Helene McMurray

Biology of human papillomaviruses

Human papillomaviruses (HPVs) cause squamous cancers of epithelial surfaces, of which genital cancers are the most common. In this article we have attempted to describe the properties and functions of the viral proteins of HPV type 16, a common cause of genital cancers, and have tried to suggest how their expression may lead to a dysregulated cell which may become malignant. These viruses are attempting to replicate in terminally differentiating keratinocytes and must stimulate G1 to S‐phase progression for the replication of their genome. As part of the successful completion of replication and assembly of infectious virus particles, the virus needs at least partial differentiation to occur. Therefore, at the same time as differentiation is occurring, the nuclei of infected cells are in S‐phase. While the mechanisms of action of the viral proteins are not completely understood, researchers are making progress and this article strives to bring together the conclusions from some of this work.

Human Papillomavirus Type 16 E6 Activates TERT Gene Transcription through Induction of c-Myc and Release of USF-Mediated Repression

ABSTRACT Human papillomavirus type 16 (HPV-16), a DNA tumor virus, has a causal role in cervical cancer, and the viral oncoproteins E6 and E7 contribute to oncogenesis in multiple ways. E6 increases telomerase activity in keratinocytes through increased transcription of the telomerase catalytic subunit gene (TERT), but the factors involved in this have been elusive. We have found that mutation of the proximal E box in the TERT promoter has an activating effect in luciferase assays. This suggested that a repressive complex might be present at this site. HPV-16 E6 activated the TERT promoter predominantly through the proximal E box, and thus, might be acting on this repressive complex. This site is specific for the Myc/Mad/Max transcription factors as well as USF1 and USF2. Addition of exogenous USF1 or USF2 repressed activation of the TERT promoter by E6, dependent on the proximal E box. Using siRNA against USF1 or USF2 allowed for greater activation of the TERT promoter by E6. Conversely, loss of c-Myc function, through a dominant-negative Myc molecule, reduced activation by E6. Chromatin immunoprecipitations showed that in the presence of E6, there was a reduction in binding of USF1 and USF2 at the TERT promoter proximal E box, and a concomitant increase in c-Myc bound to this site. This shows that a repressive complex containing USF1 and USF2 is present in normal cells with little or no telomerase activity. In E6 keratinocytes, this repressive complex is replaced by c-Myc, which corresponds to higher levels of TERT transcription and consequently, telomerase activity.

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype

Understanding the molecular underpinnings of cancer is of critical importance to the development of targeted intervention strategies. Identification of such targets, however, is notoriously difficult and unpredictable. Malignant cell transformation requires the cooperation of a few oncogenic mutations that cause substantial reorganization of many cell features and induce complex changes in gene expression patterns. Genes critical to this multifaceted cellular phenotype have therefore only been identified after signalling pathway analysis or on an ad hoc basis. Our observations that cell transformation by cooperating oncogenic lesions depends on synergistic modulation of downstream signalling circuitry suggest that malignant transformation is a highly cooperative process, involving synergy at multiple levels of regulation, including gene expression. Here we show that a large proportion of genes controlled synergistically by loss-of-function p53 and Ras activation are critical to the malignant state of murine and human colon cells. Notably, 14 out of 24 ‘cooperation response genes’ were found to contribute to tumour formation in gene perturbation experiments. In contrast, only 1 in 14 perturbations of the genes responding in a non-synergistic manner had a similar effect. Synergistic control of gene expression by oncogenic mutations thus emerges as an underlying key to malignancy, and provides an attractive rationale for identifying intervention targets in gene networks downstream of oncogenic gain- and loss-of-function mutations.

On non-detects in qPCR data

Motivation: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects—those reactions failing to produce a minimum amount of signal. Results: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. Availability and implementation: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project. Contact: mccallm@gmail.com

Degradation of p53, not telomerase activation, by E6 is required for bypass of crisis and immortalization by human papillomavirus type 16 E6/E7.

ABSTRACT Bypass of two arrest points is essential in the process of cellular immortalization, one of the components of the transformation process. Expression of human papillomavirus type 16 E6 and E7 together can escape both senescence and crisis, processes which normally limit the proliferative capacity of primary human keratinocytes. Crisis is thought to be mediated by telomere shortening. Because E6 stimulates telomerase activity and exogenous expression of the TERT gene with E7 can immortalize keratinocytes, this function is thought to be important for E6 to cooperate with E7 to bypass crisis. However, it has also been reported that E6 dissociates increased telomerase activity from maintenance of telomere length and that a dominant-negative p53 molecule can substitute for E6 in cooperative immortalization of keratinocytes with E7. Thus, to determine which functions of E6 are required to allow bypass of crisis and immortalization of keratinocytes with E7, immortalization assays were performed using specific mutants of E6, in tandem with E7. In these experiments, every clone expressing an E6 mutant capable of degrading p53 was able to bypass crisis and immortalize, regardless of telomerase induction. All clones containing E6 mutants incapable of degrading p53 died at crisis. These results suggest that the ability of E6 to induce degradation of p53 compensates for continued telomere shortening in E6/E7 cells and demonstrate that degradation of p53 is required for immortalization by E6/E7, while increased telomerase activity is dispensable.

Gene signature critical to cancer phenotype as a paradigm for anticancer drug discovery.

Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called ‘cooperation response genes’ (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here, we show that CRGs have an essential role in drug-mediated anticancer activity and that anticancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anticancer drug mechanisms.

Fitting Boolean networks from steady state perturbation data.

Gene perturbation experiments are commonly used for the reconstruction of gene regulatory networks. Typical experimental methodology imposes persistent changes on the network. The resulting data must therefore be interpreted as a steady state from an altered gene regulatory network, rather than a direct observation of the original network. In this article an implicit modeling methodology is proposed in which the unperturbed network of interest is scored by first modeling the persistent perturbation, then predicting the steady state, which may then be compared to the observed data. This results in a many-to-one inverse problem, so a computational Bayesian approach is used to assess model uncertainty. The methodology is first demonstrated on a number of synthetic networks. It is shown that the Bayesian approach correctly assigns high posterior probability to the network structure and steady state behavior. Further, it is demonstrated that where uncertainty of model features is indicated, the uncertainty may be accurately resolved with further perturbation experiments. The methodology is then applied to the modeling of a gene regulatory network using perturbation data from nine genes which have been shown to respond synergistically to known oncogenic mutations. A hypothetical model emerges which conforms to reported regulatory properties of these genes. Furthermore, the Bayesian methodology is shown to be consistent in the sense that multiple randomized applications of the fitting algorithm converge to an approximately common posterior density on the space of models. Such consistency is generally not feasible for algorithms which report only single models. We conclude that fully Bayesian methods, coupled with models which accurately account for experimental constraints, are a suitable tool for the inference of gene regulatory networks, in terms of accuracy, estimation of model uncertainty, and experimental design.

Developmental alcohol exposure impairs synaptic plasticity without overtly altering microglial function in mouse visual cortex

Fetal alcohol spectrum disorder (FASD), caused by gestational ethanol (EtOH) exposure, is one of the most common causes of non-heritable and life-long mental disability worldwide, with no standard treatment or therapy available. While EtOH exposure can alter the function of both neurons and glia, it is still unclear how EtOH influences brain development to cause deficits in sensory and cognitive processing later in life. Microglia play an important role in shaping synaptic function and plasticity during neural circuit development and have been shown to mount an acute immunological response to EtOH exposure in certain brain regions. Therefore, we hypothesized that microglial roles in the healthy brain could be permanently altered by early EtOH exposure leading to deficits in experience-dependent plasticity. We used a mouse model of human third trimester high binge EtOH exposure, administering EtOH twice daily by subcutaneous injections from postnatal day 4 through postnatal day 9 (P4-:P9). Using a monocular deprivation model to assess ocular dominance plasticity, we found an EtOH-induced deficit in this type of visually driven experience-dependent plasticity. However, using a combination of immunohistochemistry, confocal microscopy, and in vivo two-photon microscopy to assay microglial morphology and dynamics, as well as fluorescence activated cell sorting (FACS) and RNA-seq to examine the microglial transcriptome, we found no evidence of microglial dysfunction in early adolescence. We also found no evidence of microglial activation in visual cortex acutely after early ethanol exposure, possibly because we also did not observe EtOH-induced neuronal cell death in this brain region. We conclude that early EtOH exposure caused a deficit in experience-dependent synaptic plasticity in the visual cortex that was independent of changes in microglial phenotype or function. This demonstrates that neural plasticity can remain impaired by developmental ethanol exposure even in a brain region where microglia do not acutely assume nor maintain an activated phenotype.

On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior.

Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation while maintaining their Markovian nature increases the applicability of these models to genetic regulatory networks (GRN). We explore a previously-proposed class of BoNs characterized by linear threshold functions, which we refer to as threshold Boolean networks (TBN). Compared to traditional BoNs with unconstrained transition functions, these models require far fewer parameters and offer a more direct interpretation. However, the functional form of a TBN does result in a reduction in the regulatory relationships which can be modeled. We show that TBNs can be readily extended to permit self-degradation, with explicitly modeled degradation rates. We note that the introduction of variable degradation compromises the Markovian property fundamental to BoN models but show that a simple state augmentation procedure restores their Markovian nature. Next, we study the effect of assumptions regarding self-degradation on the set of possible steady states. Our findings are captured in two theorems relating self-degradation and regulatory feedback to the steady state behavior of a TBN. Finally, we explore assumptions of synchronous gene response and asynergistic regulation and show that TBNs can be easily extended to relax these assumptions. Applying our methods to the budding yeast cell-cycle network revealed that although the network is complex, its steady state is simplified by the presence of self-degradation and lack of purely positive regulatory cycles.

Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects – those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis.