Deeply conserved susceptibility in a multi‐host,multi‐parasite system

Variation in susceptibility is ubiquitous in multi‐host, multi‐parasite assemblages, and can have profound implications for ecology and evolution in these systems. The extent to which susceptibility to parasites is phylogenetically conserved among hosts can be revealed by analysing diverse regional communities. We screened for haemosporidian parasites in 3983 birds representing 40 families and 523 species, spanning ~ 4500 m elevation in the tropical Andes. To quantify the influence of host phylogeny on infection status, we applied Bayesian phylogenetic multilevel models that included a suite of environmental, spatial, temporal, life history and ecological predictors. We found evidence of deeply conserved susceptibility across the avian tree; host phylogeny explained substantial variation in infection status, and results were robust to phylogenetic uncertainty. Our study suggests that susceptibility is governed, in part, by conserved, latent aspects of anti‐parasite defence. This demonstrates the importance of deep phylogeny for understanding present‐day ecological interactions.     

A multi‐colour/multi‐affinity marker set to visualize phosphoinositide dynamics in Arabidopsis

Phosphatidylinositolphosphates (PIPs) are phospholipids that contain a phosphorylated inositol head group. PIPs represent a minor fraction of total phospholipids, but are involved in many regulatory processes, such as cell signalling and intracellular trafficking. Membrane compartments are enriched or depleted in specific PIPs, providing a unique composition for these compartments and contributing to their identity. The precise subcellular localization and dynamics of most PIP species is not fully understood in plants. Here, we designed genetically encoded biosensors with distinct relative affinities and expressed them stably in Arabidopsis thaliana. Analysis of this multi‐affinity ‘PIPline’ marker set revealed previously unrecognized localization of various PIPs in root epidermis. Notably, we found that PI(4,5)P₂ is able to localize PIP₂‐interacting protein domains to the plasma membrane in non‐stressed root epidermal cells. Our analysis further revealed that there is a gradient of PI4P, with the highest concentration at the plasma membrane, intermediate concentration in post‐Golgi/endosomal compartments, and the lowest concentration in the Golgi. Finally, we also found a similar gradient of PI3P from high in late endosomes to low in the tonoplast. Our library extends the range of available PIP biosensors, and will allow rapid progress in our understanding of PIP dynamics in plants.     

Clustering of multi‐domain protein sequences

The overall function of a multi‐domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence alignment‐based methods commonly utilize domain‐level information and provide classification only at the level of domains. Such methods are not capable of taking into account the contributions of other domains in the proteins, and domain‐linker regions and classify multi‐domain proteins. An alignment‐free protein sequence comparison tool, CLAP (CLAssification of Proteins) was previously developed in our laboratory to especially handle multi‐domain protein sequences without a requirement of defining domain boundaries and sequential order of domains. Through this method we aim to achieve a biologically meaningful classification scheme for multi‐domain protein sequences. In this article, CLAP‐based classification has been explored on 5 datasets of multi‐domain proteins and we present detailed analysis for proteins containing (1) Tyrosine phosphatase and (2) SH3 domain. At the domain‐level CLAP‐based classification scheme resulted in a clustering similar to that obtained from an alignment‐based method. CLAP‐based clusters obtained for full‐length datasets were shown to comprise of proteins with similar functions and domain architectures. Our study demonstrates that multi‐domain proteins could be classified effectively by considering full‐length sequences without a requirement of identification of domains in the sequence.     

Integrative analysis from multi‐centre studies identifies a function‐derived personalized multi‐gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.     

A stage‐wise approach for the analysis of multi‐environment trials

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi‐environment trials can be efficiently analysed by mixed models. A single‐stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two‐stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage‐wise approaches suggested so far are approximate in that they cannot fully reproduce a single‐stage analysis, except in very simple cases, because the variance–covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage‐wise method, which carries forward the full variance–covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single‐stage analysis. Computations are made efficient by a diagonalisation of the residual variance–covariance matrix, which necessitates a corresponding linear transformation of both the first‐stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three‐stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta‐analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta‐analysis.     

Spatial insurance in multi‐trophic metacommunities

Metacommunity theory suggests that dispersal is a key driver of diversity and ecosystem functioning in changing environments. The capacity of dispersal to mitigate effects of environmental change might vary among trophic groups, potentially resulting in changes in trophic interactions and food web structure. In a mesocosm experiment, we compared the compositional response of bacteria, phyto‐ and zooplankton to a factorial manipulation of acidification and dispersal. We found that the buffering capacity of dispersal varied among trophic groups: dispersal alleviated the negative effect of acidification on phytoplankton diversity mid‐experiment, but had no effect on the diversity of zooplankton and bacteria. Likewise, trophic groups differed in whether dispersal facilitated compositional change. Dispersal accelerated changes in phytoplankton composition under acidification, possibly mediated by changes in trophic interactions, but had no effect on the composition of zooplankton and bacteria. Overall, our results suggest that the potential for spatial insurance can vary among trophic groups.     

The hidden complexity of multi‐species interactions

In this issue of the Journal of Vegetation Science, Michalet et al. used species removal experiments to detect direct and indirect species interactions in a sub‐alpine grassland. They found evidence for competition, facilitation and a range of indirect interactions at the species level, but no measurable effects when aggregated at the community level. Their results raise fundamental questions regarding the role and importance of indirect interactions in structuring ecological communities.