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The epitope imprinting approach applies exposed peptides as templates to synthesize Molecularly Imprinted Polymers (MIPs) for the recognition of the parent protein. While generally the template protein binding to such MIPs is considered to occur via the epitope-shaped cavities, unspecific interactions of the analyte with non-imprinted polymer as well as the detection method used may add to the complexity and interpretation of the target rebinding. To get new insights on the effects governing the rebinding of analytes, we electrosynthesized two epitope-imprinted polymers using the N-terminal pentapeptide VHLTP-amide of human hemoglobin (HbA) as the template. MIPs were prepared either by single-step electrosynthesis of scopoletin/pentapeptide mixtures or electropolymerization was performed after chemisorption of the cysteine extended VHLTP peptide. Rebinding of the target peptide and the parent HbA protein to the MIP nanofilms was quantified by square wave voltammetry using a redox probe gating, surface enhanced infrared absorption spectroscopy, and atomic force microscopy. While binding of the pentapeptide shows large influence of the amino acid sequence, all three methods revealed strong non-specific binding of HbA to both polyscopoletin-based MIPs with even higher affinities than the target peptides.
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.
A drop of immunity
(2021)
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
Almost one third of global drylands are open forests and savannas, which are typically shaped by frequent natural disturbances such as wildfire and herbivory. Studies on ecosystem functions and services of woody vegetation require robust estimates of aboveground biomass (AGB). However, most methods have been developed for comparatively undisturbed forest ecosystems. As they are not tailored to accurately quantify AGB of small and irregular growth forms, their application on these growth forms may lead to unreliable or even biased AGB estimates in disturbance-prone dryland ecosystems. Moreover, these methods cannot quantify AGB losses caused by disturbance agents. Here we propose a methodology to estimate individual-and stand-level woody AGB in disturbance-prone ecosystems. It consists of flexible field sampling routines and estimation workflows for six growth classes, delineated by size and damage criteria. It also comprises a detailed damage assessment, harnessing the ecological archive of woody growth for past disturbances.
Based on large inventories collected along steep gradients of elephant disturbances in African dryland ecosystems, we compared the AGB estimates generated with our proposed method against estimates from a less adapted forest inventory method. We evaluated the necessary stepwise procedures of method adaptation and analyzed each step's effect on stand-level AGB estimation. We further explored additional advantages of our proposed method with regard to disturbance impact quantification. Results indicate that a majority of growth forms and individuals in savanna vegetation could only be assessed if methods of AGB estimation were adapted to the conditions of a disturbance-prone ecosystem. Furthermore, our damage assessment demonstrated that one third to half of all woody AGB was lost to disturbances. Consequently, less adapted methods may be insufficient and are likely to render inaccurate AGB estimations.
Our proposed method has the potential to accurately quantify woody AGB in disturbance-prone ecosystems, as well as AGB losses. Our method is more time consuming than conventional allometric approaches, yet it can cover sufficient areas within reasonable timespans, and can also be easily adapted to alternative sampling schemes.
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.
Human size changes over time with worldwide secular trends in height, weight, and body mass index (BMI). There is general agreement to relate the state of nutrition to height and weight, and to ratios of weight-to-height. The BMI is a ratio. It is commonly used to classify underweight, overweight and obesity in adults. Yet, the BMI is inappropriate to provide any immediate information on body composition.
It is accepted that the BMI is “a simple index to classify underweight, overweight and obesity in adults”. It is stated that “policies, programmes and investments need to be “nutrition-sensitive”, which means they must have positive impacts on nutrition”. It is also stated that “a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions“. But these statements are neither warranted by arithmetic considerations, nor by historic evidence.
Measuring the BMI is an appropriate screening tool for detecting an unusual weight-to-height ratio, but the BMI is an inappropriate tool for estimating body composition, or suggesting medical and health policy decisions.
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.
Purpose of review
The zebrafish embryo has emerged as a powerful model organism to investigate the mechanisms by which biophysical forces regulate vascular and cardiac cell biology during development and disease. A versatile arsenal of methods and tools is available to manipulate and analyze biomechanical signaling. This review aims to provide an overview of the experimental strategies and tools that have been utilized to study biomechanical signaling in cardiovascular developmental processes and different vascular disease models in the zebrafish embryo. Within the scope of this review, we focus on work published during the last two years.
Recent findings
Genetic and pharmacological tools for the manipulation of cardiac function allow alterations of hemodynamic flow patterns in the zebrafish embryo and various types of transgenic lines are available to report endothelial cell responses to biophysical forces. These tools have not only revealed the impact of biophysical forces on cardiovascular development but also helped to establish more accurate models for cardiovascular diseases including cerebral cavernous malformations, hereditary hemorrhagic telangiectasias, arteriovenous malformations, and lymphangiopathies.
Summary
The zebrafish embryo is a valuable vertebrate model in which in-vivo manipulations of biophysical forces due to cardiac contractility and blood flow can be performed. These analyses give important insights into biomechanical signaling pathways that control endothelial and endocardial cell behaviors. The technical advances using this vertebrate model will advance our understanding of the impact of biophysical forces in cardiovascular pathologies.
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.
Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7-91.0%/75.2-76.7%; rumination: 89.6-91.6%/53.5-86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes.
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.
Objective:
Stunting (height-for-age < −2 SD) is one of the forms of undernutrition and is frequent among children of low- and middle-income countries. But stunting perSe is not a synonym of undernutrition. We investigated association between body height and indicators of energetic undernutrition at three critical thresholds for thinness used in public health: (1) BMI SDS < −2; (2) mid-upper arm circumference divided by height (MUAC (mm) × 10/height (cm) < 1·36) and (3) mean skinfold thickness (SF) < 7 mm and to question the reliability of thresholds as indicators of undernutrition.
Design:
Cross-sectional study; breakpoint analysis.
Setting:
Rural and urban regions of Indonesia and Guatemala – different socio-economic status (SES).
Participants:
1716 Indonesian children (6·0–13·2 years) and 3838 Guatemalan children (4·0–18·9 years) with up to 50 % stunted children.
Results:
When separating the regression of BMI, MUAC or SF, on height into distinguishable segments (breakpoint analysis), we failed to detect relevant associations between height, and BMI, MUAC or SF, even in the thinnest and shortest children. For BMI and SF, the breakpoint analysis either failed to reach statistical significance or distinguished at breakpoints above critical thresholds. For MUAC, the breakpoint analysis yielded negative associations between MUAC/h and height in thin individuals. Only in high SES Guatemalan children, SF and height appeared mildly associated with R2 = 0·017.
Conclusions:
Currently used lower thresholds of height-for-age (stunting) do not show relevant associations with anthropometric indicators of energetic undernutrition. We recommend using the catch-up growth spurt during early re-feeding instead as immediate and sensitive indicator of past undernourishment. We discuss the primacy of education and social-economic-political-emotional circumstances as responsible factors for stunting.
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.
City mice and country mice
(2021)
The ability to produce innovative behaviour is a key determinant in the successful coping with environmental challenges and changes. The expansion of human-altered environments presents wildlife with multiple novel situations in which innovativeness could be beneficial. A better understanding of the drivers of within-species variation in innovation propensity and its consequences will provide insights into the traits enabling animals to thrive in the face of human-induced rapid environmental change. We compared problem-solving performance of 31 striped field mice, Apodemus agrarius, originating from rural or urban environments in a battery of eight foraging extraction tasks. We tested whether differences in problem-solving performance were mediated by the extent and duration of the animal's exploration of the experimental set-ups, the time required to solve the tasks, and their persistence. In addition, we tested the influence of the diversity of motor responses, as well as of behavioural traits boldness and activity on problem-solving performance. Urban individuals were better problem solvers despite rural individuals approaching faster and interacting longer with the test set-ups. Participation rates and time required to solve a task did not differ between rural and urban individuals. However, in case of failure to solve a task, rural mice were more persistent. The best predictors of solving success, aside from the area of origin, were the time spent exploring the set-ups and boldness, while activity and diversity of motor responses did not explain it. Problem-solving ability could thus be a contributing factor to the successful coping with the rapid and recent expansion of human-altered environments.
Coherent network partitions
(2021)
We continue to study coherent partitions of graphs whereby the vertex set is partitioned into subsets that induce biclique spanned subgraphs. The problem of identifying the minimum number of edges to obtain biclique spanned connected components (CNP), called the coherence number, is NP-hard even on bipartite graphs. Here, we propose a graph transformation geared towards obtaining an O (log n)-approximation algorithm for the CNP on a bipartite graph with n vertices. The transformation is inspired by a new characterization of biclique spanned subgraphs. In addition, we study coherent partitions on prime graphs, and show that finding coherent partitions reduces to the problem of finding coherent partitions in a prime graph. Therefore, these results provide future directions for approximation algorithms for the coherence number of a given graph.