Inheritance of somatic mutations by animal offspring.

Since 1892, it has been widely assumed that somatic mutations are evolutionarily irrelevant in animals because they cannot be inherited by offspring. However, some nonbilaterians segregate the soma and germline late in development or never, leaving the evolutionary fate of their somatic mutations unknown. By investigating uni- and biparental reproduction in the coral Acropora palmata (Cnidaria, Anthozoa), we found that uniparental, meiotic offspring harbored 50% of the 268 somatic mutations present in their parent. Thus, somatic mutations accumulated in adult coral animals, entered the germline, and were passed on to swimming larvae that grew into healthy juvenile corals. In this way, somatic mutations can increase allelic diversity and facilitate adaptation across habitats and generations in animals.

The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets.

The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the ΔK method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of K and is sensitive to uneven sampling. If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of K to re-estimate genetic structure in 41 empirical microsatellite data sets; 15 from a broad range of taxa and 26 from one phylogenetic group, coral. We compared alternative estimates of K (Puechmaille statistics) with traditional (ΔK and posterior probability) estimates and found widespread disagreement of estimators across data sets. Thus, one estimator alone is insufficient for determining the optimal number of clusters; this was regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) did not necessarily clarify which clustering solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to ΔK. Disagreement between traditional and recent estimators may have important biological implications, such as previously unrecognized population structure, as was the case for many studies reanalysed here.

Nanoscale dynamics of cellulose digestion by the cellobiohydrolase TrCel7A.

Understanding the mechanism by which cellulases from bacteria, fungi, and protozoans catalyze the digestion of lignocellulose is important for developing cost-effective strategies for bioethanol production. Cel7A from the fungus Trichoderma reesei is a model exoglucanase that degrades cellulose strands from their reducing ends by processively cleaving individual cellobiose units. Despite being one of the most studied cellulases, the binding and hydrolysis mechanisms of Cel7A are still debated. Here, we used single-molecule tracking to analyze the dynamics of 11,116 quantum dot-labeled TrCel7A molecules binding to and moving processively along immobilized cellulose. Individual enzyme molecules were localized with a spatial precision of a few nanometers and followed for hundreds of seconds. Most enzyme molecules bound to cellulose in a static state and dissociated without detectable movement, whereas a minority of molecules moved processively for an average distance of 39 nm at an average speed of 3.2 nm/s. These data were integrated into a three-state model in which TrCel7A molecules can bind from solution into either static or processive states and can reversibly switch between states before dissociating. From these results, we conclude that the rate-limiting step for cellulose degradation by Cel7A is the transition out of the static state, either by dissociation from the cellulose surface or by initiation of a processive run. Thus, accelerating the transition of Cel7A out of its static state is a potential avenue for improving cellulase efficiency.