Notes

PSMA Theranostics: Science and exercise.
RNA-sequencing analyses of E. timonensis identified a six-gene cluster, herein named the γBB utilization (gbu) gene cluster, which is upregulated in response to γBB. Combinatorial cloning and functional studies identified four genes (gbuA, gbuB, gbuC and gbuE) that are necessary and sufficient to recapitulate the conversion of γBB to TMA when coexpressed in Escherichia coli. Finally, reanalysis of samples (n = 113) from a clinical, randomized diet, intervention study showed that the abundance of faecal gbuA correlates with plasma TMAO and a red meat-rich diet. Our findings reveal a microbial gene cluster that is critical to dietary carnitine → γBB → TMA → TMAO transformation in hosts and contributes to CVD risk.Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land-climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. this website We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.Characterizing the mode-the way, manner or pattern-of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell-cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.In many species, individuals can develop into strikingly different morphs, which are determined by a simple Mendelian locus. How selection shapes loci that control complex phenotypic differences remains poorly understood. In the spider Oedothorax gibbosus, males develop either into a 'hunched' morph with conspicuous head structures or as a fast-developing 'flat' morph with a female-like appearance. We show that the hunched-determining allele contains a unique genomic fragment of approximately 3 megabases that is absent in the flat-determining allele. This fragment comprises dozens of genes that duplicated from genes found at the same as well as different chromosomes. All functional duplicates, including a duplicate of the key sexual differentiation regulatory gene doublesex, show male-specific expression, which illustrates their integrated role as a masculinizing supergene. Our findings demonstrate how extensive indel polymorphisms and duplications of regulatory genes may contribute to the evolution of co-adapted gene clusters, sex-limited reproductive morphs and the enigmatic evolution of exaggerated sexual traits in general.Genetic intra-tumour heterogeneity fuels clonal evolution, but our understanding of clinically relevant clonal dynamics remain limited. We investigated spatial and temporal features of clonal diversification in clear cell renal cell carcinoma through a combination of modelling and real tumour analysis. We observe that the mode of tumour growth, surface or volume, impacts the extent of subclonal diversification, enabling interpretation of clonal diversity in patient tumours. Specific patterns of proliferation and necrosis explain clonal expansion and emergence of parallel evolution and microdiversity in tumours. In silico time-course studies reveal the appearance of budding structures before detectable subclonal diversification. Intriguingly, we observe radiological evidence of budding structures in early-stage clear cell renal cell carcinoma, indicating that future clonal evolution may be predictable from imaging. Our findings offer a window into the temporal and spatial features of clinically relevant clonal evolution.Prochlorococcus are the most abundant free-living photosynthetic carbon-fixing organisms in the ocean. Prochlorococcus show small genome sizes, low genomic G+C content, reduced DNA repair gene pool and fast evolutionary rates, which are typical features of endosymbiotic bacteria. Nevertheless, their evolutionary mechanisms are believed to be different. Evolution of endosymbiotic bacteria is dominated by genetic drift owing to repeated population bottlenecks, whereas Prochlorococcus are postulated to have extremely large effective population sizes (Ne) and thus drift has rarely been considered. However, accurately extrapolating Ne requires measuring an unbiased global mutation rate through mutation accumulation, which is challenging for Prochlorococcus. Here, we managed this experiment over 1,065 days using Prochlorococcus marinus AS9601, sequenced genomes of 141 mutant lines and determined its mutation rate to be 3.50 × 10-10 per site per generation. Extrapolating Ne additionally requires identifying population boundaries, which we defined using PopCOGenT and over 400 genomes related to AS9601. Accordingly, we calculated its Ne to be 1.68 × 107, which is only reasonably greater than that of endosymbiotic bacteria but surprisingly smaller than that of many free-living bacteria extrapolated using the same approach. Our results therefore suggest that genetic drift is a key driver of Prochlorococcus evolution.Despite the propensity for complex and non-equilibrium dynamics in nature, eco-evolutionary analytical theory typically assumes that populations are at equilibria. In particular, pathogens often show antigenic escape from host immune defences, leading to repeated epidemics, fluctuating selection and diversification, but we do not understand how this impacts the evolution of virulence. We model the impact of antigenic drift and escape on the evolution of virulence in a generalized pathogen and apply a recently introduced oligomorphic methodology that captures the dynamics of the mean and variance of traits, to show analytically that these non-equilibrium dynamics select for the long-term persistence of more acute pathogens with higher virulence. Our analysis predicts both the timings and outcomes of antigenic shifts leading to repeated epidemics and predicts the increase in variation in both antigenicity and virulence before antigenic escape. There is considerable variation in the degree of antigenic escape that occurs across pathogens and our results may help to explain the difference in virulence between related pathogens including, potentially, human influenzas. Furthermore, it follows that these pathogens will have a lower R0, with clear implications for epidemic behaviour, endemic behaviour and control. More generally, our results show the importance of examining the evolutionary consequences of non-equilibrium dynamics.How and when the microbiome modulates host adaptation remains an evolutionary puzzle, despite evidence that the extended genetic repertoire of the microbiome can shape host phenotypes and fitness. One complicating factor is that the microbiome is often transmitted imperfectly across host generations, leading to questions about the degree to which the microbiome contributes to host adaptation. Here, using an evolutionary model, we demonstrate that decreasing vertical transmission fidelity can increase microbiome variation, and thus phenotypic variation, across hosts. When the most beneficial microbial genotypes change unpredictably from one generation to the next (for example, in variable environments), hosts can maximize fitness by increasing the microbiome variation among offspring, as this improves the chance of there being an offspring with the right microbial combination for the next generation. Imperfect vertical transmission can therefore be adaptive in varying environments. We characterize how selection on vertical transmission is shaped by environmental conditions, microbiome changes during host development and the contribution of other factors to trait variation. We illustrate how environmentally dependent microbial effects can favour intermediate transmission and set our results in the context of examples from natural systems. We also suggest research avenues to empirically test our predictions. Our model provides a basis to understand the evolutionary pathways that potentially led to the wide diversity of microbe transmission patterns found in nature.The competition between kinetic energy and Coulomb interactions in electronic systems leads to complex many-body ground states with competing orders. Here we present zinc oxide-based two-dimensional electron systems as a high-mobility system to study the low-temperature phases of strongly interacting electrons. An analysis of the electronic transport provides evidence for competing correlated metallic and insulating states with varying degrees of spin polarization. Some features bear quantitative resemblance to quantum Monte Carlo simulation results, including the transition point from the paramagnetic Fermi liquid to Wigner crystal and the absence of a Stoner transition. At very low temperatures, we resolve a non-monotonic spin polarizability of electrons across the phase transition, pointing towards a low spin phase of electrons, and a two-order-of-magnitude positive magnetoresistance that is challenging to understand within traditional metallic transport paradigms. This work establishes zinc oxide as a platform for studying strongly correlated electrons in two dimensions.Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space.
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