Inhibition of growth of Giardia lamblia by difluoromethylornithine, a specific inhibitor of polyamine biosynthesis

Difluoromethylornithine (DFMO) is a specific and irreversible inhibitor of ornithine decarboxylase, an enzyme which catalyzes the first step in the biosynthetic pathway of the polyamines. We tested the effect of DFMO on the growth of Giardia lamblia, Entamoeba histolytica, and Trichomonas vaginalis. Growth of G. lamblia was inhibited by DFMO at concentrations of greater than or equal to 1.25 mM. Culture doubling time increased with increasing DFMO concentration. Growth inhibition was reversed if spermidine was added within 53 h of addition of DFMO; no growth was observed if spermidine was added later, indicating eventual parasite death. The growth of E. histolytica and T. vaginalis, two unrelated mucosal-dwelling parasites of humans, was not inhibited by 20 mM DFMO. These studies indicate that polyamine biosynthesis from ornithine is required for growth of G. lamblia.     

Epidemiology and reporting characteristics of preclinical systematic reviews

In an effort to better utilize published evidence obtained from animal experiments, systematic reviews of preclinical studies are increasingly more common—along with the methods and tools to appraise them (e.g., SYstematic Review Center for Laboratory animal Experimentation [SYRCLE’s] risk of bias tool). We performed a cross-sectional study of a sample of recent preclinical systematic reviews (2015–2018) and examined a range of epidemiological characteristics and used a 46-item checklist to assess reporting details. We identified 442 reviews published across 43 countries in 23 different disease domains that used 26 animal species. Reporting of key details to ensure transparency and reproducibility was inconsistent across reviews and within article sections. Items were most completely reported in the title, introduction, and results sections of the reviews, while least reported in the methods and discussion sections. Less than half of reviews reported that a risk of bias assessment for internal and external validity was undertaken, and none reported methods for evaluating construct validity. Our results demonstrate that a considerable number of preclinical systematic reviews investigating diverse topics have been conducted; however, their quality of reporting is inconsistent. Our study provides the justification and evidence to inform the development of guidelines for conducting and reporting preclinical systematic reviews.

A cross sectional study of a sample of recent preclinical systematic reviews reveals deficiencies in reporting and provides the justification and evidence to inform the development of specific guidelines for conducting and reporting preclinical systematic reviews.     

The duality of stability: towards a stochastic theory of species interactions

Understanding the dynamics of ecological systems using stability concepts has been a key driver in ecological research from the inception of the field. Despite the tremendous effort put into this area, progress has been limited due to the bewildering number of metrics used to describe ecological stability. Here, we seek to resolve some of the confusion by unfolding the dynamics of a simple consumer-resource interaction module. In what follows, we first review common dynamical metrics of stability (CV, eigenvalues). We argue using the classical type II consumer-resource model as an example where the empirical stability metric, CV, hides two different, but important, aspects of stability: (i) stability due to mean population density processes and (ii) stability due to population density variance processes. We then employ a simple stochastic consumer-resource framework in order to elucidate (i) when we expect these two different aspects of stability to arise in ecological systems and, importantly, highlight (ii) the fact that these two different aspects of stability respond differentially, but predictably, to changes in fundamental parameters that govern biomass flux and loss in any consumer-resource interaction (e.g., attack rates, carrying capacity, mortality).     

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as “heterogenization” of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research.

A meta-analysis study of the variability in phenotypic outcomes in both control and experimental animal models of ischaemic stroke provides novel perspectives in which heterogeneity can be embraced to improve the reproducibility and translation of preclinical studies.