A systems view of the protein expression process

The Role of HDACs in the Response of Cancer Cells to Cellular Stress and the Potential for Therapeutic Intervention

Throughout the process of carcinogenesis, cancer cells develop intricate networks to adapt to a variety of stressful conditions including DNA damage, nutrient deprivation, and hypoxia. These molecular networks encounter genomic instability and mutations coupled with changes in the gene expression programs due to genetic and epigenetic alterations. Histone deacetylases (HDACs) are important modulators of the epigenetic constitution of cancer cells. It has become increasingly known that HDACs have the capacity to regulate various cellular systems through the deacetylation of histone and bounteous nonhistone proteins that are rooted in complex pathways in cancer cells to evade death pathways and immune surveillance. Elucidation of the signaling pathways involved in the adaptive responses to cellular stress and the role of HDACs may lead to the development of novel therapeutic agents. In this article, we overview the dominant stress types including metabolic, oxidative, genotoxic, and proteotoxic stress imposed on cancer cells in the context of HDACs, which guide stress adaptation responses. Next, we expose a closer view on the therapeutic interventions and clinical trials that involve HDACs inhibitors, in addition to highlighting the impact of using HDAC inhibitors in combination with stress-inducing agents for the management of cancer and to overcome the resistance to current cancer therapy.

Proteotoxic Stress and Cell Death in Cancer Cells

To maintain proteostasis, cells must integrate information and activities that supervise protein synthesis, protein folding, conformational stability, and also protein degradation. Extrinsic and intrinsic conditions can both impact normal proteostasis, causing the appearance of proteotoxic stress. Initially, proteotoxic stress elicits adaptive responses aimed at restoring proteostasis, allowing cells to survive the stress condition. However, if the proteostasis restoration fails, a permanent and sustained proteotoxic stress can be deleterious, and cell death ensues. Many cancer cells convive with high levels of proteotoxic stress, and this condition could be exploited from a therapeutic perspective. Understanding the cell death pathways engaged by proteotoxic stress is instrumental to better hijack the proliferative fate of cancer cells.

microRNA-92a regulates the expression of aphid bacteriocyte-specific secreted protein 1

OBJECTIVE

Aphids harbor a nutritional obligate endosymbiont in specialized cells called bacteriocytes, which aggregate to form an organ known as the bacteriome. Aphid bacteriomes display distinct gene expression profiles that facilitate the symbiotic relationship. Currently, the mechanisms that regulate these patterns of gene expression are unknown. Recently using computational pipelines, we identified miRNAs that are conserved in expression in the bacteriomes of two aphid species and proposed that they function as important regulators of bacteriocyte gene expression. Here using a dual luciferase assay in mouse NIH/3T3 cell culture, we aimed to experimentally validate the computationally predicted interaction between Myzus persicae miR-92a and the predicted target region of M. persicae bacteriocyte-specific secreted protein 1 (SP1) mRNA.

RESULTS

In the dual luciferase assay, miR-92a interacted with the SP1 target region resulting in a significant downregulation of the luciferase signal. Our results demonstrate that miR-92a interacts with SP1 to alter expression in a heterologous expression system, thereby supporting our earlier assertion that miRNAs are regulators of the aphid/Buchnera symbiotic interaction.

Targeting Proteotoxic Stress in Cancer: A Review of the Role that Protein Quality Control Pathways Play in Oncogenesis

Despite significant advances in cancer diagnostics and therapeutics the majority of cancer unfortunately remains incurable, which has led to continued research to better understand its exceptionally diverse biology. As a result of genomic instability, cancer cells typically have elevated proteotoxic stress. Recent appreciation of this functional link between the two secondary hallmarks of cancer: aneuploidy (oxidative stress) and proteotoxic stress, has therefore led to the development of new anticancer therapies targeting this emerging “Achilles heel” of malignancy. This review highlights the importance of managing proteotoxic stress for cancer cell survival and provides an overview of the integral role proteostasis pathways play in the maintenance of protein homeostasis. We further review the efforts undertaken to exploit proteotoxic stress in multiple myeloma (as an example of a hematologic malignancy) and triple negative breast cancer (as an example of a solid tumor), and give examples of: (1) FDA-approved therapies in routine clinical use; and (2) promising therapies currently in clinical trials. Finally, we provide new insights gleaned from the use of emerging technologies to disrupt the protein secretory pathway and repurpose E3 ligases to achieve targeted protein degradation.

Systematic comparison of the response properties of protein and RNA mediated gene regulatory motifs

We present a framework enabling the dissection of the effects of motif structure (feedback or feedforward), the nature of the controller (RNA or protein), and the regulation mode (transcriptional, post-transcriptional or translational) on the response to a step change in the input. We have used a common model framework for gene expression where both motif structures have an activating input and repressing regulator, with the same set of parameters, to enable a comparison of the responses. We studied the global sensitivity of the system properties, such as steady-state gain, overshoot, peak time, and peak duration, to parameters. We find that, in all motifs, overshoot correlated negatively whereas peak duration varied concavely with peak time. Differences in the other system properties were found to be mainly dependent on the nature of the controller rather than the motif structure. Protein mediated motifs showed a higher degree of adaptation i.e. a tendency to return to baseline levels; in particular, feedforward motifs exhibited perfect adaptation. RNA mediated motifs had a mild regulatory effect; they also exhibited a lower peaking tendency and mean overshoot. Protein mediated feedforward motifs showed higher overshoot and lower peak time compared to the corresponding feedback motifs.

Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation

mRNA translation is a complex process involving the progression of ribosomes on the mRNA, resulting in the synthesis of proteins, and is subject to multiple layers of regulation. This process has been modelled using different formalisms, both stochastic and deterministic. Recently, we introduced a Probabilistic Boolean modelling framework for mRNA translation, which possesses the advantage of tools for numerically exact computation of steady state probability distribution, without requiring simulation. Here, we extend this model to incorporate both random sequential and parallel update rules, and demonstrate its effectiveness in various settings, including its flexibility in accommodating additional static and dynamic biological complexities and its role in parameter sensitivity analysis. In these applications, the results from the model analysis match those of TASEP model simulations. Importantly, the proposed modelling framework maintains the stochastic aspects of mRNA translation and provides a way to exactly calculate probability distributions, providing additional tools of analysis in this context. Finally, the proposed modelling methodology provides an alternative approach to the understanding of the mRNA translation process, by bridging the gap between existing approaches, providing new analysis tools, and contributing to a more robust platform for modelling and understanding translation.

mRNA translation and protein synthesis: an analysis of different modelling methodologies and a new PBN based approach

Background

mRNA translation involves simultaneous movement of multiple ribosomes on the mRNA and is also subject to regulatory mechanisms at different stages. Translation can be described by various codon-based models, including ODE, TASEP, and Petri net models. Although such models have been extensively used, the overlap and differences between these models and the implications of the assumptions of each model has not been systematically elucidated. The selection of the most appropriate modelling framework, and the most appropriate way to develop coarse-grained/fine-grained models in different contexts is not clear.

Results

We systematically analyze and compare how different modelling methodologies can be used to describe translation. We define various statistically equivalent codon-based simulation algorithms and analyze the importance of the update rule in determining the steady state, an aspect often neglected. Then a novel probabilistic Boolean network (PBN) model is proposed for modelling translation, which enjoys an exact numerical solution. This solution matches those of numerical simulation from other methods and acts as a complementary tool to analytical approximations and simulations. The advantages and limitations of various codon-based models are compared, and illustrated by examples with real biological complexities such as slow codons, premature termination and feedback regulation. Our studies reveal that while different models gives broadly similiar trends in many cases, important differences also arise and can be clearly seen, in the dependence of the translation rate on different parameters. Furthermore, the update rule affects the steady state solution.

Conclusions

The codon-based models are based on different levels of abstraction. Our analysis suggests that a multiple model approach to understanding translation allows one to ascertain which aspects of the conclusions are robust with respect to the choice of modelling methodology, and when (and why) important differences may arise. This approach also allows for an optimal use of analysis tools, which is especially important when additional complexities or regulatory mechanisms are included. This approach can provide a robust platform for dissecting translation, and results in an improved predictive framework for applications in systems and synthetic biology.