A single peroxisomal targeting signal mediates matrix protein import in diatoms

Peroxisomes are single membrane bound compartments. They are thought to be present in almost all eukaryotic cells, although the bulk of our knowledge about peroxisomes has been generated from only a handful of model organisms. Peroxisomal matrix proteins are synthesized cytosolically and posttranslationally imported into the peroxisomal matrix. The import is generally thought to be mediated by two different targeting signals. These are respectively recognized by the two import receptor proteins Pex5 and Pex7, which facilitate transport across the peroxisomal membrane. Here, we show the first in vivo localization studies of peroxisomes in a representative organism of the ecologically relevant group of diatoms using fluorescence and transmission electron microscopy. By expression of various homologous and heterologous fusion proteins we demonstrate that targeting of Phaeodactylum tricornutum peroxisomal matrix proteins is mediated only by PTS1 targeting signals, also for proteins that are in other systems imported via a PTS2 mode of action. Additional in silico analyses suggest this surprising finding may also apply to further diatoms. Our data suggest that loss of the PTS2 peroxisomal import signal is not reserved to Caenorhabditis elegans as a single exception, but has also occurred in evolutionary divergent organisms. Obviously, targeting switching from PTS2 to PTS1 across different major eukaryotic groups might have occurred for different reasons. Thus, our findings question the widespread assumption that import of peroxisomal matrix proteins is generally mediated by two different targeting signals. Our results implicate that there apparently must have been an event causing the loss of one targeting signal even in the group of diatoms. Different possibilities are discussed that indicate multiple reasons for the detected targeting switching from PTS2 to PTS1.     

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins

We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of beta-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human disease-related proteins, including prion protein, lysozyme and beta2-microglobulin. TANGO also correctly predicts pathogenic as well as protective mutations of the Alzheimer beta-peptide, human lysozyme and transthyretin, and discriminates between beta-sheet propensity and aggregation. Our results confirm the model of intermolecular beta-sheet formation as a widespread underlying mechanism of protein aggregation. Furthermore, the algorithm opens the door to a fully automated, sequence-based design strategy to improve the aggregation properties of proteins of scientific or industrial interest.     

RAT BRAIN SYNAPTIC VESICLES: UPTAKE SPECIFICITIES OF [3H]NOREPINEPHRINE AND [3H]SEROTONIN IN PREPARATIONS FROM WHOLE BRAIN AND BRAIN REGIONS1

A fraction containing neurotransmitter storage vesicles was isolated from rat whole brain and brain regions, and the uptakes of [³H]norepinephrine and [³H]serotonin were determined in vitro. Norepinephrine uptake in vesicle preparations from corpus striatum was higher than in prep arations from cerebral cortex, and uptake in vesicles from the remainder (midbrain + brainstem + cerebellum) was intermediate. The K_m for norepinephrine uptake was the same in the three brain regions, but the regions differed in maximal uptake capacity by factors which paralleled total catecholamine concentration rather than content of norepinephrine alone. Intracisternal administration of 6-hydroxydopamine, but not of 5,6-dihydroxytryptamine, reduced vesicular norepinephrine uptake, and pretreat-ment with desmethylimipramine (which protects specifically norepinephrine neurons but not dopamine neurons from the 6-hydroxydopamine) only partially prevented the loss of vesicular norepinephrine uptake. These studies indicate that uptake of norepinephrine by rat brain vesicle preparations occurs in vesicles from norepinephrine and dopamine neurons, but probably not in vesicles from serotonin neurons. Uptake of serotonin by brain vesicle preparations exhibited time, temperature and ATP-Mg²⁺ requirements nearly identical to those of norepinephrine uptake. The affinity of serotonin uptake matched that of serotonin for inhibition of norepinephrine uptake, and the maximal capacity was the same for serotonin as for norepinephrine. Norepinephrine, dopamine and reserpine inhibited serotonin uptake in a purely competitive fashion, with K_is similar to those for inhibition of norepinephrine uptake. Whereas 5,6-dihydroxytryptamine treatment reduced synaptosomal serotonin uptake but not vesicular serotonin uptake, 6-hydroxydopamine reduced vesicular serotonin uptake in the absence of reductions in synaptosomal serotonin uptake. Thus, in this preparation, serotonin appears to be taken up in vitro into catecholamine vesicles, rather than into serotonin vesicles.     

Testing for Spatially Divergent Selection: Comparing QST to FST

Q_ST is a standardized measure of the genetic differentiation of a quantitative trait among populations. The distribution of Q_ST''s for neutral traits can be predicted from the F_ST for neutral marker loci. To test for the neutral differentiation of a quantitative trait among populations, it is necessary to ask whether the Q_ST of that trait is in the tail of the probability distribution of neutral traits. This neutral distribution can be estimated using the Lewontin–Krakauer distribution and the F_ST from a relatively small number of marker loci. We develop a simulation method to test whether the Q_ST of a given trait is consistent with the null hypothesis of selective neutrality over space. The method is most powerful with small mean F_ST, strong selection, and a large number (>10) of measured populations. The power and type I error rate of the new method are far superior to the traditional method of comparing Q_ST and F_ST.IN 1993, Spitze (1993) and Prout and Barker (1993) introduced Q_ST, a quantitative genetic analog of Wright''s F_ST. Just as F_ST gives a standardized measure of the genetic differentiation among populations for a genetic locus, Q_ST measures the amount of genetic variance among populations relative to the total genetic variance. In the years since, Q_ST has been frequently used to test for the effects of spatially divergent (or less commonly, spatially uniform) selection (see reviews in Lynch et al. 1999; Merilä and Crnokrak 2001; McKay and Latta 2002; Howe et al. 2003; Leinonen et al. 2008; Whitlock 2008). In principle, the average Q_ST of a neutral additive quantitative trait is expected to be equal to the mean value of F_ST for neutral genetic loci. F_ST can be readily measured on commonly available genetic markers, and Q_ST can be measured as well with an appropriate breeding design in a common-garden setting. As a result, Q_ST promises to be an index of the effect of selection on the quantitative trait. If Q_ST is higher than F_ST, then this is taken as evidence of spatially divergent selection on the trait. If Q_ST is much smaller than F_ST, then this has been taken as evidence of spatially uniform stabilizing selection, which makes the trait diverge less than expected by chance.The comparison with F_ST is essential to rule out genetic drift as an alternative mechanism for phenotypic divergence among populations. Because finite populations may diverge genetically in the absence of selection, divergence must be greater than expected by drift alone if we are to conclusively demonstrate that divergent selection has played a role in genetic differentiation among populations. Therefore it has become common practice to use F_ST of putatively neutral markers as a control for the effects of genetic drift and to compare observed Q_ST values for traits to these neutral F_ST values.These comparisons follow two separate methods, to address related but distinct questions. First, many studies of quantitative genetic differentiation measure the Q_ST of many traits and the F_ST of many loci, followed by a comparison of the mean Q_ST to the mean F_ST. Such a comparison may judge whether the conditions are suitable in that species for local adaptation, that is, whether selective differences between populations are large enough relative to gene flow to allow adaptive differentiation (Whitlock 2008). We do not consider this sort of comparison in this article.The other type of comparison asks whether the Q_ST of a single trait is greater than expected by drift, as measured by F_ST. This type of comparison is most common, but it is statistically difficult. Unfortunately, as emphasized in a recent review by Whitlock (2008), there is great variation in the expected F_ST among neutral loci and among the Q_ST of different neutral traits (see Figure 1). The majority of this variation results from evolutionary differences between loci and not sampling error in the observations. Rogers and Harpending (1983) imply that the distribution of Q_ST of a single neutral trait should be approximately equivalent to that for F_ST of a single neutral locus, and this has been confirmed by simulation for traits determined by additive loci compared to biallelic marker loci (Whitlock 2008). The two distributions are similar, but there is great heterogeneity among traits or loci. As a result, to show that selection is acting on a trait, it is necessary to show that the value of Q_ST has a low probability of being observed given the distribution of neutral Q_ST.Open in a separate windowFigure 1.—The distribution of F_ST for neutral loci and Q_ST for neutral quantitative traits. The histograms show the results of simulations of a set of 10 local populations each of 100 individuals, connected by 5% migration following island model assumptions. The solid line shows the distribution predicted by the Lewontin–Krakauer distribution. The distribution of Q_ST for neutral traits is very similar to the distribution of F_ST for single neutral loci, as can be seen by their mutual good fit to the Lewontin–Krakauer distribution (Figure modified from Whitlock 2008).Comparing Q_ST to the distribution inferred from F_ST is difficult for two reasons. First, typical data sets rarely include enough loci to directly infer the distribution of F_ST without extra inferential steps. In our approach, we use the distribution of Q_ST predicted from the mean F_ST and the χ² distribution by Lewontin and Krakauer (1973) to bridge this gap. Whitlock (2008) has shown that this distribution is appropriate for nearly all realistic situations for traits determined by additive genetic effects. Second, Q_ST for a trait is rarely measured with high precision, so the position of a given estimated Q_ST value in the distribution cannot be known without error.To test the null hypothesis that the spatial distribution of a particular trait is not affected by selection, we wish to compare the observed of that trait (marked with a hat to indicate it is an estimate) to the distribution of Q_ST expected for neutral traits. Unfortunately, calculating the distribution of Q_ST for neutral traits is not straightforward, because the estimate of Q_ST for a particular trait is variable for several reasons. The estimate of Q_ST is subject to measurement error, caused by the finite samples of families and individuals in the quantitative genetic experiment. These cause error in the estimate of the additive genetic variance within populations (V_A,within) and the genetic variance among populations (V_G,among), which translate into error of the estimate of Q_ST. In addition, there is another source of variation in Q_ST among neutral traits, caused by the idiosyncrasies of the evolutionary process in each local population in the study. The true value of Q_ST for the set of populations being studied can vary tremendously around its expectation, even for neutral traits, because by chance a finite set of populations may drift in a similar direction (Whitlock 2008). As a result, measurements of Q_ST can vary because of both statistical and evolutionary variation.Fortunately, these two sources of variation are fairly well understood individually. The sampling error for the estimates of the variance components can be estimated from standard approaches, and this variation can be well approximated using information from the mean squares of the analysis of the breeding experiment (O''Hara and Merilä 2005). The variation in neutral Q_ST that results from heterogeneity of evolutionary history can be approximated by the Lewontin–Krakauer distribution (Lewontin and Krakauer 1973), if information is available on the mean Q_ST of neutral traits (Whitlock 2008). This approximation does not depend on the demographic details of the populations in question (Whitlock 2008), but the effects of deviations from assumptions of additive gene effect have not yet been tested. The mean of the distribution of values of Q_ST for neutral traits is usually not known, but fortunately the mean of the distribution of F_ST of neutral loci is expected to be approximately equal to the mean Q_ST of neutral traits (Spitze 1993), and this does not depend on demographic details (Whitlock 1999). Therefore the mean F_ST measured from a series of genetic markers thought to be selectively neutral can be combined with the Lewontin–Krakauer distribution to predict the distribution of true neutral Q_ST across the range of possible evolutionary trajectories.Given that the mean value of of neutral traits is expected to equal the mean F_ST of neutral markers under certain assumptions (discussed later), we will use as a test statistic and compare the observed quantity to the zero value proposed by the null hypothesis. We will use a traditional hypothesis testing approach, which means that we need to specify the sampling distribution of under the assumption of neutrality. Traditionally, the sampling distribution of is inferred from the data on the trait itself, for example, using bootstrapping to infer the sampling distribution. This is appropriate when calculating a confidence interval for Q_ST but is a biased measure of the sampling variance of neutral Q_ST. The variance of the sampling distribution of varies with its expected value; larger values of true Q_ST have more variable sampling distributions than traits with smaller true Q_ST. This association between Q_ST and its sampling error is quite strong, as shown in Figure 2. As a result, if the sampling properties of neutral are inferred from a trait with high Q_ST, the estimate of the variance of the null distribution will be too high, and the hypothesis test comparing to F_ST will be conservative. On the other hand, if a low Q_ST is used to estimate the variance of the null distribution, the estimated error will be too small, and the test will reject true null hypotheses too often.Open in a separate windowFigure 2.—The width of the estimated sampling distribution of varies with mean Q_ST. The solid line shows the sampling distribution of Q_ST when the true mean Q_ST value is 0.05. The dotted line shows the sampling distribution that would be estimated for Q_ST from a trait that by chance was at the first percentile of this distribution, and the dashed line shows the sampling distribution that would be inferred from a value taken at the 99th percentile. If the Q_ST of a trait differs from the expectation by chance, then the width of the sampling distribution will also be estimated with substantial error. In particular, the error variance of is overestimated with Q_ST estimates that are too high and underestimated for small Q_ST values.We address this problem by using F_ST from putatively neutral maker loci in combination with estimates of the additive genetic variance within populations to predict the sampling variance that would be expected for the Q_ST of a neutral trait. We show that the power and type I error rate of this test are greatly superior to traditional methods.     

Tooth morphogenesis and ameloblast differentiation are regulated by micro-RNAs

Teeth form as appendages of the ectoderm and their morphogenesis is regulated by tissue interactions mediated by networks of conserved signal pathways. Micro-RNA (miRNA) pathway has emerged as important regulator of various aspects of embryonic development, but its function in odontogenesis has not been elucidated. We show that the expression of RNAi pathway effectors is dynamic during tooth morphogenesis and differentiation of dental cells. Based on microarray profiling we selected 8 miRNAs expressed during morphogenesis and 7 miRNAs in the incisor cervical loop containing the stem cell niche. These miRNAs were mainly expressed in the dental epithelium. Conditional deletion of Dicer-1 in the epithelium (Dcr^K14−/−) resulted in rather mild but significant aberrations in tooth shape and enamel formation. The cusp patterns of the Dcr^K14−/− molar crowns resembled the patterns of both ancestral muroid rodents and mouse mutants with modulated signal pathways. In the Dcr^K14−/− incisors, longitudinal grooves formed on the labial surface and these were shown to result from ectopic budding of the progenitor epithelium in the cervical loop. In addition, ameloblast differentiation was impaired and resulted in deficient enamel formation in molars and incisors. To help the identification of candidate target genes of the selected tooth enriched miRNAs, we constructed a new ectodermal organ oriented database, miRTooth. The predicted targets of the selected miRNAs included several components of the main morphogenetic signal pathways regulating tooth development. Based on our findings we suggest that miRNAs modulate tooth morphogenesis largely by fine tuning conserved signaling networks and that miRNAs may have played important roles during tooth evolution.     

Biogeography of microbial communities in high-latitude ecosystems: Contrasting drivers for methanogens,methanotrophs and global prokaryotes

Methane-cycling is becoming more important in high-latitude ecosystems as global warming makes permafrost organic carbon increasingly available. We explored 387 samples from three high-latitudes regions (Siberia, Alaska and Patagonia) focusing on mineral/organic soils (wetlands, peatlands, forest), lake/pond sediment and water. Physicochemical, climatic and geographic variables were integrated with 16S rDNA amplicon sequences to determine the structure of the overall microbial communities and of specific methanogenic and methanotrophic guilds. Physicochemistry (especially pH) explained the largest proportion of variation in guild composition, confirming species sorting (i.e., environmental filtering) as a key mechanism in microbial assembly. Geographic distance impacted more strongly beta diversity for (i) methanogens and methanotrophs than the overall prokaryotes and, (ii) the sediment habitat, suggesting that dispersal limitation contributed to shape the communities of methane-cycling microorganisms. Bioindicator taxa characterising different ecological niches (i.e., specific combinations of geographic, climatic and physicochemical variables) were identified, highlighting the importance of Methanoregula as generalist methanogens. Methylocystis and Methylocapsa were key methanotrophs in low pH niches while Methylobacter and Methylomonadaceae in neutral environments. This work gives insight into the present and projected distribution of methane-cycling microbes at high latitudes under climate change predictions, which is crucial for constraining their impact on greenhouse gas budgets.     

Expectation-Maximization Binary Clustering for Behavioural Annotation

The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into elementary units of movement. Key challenges for movement trajectory segmentation are to (i) minimize the need of supervision, (ii) reduce computational costs, (iii) minimize the need of prior assumptions (e.g. simple parametrizations), and (iv) capture biologically meaningful semantics, useful across a broad range of species. We introduce the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering algorithm based on the maximum likelihood estimation of a Gaussian mixture model. This is an iterative algorithm with a closed form step solution and hence a reasonable computational cost. The method looks for a good compromise between statistical soundness and ease and generality of use (by minimizing prior assumptions and favouring the semantic interpretation of the final clustering). Here we focus on the suitability of the EMbC algorithm for behavioural annotation of movement data. We show and discuss the EMbC outputs in both simulated trajectories and empirical movement trajectories including different species and different tracking methodologies. We use synthetic trajectories to assess the performance of EMbC compared to classic EMC and Hidden Markov Models. Empirical trajectories allow us to explore the robustness of the EMbC to data loss and data inaccuracies, and assess the relationship between EMbC output and expert label assignments. Additionally, we suggest a smoothing procedure to account for temporal correlations among labels, and a proper visualization of the output for movement trajectories. Our algorithm is available as an R-package with a set of complementary functions to ease the analysis.