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Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers.
The manuscript describes the phytochemical investigation of the roots, leaves and stem bark of Millettia lasiantha resulting in the isolation of twelve compounds including two new isomeric isoflavones lascoumestan and las-coumaronochromone. The structures of the new compounds were determined using different spectroscopic techniques.
A matter of concern
(2021)
Neurons are post-mitotic cells in the brain and their integrity is of central importance to avoid neurodegeneration. Yet, the inability of self-replenishment of post-mitotic cells results in the need to withstand challenges from numerous stressors during life. Neurons are exposed to oxidative stress due to high oxygen consumption during metabolic activity in the brain. Accordingly, DNA damage can occur and accumulate, resulting in genome instability. In this context, imbalances in brain trace element homeostasis are a matter of concern, especially regarding iron, copper, manganese, zinc, and selenium. Although trace elements are essential for brain physiology, excess and deficient conditions are considered to impair neuronal maintenance. Besides increasing oxidative stress, DNA damage response and repair of oxidative DNA damage are affected by trace elements. Hence, a balanced trace element homeostasis is of particular importance to safeguard neuronal genome integrity and prevent neuronal loss. This review summarises the current state of knowledge on the impact of deficient, as well as excessive iron, copper, manganese, zinc, and selenium levels on neuronal genome stability
Monoclonal antibodies are used worldwide as highly potent and efficient detection reagents for research and diagnostic applications. Nevertheless, the specific targeting of complex antigens such as whole microorganisms remains a challenge. To provide a comprehensive workflow, we combined bioinformatic analyses with novel immunization and selection tools to design monoclonal antibodies for the detection of whole microorganisms. In our initial study, we used the human pathogenic strain E. coli O157:H7 as a model target and identified 53 potential protein candidates by using reverse vaccinology methodology. Five different peptide epitopes were selected for immunization using epitope-engineered viral proteins. The identification of antibody-producing hybridomas was performed by using a novel screening technology based on transgenic fusion cell lines. Using an artificial cell surface receptor expressed by all hybridomas, the desired antigen-specific cells can be sorted fast and efficiently out of the fusion cell pool. Selected antibody candidates were characterized and showed strong binding to the target strain E. coli O157:H7 with minor or no cross-reactivity to other relevant microorganisms such as Legionella pneumophila and Bacillus ssp. This approach could be useful as a highly efficient workflow for the generation of antibodies against microorganisms.
Objective
The Caribbean is an important global biodiversity hotspot. Adaptive radiations there lead to many speciation events within a limited period and hence are particularly prominent biodiversity generators. A prime example are freshwater fish of the genus Limia, endemic to the Greater Antilles. Within Hispaniola, nine species have been described from a single isolated site, Lake Miragoâne, pointing towards extraordinary sympatric speciation. This study examines the evolutionary history of the Limia species in Lake Miragoâne, relative to their congeners throughout the Caribbean.
Results
For 12 Limia species, we obtained almost complete sequences of the mitochondrial cytochrome b gene, a well-established marker for lower-level taxonomic relationships. We included sequences of six further Limia species from GenBank (total N = 18 species). Our phylogenies are in concordance with other published phylogenies of Limia. There is strong support that the species found in Lake Miragoâne in Haiti are monophyletic, confirming a recent local radiation. Within Lake Miragoâne, speciation is likely extremely recent, leading to incomplete lineage sorting in the mtDNA. Future studies using multiple unlinked genetic markers are needed to disentangle the relationships within the Lake Miragoâne clade.
AAA+ proteins (ATPases associated with various cellular activities) catalyze the energy-dependent movement or rearrangement of macromolecules. A new study addresses the important question of how to design a selective chemical inhibitor for specific proteins in this diverse superfamily. The powerful chemical genetics approach adds to a growing toolbox of applications that allow dissection of the functions of distinct AAA+ proteins in vivo, facilitating the first steps toward effective drug development.
Iron sulfur (Fe-S) clusters are important biological cofactors present in proteins with crucial biological functions, from photosynthesis to DNA repair, gene expression, and bioenergetic processes. For the insertion of Fe-S clusters into proteins, A-type carrier proteins have been identified. So far, three of them have been characterized in detail in Escherichia coli, namely, IscA, SufA, and ErpA, which were shown to partially replace each other in their roles in [4Fe-4S] cluster insertion into specific target proteins. To further expand the knowledge of [4Fe-4S] cluster insertion into proteins, we analyzed the complex Fe-S cluster-dependent network for the synthesis of the molybdenum cofactor (Moco) and the expression of genes encoding nitrate reductase in E. coli. Our studies include the identification of the A-type carrier proteins ErpA and IscA, involved in [4Fe-4S] cluster insertion into the radical Sadenosyl-methionine (SAM) enzyme MoaA. We show that ErpA and IscA can partially replace each other in their role to provide [4Fe-4S] clusters for MoaA. Since most genes expressing molybdoenzymes are regulated by the transcriptional regulator for fumarate and nitrate reduction (FNR) under anaerobic conditions, we also identified the proteins that are crucial to obtain an active FNR under conditions of nitrate respiration. We show that ErpA is essential for the FNR-dependent expression of the narGHJI operon, a role that cannot be compensated by IscA under the growth conditions tested. SufA does not appear to have a role in Fe-S cluster insertion into MoaA or FNR under anaerobic growth employing nitrate respiration, based on the low level of gene expression. <br /> IMPORTANCE Understanding the assembly of iron-sulfur (Fe-S) proteins is relevant to many fields, including nitrogen fixation, photosynthesis, bioenergetics, and gene regulation. Remaining critical gaps in our knowledge include how Fe-S clusters are transferred to their target proteins and how the specificity in this process is achieved, since different forms of Fe-S clusters need to be delivered to structurally highly diverse target proteins. Numerous Fe-S carrier proteins have been identified in prokaryotes like Escherichia coli, including ErpA, IscA, SufA, and NfuA. In addition, the diverse Fe-S cluster delivery proteins and their target proteins underlie a complex regulatory network of expression, to ensure that both proteins are synthesized under particular growth conditions.
Populations adapt to novel environmental conditions by genetic changes or phenotypic plasticity. Plastic responses are generally faster and can buffer fitness losses under variable conditions. Plasticity is typically modeled as random noise and linear reaction norms that assume simple one-to- one genotype–phenotype maps and no limits to the phenotypic response. Most studies on plasticity have focused on its effect on population viability. However, it is not clear, whether the advantage of plasticity depends solely on environmental fluctuations or also on the genetic and demographic properties (life histories) of populations. Here we present an individual-based model and study the relative importance of adaptive and nonadaptive plasticity for populations of sexual species with different life histories experiencing directional stochastic climate change. Environmental fluctuations were simulated using differentially autocorrelated climatic stochasticity or noise color, and scenarios of directiona
climate change. Nonadaptive plasticity was simulated as a random environmental effect on trait development, while adaptive plasticity as a linear, saturating, or sinusoidal reaction norm. The last two imposed limits to the plastic response and emphasized flexible interactions of the genotype with the environment. Interestingly, this assumption led to (a) smaller phenotypic than genotypic variance in the population (many-to- one genotype–phenotype map) and the coexistence of polymorphisms, and (b) the maintenance of higher genetic variation—compared to linear reaction norms and genetic determinism—even when the population was exposed to a constant environment for several generations. Limits to plasticity led to genetic accommodation, when costs were negligible, and to the appearance of cryptic variation when limits were exceeded. We found that adaptive plasticity promoted population persistence under red environmental noise and was particularly important for life histories with low fecundity. Populations produing more offspring could cope with environmental fluctuations solely by genetic changes or random plasticity, unless environmental change was too fast.
Due to their electrically polarized air-filled internal pores, optimized ferroelectrets exhibit a remarkable piezoelectric response, making them suitable for energy harvesting. Expanded polytetrafluoroethylene (ePTFE) ferroelectret films are laminated with two fluorinated-ethylene-propylene (FEP) copolymer films and internally polarized by corona discharge. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-coated spandex fabric is employed for the electrodes to assemble an all-organic ferroelectret nanogenerator (FENG). The outer electret-plus-electrode double layers form active device layers with deformable electric dipoles that strongly contribute to the overall piezoelectric response in the proposed concept of wearable nanogenerators. Thus, the FENG with spandex electrodes generates a short-circuit current which is twice as high as that with aluminum electrodes. The stacking sequence spandex/FEP/ePTFE/FEP/ePTFE/FEP/spandex with an average pore size of 3 mu m in the ePTFE films yields the best overall performance, which is also demonstrated by the displacement-versus-electric-field loop results. The all-organic FENGs are stable up to 90 degrees C and still perform well 9 months after being polarized. An optimized FENG makes three light emitting diodes (LEDs) blink twice with the energy generated during a single footstep. The new all-organic FENG can thus continuously power wearable electronic devices and is easily integrated, for example, with clothing, other textiles, or shoe insoles.
Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows
(2021)
Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. <br /> Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.
A symmetry-breaking mechanism is investigated that creates bistability between fully and partially synchronized states in oscillator networks. Two populations of oscillators with unimodal frequency distribution and different amplitudes, in the presence of weak global coupling, are shown to simplify to a modular network with asymmetrical coupling. With increasing the coupling strength, a synchronization transition is observed with an isolated fully synchronized state. The results are interpreted theoretically in the thermodynamic limit and confirmed in experiments with chemical oscillators.
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.
Self-organized coherence-incoherence patterns, called chimera states, have first been reported in systems of Kuramoto oscillators. For coupled excitable units, similar patterns where coherent units are at rest are called bump states. Here, we study bumps in an array of active rotators coupled by nonlocal attraction and global repulsion. We demonstrate how they can emerge in a supercritical scenario from completely coherent Turing patterns: a single incoherent unit appears in a homoclinic bifurcation, undergoing subsequent transitions to quasiperiodic and chaotic behavior, which eventually transforms into extensive chaos with many incoherent units. We present different types of transitions and explain the formation of coherence-incoherence patterns according to the classical paradigm of short-range activation and long-range inhibition.
Mitochondria are critical for hypothalamic function and regulators of metabolism. Hypothalamic mitochondrial dysfunction with decreased mitochondrial chaperone expression is present in type 2 diabetes (T2D). Recently, we demonstrated that a dysregulated mitochondrial stress response (MSR) with reduced chaperone expression in the hypothalamus is an early event in obesity development due to insufficient insulin signaling. Although insulin activates this response and improves metabolism, the metabolic impact of one of its members, the mitochondrial chaperone heat shock protein 10 (Hsp10), is unknown. Thus, we hypothesized that a reduction of Hsp10 in hypothalamic neurons will impair mitochondrial function and impact brain insulin action. Therefore, we investigated the role of chaperone Hsp10 by introducing a lentiviral-mediated Hsp10 knockdown (KD) in the hypothalamic cell line CLU-183 and in the arcuate nucleus (ARC) of C57BL/6N male mice. We analyzed mitochondrial function and insulin signaling utilizing qPCR, Western blot, XF96 Analyzer, immunohistochemistry, and microscopy techniques. We show that Hsp10 expression is reduced in T2D mice brains and regulated by leptin in vitro. Hsp10 KD in hypothalamic cells induced mitochondrial dysfunction with altered fatty acid metabolism and increased mitochondria-specific oxidative stress resulting in neuronal insulin resistance. Consequently, the reduction of Hsp10 in the ARC of C57BL/6N mice caused hypothalamic insulin resistance with acute liver insulin resistance.
Cep192, a novel missing link between the centrosomal core and corona in Dictyostelium amoebae
(2021)
The Dictyostelium centrosome is a nucleus-associated body with a diameter of approx. 500 nm. It contains no centrioles but consists of a cylindrical layered core structure surrounded by a microtubule-nucleating corona. At the onset of mitosis, the corona disassembles and the core structure duplicates through growth, splitting, and reorganization of the outer core layers. During the last decades our research group has characterized the majority of the 42 known centrosomal proteins. In this work we focus on the conserved, previously uncharacterized Cep192 protein. We use superresolution expansion microscopy (ExM) to show that Cep192 is a component of the outer core layers. Furthermore, ExM with centrosomal marker proteins nicely mirrored all ultrastructurally known centrosomal substructures. Furthermore, we improved the proximity-dependent biotin identification assay (BioID) by adapting the biotinylase BioID2 for expression in Dictyostelium and applying a knock-in strategy for the expression of BioID2-tagged centrosomal fusion proteins. Thus, we were able to identify various centrosomal Cep192 interaction partners, including CDK5RAP2, which was previously allocated to the inner corona structure, and several core components. Studies employing overexpression of GFP-Cep192 as well as depletion of endogenous Cep192 revealed that Cep192 is a key protein for the recruitment of corona components during centrosome biogenesis and is required to maintain a stable corona structure.
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
Inhibition of acid sphingomyelinase (ASM), a lysosomal enzyme that catalyzes the hydrolysis of sphingomyelin into ceramide and phosphorylcholine, may serve as an investigational tool or a therapeutic intervention to control many diseases. Specific ASM inhibitors are currently not sufficiently characterized. Here, we found that 1-aminodecylidene bis-phosphonic acid (ARC39) specifically and efficiently (>90%) inhibits both lysosomal and secretory ASM in vitro. Results from investigating sphingomyelin phosphodiesterase 1 (SMPD1/Smpd1) mRNA and ASM protein levels suggested that ARC39 directly inhibits ASM's catalytic activity in cultured cells, a mechanism that differs from that of functional inhibitors of ASM. We further provide evidence that ARC39 dose- and time-dependently inhibits lysosomal ASM in intact cells, and we show that ARC39 also reduces platelet- and ASM-promoted adhesion of tumor cells. The observed toxicity of ARC39 is low at concentrations relevant for ASM inhibition in vitro, and it does not strongly alter the lysosomal compartment or induce phospholipidosis in vitro. When applied intraperitoneally in vivo, even subtoxic high doses administered short-term induced sphingomyelin accumulation only locally in the peritoneal lavage without significant accumulation in plasma, liver, spleen, or brain. These findings require further investigation with other possible chemical modifications. In conclusion, our results indicate that ARC39 potently and selectively inhibits ASM in vitro and highlight the need for developing compounds that can reach tissue concentrations sufficient for ASM inhibition in vivo.
Background: Being an essential trace element, copper is involved in diverse physiological processes. However, excess levels might lead to adverse effects. Disrupted copper homeostasis, particularly in the brain, has been associated with human diseases including the neurodegenerative disorders Wilson and Alzheimer?s disease. In this context, astrocytes play an important role in the regulation of the copper homeostasis in the brain and likely in the prevention against neuronal toxicity, consequently pointing them out as a potential target for the neurotoxicity of copper. Major toxic mechanisms are discussed to be directed against mitochondria probably via oxidative stress. However, the toxic potential and mode of action of copper in astrocytes is poorly understood, so far. Methods: In this study, excess copper levels affecting human astrocytic cell model and their involvement in the neurotoxic mode of action of copper, as well as, effects on the homeostasis of other trace elements (Mn, Fe, Ca and Mg) were investigated. Results: Copper induced substantial cytotoxic effects in the human astrocytic cell line following 48 h incubation (EC30: 250 ?M) and affected mitochondrial function, as observed via reduction of mitochondrial membrane potential and increased ROS production, likely originating from mitochondria. Moreover, cellular GSH metabolism was altered as well. Interestingly, not only cellular copper levels were affected, but also the homeostasis of other elements (Ca, Fe and Mn) were disrupted. Conclusion: One potential toxic mode of action of copper seems to be effects on the mitochondria along with induction of oxidative stress in the human astrocytic cell model. Moreover, excess copper levels seem to interact with the homeostasis of other essential elements such as Ca, Fe and Mn. Disrupted element homeostasis might also contribute to the induction of oxidative stress, likely involved in the onset and progression of neurodegenerative disorders. These insights in the toxic mechanisms will help to develop ideas and approaches for therapeutic strategies against copper-mediated diseases.
Comprior
(2021)
Background
Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness.
Results
We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance.
Conclusion
Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness
Nature conservation and restoration in terrestrial ecosystems is often focused on increasing the numbers of megafauna, expecting them to have positive impacts on ecological self-regulation processes and biodiversity. In sub-Saharan Africa, conservation efforts also aspire to protect and enhance biodiversity with particular focus on elephants. However, elephant browsing carries the risk of woody biomass losses. In this context, little is known about how increasing elephant numbers affects carbon stocks in soils, including the subsoils. We hypothesized that (1) increasing numbers of elephants reduce tree biomass, and thus the amount of C stored therein, resulting (2) in a loss of soil organic carbon (SOC). If true, a negative carbon footprint could limit the sustainability of elephant conservation from a global carbon perspective. To test these hypotheses, we selected plots of low, medium, and high elephant densities in two national parks and adjacent conservancies in the Namibian component of the Kavango Zambezi Transfrontier Area (KAZA), and quantified carbon storage in both woody vegetation and soils (1 m). Analyses were supplemented by the assessment of soil carbon isotopic composition. We found that increasing elephant densities resulted in a loss of tree carbon storage by 6.4 t ha(-1). However, and in contrast to our second hypothesis, SOC stocks increased by 4.7 t ha(-1) with increasing elephant densities. These higher SOC stocks were mainly found in the topsoil (0-30 cm) and were largely due to the formation of SOC from woody biomass. A second carbon input source into the soils was megaherbivore dung, which contributed with 0.02-0.323 t C ha(-1) year(-1) to ecosystem carbon storage in the low and high elephant density plots, respectively. Consequently, increasing elephant density does not necessarily lead to a negative C footprint, as soil carbon sequestration and transient C storage in dung almost compensate for losses in tree biomass.