Institut für Biochemie und Biologie
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Nature conservation is currently shaping many terrestrial ecosystems in Africa.
This is particularly evident in Sub-Saharan Africa (SSA), where conservation is intended to recover wildlife populations, with special focus on elephants.
Rising numbers of elephants induce woody biomass losses but increase soil organic carbon (SOC) stocks from decaying wood and dung.
We hypothesized that these increases under wildlife conservation in SSA go along with rising contents of plant residues in SOC, traceable by the molecular markers lignin and n-alkanes.
In contrast, agricultural intensification would reduce them due to lower C input and faster SOC turnover through tillage.
To test this, we analyzed lignin by the CuO oxidation method and n-alkanes by fast pressurized solvent extraction in topsoils (0-10 cm) of Arenosols and corresponding plant samples (trees, grasses and crops).
Sampling sites followed conservation gradients with low, medium and high elephant densities and intensification gradients with rangeland and cropland in the woodland savanna of the Namibian Zambezi Region.
Patterns of lignin-derived phenols were retained in the soil, whereas n-alkanes showed shifts in chain lengths. n-Alkanes also showed no clear increase or decrease under conservation or intensification, respectively.
Differently, lignin-derived phenols showed lower values under intensification than under conservation. Confirming our hypothesis, rising SOC contents with rising elephant densities (from 4.4 at low to 5.7 g kg(-1) SOC at high elephant densities) went along with an increasing accumulation of lignin-derived phenols (24.4-34.8 g kg(-1) VSCOC).
This increase is associated with the input of woody debris to the soil, as indicated by V-units and carbon isotopes, modulated by clay and woody biomass.
We conclude, that increasing input of woody residues into soil by browsing behaviour of elephants is an important mechanism for controlling SOC supply in the context of wildlife conservation and is traceable with lignin-derived phenols, but not with n-alkanes.
Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants.
Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (rS = 0.74) to measured hyphal dry weight; and (iii) simulate growth rate increases with tighter association of this AMF with the host plant across three fungal structures.
Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis.
Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues.
In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance.
IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown.
We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes.
We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients.
The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants.
Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues.
Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology.
Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level.
We show that a support vector machine classifier based on the SAM network properties is predictive of cell division events, with test accuracy of 76%, which matches that based on cell size alone.
Furthermore, we demonstrate that the combination of topological and biological properties, including cell size, perimeter, distance and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division.
Using our classifiers, we demonstrate the importance of microtubule-mediated cell-to-cell growth coordination in influencing tissue-level topology.
Together, the results from our network-based analysis demonstrate a feedback mechanism between tissue topology and cell division in A. thaliana SAMs.
In plants, the trehalose biosynthetic pathway plays key roles in the regulation of carbon allocation and stress adaptation.
Engineering of the pathway holds great promise to increase the stress resilience of crop plants.
The synthesis of trehalose proceeds by a two-step pathway in which a trehalose-phosphate synthase (TPS) uses UDP-glucose and glucose-6-phosphate to produce trehalose-6 phosphate (T6P) that is subsequently dephosphorylated by trehalose-6 phosphate phosphatase (TPP).
While plants usually do not accumulate high amounts of trehalose, their genome encodes large families of putative trehalose biosynthesis genes, with many members lacking obvious enzymatic activity.
Thus, the function of putative trehalose biosynthetic proteins in plants is only vaguely understood. To gain a deeper insight into the role of trehalose biosynthetic proteins in crops, we assessed the enzymatic activity of the TPS/TPP family from tomato (Solanum lycopersicum L.) and investigated their expression pattern in different tissues as well as in response to temperature shifts. From the 10 TPS isoforms tested, only the 2 proteins belonging to class I showed enzymatic activity, while all 5 TPP isoforms investigated were catalytically active.
Most of the TPS/TPP family members showed the highest expression in mature leaves, and promoter-reporter gene studies suggest that the two class I TPS genes have largely overlapping expression patterns within the vasculature, with only subtle differences in expression in fruits and flowers.
The majority of tomato TPS/TPP genes were induced by heat stress, and individual family members also responded to cold.
This suggests that trehalose biosynthetic pathway genes could play an important role during temperature stress adaptation.
In summary, our study represents a further step toward the exploitation of the TPS and TPP gene families for the improvement of tomato stress resistance.
Pichia pastoris (syn. Komagataella phaffii) represents a commonly used expression system in the biotech industry. High clonal variation of transformants, however, typically results in a broad range of specific productivities for secreted proteins. To isolate rare clones with exceedingly high product titers, an extensive number of clones need to be screened.
In contrast to high-throughput screenings of P. pastoris clones in microtiter plates, secrete-and -capture methodologies have the potential to efficiently isolate high-producer clones among millions of cells through fluorescence-activated cell sorting (FACS).Here, we describe a novel approach for the non-covalent binding of fragment antigen-binding (Fab) proteins to the cell surface for the isolation of high-producing clones.
Eight different single-chain variable fragment (scFv)-based capture matrices specific for the constant part of the Fabs were fused to the Saccharomyces cerevisiae alpha -agglutinin (SAG1) anchor protein for surface display in P. pastoris. By encoding the capture matrix on an episomal plasmid harboring inherently unstable autonomously replicating sequences (ARS), this secrete-and -capture system offers a switchable scFv display.
Efficient plasmid clearance upon removal of selective pres-sure enabled the direct use of isolated clones for subsequent Fab production. Flow-sorted clones (n = 276) displaying high amounts of Fabs showed a significant increase in median Fab titers detected in the cell-free supernatant (CFS) compared to unsorted clones (n = 276) when cells were cultivated in microtiter plates (fac-tor in the range of-21-49). Fab titers of clones exhibiting the highest product titer observed for each of the two approaches were increased by up to 8-fold for the sorted clone.
Improved Fab yields of sorted cells vs. unsorted cells were confirmed in an upscaled shake flask cultivation of selected candidates (factor in the range of-2-3). Hence, the developed display-based selection method proved to be a valuable tool for efficient clone screening in the early stages of our bioprocess development.
Many proteins are anchored to the cell surface of eukaryotes using a unique family of glycolipids called glycosylphosphatidylinositol (GPI) anchors.
These glycolipids also exist without a covalently bound protein, in particular on the cell surfaces of protozoan parasites where they are densely populated.
GPIs and GPI-anchored proteins participate in multiple cellular processes such as signal transduction, cell adhesion, protein trafficking and pathogenesis of Malaria, Toxoplasmosis, Trypanosomiasis and prion diseases, among others.
All GPIs share a common conserved glycan core modified in a cell-dependent manner with additional side glycans or phosphoethanolamine residues. Here, we use atomistic molecular dynamic simulations and perform a systematic study to evaluate the structural properties of GPIs with different side chains inserted in lipid bilayers.
Our results show a flop-down orientation of GPIs with respect to the membrane surface and the presentation of the side chain residues to the solvent.
This finding agrees well with experiments showing the role of the side residues as active epitopes for recognition of GPIs by macrophages and induction of GPI-glycan-specific immune responses.
Protein-GPI interactions were investigated by attaching parasitic GPIs to Green Fluorescent Protein. GPIs are observed to recline on the membrane surface and pull down the attached protein close to the membrane facilitating mutual contacts between protein, GPI and the lipid bilayer.
This model is efficient in evaluating the interaction of GPIs and GPI-anchored proteins with membranes and can be extended to study other parasitic GPIs and proteins and develop GPI-based immunoprophylaxis to treat infectious diseases.
The debate whether single large or several small (SLOSS) patches benefit biodiversity has existed for decades, but recent literature provides increasing evidence for the importance of small habitats.
Possible beneficial mechanisms include reduced presence of preda-tors and competitors in small habitat areas or specific functions such as stepping stones for dispersal.
Given the increasing amount of studies highlighting individual behavioral differences that may influence these functions, we hypothesize that the advantage of small versus large habitat patches not only depends on patch functionality but also on the presence of animal personalities (i.e., risk-tolerant vs. risk-averse). Using an individual-based, spatially-explicit community model, we analyzed the diversity of mammal communities in landscapes consisting of a few large habitat islands interspersed with different amounts and sizes of small habitat patches.
Within these heterogeneous environments, individuals compete for resources and form home-ranges, with only risk-tolerant individuals using habitat edges. Results show that when risk-tolerant individuals exist, small patches increase species diversity. A strong peak occurs at approximately 20% habitat cover in small patches when those small habitats are only used for foraging but not for breeding and home-range core position. Additional usage as stepping stones for juvenile dispersal further increases species persistence. Over-all, our results reveal that a combination of a few large and several small habitat patches promotes biodiversity by enhancing land-scape heterogeneity.
Here, heterogeneity is created by pronounced differences in habitat functionality, increasing edge density, and variability in habitat use by different behavioral types. The finding that a combination of single large AND several small (SLASS) patches is needed for effective biodiversity preservation has implications for advancing landscape conservation.
Particularly in struc-turally poor agricultural areas, modern technology enables precise management with the opportunity to create small foraging habitats by excluding less profitable agricultural land from cultivation.
Crop rotation, fertilization and residue management affect the water balance and crop production and can lead to different sensitivities to climate change. To assess the impacts of climate change on crop rotations (CRs), the crop model ensemble (APSIM,AQUACROP, CROPSYST, DAISY, DSSAT, HERMES, MONICA) was used. The yields and water balance of two CRs with the same set of crops (winter wheat, silage maize, spring barley and winter rape) in a continuous transient run from 1961 to 2080 were simulated. CR1 was without cover crops and without manure application. Straw after the harvest was exported from the fields. CR2 included cover crops, manure application and crop residue retention left on field. Simulations were performed using two soil types (Chernozem, Cambisol) within three sites in the Czech Republic, which represent temperature and precipitation gradients for crops in Central Europe. For the description of future climatic conditions, seven climate scenarios were used. Six of them had increasing CO & nbsp;concentrations according RCP 8.5, one had no CO2 increase in the future. The output of an ensemble expected higher productivity by 0.82 t/ha/year and 2.04 t/ha/year for yields and aboveground biomass in the future (2051-2080). However, if the direct effect of a CO2 increase is not considered, the average yields for lowlands will be lower. Compared to CR1, CR2 showed higher average yields of 1.26 t/ha/year for current climatic conditions and 1.41 t/ha/year for future climatic conditions. For the majority of climate change scenarios, the crop model ensemble agrees on the projected yield increase in C3 crops in the future for CR2 but not for CR1. Higher agreement for future yield increases was found for Chernozem, while for Cambisol, lower yields under dry climate scenarios are expected. For silage maize, changes in simulated yields depend on locality. If the same hybrid will be used in the future, then yield reductions should be expected within lower altitudes. The results indicate the potential for higher biomass production from cover crops, but CR2 is associated with almost 120 mm higher evapotranspiration compared to that of CR1 over a 5-year cycle for lowland stations in the future, which in the case of the rainfed agriculture could affect the long-term soil water balance. This could affect groundwater replenishment, especially for locations with fine textured soils, although the findings of this study highlight the potential for the soil water-holding capacity to buffer against the adverse weather conditions.
To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5-5.7% per degrees C), but to increase with increasing CO2 concentration (4.6-8.3% per 100-ppm), rainfall (2.1-6.1% per 10% increase), and nitrogen rates (1.3-6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments.