Sexual size dimorphism and Rensch's rule in Canidae

The size variation between males and females of a species is a phenomenon known as sexual size dimorphism (SSD). The observed patterns of variation in SSD among species has led to the formulation of Rensch's rule, which establishes that, in species showing a male size bias, SSD increases with an increase in the body size of the species. However, for species in which there is a female size bias, the SSD would decrease when the body size of the species increases. In the present study, we examined the variation in body size and SSD of 33 species of canids from estimates of body mass and body length. We studied its relationship with life‐history characteristics and tested Rensch's rule using phylogenetic generalized least squares and phylogenetic reduced major axis regressions, respectively. We observed the existence of correlation between body mass and body length, although the SSDs from these estimators are uncorrelated. SSD did not show the pattern predicted by Rensch's rule. SSD also did not show any correlation with life‐history traits. It is likely that the low SSD observed in canids is related to the monogamy observed in the family, which is a rare situation in mammals.     

Translating niche features: Modelling differential exposure of Argentine reptiles to global climate change

Global climate change affects the distributions of ectotherms and may be the cause of several conservation problems, such as great displacement of climatic suitable spaces for species and, consequently, important reductions of the extent of liveable places, threatening the existence of many of them. Species exposure (and hence vulnerability) to global climate change is linked to features of their climatic niches (such as the relative position of the inhabited localities of each species in the climatic space), and therefore to characteristics of their geographic ranges (such as the extent of the distributions or altitudinal range inhabited by the species). In order to analyze the pattern of response of Argentine reptiles to global climate change, we ran phylogenetic generalized least squares models using species exposure to global climate change as a response variable, and (i) niche properties (breadth and position of the species in the climate space) and (ii) general features of the distribution of species (maximum latitude, altitudinal range, maximum elevation, distributional range and proximity to the most important dispersal barrier) as predictors. Our results suggest that the best way to explain climate change exposure is by combining breadth and position of climatic niche of the species or combining geographic features that are indicators of both niche characteristics. Our best model shows that in our study area, species with the narrowest distributional ranges that also inhabit the highest elevations are the most exposed to the effects of global climate change. In this sense, reptile species from Yungas, Puna and Andes ecoregions could be especially vulnerable to the effects of climate change. We believe that these types of models may represent an interesting tool for determining species and places particularly threatened by the effects of global climate change, which should be strongly considered in conservation planning.     

A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F₁ hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.     

Determination of community structure through deconvolution of PLFA-FAME signature of mixed population

Phospholipid fatty acids (PLFAs) as biomarkers are well established in the literature. A general method based on least square approximation (LSA) was developed for the estimation of community structure from the PLFA signature of a mixed population where biomarker PLFA signatures of the component species were known. Fatty acid methyl ester (FAME) standards were used as species analogs and mixture of the standards as representative of the mixed population. The PLFA/FAME signatures were analyzed by gas chromatographic separation, followed by detection in flame ionization detector (GC-FID). The PLFAs in the signature were quantified as relative weight percent of the total PLFA. The PLFA signatures were analyzed by the models to predict community structure of the mixture. The LSA model results were compared with the existing "functional group" approach. Both successfully predicted community structure of mixed population containing completely unrelated species with uncommon PLFAs. For slightest intersection in PLFA signatures of component species, the LSA model produced better results. This was mainly due to inability of the "functional group" approach to distinguish the relative amounts of the common PLFA coming from more than one species. The performance of the LSA model was influenced by errors in the chromatographic analyses. Suppression (or enhancement) of a component's PLFA signature in chromatographic analysis of the mixture, led to underestimation (or overestimation) of the component's proportion in the mixture by the model. In mixtures of closely related species with common PLFAs, the errors in the common components were adjusted across the species by the model.     

Dynamic analysis of GS-NS0 cells producing a recombinant monoclonal antibody during fed-batch culture

In this study we have analyzed the dynamic covariation of the mammalian cell proteome with respect to functional phenotype during fed-batch culture of NS0 murine myeloma cells producing a recombinant IgG(4) monoclonal antibody. GS-NS0 cells were cultured in duplicate 10 L bioreactors (36.5 degrees C, 15% DOT, pH 7.0) for 335 h and supplemented with a continuous feed stream after 120 h. Cell-specific growth rate declined continuously after 72 h of culture. Cell-specific recombinant monoclonal antibody production rate (qP) varied sixfold through culture. Whilst qP correlated with relative recombinant heavy chain mRNA abundance up to 216 h, qP subsequently declined, independent of recombinant heavy chain or light chain mRNA abundance. GS-NS0 cultures were sampled at 48 h intervals between 24 and 264 h of culture for proteomic analyses. Total protein abundance and nascent polypeptide synthesis was determined by 2D PAGE of unlabeled proteins visualized by SYPRO Ruby and autoradiography of (35)S-labeled polypeptides, respectively. Covariation of nascent polypeptide synthesis and abundance with biomass-specific cell growth, glucose and glutamate consumption, lactate and Mab production rates were then examined using two partial least squares regression models. Most changes in polypeptide synthesis or abundance for proteins previously identified by mass spectrometry were positively correlated with biomass-specific growth rate. We conclude that the substantial transitions in cell physiology and qP that occur during culture utilize a relatively constant complement of the most abundant host cell machines that vary primarily with respect to induced changes in cell growth rate.     

WHY DO SOME SIBLINGS ATTACK EACH OTHER? COMPARATIVE ANALYSIS OF AGGRESSION IN AVIAN BROODS

In many parentally fed species, siblings compete for food not only by begging and scrambling, but also by violently attacking each other. This aggressive competition has mostly been studied in birds, where it is often combined with dominance subordination, aggressive intimidation, and siblicide. Previous experimental and theoretical studies proposed several life-history, morphological, and behavioral variables that may facilitate the evolution of broodmate aggression, and explain its taxonomic distribution. Here we apply phylogenetic comparative analyses for the first time to test the influence of five hypothesized facilitators of the evolution of broodmate aggression, analyzing 69 species in seven avian families using two quantitative measures of aggression: incidence and intensity. We show that incidence and intensity of aggression increase with long nestling periods and indirect feeding, and small brood size is associated with intense aggression. Large food parcels were not correlated with either the incidence or intensity of aggression. Our study suggests that indirect feeding, long nestling periods, and small broods, possibly in combination with other factors, have tended to favor the evolution of aggressive broodmate competition.     

Expression and functional analysis of flotillins in Dugesia japonica

FLOTILLIN-1 and FLOTILLIN-2 are membrane rafts associated proteins that have been implicated in insulin and growth factor signaling, endocytosis, cell migration, proliferation, differentiation, cytoskeleton remodeling and membrane trafficking. Furthermore, FLOTILLINs also play important roles in the progression of cancer and neurodegenerative diseases. In this study, the roles of flotillins are investigated in planarian Dugesia japonica. The results show that Djflotillin-1 and Djflotillin-2 play a key role in homeostasis maintenance and regeneration process by regulating the proliferation of the neoblast cells, they are not involved in the maintenance and regeneration of the central nervous system in planarians.     

Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.     

Nonparametric estimation of the effects of quantitative trait loci

Interval mapping of quantitative trait loci from breeding experiments plays an important role in understanding the mechanisms of disease, both in humans and other organisms. Standard approaches to estimation involve parametric assumptions for the component distributions and may be sensitive to model misspecification. Some nonparametric tests have been studied. However, nonparametric estimation of the phenotypic distributions has not been considered in the genetics literature, even though such methods might provide essential nonparametric summaries for comparing different loci. We develop a sufficient condition for identifiability of the phenotypic distributions. Simple nonparametric estimators for the distributions are proposed for uncensored and right censored data. They have a closed form and their small and large sample properties are readily established. Their practical utility as numerical summaries which complement nonparametric tests is demonstrated on two recent genetics examples.