Biology subjects

Ho, T. H.

Publications and source records attributed to Ho, T. H..

7 recordsLinked to original sources

bioRxiv2025

Collagen Prolyl Hydroxylases Regulate HIF-α Levels Independently of pVHL in ccRCC

The hypoxia-inducible factors, HIF-1 and HIF-2, are master regulators of the hypoxia response. Under ambient conditions, both are hydroxylated by the HIF prolyl hydroxylases (HIF PHDs) resulting in ubiquitination by the pVHL E3 ligase complex, leading to subsequent proteasomal degradation. During hypoxia, the HIF PHDs are inhibited, resulting in HIF- stabilization and transcriptional activation of genes involved in the adaptation to hypoxia. Previous studies have shown that the collagen PHD, P4HA1, which promotes proline hydroxylation of collagen, inhibits the HIF PHDs by modulating levels of -ketoglutarate and succinate, thus enhancing HIF-1 stability by preventing pVHL-mediate degradation. Here, we investigate the role of collagen PHDs in the regulation of HIF-1/2 in the setting of pVHL deficiency in ccRCC. We show that the collagen PHDs P4HA1 and P4HA2 are required for HIF-1 translation and HIF-2 transcription and translation independently of pVHL function, through a mechanism regulated in part by P4HA1/2-driven collagen production. Thus, we reveal a novel pVHL-independent mechanism of HIF-1/2 regulation driven by P4HA1/2 in ccRCC. Since the HIFs have been strongly implicated in ccRCC initiation and progression, our data suggests that inhibition of P4HA1/2 may be a promising therapeutic strategy in ccRCC.

cancer biology↗

bioRxiv2024

Deciphering tumour microenvironment and elucidating the origin of cancer cells in ovarian clear cell carcinoma

Ovarian clear cell carcinoma (CCC) has an East Asian preponderance. It is associated with endometriosis, a benign condition where endometrial (inner lining of the uterus) tissue is found outside the uterus and on the peritoneal surface, in the abdominal or pelvic space. CCC is relatively more resistant to conventional chemotherapy compared to other ovarian cancer subtypes and is associated with a poorer prognosis. In this study, we recruited and obtained tumour tissues from seven patients across the four stages of CCC. The tumour and the tumour microenvironment (TME) from 7 CCC patients spanning clinical stages 1-4 were transcriptionally profiled using high-resolution scRNA-seq to gain insight into CCCs biological mechanisms. Firstly, we built a scRNA-seq resource for the CCC tumour microenvironment (TME). Secondly, we identified the different cell type proportions and found high levels of immune infiltration in CCC. Thirdly, since CCC is associated with endometriosis, we compared CCC with two publicly available endometriosis scRNA-seq datasets. The CCC malignant cells showed similarities with glandular secretory and ciliated epithelial cells found in endometriosis. Finally, we determined the differences in cell-cell communication between various cell types present in CCC TME and endometriosis conditions to gain insights into the transformations in CCC.

cancer biology↗

bioRxiv2024

AMRomics: a scalable workflow to analyze large microbial genome collection

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, a minimalized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

bioinformatics↗

bioRxiv2023

Pasa: Leverage population pangenome graph to scaffold prokaryote genome assemblies.

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the art in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.

bioinformatics↗

bioRxiv2023

PanTA: An ultra-fast method for constructing large and growing microbial pangenomes

Pangenome analysis is an indispensable step in bacterial genomics to address the high variability of bacteria genomes. However, speed and scalability remain a challenge for pangenome inference software tools to cope with the fast-growing genomic collections. We present PanTA, a software package for constructing the pangenomes of large bacterial collections. We show that PanTA exhibits an unprecedented multiple times more efficient than the current state-of-the-arts while maintaining a similar pangenome accuracy. In addition, PanTA introduces a novel mechanism to construct the pangenome progressively where new samples are added into an existing pangenome without rebuilding the accumulated collection from scratch. In the progressive mode, PanTA is demonstrated to consume orders of magnitude less computational resource than existing solutions in managing the pangenomes of growing microbial datasets. We further show that PanTA can build the pangenome of the entire collection of >28000 Escherichia coli genomes from the RefSeq database on a laptop computer in 32 hours, highlighting the scalability and practicality of PanTA.The software is open source and is publicly available at https://github.com/amromics/panta under an MIT license.

bioinformatics↗

bioRxiv2020

Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors1-3, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings shed light on the molecular drivers of aggressivity and responsiveness to immune checkpoint inhibitors of S/R RCC tumors.

cancer biology↗

bioRxiv2017

Inhibition of Intracellular Lipolysis Promotes Cancer Cell Adaptation to Hypoxia

Tumor tissues are chronically exposed to hypoxia owing to aberrant vascularity. Lipid droplet (LD) accumulation is a hallmark of hypoxic cancer cells, yet how LDs form and function during hypoxia remains poorly understood. Herein, we report that in various cancer cells upon oxygen deprivation, HIF-1 activation down-modulates LD catabolism mediated by adipose triglyceride lipase (ATGL), the key enzyme for intracellular lipolysis. Proteomics and functional analyses identified hypoxia-inducible gene 2 (HIG2), a HIF-1 target, as a new inhibitor of ATGL. Knockout of HIG2 enhanced LD breakdown and fatty acid (FA) oxidation, leading to increased ROS production and apoptosis in hypoxic cancer cells as well as impaired growth of tumor xenografts. All of these effects were reversed by co-ablation of ATGL. Thus, by inhibiting ATGL, HIG2 acts downstream of HIF-1 to sequester FAs in LDs away from the mitochondrial pathways for oxidation and ROS generation, thereby sustaining cancer cell survival in hypoxia.

biochemistry↗