Biology subjects

Tran, P. H.

Publications and source records attributed to Tran, P. H..

3 recordsLinked to original sources

bioRxiv2026

Cell fusion reprograms tumor cells and promotes RUNX1-mediated invasion and dissemination in colorectal cancer

Metastasis remains the primary cause of cancer-related morbidity and mortality, despite significant advances in targeted therapies. Although metastatic dissemination requires tumor cells to escape the primary lesion and colonize distant organs, the mechanisms by which primary tumor cells gain metastatic competence remain poorly understood. Increasing evidence demonstrates that fusion of tumor (i.e., neoplastic) and immune (e.g., macrophages) cells generate a distinct population of tumor-immune hybrid cells with enhanced functional ability to migrate and disseminate into peripheral blood. Herein, our study investigates tumor-macrophage hybrid cells, an underexplored population of disseminated tumor cells, and their inherent heterogeneity and acquisition of molecular mechanisms underlying their dissemination as metastatic effectors in colorectal cancer (CRC). Through hybrid cell phenotyping utilizing integrative single-cell RNA sequencing (scRNA-seq), cyclic immunofluorescence (cyCIF) and functional assays with an in vitro model of CRC hybrid cells, we identify Runt-related transcription factor 1 (Runx1) as a central regulator of hybrid cell motility and invasion. Runx1 depletion in hybrid cells suppressed functional protease expression, chemotactic activity and extracellular matrix (ECM) invasion. Furthermore, pharmacologic inhibition of RUNX1 in an in vivo model reduced hybrid tumor growth and dissemination into peripheral blood, key attributes of metastatic spread of disease. In patients with CRC, RUNX1+ hybrid cells were identified in both primary tumor and peripheral blood, where circulating hybrid cells (CHCs) exhibited enriched migratory and epithelial-to-mesenchymal transition (EMT) phenotypes. Taken together, these findings reveal a mechanistic role for RUNX1 in driving invasive behavior of tumor-immune hybrids and highlight disseminated CHCs as an under-recognized contributor to metastatic spread and a promising noninvasive biomarker for tumor progression.

cancer biology↗

bioRxiv2024

A genome-wide ATLAS of liver chromatin architecture reveals that sex dictates diet-induced nucleosome dynamics

The three-dimensional organization of the genome plays an important role in cellular function. Alterations between open and closed chromatin states contributes to DNA binding, collaborative transcriptional activities and informs post-transcriptional processing. The liver orchestrates systemic metabolic control and has the ability to mount a rapid adaptive response to environmental challenges. We interrogated the chromatin architecture in liver under different dietary cues. Using ATAC-seq, we mapped over 120,000 nucleosome peaks, revealing a remarkably preserved hepatic chromatin landscape across feeding conditions. Stringent analysis of nucleosome rearrangements in response to diet revealed that sex is the dominant factor segregating changes in chromatin accessibility. A lipid-rich diet led to a more accessible chromatin confirmation at promoter regions in female mice along with enrichment of promoter binding CCAAT-binding domain proteins. Male liver exhibited stronger binding for nutrient sensing nuclear receptors. Integrative analysis with gene expression corroborated a role for chromatin states in informing functional differences in metabolic traits. We distinguished the impact of gonadal sex and chromosomal sex as determinants of chromatin modulation by diet using the Four Core Genotypes mouse model. Our data provide mechanistic evidence underlying the regulation for the critical sex-dimorphic GWAS gene, Pnpla3. In summary, we provide a comprehensive epigenetic resource in murine liver that uncovers the complexity of chromatin dynamics in response to diet and sex. HighlightsATAC-Seq, RNA-Seq, and FCG model-integrated analysis unravel sex differences in chromatin accessibility and transcriptome responses to dietary challenges. Lipid-rich diet led to sex-biased chromatin confirmation at promoter regions. Gonadal sex emerged as the most prevalent determinant of the sex bias hepatic chromatin modulation by lipid-rich diets. The critical sex-dimorphic GWAS gene Pnpla3 is suppressed by testosterone, which underlies hepatic differences in expression between the sexes.

molecular biology↗

bioRxiv2021

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6470 SNPs after quality control showed that AI methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1 - 15.4%. However, the prediction accuracies obtained from AI methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3 - 19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3 - 5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1362 - 1589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs.

genetics↗