Biology subjects

Hughes, F.

Publications and source records attributed to Hughes, F..

4 recordsLinked to original sources

bioRxiv2025

LOX inhibition disrupts a collagen-integrin-MYC axis as a translatable targeting strategy in invasive lobular carcinoma

Invasive lobular carcinoma (ILC) accounts for 15% of breast cancers yet lacks specific therapy because ILCs are underrepresented in clinical trials and preclinical models are lacking. We established intraductal xenograft models to test whether the clinical pan-lysyl-oxidase PXS-5505, now in phase trials for myelofibrosis can exploit the collagen-rich matrix dependency created by CDH1 loss. PXS-5505 remodels fibrillar collagen, and halts tumor expansion and metastatic seeding across ER+ and triple negative models without systemic toxicity. Genome-wide CRISPR screens reveal ITGAV and ITGB5 as synthetic lethal partners of CDH1 and LOX inhibition downregulates their expression together with MYC, NF-{kappa}B, and AP-1 transcriptional programmes. Collagen fibre density/alignment, and MYC/AP-1 gene signatures serve as pharmacodynamic readouts of drug activity. These data uncover a tractable ECM-integrin-MYC axis in ILC and nominate PXS-5505, alone or with endocrine therapy, for window of opportunity trials in this understudied breast cancer subtype. One Sentence SummaryTargeting matrix remodelling in ILC inhibits ILC progression and alters multiple molecular endpoints, providing a translatable therapeutic strategy for this understudied subtype that requires better treatments.

cancer biology↗

bioRxiv2022

Patterns of interdivision time correlations reveal hidden cell cycle factors

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.

systems biology↗

bioRxiv2021

Excessive fetal growth affects HSC quiescence maintenance through epigenetic programming of EGR1 transcriptional network

Excessive fetal growth is associated with DNA methylation alterations in human hematopoietic stem and progenitor cells (HSPC), but their functional impact remains elusive. We implemented an integrative analysis combining single-cell epigenomics, single-cell transcriptomics, and in vitro analyses to functionally link DNA methylation changes to putative alterations of HSPC functions. We showed in hematopoietic stem cells (HSC) from large for gestational age neonates that both DNA hypermethylation and chromatin rearrangement target a specific network of transcription factors known to sustain stem cell quiescence. In parallel, we found a decrease expression of key genes regulating HSC differentiation including EGR1, KLF2, SOCS3, and JUNB. Our functional analyses showed that this epigenetic programming was associated with a decreased ability for HSCs to stay quiescent. Taken together, our multimodal approach using single-cell (epi)genomics showed that human fetal overgrowth affects hematopoietic stem cells quiescence maintenance via epigenetic programming.

genomics↗

bioRxiv2018

Genetic variants influence on the placenta regulatory landscape

BackgroundFrom genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications.\n\nResultsBy producing and analyzing DNA sequence variation (n=303), DNA methylation (n=303) and mRNA expression data (n=80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations.\n\nConclusionsThese findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.\n\nAuthor summaryThe placenta is a critical organ playing multiple roles including oxygen and metabolite transfer from mother to fetus, hormone production, and vascular perfusion. With this study, we aimed to deliver a placenta-specific regulatory map based on a combination of publicly available and newly generated data. To complete this reference, we obtained genotype information (n=303), DNA methylation (n=303) and expression data (n=80) for placentas from healthy women. Our analysis of methylation and expression quantitative trait loci (QTLs) and correlations between methylation and expression data were designed to identify fundamental associations between genome, transcriptome, and epigenome in this key fetal organ. The results provide high-resolution genetic and epigenetic maps specific to the placenta based on a representative ethnically diverse cohort. As interest and efforts are growing to better understand the etiology of placental disease and the impact of the environment on placental function these data will provide a reference and enhance future investigations.

genomics↗