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Strauss, C.

Publications and source records attributed to Strauss, C..

6 recordsLinked to original sources

Liquid Crystal-like self-organization of Glioblastoma Prevents cell density induced migratory arrest

Glioblastoma is the most lethal and frequent type of primary brain tumors, characterized by a high proliferative and infiltrative capacity, leading to frequent recurrences after therapy. Here, we used live cell imaging to analyze the effect of cell density variations on the migratory capacity of established and primary glioblastoma cell lines. We found that proliferation events promoted local velocity of glioblastoma cells, but had only a small effect on large spatial scales. Furthermore, two phenotypes were found when subjecting glioblastoma cells to cell density gradient. One phenotype was characterized by the active migration of cells, independent of proliferation, while the other was mostly driven by cell proliferation. Lastly, the analysis of effects of an overall increasing cell density demonstrated that cells showing signs of self-organization, forming liquid crystal-like structures, are able to escape the expected cell-density induced migratory arrest. Notably, the emergence of small-scale liquid crystal-like order was associated with a promotion of cellular migration, even in cell populations that were largely in a state of migratory arrest. Thus, structure formation could help glioblastoma cells to move efficiently in states of high confinement, maintaining infiltrative properties.

biophysics↗

Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search

Prior behavioral work showed that event structure supports our ability to search through continuous naturalistic memories. We hypothesized that, neurally, this memory search process involves a division of labor between slowly unfurling neocortical states representing event knowledge and fast hippocampal-neocortical communication supporting retrieval of new information at transitions between events. In a sample of ten intracranial EEG patients we tracked slow neural state-patterns as patients viewed a movie and then searched their memories in a structured naturalistic interview. As patients answered questions ("after A, when does B happen next?"), state-patterns from movie-viewing were reinstated in neocortex. During the critical memory-search period, states unfurled in a forward direction, realizing a trajectory from A to B in memory. Punctate moments of state-transition during memory-search were marked by low-frequency power decreases in cortex and preceded by power decreases in hippocampus that correlated with reinstatement. Connectivity-analysis revealed information-flow from hippocampus to cortex underpinning state-transitions.

neuroscience↗

The role of Smarcad1 in retroviral repression in mouse embryonic stem cells

BackgroundMoloney murine leukemia virus (MLV) replication is suppressed in mouse embryonic stem cells (ESCs) by the Trim28-SETDB1 complex. The chromatin remodeler Smarcad1 interacts with Trim28 and was suggested to allow the deposition of the histone variant H3.3. However, the role of Trim28, H3.3, and Smarcad1 in MLV repression in ESCs still needs to be fully understood. ResultsIn this study, we used MLV to explore the role of Smarcad1 in retroviral silencing in ESCs. We show that Smarcad1 is immediately recruited to the MLV provirus. Based on the repression dynamics of a GFP-reporter MLV, our findings suggest that Smarcad1 plays a critical role in the establishment and maintenance of MLV repression, as well as other Trim28-targeted genomic loci. Furthermore, Smarcad1 is important for stabilizing and strengthening Trim28 binding to the provirus over time, and its presence around the provirus is needed for proper deposition of H3.3 on the provirus. Surprisingly, the combined depletion of Smarcad1 and Trim28 results in enhanced MLV derepression, suggesting that these two proteins may also function independently to maintain repressive chromatin states. ConclusionsOverall, the results of this study provide evidence for the crucial role of Smarcad1 in the silencing of retroviral elements in embryonic stem cells. Further research is needed to fully understand how Smarcad1 and Trim28 cooperate and their implications for gene expression and genomic stability. Take homeO_LIDepletion of Smarcad1 impairs retroviral repression. C_LIO_LISmarcad1 is necessary for proper recruitment of Trim28 and H3.3 deposition. C_LIO_LIDepleting Smarcad1 and Trim28 results in enhanced derepression of the MLV provirus. C_LI

molecular biology↗

Short heat shock has a long-term effect on mesenchymal stem cells' transcriptome

The harmful consequences of heat stress (HS) on physiology are well documented, but the molecular aspects of changing thermal conditions are poorly understood. Therefore, a better understanding of the effects of this stress on the morphology, phenotype, proliferative capacity, and fate decision of MSCs is required. Our thorough characterization of MSCs transcriptome showed a major effect of HS on the transcriptional landscape. Specifically, examining the effect after three days of moderate HS shows changes in many cell processes, such as immune response, cell cycle, and differentiation. Surprisingly, we detected a long-term effect on cell identity even after short stress, possibly through the activation of bivalent genes related to cell lineage decisions. Finally, comparing the differentially expressed genes following short HS with their transcriptional state after three days of recovery, we find transient upregulation of many members of the MLL family and other histone modifiers; a finding which offers a potential mechanistic account for the stable bivalent genes activation. This could be used to predict and modify the long-term effect of HS on cell identity. Summary blurbHeat shock alters mesenchymal stem cells transcriptional programs, resulting in stable activation of lineage commitment genes, thus explaining the shift in the identity and fate of the cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/517224v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@91f54dorg.highwire.dtl.DTLVardef@7fa0e2org.highwire.dtl.DTLVardef@11b6f54org.highwire.dtl.DTLVardef@2a2180_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

TM-Vec: template modeling vectors for fast homology detection and alignment

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

bioinformatics↗

Protein Structural Alignments From Sequence

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

bioinformatics↗