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Institute
- Theodor-Boveri-Institut für Biowissenschaften (112) (remove)
Sonstige beteiligte Institutionen
ResearcherID
- J-8841-2015 (1)
Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F1 interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F1 interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability.
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.
Deadwood represents an important structural component of forest ecosystems, where it provides diverse niches for saproxylic biota. Although wood-inhabiting prokaryotes are involved in its degradation, knowledge about their diversity and the drivers of community structure is scarce. To explore the effect of deadwood substrate on microbial distribution, the present study focuses on the microbial communities of deadwood logs from 13 different tree species investigated using an amplicon based deep-sequencing analysis. Sapwood and heartwood communities were analysed separately and linked to various relevant wood physico-chemical parameters. Overall, Proteobacteria, Acidobacteria and Actinobacteria represented the most dominant phyla. Microbial OTU richness and community structure differed significantly between tree species and between sapwood and heartwood. These differences were more pronounced for heartwood than for sapwood. The pH value and water content were the most important drivers in both wood compartments. Overall, investigating numerous tree species and two compartments provided a remarkably comprehensive view of microbial diversity in deadwood.
1.
The successional dynamics of forests—from canopy openings to regeneration, maturation, and decay—influence the amount and heterogeneity of resources available for forest-dwelling organisms. Conservation has largely focused only on selected stages of forest succession (e.g., late-seral stages). However, to develop comprehensive conservation strategies and to understand the impact of forest management on biodiversity, a quantitative understanding of how different trophic groups vary over the course of succession is needed.
2.
We classified mixed mountain forests in Central Europe into nine successional stages using airborne LiDAR. We analysed α- and β-diversity of six trophic groups encompassing approximately 3,000 species from three kingdoms. We quantified the effect of successional stage on the number of species with and without controlling for species abundances and tested whether the data fit the more-individuals hypothesis or the habitat heterogeneity hypothesis. Furthermore, we analysed the similarity of assemblages along successional development.
3.
The abundance of producers, first-order consumers, and saprotrophic species showed a U-shaped response to forest succession. The number of species of producer and consumer groups generally followed this U-shaped pattern. In contrast to our expectation, the number of saprotrophic species did not change along succession. When we controlled for the effect of abundance, the number of producer and saproxylic beetle species increased linearly with forest succession, whereas the U-shaped response of the number of consumer species persisted. The analysis of assemblages indicated a large contribution of succession-mediated β-diversity to regional γ-diversity.
4.
Synthesis and applications. Depending on the species group, our data supported both the more-individuals hypothesis and the habitat heterogeneity hypothesis. Our results highlight the strong influence of forest succession on biodiversity and underline the importance of controlling for successional dynamics when assessing biodiversity change in response to external drivers such as climate change. The successional stages with highest diversity (early and late successional stages) are currently strongly underrepresented in the forests of Central Europe. We thus recommend that conservation strategies aim at a more balanced representation of all successional stages.
Body size is a fundamental trait known to allometrically scale with metabolic rate and therefore a key determinant of individual development, life history, and consequently fitness. In spatially structured environments, movement is an equally important driver of fitness. Because movement is tightly coupled with body size, we expect habitat fragmentation to induce a strong selection pressure on size variation across and within species. Changes in body size distributions are then, in turn, expected to alter food web dynamics. However, no consensus has been reached on how spatial isolation and resource growth affect consumer body size distributions. Our aim was to investigate how these two factors shape the body size distribution of consumers under scenarios of size-dependent and size-independent consumer movement by applying a mechanistic, individual-based resource–consumer model. We also assessed the consequences of altered body size distributions for important ecosystem traits such as resource abundance and consumer stability. Finally, we determined those factors that explain most variation in size distributions. We demonstrate that decreasing connectivity and resource growth select for communities (or populations) consisting of larger species (or individuals) due to strong selection for the ability to move over longer distances if the movement is size-dependent. When including size-dependent movement, intermediate levels of connectivity result in increases in local size diversity. Due to this elevated functional diversity, resource uptake is maximized at the metapopulation or metacommunity level. At these intermediate levels of connectivity, size-dependent movement explains most of the observed variation in size distributions. Interestingly, local and spatial stability of consumer biomass is lowest when isolation and resource growth are high. Finally, we highlight that size-dependent movement is of vital importance for the survival of populations or communities within highly fragmented landscapes. Our results demonstrate that considering size-dependent movement is essential to understand how habitat fragmentation and resource growth shape body size distributions—and the resulting metapopulation or metacommunity dynamics—of consumers.
All over the world, pollinators are threatened by land-use change involving degradation of seminatural habitats or conversion into agricultural land. Such disturbance often leads to lowered pollinator abundance and/or diversity, which might reduce crop yield in adjacent agricultural areas. For West Africa, changes in bee communities across disturbance gradients from savanna to agricultural land are mainly unknown. In this study, we monitored for the impact of human disturbance on bee communities in savanna and crop fields. We chose three savanna areas of varying disturbance intensity (low, medium, and high) in the South Sudanian zone of Burkina Faso, based on land-use/land cover data via Landsat images, and selected nearby cotton and sesame fields. During 21 months covering two rainy and two dry seasons in 2014 and 2015, we captured bees using pan traps. Spatial and temporal patterns of bee species abundance, richness, evenness and community structure were assessed. In total, 35,469 bee specimens were caught on 12 savanna sites and 22 fields, comprising 97 species of 32 genera. Bee abundance was highest at intermediate disturbance in the rainy season. Species richness and evenness did not differ significantly. Bee communities at medium and highly disturbed savanna sites comprised only subsets of those at low disturbed sites. An across-habitat spillover of bees (mostly abundant social bee species) from savanna into crop fields was observed during the rainy season when crops are mass-flowering, whereas most savanna plants are not in bloom. Despite disturbance intensification, our findings suggest that wild bee communities can persist in anthropogenic landscapes and that some species even benefitted disproportionally. West African areas of crop production such as for cotton and sesame may serve as important food resources for bee species in times when resources in the savanna are scarce and receive at the same time considerable pollination service.
Plant performance is correlated with element concentrations in plant tissue, which may be impacted by adverse chemical soil conditions. Antibiotics of veterinary origin can adversely affect plant performance. They are released to agricultural fields via grazing animals or manure, taken up by plants and may be stored, transformed or sequestered by plant metabolic processes. We studied the potential effects of three antibiotics (penicillin, sulfadiazine, and tetracycline) on plant element contents (macro- and microelements). Plant species included two herb species (Brassica napus and Capsella bursa-pastoris) and two grass species (Triticum aestivum and Apera spica-venti), representing two crop species and two noncrop species commonly found in field margins, respectively. Antibiotic concentrations were chosen as to reflect in vivo situations, that is, relatively low concentrations similar to those detected in soils. In a greenhouse experiment, plants were raised in soil spiked with antibiotics. After harvest, macro- and microelements in plant leaves, stems, and roots were determined (mg/g). Results indicate that antibiotics can affect element contents in plants. Penicillin exerted the greatest effect both on element contents and on scaling relationships of elements between plant organs. Roots responded strongest to antibiotics compared to stems and leaves. We conclude that antibiotics in the soil, even in low concentrations, lead to low-element homeostasis, altering the scaling relationships between roots and other plant organs, which may affect metabolic processes and ultimately the performance of a plant.
Drosophila melanogaster is a long-standing model organism in the circadian clock research. A major advantage is the relative small number of about 150 neurons, which built the circadian clock in Drosophila. In our recent work, we focused on the neuroanatomical properties of the lateral neurons of the clock network. By applying the multicolor-labeling technique Flybow we were able to identify the anatomical similarity of the previously described E2 subunit of the evening oscillator of the clock, which is built by the 5th small ventrolateral neuron (5th s-LNv) and one ITP positive dorsolateral neuron (LNd). These two clock neurons share the same spatial and functional properties. We found both neurons innervating the same brain areas with similar pre- and postsynaptic sites in the brain. Here the anatomical findings support their shared function as a main evening oscillator in the clock network like also found in previous studies. A second quite surprising finding addresses the large lateral ventral PDF-neurons (l-LNvs). We could show that the four hardly distinguishable l-LNvs consist of two subgroups with different innervation patterns. While three of the neurons reflect the well-known branching pattern reproduced by PDF immunohistochemistry, one neuron per brain hemisphere has a distinguished innervation profile and is restricted only to the proximal part of the medulla-surface. We named this neuron “extra” l-LNv (l-LNvx). We suggest the anatomical findings reflect different functional properties of the two l-LNv subgroups.
To study delayed genetic and epigenetic radiation effects, which may trigger radiation-induced carcinogenesis, we have established single-cell clones from irradiated and non-irradiated primary human fibroblasts. Stable clones were endowed with the same karyotype in all analyzed metaphases after 20 population doublings (PDs), whereas unstable clones displayed mosaics of normal and abnormal karyotypes. To account for variation in radiation sensitivity, all experiments were performed with two different fibroblast strains. After a single X-ray dose of 2 Gy more than half of the irradiated clones exhibited radiation-induced genome instability (RIGI). Irradiated clones displayed an increased rate of loss of chromosome Y (LOY) and copy number variations (CNVs), compared to controls. CNV breakpoints clustered in specific chromosome regions, in particular 3p14.2 and 7q11.21, coinciding with common fragile sites. CNVs affecting the FHIT gene in FRA3B were observed in independent unstable clones and may drive RIGI. Bisulfite pyrosequencing of control clones and the respective primary culture revealed global hypomethylation of ALU, LINE-1, and alpha-satellite repeats as well as rDNA hypermethylation during in vitro ageing. Irradiated clones showed further reduced ALU and alpha-satellite methylation and increased rDNA methylation, compared to controls. Methylation arrays identified several hundred differentially methylated genes and several enriched pathways associated with in vitro ageing. Methylation changes in 259 genes and the MAP kinase signaling pathway were associated with delayed radiation effects (after 20 PDs). Collectively, our results suggest that both genetic (LOY and CNVs) and epigenetic changes occur in the progeny of exposed cells that were not damaged directly by irradiation, likely contributing to radiation-induced carcinogenesis. We did not observe epigenetic differences between stable and unstable irradiated clones. The fact that the DNA methylation (DNAm) age of clones derived from the same primary culture varied greatly suggests that DNAm age of a single cell (represented by a clone) can be quite different from the DNAm age of a tissue. We propose that DNAm age reflects the emergent property of a large number of individual cells whose respective DNAm ages can be highly variable.
Patient-tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix-based three-dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR-activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho-arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho-kinase array, proliferation, and apoptosis), we generated cell line-specific in silico topologies and condition-specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS-mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.