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Institute
- Theodor-Boveri-Institut für Biowissenschaften (58) (remove)
The phylogeny of Euglenophyceae (Euglenozoa, Euglenida) has been discussed for decades with new genera being described in the last few years. In this study, we reconstruct a phylogeny using 18S rDNA sequence and structural data simultaneously. Using homology modeling, individual secondary structures were predicted. Sequence–structure data are encoded and automatically aligned. Here, we present a sequence–structure neighbor‐joining tree of more than 300 taxa classified as Euglenophyceae. Profile neighbor‐joining was used to resolve the basal branching pattern. Neighbor‐joining, maximum parsimony, and maximum likelihood analyses were performed using sequence–structure information for manually chosen subsets. All analyses supported the monophyly of Eutreptiella, Discoplastis, Lepocinclis, Strombomonas, Cryptoglena, Monomorphina, Euglenaria, and Colacium. Well‐supported topologies were generally consistent with previous studies using a combined dataset of genetic markers. Our study supports the simultaneous use of sequence and structural data to reconstruct more accurate and robust trees. The average bootstrap value is significantly higher than the average bootstrap value obtained from sequence‐only analyses, which is promising for resolving relationships between more closely related taxa.
Despite sometimes strong codependencies of insect herbivores and plants, the responses of individual taxa to accelerating climate change are typically studied in isolation. For this reason, biotic interactions that potentially limit species in tracking their preferred climatic niches are ignored. Here, we chose butterflies as a prominent representative of herbivorous insects to investigate the impacts of temperature changes and their larval host plant distributions along a 1.4‐km elevational gradient in the German Alps. Following a sampling protocol of 2009, we revisited 33 grassland plots in 2019 over an entire growing season. We quantified changes in butterfly abundance and richness by repeated transect walks on each plot and disentangled the direct and indirect effects of locally assessed temperature, site management, and larval and adult food resource availability on these patterns. Additionally, we determined elevational range shifts of butterflies and host plants at both the community and species level. Comparing the two sampled years (2009 and 2019), we found a severe decline in butterfly abundance and a clear upward shift of butterflies along the elevational gradient. We detected shifts in the peak of species richness, community composition, and at the species level, whereby mountainous species shifted particularly strongly. In contrast, host plants showed barely any change, neither in connection with species richness nor individual species shifts. Further, temperature and host plant richness were the main drivers of butterfly richness, with change in temperature best explaining the change in richness over time. We concluded that host plants were not yet hindering butterfly species and communities from shifting upwards. However, the mismatch between butterfly and host plant shifts might become a problem for this very close plant–herbivore relationship, especially toward higher elevations, if butterflies fail to adapt to new host plants. Further, our results support the value of conserving traditional extensive pasture use as a promoter of host plant and, hence, butterfly richness.
Earlier flowering of winter oilseed rape compensates for higher pest pressure in warmer climates
(2023)
Global warming can increase insect pest pressure by enhancing reproductive rates. Whether this translates into yield losses depends on phenological synchronisation of pests with their host plants and natural enemies. Simultaneously, landscape composition may mitigate climate effects by shaping the resource availability for pests and their antagonists. Here, we study the combined effects of temperature and landscape composition on pest abundances, larval parasitism, crop damage and yield, while also considering crop phenology, to identify strategies for sustainable management of oilseed rape (OSR) pests under warming climates.
In all, 29 winter OSR crop fields were investigated in different climates (defined by multi‐annual mean temperature, MAT) and landscape contexts in Bavaria, Germany. We measured abundances of adult pollen beetles and stem weevil larvae, pollen beetle larval parasitism, bud loss, stem damage and seed yield, and calculated the flowering date from growth stage observations. Landscape parameters (proportion of non‐crop and OSR area, change in OSR area relative to the previous year) were calculated at six spatial scales (0.6–5 km).
Pollen beetle abundance increased with MAT but to different degrees depending on the landscape context, that is, increased less strongly when OSR proportions were high (1‐km scale), interannually constant (5‐km scale) or both. In contrast, stem weevil abundance and stem damage did not respond to landscape composition nor MAT. Pollen beetle larval parasitism was overall low, but occasionally exceeded 30% under both low and high MAT and with reduced OSR area (0.6‐km scale).
Despite high pollen beetle abundance in warm climates, yields were high when OSR flowered early. Thereby, higher temperatures favoured early flowering. Only among late‐flowering OSR crop fields yield was higher in cooler than warmer climates. Bud loss responded analogously. Landscape composition did not substantially affect bud loss and yield.
Synthesis and applications: Earlier flowering of winter OSR compensates for higher pollen beetle abundance in warmer climates, while interannual continuity of OSR area prevents high pollen beetle abundance in the first place. Thus, regional coordination of crop rotation and crop management promoting early flowering may contribute to sustainable pest management in OSR under current and future climatic conditions.
Salt stress is a major abiotic stress, responsible for declining agricultural productivity. Roots are regarded as hubs for salt detoxification, however, leaf salt concentrations may exceed those of roots. How mature leaves manage acute sodium chloride (NaCl) stress is mostly unknown.
To analyze the mechanisms for NaCl redistribution in leaves, salt was infiltrated into intact tobacco leaves. It initiated pronounced osmotically‐driven leaf movements. Leaf downward movement caused by hydro‐passive turgor loss reached a maximum within 2 h.
Salt‐driven cellular water release was accompanied by a transient change in membrane depolarization but not an increase in cytosolic calcium ion (Ca\(^{2+}\)) level. Nonetheless, only half an hour later, the leaves had completely regained turgor. This recovery phase was characterized by an increase in mesophyll cell plasma membrane hydrogen ion (H\(^{+}\)) pumping, a salt uptake‐dependent cytosolic alkalization, and a return of the apoplast osmolality to pre‐stress levels. Although, transcript numbers of abscisic acid‐ and Salt Overly Sensitive pathway elements remained unchanged, salt adaptation depended on the vacuolar H\(^{+}\)/Na\(^{+}\)‐exchanger NHX1.
Altogether, tobacco leaves can detoxify sodium ions (Na\(^{+}\)) rapidly even under massive salt loads, based on pre‐established posttranslational settings and NHX1 cation/H+ antiport activity. Unlike roots, signaling and processing of salt stress in tobacco leaves does not depend on Ca\(^{2+}\) signaling.
Formation of the Aurora-A–MYCN complex increases levels of the oncogenic transcription factor MYCN in neuroblastoma cells by abrogating its degradation through the ubiquitin proteasome system. While some small-molecule inhibitors of Aurora-A were shown to destabilize MYCN, clinical trials have not been satisfactory to date. MYCN itself is considered to be `undruggable' due to its large intrinsically disordered regions. Targeting the Aurora-A–MYCN complex rather than Aurora-A or MYCN alone will open new possibilities for drug development and screening campaigns. To overcome the challenges that a ternary system composed of Aurora-A, MYCN and a small molecule entails, a covalently cross-linked construct of the Aurora-A–MYCN complex was designed, expressed and characterized, thus enabling screening and design campaigns to identify selective binders.
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R
2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R
2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
Aim
Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small‐bodied species, will increase with increasing temperature. However, the specific responses of single species to rising temperatures, such as spatial range shifts, also alter communities, calling for intensive monitoring of real‐world communities over time.
Location
German Alps and pre‐alpine forests in south‐east Germany.
Methods
We empirically examined the temporal and spatial change in wild bee communities and its drivers along two largely well‐protected elevational gradients (alpine grassland vs. pre‐alpine forest), each sampled twice within the last decade.
Results
We detected clear abundance‐based upward shifts in bee communities, particularly in cold‐adapted bumble bee species, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase in small‐bodied species in low‐ and mid‐elevations along the grassland gradient. Community responses in the pre‐alpine forest gradient were only partly consistent with community responses in alpine grasslands.
Main Conclusion
In well‐protected temperate mountain regions, small‐bodied bees may initially profit from warming temperatures, by getting more abundant and diverse. Less severe warming, and differences in habitat openness along the forested gradient, however, might moderate species responses. Our study further highlights the utility of standardized abundance data for revealing rapid changes in bee communities over only one decade.