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.     

Transferability,development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials.  The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.     

In Situ Fe and S isotope analyses in pyrite from the 3.2 Ga Mendon Formation (Barberton Greenstone Belt,South Africa): Evidence for early microbial iron reduction

On the basis of phylogenetic studies and laboratory cultures, it has been proposed that the ability of microbes to metabolize iron has emerged prior to the Archaea/Bacteria split. However, no unambiguous geochemical data supporting this claim have been put forward in rocks older than 2.7–2.5 giga years (Gyr). In the present work, we report in situ Fe and S isotope composition of pyrite from 3.28‐ to 3.26‐Gyr‐old cherts from the upper Mendon Formation, South Africa. We identified three populations of microscopic pyrites showing a wide range of Fe isotope compositions, which cluster around two δ⁵⁶Fe values of ?1.8‰ and +1‰. These three pyrite groups can also be distinguished based on the pyrite crystallinity and the S isotope mass‐independent signatures. One pyrite group displays poorly crystallized pyrite minerals with positive Δ³³S values > +3‰, while the other groups display more variable and closer to 0‰ Δ³³S values with recrystallized pyrite rims. It is worth to note that all the pyrite groups display positive Δ³³S values in the pyrite core and similar trace element compositions. We therefore suggest that two of the pyrite groups have experienced late fluid circulations that have led to partial recrystallization and dilution of S isotope mass‐independent signature but not modification of the Fe isotope record. Considering the mineralogy and geochemistry of the pyrites and associated organic material, we conclude that this iron isotope systematic derives from microbial respiration of iron oxides during early diagenesis. Our data extend the geological record of dissimilatory iron reduction (DIR) back more than 560 million years (Myr) and confirm that micro‐organisms closely related to the last common ancestor had the ability to reduce Fe(III).     

Evolution of putative barrier loci at an intermediate stage of speciation with gene flow in campions (Silene)

Understanding the origin of new species is a central goal in evolutionary biology. Diverging lineages often evolve highly heterogeneous patterns of genetic differentiation; however, the underlying mechanisms are not well understood. We investigated evolutionary processes governing genetic differentiation between the hybridizing campions Silene dioica (L.) Clairv. and S. latifolia Poiret. Demographic modelling indicated that the two species diverged with gene flow. The best‐supported scenario with heterogeneity in both migration rate and effective population size suggested that a small proportion of the loci evolved without gene flow. Differentiation (F _ST) and sequence divergence (d _XY) were correlated and both tended to peak in the middle of most linkage groups, consistent with reduced gene flow at highly differentiated loci. Highly differentiated loci further exhibited signatures of selection. In between‐species population pairs, isolation by distance was stronger for genomic regions with low between‐species differentiation than for highly differentiated regions that may contain barrier loci. Moreover, differentiation landscapes within and between species were only weakly correlated, suggesting that linked selection due to shared recombination and gene density landscapes is not the dominant determinant of genetic differentiation in these lineages. Instead, our results suggest that divergent selection shaped the genomic landscape of differentiation between the two Silene species, consistent with predictions for speciation in the face of gene flow.     

In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses

During the most recent decade, environmental DNA metabarcoding approaches have been both developed and improved to minimize the biological and technical biases in these protocols. However, challenges remain, notably those relating to primer design. In the current study, we comprehensively assessed the performance of ten COI and two 16S primer pairs for eDNA metabarcoding, including novel and previously published primers. We used a combined approach of in silico, in vivo‐mock community (33 arthropod taxa from 16 orders), and guano‐based analyses to identify primer sets that would maximize arthropod detection and taxonomic identification, successfully identify the predator (bat) species, and minimize the time and financial costs of the experiment. We focused on two insectivorous bat species that live together in mixed colonies: the greater horseshoe bat (Rhinolophus ferrumequinum) and Geoffroy's bat (Myotis emarginatus). We found that primer degeneracy is the main factor that influences arthropod detection in silico and mock community analyses, while amplicon length is critical for the detection of arthropods from degraded DNA samples. Our guano‐based results highlight the importance of detecting and identifying both predator and prey, as guano samples can be contaminated by other insectivorous species. Moreover, we demonstrate that amplifying bat DNA does not reduce the primers' capacity to detect arthropods. We therefore recommend the simultaneous identification of predator and prey. Finally, our results suggest that up to one‐third of prey occurrences may be unreliable and are probably not of primary interest in diet studies, which may decrease the relevance of combining several primer sets instead of using a single efficient one. In conclusion, this study provides a pragmatic framework for eDNA primer selection with respect to scientific and methodological constraints.     

Elicitation Improves the Leaf Area,Enzymatic Activities,Antioxidant Activity and Content of Secondary Metabolites in Achillea millefolium L. Grown in the Field

Salicylic acid (SA) is a plant hormone that stimulates the growth and metabolism of plants, also acting as an abiotic elicitor. This study aimed to evaluate the effect of SA on leaf production, leaf area and synthesis of secondary compounds in yarrow plants. The experiments were conducted under field conditions in two consecutive years and f-received SA foliar applications (T1-control; T2-1.0 mmol L⁻¹ applications at 20, 60 and 100 days after planting (DAP) and T3-1.0 mmol L⁻¹ applications at 100 DAP during 3 days). The exogenous application of SA resulted in increases in leaf area (total and specific), number of leaves and leaf mass ratio of yarrow plants, polyphenolic compounds, phenylalanine ammonia-lyase and chalcone synthase enzymes and the antioxidant activity of the plant extract. The HPLC–DAD–MS/MS analysis of phenolic compounds revealed increases in the amounts of quinic acid and rutin. The results of this research lead us to affirm that SA exerted both the hormonal effect on number of leaves and leaf area, and also acted as eliciting substance.

     

Identifying conversion efficiency as a key mechanism underlying food webs adaptive evolution: a step forward,or backward?

Body size or mass is one of the main factors underlying food webs structure. A large number of evolutionary models have shown that indeed, the adaptive evolution of body size (or mass) can give rise to hierarchically organised trophic levels with complex between and within trophic interactions. However, these models generally make strong arbitrary assumptions on how traits evolve, casting doubts on their robustness. In particular, biomass conversion efficiency is always considered independent of the predator and prey size, which contradicts with the literature. In this paper, we propose a general model encompassing most previous models which allows to show that relaxing arbitrary assumptions gives rise to unrealistic food webs. We then show that considering biomass conversion efficiency dependent on species size is certainly key for food webs adaptive evolution because realistic food webs can evolve, making obsolete the need of arbitrary constraints on traits' evolution. We finally conclude that, on the one hand, ecologists should pay attention to how biomass flows into food webs in models. On the other hand, we question more generally the robustness of evolutionary models for the study of food webs.