Biology subjects

Mora, F. V.

Publications and source records attributed to Mora, F. V..

3 recordsLinked to original sources

bioRxiv2024

Androgen stimulation rapidly reorganizes temporal 3D genome and epigenome states to trigger AR-mediated transcription in prostate cancer.

Androgen receptor (AR)-mediated transcription drives prostate cancer progression and remains a critical therapeutic target. AR activation by androgens triggers nuclear translocation, DNA binding at AR regulatory elements (AREs) and transcription initiation. This process involves co-factors such as FOXA1, epigenetic modifications, and 3D chromatin interactions. However, the temporal coordination of these events during AR-mediated transcription remains poorly understood. Using a time-course androgen stimulation model in prostate cancer cells, we integrated temporal multi-omics data within the MOFA+ framework to dissect AR-mediated transcriptional dynamics. Our analysis revealed that rapid AR binding at gene promoters is crucial for initiating nascent transcription of AR target genes. We identified H3K27 acetylation at flanking AREs, pre-marked by constitutive FOXA1 binding, occurs prior to AR recruitment. Additionally, androgen stimulation induced a temporal early shift in nascent transcription from MYC to AR target genes, associated with transient disruption of 3D chromatin interactions. Finally, we showed that CRISPR-mediated inhibition of AR-associated chromatin contacts suppresses distal linked target genes, demonstrating that dynamic genome architecture is functionally required for AR-driven transcription. Together these findings uncover the dynamic reorganization of 3D chromatin structure and transcription factor binding post-androgen stimulation, resulting in distinct temporal epigenomic states. This work provides new insights into the mechanisms governing AR-mediated transcriptional activation in prostate cancer and highlights the interplay between MYC and AR in regulating transcription in hormone-driven cancers.

genomics↗

bioRxiv2022

Pleiotrophin drives a pro-metastatic immune niche within the breast tumor microenvironment

Metastatic cancer cells adapt to thrive in secondary organs. To investigate metastatic adaptation, we performed transcriptomic analysis of metastatic and non-metastatic murine breast cancer cells. We found that pleiotrophin (PTN), a neurotrophic cytokine, is a metastasis-associated factor that is expressed highly by aggressive breast cancers. Moreover, elevated PTN in plasma correlated significantly with metastasis and reduced survival of breast cancer patients. Mechanistically, we find that PTN activates NF-kB in cancer cells leading to altered cytokine production, subsequent neutrophil recruitment and an immune suppressive microenvironment. Consequently, inhibition of PTN, pharmacologically or genetically, reduces the accumulation of tumor associated neutrophils and reverts local immune suppression resulting in increased T cell activation and attenuated metastasis. Furthermore, inhibition of PTN significantly enhanced the efficacy of immune checkpoint blockade + chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a pro-metastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer.

cancer biology↗

bioRxiv2021

Disentangling single-cell omics representation with a power spectral density-based feature extraction

Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data.

bioinformatics↗