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Karl, T.

Publications and source records attributed to Karl, T..

6 recordsLinked to original sources

Knock-out of Tpm4.2/actin filaments alters neuronal signaling, neurite outgrowth and behavioral phenotypes in mice.

Tropomyosins (Tpm) are master regulators of actin dynamics through forming co-polymers with filamentous actin. Despite the well-understood function of muscle Tpms in the contractile apparatus of muscle cells, much less is known about the diverse physiological function of cytoplasmic Tpms in eukaryotic cells. Here, we investigated the role of the Tpm4.2 isoform in neuronal processes including signaling, neurite outgrowth and receptor recycling using primary neurons from Tpm4.2 knock-out mice. Live imaging of calcium and electro-physiology data demonstrated increased frequency, yet reduced strength of single neuron spikes. Calcium imaging further showed increase in neuronal networks. In vitro assays of Tpm4.2 knock-out neurons displayed impaired recycling of the AMPA neurotransmitter receptor subunit GluA1. Morphometric analysis of neurite growth showed increased dendritic complexity and altered dendritic spine morphology in Tpm4.2 knock-out primary neurons. Behavioral analysis of Tpm4.2 knock-out mice displayed heightened anxiety in Open field test whilst Elevated Plus maze displayed heightened anxiety only in females. A sex-dependent phenotype was also seen in the Social Preference test with impaired social memory and socialization in female Tpm4.2 knock-out mice, whilst male and female knock-out mice had impaired recognition memory in a Novel Object Recognition test. Our study depicts the multi-faceted role of the Tpm4.2 isoform and its co-polymer F-actin population in neurons, with potential implications for better understanding diseases of the nervous system which involve actin cytoskeleton dys-function.

neuroscience↗

Deciphering plant-beneficial fungal interactions: Unravelling metabolic diversity that underpins communication between Laccaria bicolor and Trichoderma

With over 250 known species, the genus Trichoderma (Ascomycota, Hypocreaceae) is found in various soils, on plant surfaces and as plant endophytes. While Trichoderma species are known as mycoparasites, their antagonistic behaviour can also negatively affect other beneficial fungi, such as mycorrhizal fungi. To gain insight into the metabolic signals involved in the interactions between the ectomycorrhizal fungus (ECM) Laccaria bicolor (Basidomycota, Hydnangiaceae), and different mycoparasitic Trichoderma spp. (T. harzianum strains WM24a1, MS8a1 and ES8g1, and T. atrobrunneum), we performed in vitro dual-confrontation experiments. We studied the volatile organic compounds (VOCs), hyphal metabolomes and soluble metabolites released by each of the fungi in various co-cultivation scenarios. The results revealed an altered growth of the mycelia depending on the degree of contact: When Trichoderma spp. and L. bicolor shared only the same headspace, Trichoderma spp. growth was at least partially inhibited, whereas in direct contact the growth of L. bicolor was impaired. Distinct strain- and species-specific changes in hyphal metabolites, in exudates and volatile emission were revealed from each of the studied fungi. We identified both core metabolite profiles and interaction-specific metabolic responses that were related to carbohydrate, lipid, nucleotide, energy and amino acid metabolisms. Volatile and soluble metabolites revealed temporal and spatial adjustments in dual cultures compared to solitary cultures, suggesting rapid contact-dependent adaptations and demonstrating the dynamic communication mechanisms between Trichoderma spp. and the ECM. These results suggest a central role for both emitted and secreted fungal metabolites in the fungal non-self-recognition and in interaction with each other.

microbiology↗

Lifestyle-specific responses of Trichoderma spp. in mycoparasitic confrontations and implications for biocontrol of Populus x canescens

O_LITrichoderma spp. are gaining popularity in agriculture and forestry due to their multifaceted roles in promoting plant growth through e.g. nutrient translocation, hormone production, induction of plant systemic resistance, but also direct antagonism of other fungi. However, the mycotrophic nature of the genus bears the risk of possible interference with other native plant-beneficial fungi, such as ectomycorrhiza, in the rhizosphere. Such interference could yield unpredictable consequences for the host plants of these ecosystems. C_LIO_LIWe investigated whether Trichoderma spp. can differentiate between beneficial ectomycorrhizal fungi (represented by Laccaria bicolor and Hebeloma cylindrosporum) and pathogenic fungi (represented by Fusarium graminearum and Alternaria alternata) in different confrontation scenarios, including a newly developed olfactometer "race tube"-like system. C_LIO_LIUsing two independent species, T. harzianum, and T. atrobrunneum, with plant-growth-promoting and immune-stimulating properties towards Populus x canescens, our study revealed robustly accelerated growth towards phytopathogens, while showing a contrary response to ectomycorrhizal fungi. Transcriptomic analyses identified distinct genetic programs during interaction corresponding to the lifestyles, emphasizing the expression of mycoparasitism-related genes only in the presence of phytopathogens. C_LIO_LIThe findings reveal a critical mode of fungal community interactions belowground and suggest that Trichoderma spp. can distinguish between fungal partners of different lifestyles already at a distance. C_LI

microbiology↗

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

pharmacology and toxicology↗

TMvisDB: resource for transmembrane protein annotation and 3D visualization

Since the rise of cellular organisms, transmembrane proteins (TMPs) have been crucial to a variety of cellular processes due to their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to technical limitations. With structure prediction methods coming of age, predictions might fill some of the need. However, identifying the membrane regions and topology in three-dimensional structure files requires additional in silico prediction. Here, we introduce TMvisDB to sieve through millions of predicted structures for TMPs. This resource enables both, to browse through 46 million predicted TMPs and to visualize those along with their topological annotations. The database was created by joining AlphaFold DB structure predictions and transmembrane topology predictions from the protein language model based method TMbed. We show the utility of TMvisDB for individual proteins through two single use cases, namely the B-lymphocyte antigen CD20 (Homo sapiens) and the cellulose synthase (Novosphingobium sp. P6W). To demonstrate the value for large scale analyses, we focus on all TMPs predicted for the human proteome. TMvisDB is freely available at tmvis.predictprotein.org.

bioinformatics↗

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗