Biology subjects

Hawkins, W.

Publications and source records attributed to Hawkins, W..

4 recordsLinked to original sources

bioRxiv2024

Chemical perturbations impacting histone acetylation governcolorectal cancer differentiation

Dysregulated epigenetic programs that restrict differentiation, reactivate fetal genes, and confer phenotypic plasticity are critical to colorectal cancer (CRC) development. By screening a small molecule library targeting epigenetic regulators using our dual reporter system, we found that inhibiting histone deacetylase (HDAC) 1/2 promotes CRC differentiation and anti-tumor activity. Comprehensive biochemical, chemical, and genetic experiments revealed that on-target blockade of the HDAC1/2 catalytic domain mediated the differentiated phenotype. Unbiased profiling of histone posttranslational modifications induced by HDAC1/2 inhibition nominated acetylation of specific histone lysine residues as potential regulators of differentiation. Genome-wide assessment of implicated marks indicated that H3K27ac gains at HDAC1/2-bound regions associated with open chromatin and upregulation of differentiation genes upon HDAC1/2 inhibition. Disrupting H3K27ac by degrading acetyltransferase EP300 rescued HDAC1/2 inhibitor-mediated differentiation of a patient-derived CRC model using single cell RNA-sequencing. Genetic screens revealed that DAPK3 contributes to CRC differentiation induced by HDAC1/2 inhibition. These results highlight the importance of specific chemically targetable histone modifications in governing cancer cell states and epigenetic reprogramming as a therapeutic strategy in CRC. BRIEF SUMMARYHDAC1/2 inhibition promotes colorectal cancer differentiation via gains in H3K27ac, which can be reversed by blocking its acetyltransferase EP300.

cancer biology↗

bioRxiv2024

Modulation of blood-tumor barrier transcriptional programs improves intra-tumoral drug delivery and potentiates chemotherapy in GBM

Glioblastoma (GBM) is the most common malignant primary brain tumor. GBM has an extremely poor prognosis and new treatments are badly needed. Efficient drug delivery to GBM is a major obstacle as the blood-brain barrier (BBB) prevents passage of the majority of cancer drugs into the brain. It is also recognized that the blood-brain tumor barrier (BTB) in the growing tumor represents a challenge. The BTB is heterogeneous and poorly characterized, but similar to the BBB it can prevent therapeutics from reaching effective intra-tumoral doses, dramatically hindering their potential. Here, we identified a 12-gene signature associated with the BTB, with functions related to vasculature development, morphogenesis and cell migration. We identified CDH5 as a core molecule in this set and confirmed its over-expression in GBM vasculature using spatial transcriptomics of GBM patient specimens. We found that the indirubin-derivative, 6-bromoindirubin acetoxime (BIA), could downregulate CDH5 and other BTB signature genes, causing endothelial barrier disruption in endothelial monolayers and BBB 3D spheroids in vitro. Treatment of tumor-bearing mice with BIA enabled increased intra-tumoral accumulation of the BBB non-penetrant chemotherapeutic drug cisplatin and potentiated cisplatin-mediated DNA damage by targeting DNA repair pathways. Finally, using an injectable BIA nanoparticle formulation, PPRX-1701, we significantly improved the efficacy of cisplatin in patient-derived GBM xenografts and prolonged their survival. Overall, our work reveals potential targets at the BTB for improved chemotherapy delivery and the bifunctional properties of BIA as a BTB modulator and potentiator of chemotherapy, supporting its further development.

cancer biology↗

bioRxiv2023

M3NetFlow: a novel multi-scale multi-hop modular graph AI model for multi-omics data integration and signaling network inference

SummaryMulti-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer s disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow

bioinformatics↗

bioRxiv2021

SC2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimers Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

bioinformatics↗