Biology subjects

Petrovic, J.

Publications and source records attributed to Petrovic, J..

5 recordsLinked to original sources

bioRxiv2024

Catalytic inhibition of KAT6/KAT7 enhances the efficacy and overcomes primary and acquired resistance to Menin inhibitors in MLL leukaemia

Understanding the molecular pathogenesis of MLL fusion oncoprotein (MLL-FP) leukaemia has spawned epigenetic therapies that have improved clinical outcomes in this often-incurable disease. Using genetic and pharmacological approaches, we define the individual and combined contribution of KAT6A, KAT6B and KAT7, in MLL-FP leukaemia. Whilst inhibition of KAT6A/B is efficacious in some pre-clinical models, simultaneous targeting of KAT7, with the novel inhibitor PF-9363, increases the therapeutic efficacy. KAT7 interacts with Menin and the MLL complex and is co-localised at chromatin to co-regulate the MLL-FP transcriptional program. Inhibition of KAT6/KAT7 provides an orthogonal route to targeting Menin to disable the transcriptional activity of MLL-FP. Consequently, combined inhibition rapidly evicts the MLL-FP from chromatin, potently represses oncogenic transcription and overcomes primary resistance to Menin inhibitors. Moreover, PF-9363 or genetic depletion of KAT7 can also overcome acquired genetic/non-genetic resistance to Menin inhibition. These data provide the molecular rationale for rapid clinical translation of combination therapy in MLL-FP leukaemia.

cancer biology↗

bioRxiv2024

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remains unclear. Here, we systematically identified enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We found that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further showed that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

molecular biology↗

bioRxiv2019

TooManyCells identifies and visualizes relationships of single-cell clades

Transcriptional programs contribute to phenotypic and functional cell states. While elucidation of cell state heterogeneity and its role in biology and pathobiology has been advanced by studying single cell level measurements, the underlying assumptions of current analytical methods limit the identification and exploration of cell clades. Unlike other methods, which produce a single uni-layer partition of cells ignoring echelons of cell states, we present TooManyCells, a software consisting of a suite of graph-based tools for efficient, global, and unbiased identification and visualization of cell clades while maintaining and presenting the relationship between cell states. TooManyCells provides a set of tools based on a matrix-free efficient divisive hierarchical spectral clustering algorithm wholly different from the prevalent Louvain-based methods. BirchBeer, the visualization component of TooManyCells, introduces a new approach for single cell analysis that is built on a concept intentionally orthogonal to the widely used dimensionality reduction methods. Together, this suite of tools provide a paradigm shift in the analysis and interpretation of single cell data by enabling simultaneous comparisons of cell states at context-and application-dependent scales. A byproduct of this shift is the immediate detection and visualization of rare populations that outperforms previous algorithms as demonstrated by applying these tools to existing single cell RNA-seq data sets from various mouse organs.

bioinformatics↗

bioRxiv2018

γδ T cells do not contribute to peripheral inflammatory pain

Circulating immune cells, which are recruited to the site of injury/disease, secrete various inflammatory mediators that are critical to nociception and pain. The role of tissue-resident immune cells, however, remains poorly characterized. One of the first cells to be activated in peripheral tissues following injury are {gamma}{delta} T cells, which serve important roles in infection and disease. Using a transgenic mouse line lacking these cells, we sought to identify their contribution to inflammatory pain. Three distinct models of inflammatory pain were used: intraplantar injection of formalin and incisional wound (as models of acute inflammatory pain) and intraplantar injection of complete Freunds adjuvant (as a model of chronic inflammatory pain). Our results show that absence of these cells does not alter baseline sensitivity, nor does it result in changes to mechanical or thermal hypersensitivity after tissue injury. These results were consistent in both male and female mice, suggesting that there are no sex differences in these outcomes. This comprehensive characterization suggests that {gamma}{delta} T cells do not contribute to basal sensitivity or the development and maintenance of inflammatory pain.

neuroscience↗

bioRxiv2018

Differential Integration of Transcriptome and Proteome Identifies Pan-cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene-programs and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.

bioinformatics↗