Noncircadian regulation and function of clock genes period and timeless in oogenesis of Drosophila melanogaster

Circadian clock genes are ubiquitously expressed in the nervous system and peripheral tissues of complex animals. While clock genes in the brain are essential for behavioral rhythms, the physiological roles of these genes in the periphery are not well understood. Constitutive expression of the clock gene period was reported in the ovaries of Drosophila melanogaster; however, its molecular interactions and functional significance remained unknown. This study demonstrates that period (per) and timeless (tim) are involved in a novel noncircadian function in the ovary. PER and TIM are constantly expressed in the follicle cells enveloping young oocytes. Genetic evidence suggests that PER and TIM interact in these cells, yet they do not translocate to the nucleus. The levels of TIM and PER in the ovary are affected neither by light nor by the lack of clock-positive elements Clock (Clk) and cycle (cyc). Taken together, these data suggest that per and tim are regulated differently in follicle cells than in clock cells. Experimental evidence suggests that a novel fitness-related phenotype may be linked to noncircadian expression of clock genes in the ovaries. Mated females lacking either per or tim show nearly a 50% decline in progeny, and virgin females show a similar decline in the production of mature oocytes. Disruption of circadian mechanism by either the depletion of TIM via constant light treatment or continuous expression of PER via GAL4/UAS expression system has no adverse effect on the production of mature oocytes.     

A Hierarchical Classification of Benthic Biodiversity and Assessment of Protected Areas in the Southern Ocean

An international effort is underway to establish a representative system of marine protected areas (MPAs) in the Southern Ocean to help provide for the long-term conservation of marine biodiversity in the region. Important to this undertaking is knowledge of the distribution of benthic assemblages. Here, our aim is to identify the areas where benthic marine assemblages are likely to differ from each other in the Southern Ocean including near-shore Antarctica. We achieve this by using a hierarchical spatial classification of ecoregions, bathomes and environmental types. Ecoregions are defined according to available data on biogeographic patterns and environmental drivers on dispersal. Bathomes are identified according to depth strata defined by species distributions. Environmental types are uniquely classified according to the geomorphic features found within the bathomes in each ecoregion. We identified 23 ecoregions and nine bathomes. From a set of 28 types of geomorphic features of the seabed, 562 unique environmental types were classified for the Southern Ocean. We applied the environmental types as surrogates of different assemblages of biodiversity to assess the representativeness of existing MPAs. We found that 12 ecoregions are not represented in MPAs and that no ecoregion has their full range of environmental types represented in MPAs. Current MPA planning processes, if implemented, will substantially increase the representation of environmental types particularly within 8 ecoregions. To meet internationally agreed conservation goals, additional MPAs will be needed. To assist with this process, we identified 107 spatially restricted environmental types, which should be considered for inclusion in future MPAs. Detailed supplementary data including a spatial dataset are provided.     

Rapamycin inhibits the secretory phenotype of senescent cells by a Nrf2‐independent mechanism

Senescent cells contribute to age‐related pathology and loss of function, and their selective removal improves physiological function and extends longevity. Rapamycin, an inhibitor of mTOR, inhibits cell senescence in vitro and increases longevity in several species. Nrf2 levels have been shown to decrease with aging and silencing Nrf2 gene induces premature senescence. Therefore, we explored whether Nrf2 is involved in the mechanism by which rapamycin delays cell senescence. In wild‐type (WT) mouse fibroblasts, rapamycin increased the levels of Nrf2, and this correlates with the activation of autophagy and a reduction in the induction of cell senescence, as measured by SA‐β‐galactosidase (β‐gal) staining, senescence‐associated secretory phenotype (SASP), and p16 and p21 molecular markers. In Nrf2KO fibroblasts, however, rapamycin still decreased β‐gal staining and the SASP, but rapamycin did not activate the autophagy pathway or decrease p16 and p21 levels. These observations were further confirmed in vivo using Nrf2KO mice, where rapamycin treatment led to a decrease in β‐gal staining and pro‐inflammatory cytokines in serum and fat tissue; however, p16 levels were not significantly decreased in fat tissue. Consistent with literature demonstrating that the Stat3 pathway is linked to the production of SASP, we found that rapamycin decreased activation of the Stat3 pathway in cells or tissue samples from both WT and Nrf2KO mice. Our data thus suggest that cell senescence is a complex process that involves at least two arms, and rapamycin uses Nrf2 to regulate cell cycle arrest, but not the production of SASP.     

Development of a QTL-environment-based predictive model for node addition rate in common bean

Key message

This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate.

Abstract

To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day^??1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50–90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.