Stress-Induced Mutagenesis in Bacteria

ABSTRACT

Bacteria spend their lives buffeted by changing environmental conditions. To adapt to and survive these stresses, bacteria have global response systems that result in sweeping changes in gene expression and cellular metabolism. These responses are controlled by master regulators, which include: alternative sigma factors, such as RpoS and RpoH; small molecule effectors, such as ppGpp; gene repressors such as LexA; and, inorganic molecules, such as polyphosphate. The response pathways extensively overlap and are induced to various extents by the same environmental stresses. These stresses include nutritional deprivation, DNA damage, temperature shift, and exposure to antibiotics. All of these global stress responses include functions that can increase genetic variability. In particular, up-regulation and activation of error-prone DNA polymerases, down-regulation of error-correcting enzymes, and movement of mobile genetic elements are common features of several stress responses. The result is that under a variety of stressful conditions, bacteria are induced for genetic change. This transient mutator state may be important for adaptive evolution.     

Proteomics advances for precision therapy in ovarian cancer

ABSTRACT

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior.

Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with ‘ovarian cancer’ including ‘proteomics’, ‘proteogenomic’, ‘reverse-phase protein array’, ‘mass spectrometry’, and ‘adaptive response’, were used to search PubMed.

Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.     

Climate change and the optimal flowering time of annual plants in seasonal environments

Long‐term phenology monitoring has documented numerous examples of changing flowering dates during the last century. A pivotal question is whether these phenological responses are adaptive or not under directionally changing climatic conditions. We use a classic dynamic growth model for annual plants, based on optimal control theory, to find the fitness‐maximizing flowering time, defined as the switching time from vegetative to reproductive growth. In a typical scenario of global warming, with advanced growing season and increased productivity, optimal flowering time advances less than the start of the growing season. Interestingly, increased temporal spread in production over the season may either advance or delay the optimal flowering time depending on overall productivity or season length. We identify situations where large phenological changes are necessary for flowering time to remain optimal. Such changes also indicate changed selection pressures. In other situations, the model predicts advanced phenology on a calendar scale, but no selection for early flowering in relation to the start of the season. We also show that the optimum is more sensitive to increased productivity when productivity is low than when productivity is high. All our results are derived using a general, graphical method to calculate the optimal flowering time applicable for a large range of shapes of the seasonal production curve. The model can thus explain apparent maladaptation in phenological responses in a multitude of scenarios of climate change. We conclude that taking energy allocation trade‐offs and appropriate time scales into account is critical when interpreting phenological patterns.     

Variation in hippocampal glial cell numbers in food‐caching birds from different climates

Enhancements to memory are associated with enhanced neural structures that support those capabilities. A great deal of work has examined this relationship in the context of natural variation in spatial memory capability and hippocampal (Hp) structure. Most studies have focused on volumetric and neuron measures, but have seldom examined the role of glial cells. Once considered involved only in supportive functions associated with neurons, the importance of glial cells in cognitive processes, including memory, is gaining more attention. Building upon our previous study on the relationship between the brain, memory, and environmental severity in food‐caching birds, we compared the total number of Hp glial cells in wild‐sampled and in lab‐reared (common garden) black‐capped chickadees (Poecile atricapillus) originating from two different environmental extremes. We found that birds from more harsh climate tended to have significantly more Hp glial cells than those from more mild climate and that lab‐reared chickadees had significantly fewer Hp glial cells compared to the wild‐sampled birds. These results suggest that population differences in glial numbers may be controlled, at least in part, by heritable mechanisms, but glial numbers appear to be additionally regulated by an individual's environment. The pattern of Hp glial cell abundance among our treatment groups closely followed that of the Hp volume, suggesting that Hp glial cell number may be associated with the Hp volume. Unlike Hp neurons, however, the number of Hp glial cells may be, at least in part, affected by an individual's experiences and environment. © 2013 Wiley Periodicals, Inc. Develop Neurobiol 73: 480–485, 2013     

Hypoxia-induced autophagy

A major challenge in formulating an effective immunotherapy is to overcome the mechanisms of tumor escape from immunosurveillance. We showed that hypoxia-induced autophagy impairs cytotoxic T-lymphocyte (CTL)-mediated tumor cell lysis by regulating phospho-STAT3 in target cells. Autophagy inhibition in hypoxic cells decreases phospho-STAT3 and restores CTL-mediated tumor cell killing by a mechanism involving the ubiquitin proteasome system and SQSTM1/p62. Simultaneously boosting the CTL-response, using a TRP-peptide vaccination strategy, and targeting autophagy in hypoxic tumors, improves the efficacy of cancer vaccines and promotes tumor regression in vivo. Overall, in addition to its immunosuppressive effect, the hypoxic microenvironment also contributes to immunoresistance and can be detrimental to antitumor effector cell functions.     

Lifespan Extension Via Dietary Restriction: Time to Reconsider the Evolutionary Mechanisms?

Dietary restriction (DR) is the most consistent environmental manipulation to extend lifespan. Originally thought to be caused by a reduction in caloric intake, recent evidence suggests that macronutrient intake underpins the effect of DR. The prevailing evolutionary explanations for the DR response are conceptualized under the caloric restriction paradigm, necessitating reconsideration of how or whether these evolutionary explanations fit this macronutrient perspective. In the authors’ opinion, none of the current evolutionary explanations of DR adequately explain the intricacies of observed results; instead a context-dependent combination of these theories is suggested which is likely to reflect reality. In reviewing the field, it is proposed that the ability to track the destination of different macronutrients within the body will be key to establishing the relative roles of the competing theories. Understanding the evolution of the DR response and its ecological relevance is critical to understanding variation in DR responses and their relevance outside laboratory environments.     

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods,simulation model and their implementation in R

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd , previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so-called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.     

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between-center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between-center heterogeneity, an unadjusted and a bias-adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.