Biology subjects

Leung, E.

Publications and source records attributed to Leung, E..

8 recordsLinked to original sources

bioRxiv2026

Range-wide genetic population structure and environmental adaptation in the eastern oyster (Crassostrea virginica) provides insight for aquaculture

Selective breeding in aquaculture is necessary to establish food security and meet demand for sustainably produced protein. An informed selective breeding program requires understanding how population structure, environmental adaptation, and human activities shape natural genetic variation in wild conspecifics. Unfortunately, wild variation remains poorly characterized for many commercially important aquaculture species. Here, we conduct the first range-wide study of genomic population structure for the eastern oyster (Crassostrea virginica) across thousands of miles (Texas, USA to Eastern Canada) using a 200K SNP array. We integrate population structure analyses, genotype-environmental associations, and structural variant detection to identify adaptive loci and quantify human-mediated genetic impacts. Our data confirms two ancestral clusters with a phylogeographic break between the Gulf and Atlantic (FST = 0.06) and highlights patterns of substructure within each region. We find evidence of unexpected patterns of genomic variation in two locations: evidence of Gulf ancestry in a mid-Atlantic estuary (Chesapeake Bay), and evidence of Atlantic ancestry in a Gulf estuary (Apalachicola Bay). While we cannot definitively determine the causes of these unexpected patterns, we show that they are consistent with direct and indirect human impacts in these estuaries. Genotype-environment association analyses with in situ temperature and salinity measurements were used to identify putatively adaptive loci, including SNPs within large structural variants (>1Mb). Our results identified genomic targets for aquaculture breeding programs aimed at climate resilience, reveal complex patterns of human impacts in managed systems, and demonstrate how seascape genomics can be used to improve aquaculture outcomes.

evolutionary biology↗

bioRxiv2022

Alcohol, Opioid, and Combined Alcohol and Opioid Use Disorders Affect Shared and Unique Pathways: A Proteomic Analysis of Postmortem Brains

Substance misuse is a major burden for not only patients but the healthcare system. Understanding how substance misuse alters a patients genome, proteome, and neurocircuitry is an important step toward identifying targets for treatments. In this exploratory study, we performed a proteomic analysis of postmortem brain from individuals with substance misuse disorder (n=29), including alcohol (n=11) and opioid misuse disorder (n=12), or comorbidity with both disorders (n=6), compared to controls (n=12). The results demonstrate that these substances affect both common and different biological pathways compared to controls. Alcohol misuse affects primarily protein translation, rRNA processing, and energy metabolism pathways, while opioid influences G-protein signaling, protein translation, rRNA processing, energy metabolism, and angiogenesis pathways. Alcohol and opioid misuse combined appears to have a synergistic effect on protein expression of the TCA cycle, respiratory transport, mitochondrial function, LGI-ADAM interactions, interleukin signaling, neuronal system changes, and actin polymerization. These findings offer new insights into how substance misuse leads to protein alterations and how combined substance misuse leads to different protein changes compared to single substance misuse.

neuroscience↗

bioRxiv2022

Characterization of TR-107, a Novel Chemical Activator of the Human Mitochondrial Protease ClpP

We recently described the identification of a new class of small molecule activators of the mitochondrial protease ClpP. These compounds synthesized by Madera Therapeutics showed increased potency of cancer growth inhibition over the related compound ONC201. In this study, we describe chemical optimization and characterization of the next generation of highly potent and selective small molecule ClpP activators (TR compounds) and demonstrate their efficacy against breast cancer models in vitro and in vivo. One of these compounds (TR-107) with excellent potency, specificity and drug-like properties was selected for further evaluation. Examination of TR-107 effects in triple-negative breast cancer (TNBC) cell models showed growth inhibition in the low nanomolar range, equipotent to paclitaxel, in a ClpP-dependent manner. TR-107 reduced specific mitochondrial proteins including OXPHOS and TCA cycle components, in a time, dose and ClpP-dependent manner. Seahorse XF analysis and glucose deprivation experiments confirmed inactivation of OXPHOS and demonstrated an increased dependence on glycolysis following TR-107 exposure. The pharmacokinetic properties of TR-107 were compared to other known ClpP activators including ONC201 and ONC212. TR-107 displayed excellent exposure and serum t1/2 after oral administration. The antitumor response to TR-107 was investigated using human TNBC MDA-MB-231 cell line-induced xenograft tumors. Oral administration of TR-107 resulted in reduction in tumor volume and extension of survival in the treated compared with vehicle control mice. In summary, we describe the identification of highly potent new ClpP agonists with improved efficacy against TNBC, through targeted inactivation of OXPHOS and disruption of mitochondrial metabolism.

pharmacology and toxicology↗

bioRxiv2021

Conjugation of palbociclib with MHI-148 has an increased cytotoxic effect for breast cancer cells and an altered mechanism of action

The CDK4/6 inhibitor palbociclib, combined with endocrine therapy, has been shown to be effective in postmenopausal women with estrogen receptor positive, HER2-negative advanced or metastatic breast cancer. However, palbociclib is not as effective in the highly aggressive triple-negative breast cancer that lacks sensitivity to chemotherapy or endocrine therapy. We hypothesized that conjugation of the near-infrared dye MHI-148 with palbociclib can produce a potential theranostic in triple-negative as well as estrogen receptor positive breast cancer cells. In our study, the conjugate was found to have enhanced activity in all mammalian cell lines tested in vitro. However, the conjugate was cytotoxic and did not induce G1 cell cycle arrest in breast cancer cells suggesting the mechanism of action differed from the parent compound palbociclib. The study highlights the importance of investigating the mechanism of conjugates of near-infrared dyes to therapeutic compounds as conjugation can potentially result in a change of mechanism or target, with an enhanced cytotoxic effect in this case.

cancer biology↗

bioRxiv2021

Effect of SFX-01 on proliferation of human glioblastoma cell lines

Glioblastoma (GBM) is the most aggressive and lethal of brain tumours and there is a clear need for novel therapies. SFX-01, a stabilised formu of sulforaphane (SFN) that is complexed with -cyclodextrin, is being investigated as a potential anticancer drug. And as part of this investigation we have determined its effect on the proliferation of a series of tumour cell lines. Growth-inhibitory concentrations (IC50 values) of SFX-01 and reference SFN were highly correlated but showed a divergence at higher drug concentrations that is likely to reflect drug binding to -cyclodextrin complexes. Because SFN is redox-sensitive, we also compared the effects of these drugs on four GBM lines that had been derived and cultured under physiological oxygen (5%) conditions. Comparisons were also made using a three-dimensional spheroid model, used to mimic the in vivo state of glioblastomas and to simulate barriers to drug delivery in vivo. SFX-01 and reference SFN and inhibited proliferation of four GBM cell lines in monolayers as well as in spheroids; -cyclodextrin alone had no significant effect. Our results are consistent with the hypothesis that SFN is liberated from SFX-01 at concentrations sufficient to inhibit cell growth. We envision that SFX-01 will have improved pharmacokinetic properties in vivo, warranting further pre-clinical investigation and clinical development.

cancer biology↗

bioRxiv2020

SUN-MKL1 crosstalk regulates nuclear deformation and fast motility of breast carcinoma cells in fibrillar ECM micro-environment

Aligned collagen fibers provide topography for the rapid migration of single tumor cells (streaming migration) to invade the surrounding stroma, move within tumor nests towards blood vessels to intravasate and form distant metastases. Mechanisms of tumor cell motility have been studied extensively in the 2D context, but the mechanistic understanding of rapid single tumor cell motility in the in vivo context is still lacking. Here, we show that streaming tumor cells in vivo use collagen fibers with diameters below 3 m. Employing 1D migration assays with matching in vivo fiber dimensions, we found a dependence of tumor cell motility on 1D substrate width, with cells moving the fastest and the most persistently on the narrowest 1D fibers (700 nm - 2.5 m). Interestingly, we also observed nuclear deformation in the absence of restricting extracellular matrix pores during high speed carcinoma cell migration in 1D, similar to the nuclear deformation observed in tumor cells in vivo. Further, we found that actomyosin machinery is aligned along the 1D axis and actomyosin contractility synchronously regulates cell motility and nuclear deformation. To further investigate the link between cell speed and nuclear deformation, we focused on the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex proteins and SRF-MKL1 signaling, key regulators of mechanotransduction, actomyosin contractility and actin-based cell motility. Analysis of The Cancer Genome Atlas dataset showed a dramatic decrease in the LINC complex proteins SUN1 and SUN2 in primary tumor compared to the normal tissue. Disruption of LINC complex by SUN1+2 KD led to multi-lobular elongated nuclei, increased tumor cell motility and concomitant increase in F-actin, without affecting Lamin proteins. Mechanistically, we found that MKL1, an effector of changes in cellular G-actin to F-actin ratio, is required for increased 1D motility seen in SUN1+2 KD cells. Thus, we demonstrate a previously unrecognized crosstalk between SUN proteins and MKL1 transcription factor in modulating nuclear shape and carcinoma cell motility in an in vivo relevant 1D microenvironment.

cancer biology↗

bioRxiv2020

Towards establishing Extracellular Vesicle-associated RNAs as biomarkers for HER2+ breast cancer

Extracellular vesicles (EVs) are emerging as key players in breast cancer progression and hold immense promise as cancer biomarkers. However, difficulties in obtaining sufficient quantities of EVs for the identification of potential biomarkers hampers progress in this area. To circumvent this obstacle, we cultured BT-474 breast cancer cells in a two chambered bioreactor with CDM-HD serum replacement to significantly improve the yield of cancer cell-associated EVs and eliminate bovine EV contamination. Cancer-relevant mRNAs BIRC5 (Survivin) and YBX1 as well as long-noncoding RNAs HOTAIR, ZFAS1, and AGAP2-AS1 were detected in BT-474 EVs by quantitative RT-PCR. Bioinformatics meta-analyses showed that BIRC5 and HOTAIR RNAs were substantially upregulated compared to non-tumour breast tissue, encouraging further studies to explore their usefulness as biomarkers in patient EV samples. We contend that this effective procedure for obtaining large amounts of cancer-specific EVs will accelerate discovery of EV-associated RNA biomarkers for detection of HER2+ breast cancer.

cancer biology↗

bioRxiv2019

Tensor Factorization-based Prediction with an Application to Estimating the Risk of Chronic Diseases

Tensor factorization has emerged as a powerful method to address the challenges of high dimensionality regarding disease development and comorbidity. Chronic diseases have a high likelihood to co-occur, making patients suffering from one chronic disease to have an elevated risk for the other diseases in the course of aging. Individualized prediction of chronic diseases can help patients prevent new diseases and reduce the healthcare costs. Despite rich results of risk assessment models for chronic diseases, individualized risk prediction considering the complex mechanisms of disease development and comorbidity remains to be under-researched. This research aims to develop tensor factorization-based machine learning models to predict the onset of new chronic diseases for individual patients through incorporating the comorbidity patterns with the clinical and sequential factors revealed in the electronic health records (EHR) data. We propose two tensor factorization-based methods to incorporate the clinical and sequential factors to reveal the latent patterns of co-occurring chronic diseases. The efficacy of the proposed methods was validated through predicting the onset of new chronic diseases for individual patients using the EHR data for 23 years from a major hospital in Hong Kong. The proposed methods consistently outperform benchmark predictive models. The top 10 predictions of new chronic diseases have approximately 60% recall. Tensor factorization is an appropriate method for predicting the onset of chronic diseases at the individual level. The proposed predictive models could inform proactive health management programs for at-risk patients with different chronic conditions at discharge.\n\nAuthor summaryThe existing risk assessment models mainly focused on the prediction of single diseases in the population base. Chronic disease risk prediction considering the complex mechanisms of disease development and comorbidity is under-researched. To support and inform clinical decision making for healthcare professionals in the aging society, this study provides an innovative approach to mapping an interconnected web of chronic illnesses and investigated the performance of chronic disease prediction using 2 years worth of patient assessment records and 23 years admission history data from a major hospital in Hong Kong. We proposed matrix and tensor-based methods to represent the high-order interrelations of patients, chronic diseases and additional features, which can reveal the latent patterns of co-occurring chronic diseases to enable more effective prediction. The proposed methods exhibit state-of-the-art performance in predicting the onset of new chronic diseases for individual patients.

bioinformatics↗