Center for Computational and Theoretical Biology
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After the recent emergence of SARS-CoV-2 infection, unanswered questions remain related to its evolutionary history, path of transmission or divergence and role of recombination. There is emerging evidence on amino acid substitutions occurring in key residues of the receptor-binding domain of the spike glycoprotein in coronavirus isolates from bat and pangolins. In this article, we summarize our current knowledge on the origin of SARS-CoV-2. We also analyze the host ACE2-interacting residues of the receptor-binding domain of spike glycoprotein in SARS-CoV-2 isolates from bats, and compare it to pangolin SARS-CoV-2 isolates collected from Guangdong province (GD Pangolin-CoV) and Guangxi autonomous regions (GX Pangolin-CoV) of South China. Based on our comparative analysis, we support the view that the Guangdong Pangolins are the intermediate hosts that adapted the SARS-CoV-2 and represented a significant evolutionary link in the path of transmission of SARS-CoV-2 virus. We also discuss the role of intermediate hosts in the origin of Omicron.
Osmotic stress can be detrimental to plants, whose survival relies heavily on proteomic plasticity. Protein ubiquitination is a central post-translational modification in osmotic-mediated stress. In this study, we used the K-Ɛ-GG antibody enrichment method integrated with high-resolution mass spectrometry to compile a list of 719 ubiquitinated lysine (K-Ub) residues from 450 Arabidopsis root membrane proteins (58% of which are transmembrane proteins), thereby adding to the database of ubiquitinated substrates in plants. Although no ubiquitin (Ub) motifs could be identified, the presence of acidic residues close to K-Ub was revealed. Our ubiquitinome analysis pointed to a broad role of ubiquitination in the internalization and sorting of cargo proteins. Moreover, the simultaneous proteome and ubiquitinome quantification showed that ubiquitination is mostly not involved in membrane protein degradation in response to short osmotic treatment but that it is putatively involved in protein internalization, as described for the aquaporin PIP2;1. Our in silico analysis of ubiquitinated proteins shows that two E2 Ub-conjugating enzymes, UBC32 and UBC34, putatively target membrane proteins under osmotic stress. Finally, we revealed a positive role for UBC32 and UBC34 in primary root growth under osmotic stress.
Purpose
To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy.
Methods
In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework (“nnU-net”) was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels.
Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson’s r correlation and Bland-Altman analysis.
Results
The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network.
Conclusion
The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.
Spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes under environmental change
(2022)
Macrophytes are key components of freshwater ecosystems because they provide habitat, food, and improve the water quality. Macrophyte are vulnerable to environmental change as their physiological processes depend on changing environmental factors, which themselves vary within a geographical region and along lake depth. Their spatial distribution is not well understood and their importance is publicly little-known. In this thesis, I have investigated the spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes to understand their diversity pattern along different scales and to predict and communicate potential consequences of global change on their richness.
In the introduction (Chapter 1), I provide an overview of the current scientific knowledge of the species richness patterns of macrophytes in freshwater lakes, the influences of climate and land-use change on macrophyte growth, and different modelling approaches of macrophytes.
The main part of the thesis starts with a study about submerged and emergent macrophyte species richness in natural and artificial lakes of Bavaria (Chapter 2). By analysing publicly available monitoring data, I have found a higher species richness of submerged macrophytes in natural lakes than in artificial lakes. Furthermore, I showed that the richness of submerged species is better explained by physio-chemical lake parameters than the richness of emergent species. In Chapter 3, I considered that submerged macrophytes grow along a depth gradient that provides a sharp environmental gradient on a short spatial scale. This study is the first comparative assessment of the depth diversity gradient (DDG) of macrophytes. I have found a hump-shaped pattern of different diversity components. Generalised additive mixed-effect models indicate that the shape of the DDG is influenced mainly by light quality, light quantity, layering depth, and lake area. I could not identify a general trend of the DDG within recent years, but single lakes show trends leading into different directions. In Chapter 4, I used a mechanistic eco-physiological model to explore changes in the distribution of macrophyte species richness under different scenarios of environmental conditions across lakes and with depths. I could replicate the hump-shaped pattern of potential species richness along depth. Rising temperature leads to increased species richness in all lake types, and depths. The effect of turbidity and nutrient change depends on depth and lake type. Traits that characterise “loser species” under increased turbidity and nutrients are a high light consumption and a high sensibility to disturbances. “Winner species” can be identified by a high biomass production. In Chapter 5, I discuss the image problem of macrophytes. Unawareness, ignorance, and the poor accessibility of macrophytes can lead to conflicts of use. I assumed that an increased engagement and education could counteract this. Because computer games can transfer knowledge interactively while creating an immersive experience, I present in the chapter an interactive single-player game for children.
Finally, I discuss the findings of this thesis in the light of their implications for ecological theory, their implications for conservation, and future research ideas (Chapter 6). The findings help to understand the regional distribution and the drivers of macrophyte species richness. By applying eco-physiological models, multiple environmental shaping factors for species richness were tested and scenarios of climate and land-use change were explored.
At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam-scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include the following: (1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; (2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; (3) variability in amphid sensory dendrites among dauers; and (4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments.
Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.
Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).
Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.
Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.
Solitary bees are subject to a variety of pressures that cause severe population declines. Currently, habitat loss, temperature shifts, agrochemical exposure, and new parasites are identified as major threats. However, knowledge about detrimental bacteria is scarce, although they may disturb natural microbiomes, disturb nest environments, or harm the larvae directly. To address this gap, we investigated 12 Osmia bicornis nests with deceased larvae and 31 nests with healthy larvae from the same localities in a 16S ribosomal RNA (rRNA) gene metabarcoding study. We sampled larvae, pollen provisions, and nest material and then contrasted bacterial community composition and diversity in healthy and deceased nests. Microbiomes of pollen provisions and larvae showed similarities for healthy larvae, whilst this was not the case for deceased individuals. We identified three bacterial taxa assigned to Paenibacillus sp. (closely related to P. pabuli/amylolyticus/xylanexedens), Sporosarcina sp., and Bacillus sp. as indicative for bacterial communities of deceased larvae, as well as Lactobacillus for corresponding pollen provisions. Furthermore, we performed a provisioning experiment, where we fed larvae with untreated and sterilized pollens, as well as sterilized pollens inoculated with a Bacillus sp. isolate from a deceased larva. Untreated larval microbiomes were consistent with that of the pollen provided. Sterilized pollen alone did not lead to acute mortality, while no microbiome was recoverable from the larvae. In the inoculation treatment, we observed that larval microbiomes were dominated by the seeded bacterium, which resulted in enhanced mortality. These results support that larval microbiomes are strongly determined by the pollen provisions. Further, they underline the need for further investigation of the impact of detrimental bacterial acquired via pollens and potential buffering by a diverse pollen provision microbiome in solitary bees.
Revealing the molecular organization of anatomically precisely defined brain regions is necessary for refined understanding of synaptic plasticity. Although three-dimensional (3D) single-molecule localization microscopy can provide the required resolution, imaging more than a few micrometers deep into tissue remains challenging. To quantify presynaptic active zones (AZ) of entire, large, conditional detonator hippocampal mossy fiber (MF) boutons with diameters as large as 10 mu m, we developed a method for targeted volumetric direct stochastic optical reconstruction microscopy (dSTORM). An optimized protocol for fast repeated axial scanning and efficient sequential labeling of the AZ scaffold Bassoon and membrane bound GFP with Alexa Fluor 647 enabled 3D-dSTORM imaging of 25 mu m thick mouse brain sections and assignment of AZs to specific neuronal substructures. Quantitative data analysis revealed large differences in Bassoon cluster size and density for distinct hippocampal regions with largest clusters in MF boutons. Pauli et al. develop targeted volumetric dSTORM in order to image large hippocampal mossy fiber boutons (MFBs) in brain slices. They can identify synaptic targets of individual MFBs and measured size and density of Bassoon clusters within individual untruncated MFBs at nanoscopic resolution.
The abundance of high-quality genotype and phenotype data for the model organism Arabidopsis thaliana enables scientists to study the genetic architecture of many complex traits at an unprecedented level of detail using genome-wide association studies (GWAS). GWAS have been a great success in A. thaliana and many SNP-trait associations have been published. With the AraGWAS Catalog (https://aragwas.1001genomes.org) we provide a publicly available, manually curated and standardized GWAS catalog for all publicly available phenotypes from the central A. thaliana phenotype repository, AraPheno. All GWAS have been recomputed on the latest imputed genotype release of the 1001 Genomes Consortium using a standardized GWAS pipeline to ensure comparability between results. The catalog includes currently 167 phenotypes and more than 222 000 SNP-trait associations with P < 10\(^{-4}\), of which 3887 are significantly associated using permutation-based thresholds. The AraGWAS Catalog can be accessed via a modern web-interface and provides various features to easily access, download and visualize the results and summary statistics across GWAS.
Summary
Embryos develop in a concerted sequence of spatiotemporal arrangements of cells. In the preimplantation mouse embryo, the distribution of the cells in the inner cell mass evolves from a salt-and-pepper pattern to spatial segregation of two distinct cell types. The exact properties of the salt-and-pepper pattern have not been analyzed so far. We investigate the spatiotemporal distribution of NANOG- and GATA6-expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single-cell-based neighborhood analyses. A combination of spatial statistics and agent-based modeling reveals that the cell fate distribution follows a local clustering pattern. Using ordinary differential equations modeling, we show that this pattern can be established by a distance-based signaling mechanism enabling cells to integrate information from the whole inner cell mass into their cell fate decision. Our work highlights the importance of longer-range signaling to ensure coordinated decisions in groups of cells to successfully build embryos.
Highlights
• The local cell neighborhood and global ICM population composition correlate
• ICM cells show characteristics of local clustering in early and mid mouse blastocysts
• ICM patterning requires integration of signals from cells beyond the first neighbors