Biology subjects

McDaniel, J. M.

Publications and source records attributed to McDaniel, J. M..

3 recordsLinked to original sources

bioRxiv2026

Multi-omics analysis identifies intrinsic Trp53 driven metastatic breast cancer subtypes.

Metastatic breast cancer (mBC) is deadly, and its molecular drivers are largely unknown. Treatment is limited to systemic cytotoxic chemotherapies and tumors often become refractory. The most frequently mutated gene in MBC is TP53. The TP53R248W hotspot missense mutation is associated with poor prognosis. We generated a somatic mouse model of mammary epithelial specific Trp53R245Wexpression. Primary tumors were highly metastatic, reflecting the human molecular subtypes luminal A, luminal B, HER2, and TNBC. Transcriptomic profiling revealed three intrinsic subtypes: stem-cell like (SCL), well-differentiated metabolically active (WDMA), and immunosuppressed (IS). SCL tumors activate ribosome biosynthesis and E2F signaling, amplifying Met, Birc3, Yap1 and deleting Nf1, Pik3r1, and Rad17. WDMA tumors activate cytochrome P450 enzymes, estrogen signaling and branched chain amino acid degradation, with mutations activating Pi3k/Akt/mTOR signaling. IS tumors activate immune suppression, have high mutation burden, and frequently mutate Traf7 and delete Cdkn2a. This is the most comprehensive transcriptomic and genomic profiling of mutant p53-driven breast tumors, elucidating potential therapeutic targets. TeaserA single Trp53 mutation drives intrinsic metastatic breast cancer subtypes with distinct transcriptomes and genomic alterations.

cancer biology↗

bioRxiv2024

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

biochemistry↗

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↗