The Excitotoxin Quinolinic Acid Induces Tau Phosphorylation in Human Neurons

Some of the tryptophan catabolites produced through the kynurenine pathway (KP), and more particularly the excitotoxin quinolinic acid (QA), are likely to play a role in the pathogenesis of Alzheimer''s disease (AD). We have previously shown that the KP is over activated in AD brain and that QA accumulates in amyloid plaques and within dystrophic neurons. We hypothesized that QA in pathophysiological concentrations affects tau phosphorylation. Using immunohistochemistry, we found that QA is co-localized with hyperphosphorylated tau (HPT) within cortical neurons in AD brain. We then investigated in vitro the effects of QA at various pathophysiological concentrations on tau phosphorylation in primary cultures of human neurons. Using western blot, we found that QA treatment increased the phosphorylation of tau at serine 199/202, threonine 231 and serine 396/404 in a dose dependent manner. Increased accumulation of phosphorylated tau was also confirmed by immunocytochemistry. This increase in tau phosphorylation was paralleled by a substantial decrease in the total protein phosphatase activity. A substantial decrease in PP2A expression and modest decrease in PP1 expression were observed in neuronal cultures treated with QA. These data clearly demonstrate that QA can induce tau phosphorylation at residues present in the PHF in the AD brain. To induce tau phosphorylation, QA appears to act through NMDA receptor activation similar to other agonists, glutamate and NMDA. The QA effect was abrogated by the NMDA receptor antagonist memantine. Using PCR arrays, we found that QA significantly induces 10 genes in human neurons all known to be associated with AD pathology. Of these 10 genes, 6 belong to pathways involved in tau phosphorylation and 4 of them in neuroprotection. Altogether these results indicate a likely role of QA in the AD pathology through promotion of tau phosphorylation. Understanding the mechanism of the neurotoxic effects of QA is essential in developing novel therapeutic strategies for AD.     

Lethal mutagenesis and evolutionary epidemiology

The lethal mutagenesis hypothesis states that within-host populations of pathogens can be driven to extinction when the load of deleterious mutations is artificially increased with a mutagen, and becomes too high for the population to be maintained. Although chemical mutagens have been shown to lead to important reductions in viral titres for a wide variety of RNA viruses, the theoretical underpinnings of this process are still not clearly established. A few recent models sought to describe lethal mutagenesis but they often relied on restrictive assumptions. We extend this earlier work in two novel directions. First, we derive the dynamics of the genetic load in a multivariate Gaussian fitness landscape akin to classical quantitative genetics models. This fitness landscape yields a continuous distribution of mutation effects on fitness, ranging from deleterious to beneficial (i.e. compensatory) mutations. We also include an additional class of lethal mutations. Second, we couple this evolutionary model with an epidemiological model accounting for the within-host dynamics of the pathogen. We derive the epidemiological and evolutionary equilibrium of the system. At this equilibrium, the density of the pathogen is expected to decrease linearly with the genomic mutation rate U. We also provide a simple expression for the critical mutation rate leading to extinction. Stochastic simulations show that these predictions are accurate for a broad range of parameter values. As they depend on a small set of measurable epidemiological and evolutionary parameters, we used available information on several viruses to make quantitative and testable predictions on critical mutation rates. In the light of this model, we discuss the feasibility of lethal mutagenesis as an efficient therapeutic strategy.     

The ROK-family regulator Rok7B7 directly controls carbon catabolite repression,antibiotic biosynthesis,and morphological development in Streptomyces avermitilis

Carbon catabolite repression (CCR) is a common phenomenon in bacteria that modulates expression of genes involved in uptake of alternative carbon sources. In the filamentous streptomycetes, which produce half of all known antibiotics, the precise mechanism of CCR is yet unknown. We report here that the ROK-family regulator Rok7B7 pleiotropically controls xylose and glucose uptake, CCR, development, as well as production of the macrolide antibiotics avermectin and oligomycin A in Streptomyces avermitilis. Rok7B7 directly repressed structural genes for avermectin biosynthesis, whereas it activated olmRI, the cluster-situated activator gene for oligomycin A biosynthesis. Rok7B7 also directly repressed the xylose uptake operon xylFGH, whose expression was induced by xylose and repressed by glucose. Both xylose and glucose served as Rok7B7 ligands. rok7B7 deletion led to enhancement and reduction of avermectin and oligomycin A production, respectively, relieved CCR of xylFGH, and increased co-uptake efficiency of xylose and glucose. A consensus Rok7B7-binding site, 5′-TTKAMKHSTTSAV-3′, was identified within aveA1p, olmRIp, and xylFp, which allowed prediction of the Rok7B7 regulon and confirmation of 11 additional targets involved in development, secondary metabolism, glucose uptake, and primary metabolic processes. Our findings will facilitate methods for strain improvement, antibiotic overproduction, and co-uptake of xylose and glucose in Streptomyces species.     

Using anticipatory life cycle assessment to enable future sustainable construction

The built environment is the largest single emitter of CO₂ and an important consumer of energy. Much research has gone into the improved efficiency of building operation and construction products. Life Cycle Assessment (LCA) is commonly used to assess existing buildings or building products. Classic LCA, however, is not suited for evaluating the environmental performance of developing technologies. A new approach, anticipatory LCA (a‐LCA), promises various advantages and can be used as a design constraint during the product development stage. It helps overcome four challenges: (i) data availability, (ii) stakeholder inclusion, (iii) risk assessment, and (iv) multi‐criteria problems. This article's contribution to the line of research is twofold: first, it adapts the a‐LCA approach for construction‐specific purposes in theoretical terms for the four challenges. Second, it applies the method to an innovative prefabricated modular envelope system, the CleanTechBlock (CTB), focusing on challenge (i). Thirty‐six CTB designs are tested and compared to conventional walls. Inclusion of technology foresight is achieved through structured scenario analysis. Moreover, challenge (iv) is tackled through the analysis of different environmental impact categories, transport‐related impacts, and thickness of the wall assemblies of the CTB. The case study results show that optimized material choice and product design is needed to reach the lowest environmental impact. Methodological findings highlight the importance of context‐specific solutions and the need for benchmarking new products.     

Lifting the veil on the correction of double counting incidents in hybrid life cycle assessment

Life cycle assessment (LCA) and environmentally extended input–output analyses (EEIOA) are two techniques commonly used to assess environmental impacts of an activity/product. Their strengths and weaknesses are complementary, and they are thus regularly combined to obtain hybrid LCAs. A number of approaches in hybrid LCA exist, which leads to different results. One of the differences is the method used to ensure that mixed LCA and EEIOA data do not overlap, which is referred to as correction for double counting. This aspect of hybrid LCA is often ignored in reports of hybrid assessments and no comprehensive study has been carried out on it. This article strives to list, compare, and analyze the different existing methods for the correction of double counting. We first harmonize the definitions of the existing correction methods and express them in a common notation, before introducing a streamlined variant. We then compare their respective assumptions and limitations. We discuss the loss of specific information regarding the studied activity/product and the loss of coherent financial representation caused by some of the correction methods. This analysis clarifies which techniques are most applicable to different tasks, from hybridizing individual LCA processes to integrating complete databases. We finally conclude by giving recommendations for future hybrid analyses.     

Sampling strategy optimization to increase statistical power in landscape genomics: A simulation‐based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. Implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistical constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic data sets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographical representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.     

Melanocortin‐1 receptor (MC1R) genotypes do not correlate with size in two cohorts of medium‐to‐giant congenital melanocytic nevi

Congenital melanocytic nevi (CMN) are cutaneous malformations whose prevalence is inversely correlated with projected adult size. CMN are caused by somatic mutations, but epidemiological studies suggest that germline genetic factors may influence CMN development. In CMN patients from the U.K., genetic variants in MC1R, such as p.V92M and loss‐of‐function variants, have been previously associated with larger CMN. We analyzed the association of MC1R variants with CMN characteristics in two distinct cohorts of medium‐to‐giant CMN patients from Spain (N = 113) and from France, Norway, Canada, and the United States (N = 53), similar at the clinical and phenotypical level except for the number of nevi per patient. We found that the p.V92M or loss‐of‐function MC1R variants either alone or in combination did not correlate with CMN size, in contrast to the U.K. CMN patients. An additional case–control analysis with 259 unaffected Spanish individuals showed a higher frequency of MC1R compound heterozygous or homozygous variant genotypes in Spanish CMN patients compared to the control population (15.9% vs. 9.3%; p = .075). Altogether, this study suggests that MC1R variants are not associated with CMN size in these non‐UK cohorts. Additional studies are required to define the potential role of MC1R as a risk factor in CMN development.