A high-confidence Physcomitrium patens plasmodesmata proteome by iterative scoring and validation reveals diversification of cell wall proteins during evolution.

Plasmodesmata (PD) facilitate movement of molecules between plant cells. Regulation of this movement is still not understood. Plasmodesmata are hard to study, being deeply embedded within cell walls and incorporating several membrane types. Thus, structure and protein composition of PD remain enigmatic. Previous studies of PD protein composition identified protein lists with few validations, making functional conclusions difficult. We developed a PD scoring approach in iteration with large-scale systematic localization, defining a high-confidence PD proteome of Physcomitrium patens (HC300). HC300, together with bona fide PD proteins from literature, were placed in Pddb. About 65% of proteins in HC300 were not previously PD-localized. Callose-degrading glycolyl hydrolase family 17 (GHL17) is an abundant protein family with representatives across evolutionary scale. Among GHL17s, we exclusively found members of one phylogenetic clade with PD localization and orthologs occur only in species with developed PD. Phylogenetic comparison was expanded to xyloglucan endotransglucosylases/hydrolases and Exordium-like proteins, which also diversified into PD-localized and non-PD-localized members on distinct phylogenetic clades. Our high-confidence PD proteome HC300 provides insights into diversification of large protein families. Iterative and systematic large-scale localization across plant species strengthens the reliability of HC300 as basis for exploring structure, function, and evolution of this important organelle.

Gut Microbiota Patterns Predicting Long-Term Weight Loss Success in Individuals with Obesity Undergoing Nonsurgical Therapy.

Background: The long-term success of nonsurgical weight reduction programs is variable; thus, predictors of outcome are of major interest. We hypothesized that the intestinal microbiota known to be linked with diet and obesity contain such predictive elements. Methods: Metagenome analysis by shotgun sequencing of stool DNA was performed in a cohort of 15 adults with obesity (mean body mass index 43.1 kg/m2) who underwent a one-year multidisciplinary weight loss program and another year of follow-up. Eight individuals were persistently successful (mean relative weight loss 18.2%), and seven individuals were not successful (0.2%). The relationship between relative abundancies of bacterial genera/species and changes in relative weight loss or body mass index was studied using three different statistical modeling methods. Results: When combining the predictor variables selected by the applied statistical modeling, we identified seven bacterial genera and eight bacterial species as candidates for predicting success of weight loss. By classification of relative weight-loss predictions for each patient using 2-5 term models, 13 or 14 out of 15 individuals were predicted correctly. Conclusions: Our data strongly suggest that gut microbiota patterns allow individual prediction of long-term weight loss success. Prediction accuracy seems to be high but needs confirmation by larger prospective trials.

Regulatory Modules of Metabolites and Protein Phosphorylation in Arabidopsis Genotypes With Altered Sucrose Allocation.

Multi-omics data sets are increasingly being used for the interpretation of cellular processes in response to environmental cues. Especially, the posttranslational modification of proteins by phosphorylation is an important regulatory process affecting protein activity and/or localization, which, in turn, can have effects on metabolic processes and metabolite levels. Despite this importance, relationships between protein phosphorylation status and metabolite abundance remain largely underexplored. Here, we used a phosphoproteomics-metabolomics data set collected at the end of day and night in shoots and roots of Arabidopsis to propose regulatory relationships between protein phosphorylation and accumulation or allocation of metabolites. For this purpose, we introduced a novel, robust co-expression measure suited to the structure of our data sets, and we used this measure to construct metabolite-phosphopeptide networks. These networks were compared between wild type and plants with perturbations in key processes of sugar metabolism, namely, sucrose export (sweet11/12 mutant) and starch synthesis (pgm mutant). The phosphopeptide-metabolite network turned out to be highly sensitive to perturbations in sugar metabolism. Specifically, KING1, the regulatory subunit of SnRK1, was identified as a primary candidate connecting protein phosphorylation status with metabolism. We additionally identified strong changes in the fatty acid network of the sweet11/12 mutant, potentially resulting from a combination of fatty acid signaling and metabolic overflow reactions in response to high internal sucrose concentrations. Our results further suggest novel protein-metabolite relationships as candidates for future targeted research.

Predictive modeling of the early stages of semi-solid food ripening: Spatio-temporal dynamics in semi-solid casein matrices.

A mechanistic, spatio-temporal model to predict early stage semi-solid food ripening, exemplary for semi-solid casein matrices, was created using software based on the finite element method (FEM). The model was refined and validated by experimental data obtained during 8 wk of ripening of a casein matrix that was inoculated by one single central injection of starter culture. The resulting spatio-temporal distributions of lactococci strains, lactose, lactic acid/lactate and pH allowed us to optimize a number of parameters of the predictive model. Using the optimized model, the agreement between simulation and experiment was found to be satisfactory, with the pH matching best. The predictive model unveiled that effective diffusion of substrate and metabolites were crucial for an eventual homogeneous distribution of the measured substances. Hence, while using the optimized parameters from the single injection model, an injection technology for starter culture to inoculate and ferment casein matrices homogeneously was developed by means of solving another optimization problem with respect to injection positions. The casein matrix inoculated by the proposed injection pattern (21 injections, distance = 19 mm) showed sufficient homogeneity (bacterial activity and pH distribution) after the early stages of ripening, demonstrating the potential of application of the injection technology for fermentation of casein-based foods e.g. cheese.

Multiple linear regression modeling: Prediction of cheese curd dry matter during curd treatment.

Cheese curd dry matter determines functional properties and process parameters during cheese manufacture. Dry matter is affected by many internal (milk composition and pre-treatment) and external (cheese process parameters) factors that are not considered in the most common models. The purpose of this study was to consider a large number of multiple linear regression models that use these internal and external factors as predictor variables, and select the most suitable of these models in order to predict the cheese curd dry matter during curd treatment. Dry matter (DMexp,nat) was experimentally determined to create a native data set (n = 1013) for fitting the regression model. Dry matter was affected by curd treatment time (CTT), curd treatment temperature (ϑ), pH-value (pH), curd grain size (CGS), fat level (f) and degree of microfiltration (i). A large number of empirical regression models, organized into three different groups, depending on the predictors used, were developed on basis of DMexp,nat. A Monte Carlo approach was used to select the optimal model, taking into account the value of Akaike's information criterion (AICc) and the coefficient of determination (R2) of each model. The best models were further analyzed to check for potential bias and to verify that the model assumptions were met. We considered one model of group G2 with 11 terms to most closely fit the aforementioned criteria (native data set; R2 = 95.55). This model was successfully validated by an independent validation data set (n = 120; R2 = 91.95).

Divergent Allele Advantage Provides a Quantitative Model for Maintaining Alleles with a Wide Range of Intrinsic Merits.

The Major Histocompatibility Complex (MHC) is the most genetically diverse region of the genome in most vertebrates. Some form of balancing selection is necessary to account for the extreme diversity, but the precise mechanism of balancing selection is unknown. Due to the way MHC molecules determine immune recognition, overdominance (also referred to as heterozygote advantage) has been suggested as the main driving force behind this unrivalled diversity. However, both theoretical results and simulation models have shown that overdominance in its classical form cannot maintain large numbers of alleles unless all alleles confer unrealistically similar levels of fitness. There is increasing evidence that heterozygotes containing genetically divergent alleles allow for broader antigen presentation to immune cells, providing a selective mechanism for MHC polymorphism. By framing competing models of overdominance within a general framework, we show that a model based on Divergent Allele Advantage (DAA) provides a superior mechanism for maintaining alleles with a wide range of intrinsic merits, as intrinsically less-fit MHC alleles that are more divergent can survive under DAA. Specifically, our results demonstrate that a quantitative mechanism built from the DAA hypothesis is able to maintain polymorphism in the MHC. Applying such a model to both livestock breeding and conservation could provide a better way of identifying superior heterozygotes, and quantifying the advantages of genetic diversity at the MHC.

A Bayesian generalized random regression model for estimating heritability using overdispersed count data.

BACKGROUND: Faecal egg counts are a common indicator of nematode infection and since it is a heritable trait, it provides a marker for selective breeding. However, since resistance to disease changes as the adaptive immune system develops, quantifying temporal changes in heritability could help improve selective breeding programs. Faecal egg counts can be extremely skewed and difficult to handle statistically. Therefore, previous heritability analyses have log transformed faecal egg counts to estimate heritability on a latent scale. However, such transformations may not always be appropriate. In addition, analyses of faecal egg counts have typically used univariate rather than multivariate analyses such as random regression that are appropriate when traits are correlated. We present a method for estimating the heritability of untransformed faecal egg counts over the grazing season using random regression. RESULTS: Replicating standard univariate analyses, we showed the dependence of heritability estimates on choice of transformation. Then, using a multitrait model, we exposed temporal correlations, highlighting the need for a random regression approach. Since random regression can sometimes involve the estimation of more parameters than observations or result in computationally intractable problems, we chose to investigate reduced rank random regression. Using standard software (WOMBAT), we discuss the estimation of variance components for log transformed data using both full and reduced rank analyses. Then, we modelled the untransformed data assuming it to be negative binomially distributed and used Metropolis Hastings to fit a generalized reduced rank random regression model with an additive genetic, permanent environmental and maternal effect. These three variance components explained more than 80 % of the total phenotypic variation, whereas the variance components for the log transformed data accounted for considerably less. The heritability, on a link scale, increased from around 0.25 at the beginning of the grazing season to around 0.4 at the end. CONCLUSIONS: Random regressions are a useful tool for quantifying sources of variation across time. Our MCMC (Markov chain Monte Carlo) algorithm provides a flexible approach to fitting random regression models to non-normal data. Here we applied the algorithm to negative binomially distributed faecal egg count data, but this method is readily applicable to other types of overdispersed data.

Multitrait indices to predict worm length and number in sheep with natural, mixed predominantly Teladorsagia circumcincta infection.

Accurately identifying resistance to gastrointestinal nematode infections requires the ability to identify animals with low and high intensities of infection. The pathogenic effects of nematodes depend upon both the length and number of worms, neither of which can be measured in live animals. Indices that predict these quantities are urgently needed. Monthly fecal egg counts, bodyweights, IgA concentrations and pepsinogen concentrations were measured on Scottish Blackface sheep naturally infected with a mixture of nematodes, predominantly Teladorsagia circumcincta. Worm number and average worm length were available on over 500 necropsied lambs. We derived predictive indices for worm length and number using linear combinations of traits measured in live animals. The correlations between the prediction values and the observed values were 0.55 for worm length and 0.51 for worm number. These indices can be used to identify the most resistance and susceptible lambs.

An explicit immunogenetic model of gastrointestinal nematode infection in sheep.

Gastrointestinal nematodes are a global cause of disease and death in humans, wildlife and livestock. Livestock infection has historically been controlled with anthelmintic drugs, but the development of resistance means that alternative controls are needed. The most promising alternatives are vaccination, nutritional supplementation and selective breeding, all of which act by enhancing the immune response. Currently, control planning is hampered by reliance on the faecal egg count (FEC), which suffers from low accuracy and a nonlinear and indirect relationship with infection intensity and host immune responses. We address this gap by using extensive parasitological, immunological and genetic data on the sheep-Teladorsagia circumcincta interaction to create an immunologically explicit model of infection dynamics in a sheep flock that links host genetic variation with variation in the two key immune responses to predict the observed parasitological measures. Using our model, we show that the immune responses are highly heritable and by comparing selective breeding based on low FECs versus high plasma IgA responses, we show that the immune markers are a much improved measure of host resistance. In summary, we have created a model of host-parasite infections that explicitly captures the development of the adaptive immune response and show that by integrating genetic, immunological and parasitological understanding we can identify new immune-based markers for diagnosis and control.

The transfer of IgA from mucus to plasma and the implications for diagnosis and control of nematode infections.

Immunoglobulin A (IgA) activity has been associated with reduced growth and fecundity of Teladorsagia circumcincta. IgA is active at the site of infection in the abomasal mucus. However, while IgA activity in abomasal mucus is not easily measured in live animals without invasive methods, IgA activity can be readily detected in the plasma, making it a potentially valuable tool in diagnosis and control. We used a Bayesian statistical analysis to quantify the relationship between mucosal and plasma IgA in sheep deliberately infected with T. circumcincta. The transfer of IgA depends on mucosal IgA activity as well as its interaction with worm number and size; together these account for over 80% of the variation in plasma IgA activity. By quantifying the impact of mucosal IgA and worm number and size on plasma IgA, we provide a tool that can allow more meaningful interpretation of plasma IgA measurements and aid the development of efficient control programmes.