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Macrophage infectivity potentiator (MIP) proteins are widespread in human pathogens including Legionella pneumophila, the causative agent of Legionnaires’ disease and protozoans such as Trypanosoma cruzi. All MIP proteins contain a FKBP (FK506 binding protein)-like prolyl-cis/trans-isomerase domain that hence presents an attractive drug target. Some MIPs such as the Legionella protein (LpMIP) have additional appendage domains of mostly unknown function. In full-length, homodimeric LpMIP, the N-terminal dimerization domain is linked to the FKBP-like domain via a long, free-standing stalk helix. Combining X-ray crystallography, NMR and EPR spectroscopy and SAXS, we elucidated the importance of the stalk helix for protein dynamics and inhibitor binding to the FKBP-like domain and bidirectional crosstalk between the different protein regions. The first comparison of a microbial MIP and a human FKBP in complex with the same synthetic inhibitor was made possible by high-resolution structures of LpMIP with a [4.3.1]-aza-bicyclic sulfonamide and provides a basis for designing pathogen-selective inhibitors. Through stereospecific methylation, the affinity of inhibitors to to L. pneumophila and T. cruzi MIP was greatly improved. The resulting X-ray inhibitor-complex structures of LpMIP and TcMIP at 1.49 and 1.34 Å, respectively, provide a starting point for developing potent inhibitors against MIPs from multiple pathogenic microorganisms.
Deviance detection describes an increase of neural response strength caused by a stimulus with a low probability of occurrence. This ubiquitous phenomenon has been reported for multiple species, from subthalamic areas to auditory cortex. While cortical deviance detection has been well characterised by a range of studies covering neural activity at population level (mismatch negativity, MMN) as well as at cellular level (stimulus-specific adaptation, SSA), subcortical deviance detection has been studied mainly on cellular level in the form of SSA. Here, we aim to bridge this gap by using noninvasively recorded auditory brainstem responses (ABRs) to investigate deviance detection at population level in the lower stations of the auditory system of a hearing specialist: the bat Carollia perspicillata. Our present approach uses behaviourally relevant vocalisation stimuli that are closer to the animals' natural soundscape than artificial stimuli used in previous studies that focussed on subcortical areas. We show that deviance detection in ABRs is significantly stronger for echolocation pulses than for social communication calls or artificial sounds, indicating that subthalamic deviance detection depends on the behavioural meaning of a stimulus. Additionally, complex physical sound features like frequency- and amplitude-modulation affected the strength of deviance detection in the ABR. In summary, our results suggest that at population level, the bat brain can detect different types of deviants already in the brainstem. This shows that subthalamic brain structures exhibit more advanced forms of deviance detection than previously known.
The ability to vocalize is ubiquitous in vertebrates, but neural networks leading to vocalization production remain poorly understood. Here we performed simultaneous, large scale, neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus) during the production of echolocation and non-echolocation calls in bats. This approach allows to assess the general aspects underlying vocalization production in mammals and the unique evolutionary adaptations of bat echolocation. Our findings show that distinct intra-areal brain rhythms in the beta (12-30 Hz) and gamma (30-80 Hz) bands of the local field potential can be used to predict the bats’ vocal output and that phase locking between spikes and field potentials occurs prior vocalization production. Moreover, the fronto-striatal network is differentially coupled in the theta-band during the production of echolocation and non-echolocation calls. Overall, our results present evidence for fronto-striatal network oscillations in motor action prediction in mammals.
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t–distributed stochastic neighbour embedding (t–SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of ∼30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.
Bisphenols and phthalates, chemicals frequently used in plastic products, promote obesity in cell and animal models. However, these well-known metabolism disrupting chemicals (MDCs) represent only a minute fraction of all compounds found in plastics. To gain a comprehensive understanding of plastics as a source of exposure to MDCs, we characterized all chemicals present in 34 everyday products using nontarget high-resolution mass spectrometry and analyzed their joint adipogenic activities by high-content imaging. We detected 55,300 chemical features and tentatively identified 629 unique compounds, including 11 known MDCs. Importantly, chemicals that induced proliferation, growth, and triglyceride accumulation in 3T3-L1 adipocytes were found in one third of the products. Since the majority did not target peroxisome proliferator-activated receptor γ, the effects are likely to be caused by unknown MDCs. Our study demonstrates that daily-use plastics contain potent mixtures of MDCs and can, therefore, be a relevant yet underestimated environmental factor contributing to obesity.
Teaser Plastics contain a potent mixture of chemicals promoting adipogenesis, a key process in developing obesity.
The establishment and maintenance of protected areas (PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting protected areas based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences, as well as real-time comparison of the selected areas that result from such different priorities. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between species protection and ecosystem integrity. Outputs indicate that decision makers frequently face trade-offs among conflicting objectives. Nevertheless, we show that transparent decision-support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.
The establishment and maintenance of protected areas(PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet a multitude of objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation. As available land and conservation funding are limited, optimizing resources by selecting the most beneficial PAs is vital. Here we present a decision support tool that enables a flexible approach to PA selection on a global scale, allowing different conservation objectives to be weighted and prioritized according to user-specified preferences. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between biodiversity protection and ecosystem integrity. These results indicate that decision makers must usually decide among conflicting objectives. To assist this our decision support tool provides an explicitly value-based approach that can help resolve such conflicts by considering divergent societal and political demands and values.
mRNA localization to subcellular compartments has been reported across all kingdoms of life and it is generally believed to promote asymmetric protein synthesis and localization. In striking contrast to previous observations, we show that in S. cerevisiae the B-type cyclin CLB2 mRNA is localized and translated in the yeast bud, while the Clb2 protein, a key regulator of mitosis progression, is concentrated in the mother nucleus. Using single-molecule RNA imaging in fixed (smFISH) and living cells (MS2 system), we show that the CLB2 mRNA is transported to the yeast bud by the She2-She3 complex, via an mRNA ZIP-code situated in the coding sequence. In CLB2 mRNA localization mutants, Clb2 protein synthesis in the bud is decreased resulting in changes in cell cycle distribution and genetic instability. Altogether, we propose that CLB2 mRNA localization acts as a sensor for bud development to couple cell growth and cell cycle progression, revealing a novel function for mRNA localization.
Bacteria of the genera Photorhabdus and Xenorhabdus produce a plethora of natural products to support their similar symbiotic lifecycles. For many of these compounds, the specific bioactivities are unknown. One common challenge in natural product research when trying to prioritize research efforts is the rediscovery of identical (or highly similar) compounds from different strains. Linking genome sequence to metabolite production can help in overcoming this problem. However, sequences are typically not available for entire collections of organisms. Here we perform a comprehensive metabolic screening using HPLC-MS data associated with a 114-strain collection (58 Photorhabdus and 56 Xenorhabdus) from across Thailand and explore the metabolic variation among the strains, matched with several abiotic factors. We utilize machine learning in order to rank the importance of individual metabolites in determining all given metadata. With this approach, we were able to prioritize metabolites in the context of natural product investigations, leading to the identification of previously unknown compounds. The top three highest-ranking features were associated with Xenorhabdus and attributed to the same chemical entity, cyclo(tetrahydroxybutyrate). This work addresses the need for prioritization in high-throughput metabolomic studies and demonstrates the viability of such an approach in future research.
Fungi play pivotal roles in ecosystem functioning, but little is known about their global patterns of diversity, endemicity, vulnerability to global change drivers and conservation priority areas. We applied the high-resolution PacBio sequencing technique to identify fungi based on a long DNA marker that revealed a high proportion of hitherto unknown fungal taxa. We used a Global Soil Mycobiome consortium dataset to test relative performance of various sequencing depth standardization methods (calculation of residuals, exclusion of singletons, traditional and SRS rarefaction, use of Shannon index of diversity) to find optimal protocols for statistical analyses. Altogether, we used six global surveys to infer these patterns for soil-inhabiting fungi and their functional groups. We found that residuals of log-transformed richness (including singletons) against log-transformed sequencing depth yields significantly better model estimates compared with most other standardization methods. With respect to global patterns, fungal functional groups differed in the patterns of diversity, endemicity and vulnerability to main global change predictors. Unlike α-diversity, endemicity and global-change vulnerability of fungi and most functional groups were greatest in the tropics. Fungi are vulnerable mostly to drought, heat, and land cover change. Fungal conservation areas of highest priority include wetlands and moist tropical ecosystems.