Biology subjects

Greenyer, H.

Publications and source records attributed to Greenyer, H..

5 recordsLinked to original sources

bioRxiv2026

lncRNA MANCR isoforms selectively mediate multiple levels of epigenomic and P53-responsive transcriptional control in triple negative breast cancer

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and are frequently dysregulated in cancer. The mitotically associated lncRNA MANCR is highly expressed in aggressive cancers and contributes to genomic instability in triple-negative breast cancer (TNBC), but the molecular mechanisms underlying its activity remain poorly defined. Here we integrate computational and experimental approaches to examine the structure and regulatory interactions of MANCR isoforms. Analysis of transcriptomic datasets revealed tumor-type-specific expression patterns for seven MANCR isoforms in breast cancer cell lines. Computational prediction of RNA secondary structures identified conserved structural features across isoforms, suggesting potential functional specialization. We identify p53 as a MANCR-interacting protein through computational docking and RNA immunoprecipitation sequencing (RIP-seq) and demonstrate that MANCR depletion reduces p53-dependent transcriptional activity. Chromatin isolation by RNA purification sequencing (ChIRP-seq) revealed 1, 250 genomic regions associated with MANCR, including enrichment of p53 consensus motifs and GC-rich sequence elements. Motif analysis further identified candidate sequence features associated with MANCR-occupied chromatin regions. Computational prediction of RNA-miRNA interactions identified multiple potential miRNA binding sites across MANCR isoforms, including miR-6756-5p, which targets the androgen receptor (AR). Consistent with this prediction, AR expression decreased following MANCR knockdown in TNBC cells. Together, these results suggest that MANCR isoforms may contribute to transcriptional regulation in TNBC through interactions with chromatin, p53 signaling pathways, and potential miRNA regulatory networks. One Sentence SummaryMitotically-associated lncRNA (MANCR) is prevalent in aggressive cancers interacting with DNA, P53, and miRNAs, to mediate multiple levels of epigenetic transcriptional control in triple negative breast cancer.

cancer biology↗

bioRxiv2026

Acute degron-mediated RUNX1 loss reprograms enhancer activity to epigenetically drive epithelial destabilization and initiate cancer hallmarks

The RUNX1 transcription factor mediates cell-type specific gene expression. RUNX1 suppression and perturbations are recurrently associated with breast tumor initiation and progression. However, the mechanisms governing the dual roles of RUNX1 in sustaining the mammary epithelial phenotype while epigenetically suppressing initiation of cancer-compromised gene expression are poorly understood. To address this, we used the power of degron-mediated acute, selective, and complete RUNX1 ablation in human mammary epithelial cells. RUNX1 mediates promoter and distal enhancer-driven expression of a gene cohort. Dynamic epigenomic responsiveness upon RUNX1 ablation reveals a rapid and selective decrease in chromatin accessibility and H3K27ac at RUNX1-bound enhancers, but not promoters. While differentially initiated and expressed genes contacted by RUNX1-bound enhancers are enriched in pathways involved in epithelial maintenance and stemness, genes with RUNX1-promoter occupancy support DNA damage responsiveness. Modified cell morphology, metabolic control, increased breast cancer stemness, plasticity, anchorage-independent survival, chemoresistance, and perturbed DNA damage reactivity are observed upon RUNX1 ablation. Together, these findings define RUNX1 as an epigenetic tumor suppressor that maintains epithelial cell state by preserving enhancer activity and preventing gene expression associated with hallmarks of cancer.

cancer biology↗

bioRxiv2025

Evaluation of network inference algorithms for derivation of metabolic relationships from lipidomic and metabolomic datasets

MotivationVarious methods have been proposed to construct metabolic networks from metabolomic data; however, small sample sizes, multiple confounding factors, the presence of indirect interactions as well as randomness in metabolic processes are of major concern. ResultsIn this study, we benchmark existing algorithms for creating correlation- and regression-based networks of changes in metabolite abundance and we evaluate their performance across different sample sizes of a generative model. Using standard interaction-level tests and network-scale analyses based on centrality scores, we assess how well these methods capture simulated metabolomic networks. Our findings reveal significant challenges in network inference and result interpretation, even when sample sizes are significant and data are the result of computer modeling of metabolic pathways. Despite these limitations, we demonstrate that correlation-based network inference can, to some extent, discriminate between two different metabolic states. This suggests potential utility in distinguishing overarching changes in metabolic processes but not direct pathways in different conditions. AvailabilityAll relevant data is provided at https://github.com/HosnaJabbari/metabolicRelationships Contactjabbari@ualberta.ca Supplementary informationSupplementary data are available at Bioinformatics Advances

bioinformatics↗

bioRxiv2025

Genome reorganization and its functional impact during breast cancer progression

Cancer progression involves extensive alterations in epigenetic and gene expression programs, but the accompanying changes in higher-order genome organization remain less well understood. Using high-resolution Micro-C mapping in the MCF10 cell model of breast cancer, we profiled chromatin compartments, topologically associated domains, and chromatin loops. We find large-scale compartmental shifts occur predominantly in early stages of cancer development, with more fine-scale structural changes in TADs and looping accumulating during the later transition to metastasis. Relating these chromatin features to gene expression and enhancer-associated histone marks revealed that many differentially expressed genes are physically connected to distal regulatory elements. While enhancer-promoter contact frequency and distal enhancer activity correlated with gene expression, strong changes in chromatin looping were relatively infrequent during progression, suggesting that alterations in chromatin contacts are not globally necessary but may facilitate gene regulation at a subset of genes. These results elucidate the connection between gene regulation and genome remodeling in a cell-based cancer progression model.

genomics↗

bioRxiv2020

A Python-based optimization framework for high-performance genomics

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq--a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

bioinformatics↗