Biology subjects

Conway, M. E.

Publications and source records attributed to Conway, M. E..

3 recordsLinked to original sources

bioRxiv2022

TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding

c-Myc protooncogene places a demand on glucose uptake to drive glucose-dependent biosynthetic pathways. To achieve this demand, c-Myc protein (Myc henceforth) drives the expression of glucose transporters and represses the expression of Thioredoxin Interacting Protein (TXNIP), which is a potent negative regulator of glucose uptake. A Mychigh/TXNIPlow gene signature is clinically significant as it correlates with poor clinical prognosis in Triple-Negative Breast Cancer (TNBC) but not in other subtypes of breast cancer. To better understand how TXNIP function contributes to the aggressive behavior of TNBC, we generated TXNIP null MDA-MB-231 (231:TKO) cells for our study. We show here that TXNIP loss drives a transcriptional program that resembles those driven by Myc and increases global Myc genome occupancy. TXNIP loss allows Myc to invade the promoters and enhancers of target genes that are potentially relevant to cell transformation. Together, these findings suggest that TXNIP is a broad repressor of Myc genomic binding. The increase in Myc genomic binding in the 231:TKO cells expands the Myc-dependent transcriptome we identified in parental MDA-MB-231 cells. This expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Mycs intrinsic capacity to activate transcription and without increasing Myc levels. Together, our findings suggest that TXNIP loss mimics Myc overexpression, connecting Myc genomic binding and transcriptional programs to the metabolic signals that control TXNIP expression.

cancer biology↗

bioRxiv2019

STAT3 and GR cooperate to drive basal-like triple negative breast cancer gene expression and proliferation.

Breast cancers can be divided into subtypes with different prognoses and treatment responses based on global gene expression differences. Luminal breast cancer gene expression and proliferation are driven by the transcription factors Estrogen Receptor (ER), FOXA1 and GATA3. Targeting ER is the most effective therapy for treating luminal breast cancer because ER is the master regulator of the luminal gene expression program. In contrast, it is unclear which transcription factors are responsible for driving the gene expression signature that defines basal-like triple negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype of the disease. This study utilized integrated analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. The results of this study indicate that glucocorticoid receptor (GR) and signal transducer and activator of transcription 3 (STAT3) bind to the same genomic regulatory regions that are specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperate to regulate expression of hundreds of genes in the basal-like gene expression signature and these downstream genes are associated with poor prognosis in patients. Furthermore, combination treatment with small molecule drugs that inhibit both transcription factors leads to synergistic decreases in cell proliferation in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple negative breast cancer through rational combination of STAT3 and GR inhibitors.

cancer biology↗

bioRxiv2018

SV-plaudit: A cloud-based framework for manually curating thousands of structural variants

SV-plaudit is a framework for rapidly curating structural variant (SVs) predictions. For each SV, we generate an image that visualizes the coverage and alignment signals from a set of samples. Images are uploaded to our cloud framework where users assess the quality of each image using a client-side web application. Reports can then be generated as a tab-delimited file or annotated VCF. As a proof of principle, nine researchers collaborated for one hour to evaluate 1,350 SVs each. We anticipate that SV-plaudit will become a standard step in variant calling pipelines and the crowd-sourced curation of other biological results.\n\nCode available at https://github.com/jbelyeu/SV-plaudit\n\nDemonstration video available at https://www.youtube.com/watch?v=ono8kHMKxDs

bioinformatics↗