Gene Erosion Can Lead to Gain-of-Function Alleles That Contribute to Bacterial Fitness

Despite our extensive knowledge of the genetic regulation of heat shock proteins (HSPs), the evolutionary routes that allow bacteria to adaptively tune their HSP levels and corresponding proteostatic robustness have been explored less. In this report, directed evolution experiments using the Escherichia coli model system unexpectedly revealed that seemingly random single mutations in its tnaA gene can confer significant heat resistance. Closer examination, however, indicated that these mutations create folding-deficient and aggregation-prone TnaA variants that in turn can endogenously and preemptively trigger HSP expression to cause heat resistance. These findings, importantly, demonstrate that even erosive mutations with disruptive effects on protein structure and functionality can still yield true gain-of-function alleles with a selective advantage in adaptive evolution

Mapping global hotspots and trends of water quality (1992–2010): a data driven approach

Clean water is key for sustainable development. However, large gaps in monitoring data limit our understanding of global hotspots of poor water quality and their evolution over time. We demonstrate the value added of a data-driven approach (here, random forest) to provide accurate high-frequency estimates of surface water quality worldwide over the period 1992-2010. We assess water quality for six indicators (temperature, dissolved oxygen, pH, salinity, nitrate-nitrite, phosphorus) relevant for the sustainable development goals. The performance of our modeling approach compares well to, or exceeds, the performance of recently published process-based models. The model’s outputs indicate that poor water quality is a global problem that impacts low-, middle- and high-income countries but with different pollutants. When countries become richer, water pollution does not disappear but evolves. Water quality exhibited a signif icant change between 1992 and 2010 with a higher percentage of grid cells where water quality shows a statistically significant deterioration (30%) compared to where water quality improved (22%)

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.Peer reviewe

SCGLR : un package R pour la régression linéaire généralisée sur composantes supervisées.

International audienc

A new component-based approach of regularization for multivariate generalized linear regression.

International audienc

SCGLR: a component-based multivariate regression method to model species distributions.

International audienc

Extension to mixed models of the Supervised Component-based Generalised Linear Regression

International audienceWe address the component-based regularisation of a multivariate Generalized Linear Mixed Model (GLMM). A set of random responses Y is modelled by a GLMM, using a set X of explanatory variables, a set T of additional covariates, and random effects used to introduce the dependence between statistical units. Variables in X are assumed many and redundant, so that regression demands regularisation. By contrast, variables in T are assumed few and selected so as to require no regularisation. Regularisation is performed building an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X. To estimate the model, we propose to maximise a criterion specific to the Supervised Component-based Generalised Linear Regression (SCGLR) within an adaptation of Schall's algorithm. This extension of SCGLR is tested on both simulated and real data, and compared to Ridge- and Lasso-based regularisations