Biology subjects

Ball, C.

Publications and source records attributed to Ball, C..

5 recordsLinked to original sources

bioRxiv2025

AURKB-driven dissolution of CIZ1-RNA assemblies from the inactive X chromosome in mitosis

Cip1-interacting zinc-finger protein 1 (CIZ1) interacts with Xist lncRNA to form large RNA-protein assemblies at the inactive X-chromosome (Xi) in female mammalian nuclei, plus smaller assemblies in both sexes. CIZ1 assemblies influence underlying chromatin, and their disruption alters the expression of autosomal and X-linked gene clusters. Here, we explore the regulated dissolution of CIZ1-Xi assemblies during mitosis and show that, like Xist, CIZ1 is released in prometaphase under the regulation of Aurora Kinase B (AURKB). The part of human/mouse CIZ1 comprising 179/181 C-terminal amino-acids encodes a matrin-3 domain that facilitates dimerization to form a compact folded core with disordered C-terminal extensions. Mass spectrometry revealed 56 high-confidence interacting partners of the C-terminal fragment, predominantly chromatin, nuclear matrix and RNA-binding proteins. Phosphomimetic mutation of three conserved AURKB sites in the C-terminal extensions released CIZ1 from its nuclear anchor points, but did not affect its interaction with chromatin or nuclear matrix proteins. In contrast, the same mutations, or deletion of the C-terminal extensions, abolished interaction with RNAs including Xist. Together, the data suggest CIZ1 is a regulatable component of the protein-RNA assemblies that preserve epigenetic stability across the nucleus, and that AURKB drives their dissolution in mitosis via dissociation of CIZ1 from RNA. BulletsThe data show regulated dissolution of RNA-protein assemblies involved in protection of epigenetic state. RNA spatially constrains CIZ1 assemblies via multivalent interactions at sub-nuclear sites. CIZ1 dimerizes to present dual extensions whose phosphorylation by AURKB dissolves RNA interaction in mitosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/676856v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@107bec2org.highwire.dtl.DTLVardef@17cbb4aorg.highwire.dtl.DTLVardef@1ec0144org.highwire.dtl.DTLVardef@1474995_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Literature to Span Biomedical Data Repositories

MotivationAs the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. ResultsDeveloped through the National Heart, Lung, and Blood Institutes (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15,911 study variables from public datasets. On a manually curated search dataset, Dugs total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearchs total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. Availability and ImplementationDug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. Contactawaldrop@rti.org or scox@renci.org

bioinformatics↗

bioRxiv2019

Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

Over the last decades, deep learning models have rapidly gained popularity for their ability to achieve state-of-the-art performances in different inference settings. Novel domains of application define a new set of requirements that transcend accurate predictions and depend on uncertainty measures. The aims of this study are to implement Bayesian neural networks and use the corresponding uncertainty estimates to improve predictions and perform dataset analysis. We identify two main advantages in modeling the predictive uncertainty of deep neural networks performing classification tasks. The first is the possibility to discard highly uncertain predictions to increase model accuracy. The second is the identification of unfamiliar patterns in the data that correspond to outliers in the model representation of the training data distribution. Such outliers can be further characterized as either corrupted observations or data belonging to different domains. Both advantages are well demonstrated on benchmark datasets. Furthermore we apply the Bayesian approach to a biomedical imaging dataset where cancer cells are treated with diverse drugs, and show how one can increase classification accuracy and identify noise in the ground truth labels with uncertainty analysis.

bioinformatics↗

bioRxiv2019

Metadata-Guided Visual Representation Learning for Biomedical Images

MotivationThe clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the grouping according to the presented morphological characteristics.\n\nResultsWe introduce a novel approach to visual representation learning that is guided by metadata. In high-context-screening, meta-data can typically be derived from the experimental layout, which links each cell image of a particular assay to the tested chemical compound and corresponding compound concentration. In general, there exists a one-to-many relationship between phenotype and compound, since various molecules and different dosage can lead to one and the same alterations in biological cells.\n\nOur empirical results show that metadata-guided visual representation learning is an effective approach for clustering biomedical images. We have evaluated our proposed approach on both benchmark and real-world biological data. Furthermore, we have juxtaposed implicit and explicit learning techniques, where both loss function and batch construction differ. Our experiments demonstrate that metadata-guided visual representation learning is able to identify commonalities and distinguish differences in visual appearance that lead to meaningful clusters, even without image-level annotations.\n\nNotePlease refer to the supplementary material for implementation details on metadata-guided visual representation learning strategies.

bioinformatics↗

bioRxiv2018

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗