were used to detect p53 phosphorylation in vivo. Analyses of five wild-type (wt) p53 containing cell lines revealed lineage specific differences in phosphorylation of Thr18 and Ser20 after treatment with IR or UV. Importantly, the phosphorylation of p53 at Thr18 and Ser20 correlated with induction of the p53 downstream targets p21Waf1/Cip1 (p21) and Mdm-2, suggesting a transactivation enhancing role for Thr18 and Ser20 phosphorylation. Whereas Thr18 phosphorylation appears to abolish side-chain hydrogen bonding between Thr18 and Asp21, Ser20 phosphorylation may introduce charge attraction between Ser20 and Lys24. Both of these interactions could contribute to stabilizing α-helical conformation within the p53 transactivation domain. Mutagenesis-derived phosphorylation mimicry of p53 at Thr18 and Ser20 by Asp substitution (p53T18D/S20D) altered transactivation domain conformation and significantly reduced the interaction of p53 with the transactivation repressor Mdm-2. Mdm-2 interaction was also reduced with p53 containing a single site Asp substitution at Ser20 (p53S20D) and with the Thr18/Asp21 hydrogen bond disrupting p53 mutants p53T18A, p53T18D and p53D21A. In contrast, no direct effect was observed on the interaction of p53T18A, p53T18D and p53D21A with the basal transcription factor TAF II31. However, prior incubation of p53T18A, p53T18D and p53D21A with Mdm-2 modulated TAFII31 interaction, suggesting Mdm-2 blocks the accessibility of p53 to TAFII31. Consistently, p53-null cells transfected with p53S20D and p53T18A, p53T18D and p53D21A demonstrated enhanced endogenous p21 expression; transfection with p53T18D/S20D most significantly enhanced p21 and fas/APO-1 (fas ) expression. Expression of p53T18A, p53T18D and p53D21A in p53/Mdm-2-double null cells exhibited no discernible differences in p21 expression. Cell proliferation was also significantly curtailed in p53-null cells transfected with p53T18D/S20D relative to cells transfected with wt p53. We conclude the irradiation-induced phosphorylation of p53 at Thr18 and Ser20 alters the α-helical conformation of its transactivation domain. Altered conformation reduces direct interaction with the transrepressor Mdm-2, enhancing indirect recruitment of the basal transcription factor TAFII31, facilitating sequence-specific transactivation function resulting in proliferative arrest. ^

Evolution of cis-regulatory elements in a repetitive sequence adjacent to a sea urchin aboral ectoderm -specific gene

The creation, preservation, and degeneration of cis-regulatory elements controlling developmental gene expression are fundamental genome-level evolutionary processes about which little is known. In this study, critical differences in cis-regulatory elements controlling the expression of the sea urchin aboral ectoderm-specific spec genes were identified and explored. In genomes of species within the Strongylocentrotidae family, multiple copies of a repetitive sequence element termed RSR were present, but RSRs were not detected in genomes of species outside Strongylocentrotidae. RSRs are invariably associated with spec genes, and in Strongylocentrotus purpuratus, the spec2a RSR functioned as a transcriptional enhancer displaying greater activity than RSRs from the spec1 or spec2c paralogs. Single base-pair differences at two cis-regulatory elements within the spec2a RSR greatly increased the binding affinities of four transcription factors: SpCCAAT-binding factor at one element and SpOtx, SpGoosecoid, and SpGATA-E at another. The cis-regulatory elements to which SpCCAAT-binding factor, SpOtx, SpGoosecoid, and SpGATA-E bound were recent evolutionary acquisitions that could act either to activate or repress transcription, depending on the cell type. These elements were found in the spec2a RSR ortholog in Strongylocentrotus pallidus but not in the RSR orthologs of Strongylocentrotus droebachiensis or Hemicentrotus pulcherrimus. These results indicate that spec genes exhibit a dynamic pattern of cis-regulatory element evolution while stabilizing selection preserves their aboral ectoderm expression domain. ^

Heregulin -beta 1 directly associates with RhoA via the immunoglobulin -like domain thereby modulating breast cancer metastasis-related properties

Heregulins constitute a family of growth factors belonging to the epidermal growth factor (EGF) family. Breast cancers that overexpress specific members of the EGF receptor family (EGFR, ErbB2, ErbB3, ErbB4) have increased metastatic potential, and Heregulin-β1 (HRGβ1), a ligand for ErbB3 and ErbB4, has also been shown to induce metastasis-related properties in breast cancer cells in vitro. The secreted form of the HRGβ1 is composed of five distinct structural domains, including the N-terminal domain, an immunoglobulin-like domain (IgG-like), a glycosylation domain, an EGF-like domain, and a β1-specific domain. Of these, the EGF-like domain is well characterized for its function in metastasis-related properties as well as its structure. However, the contributions of the other HRGβ1 domains in breast cancer metastasis remains unclear. ^ To investigate this, HRGβ1 proteins with targeted domain deletions were purified and subjected to assays for metastasis-related properties, including aggregation, invasion, activation of EGFR family members, and motility of breast cancer cells. These assays showed that retaining the EGF-like domain of HRGβ1 is important for activation of EGFRs. Interestingly, the HRGβ1 protein lacking the IgG-like domain (NGEB) led to a decrease in breast cancer cell motility, indicating the IgG-like domain modulates cell motility, an important step in cancer metastasis. ^ To understand the underlying mechanisms, I performed protein sequence and structural analysis of HRGβ1 and identified that the IgG-like domain of HRGβ1 shares sequence homology and three-dimensional structural similarity with the IgG-like domain of TRIO. TRIO is a cytoplasmic protein that directly associates with RhoA, a GTPase involved in cell reorganization and cell motility. Therefore, I hypothesized that HRGβ1 may translocate inside the breast cancer cells through receptor mediated endocytosis and bind to RhoA via its IgG-like domain. I show wild type HRGβ1 but not NGEB binds RhoA in vitro and in vivo, leading to RhoA activation. Inhibition of HRG-β1 internalization via endocytosis disrupted HRGβ1 binding to RhoA. Additionally, breast cancer cell motility induced by HRG-β1 is reduced after treatment with inhibitors to both endocytosis and RhoA function, similar to levels seen with NGEB treatment. ^ Thus, in addition to the well-known role of HRGβ1 as an extracellular stimulator of the EGFR family members, HRGβ1 also functions within the cell as a binding partner and activator of RhoA to modulate cancer cell motility. ^