Biology subjects

Wong, E.

Publications and source records attributed to Wong, E..

46 records · Page 3Linked to original sources

bioRxiv2019

Melanoma-secreted Amyloid Beta Supresses Neuroinflammation and Promotes Brain Metastasis

Brain metastasis is a significant cause of morbidity and mortality in multiple cancer types and represents an unmet clinical need. The mechanisms that mediate metastatic cancer growth in the brain parenchyma are largely unknown. Melanoma, which has the highest rate of brain metastasis among common cancer types, is an ideal model to study how cancer cells adapt to the brain parenchyma. We performed unbiased proteomics analysis of melanoma short-term cultures, a novel model for the study of brain metastasis. Intriguingly, we found that proteins implicated in neurodegenerative pathologies are differentially expressed in melanoma cells explanted from brain metastases compared to those derived from extracranial metastases. This raised the exciting hypothesis that molecular pathways implicated in neurodegenerative disorders are critical for metastatic adaptation to the brain. Here, we show that melanoma cells require amyloid beta (A{beta}), a polypeptide heavily implicated in Alzheimers disease, for growth and survival in the brain parenchyma. Melanoma cells produce and secrete A{beta}, which activates surrounding astrocytes to a pro-metastatic, anti-inflammatory phenotype. Furthermore, we show that pharmacological inhibition of A{beta} decreases brain metastatic burden. Our results reveal a mechanistic connection between brain metastasis and Alzheimers disease - two previously unrelated pathologies, establish A{beta} as a promising therapeutic target for brain metastasis, and demonstrate suppression of neuroinflammation as a critical feature of metastatic adaptation to the brain parenchyma.

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↗

bioRxiv2019

Disease and phenotype relevant genetic variants identified from histone acetylomes in human hearts

Identifying genetic markers for heterogeneous complex diseases such as heart failure has been challenging, and may require prohibitively large cohort sizes in genome-wide association studies (GWAS) in order to demonstrate statistical significance1. On the other hand, chromatin quantitative trait loci (QTL), elucidated by direct epigenetic profiling of specific human tissues, may contribute towards prioritising variants for disease-association. Here, we captured non-coding genetic variants by performing enhancer H3K27ac ChIP-seq in 70 human control and end-stage failing hearts, mapping out a comprehensive catalogue of 47,321 putative human heart enhancers. 3,897 differential acetylation peaks (FDR < 0.05) pointed to recognizable pathways altered in heart failure (HF). To identify cardiac histone acetylation QTLs (haQTLs), we regressed out confounding factors including HF disease status, and employed the G-SCI test2 to call out 1,680 haQTLs (FDR < 0.1). A subset of these showed significant association to gene expression, either in cis (180), or through long range interactions (81), identified by Hi-C and Hi-ChIP performed on a subset of hearts. Furthermore, a concordant relationship was found between the gain or disruption of specific transcription factor (TF) binding motifs, inferred from alternative alleles at the haQTLs, associated with altered H3K27ac peak heights. Finally, colocalisation of our haQTLs with heart-related GWAS datasets allowed us to identify 62 unique loci. Disease-association for these new loci may indeed be mediated through modification of H3K27-acetylation enrichment and their corresponding gene expression differences.

genomics↗

bioRxiv2018

Spatial memory formation requires netrin-1 expression by neurons in the adult mammalian brain.

Netrin-1 was initially characterized as an axon guidance molecule that is essential for normal embryonic neural development; however, many types of neurons continue to express netrin-1 in the post-natal and adult mammalian brain. Netrin-1 and the netrin receptor DCC are both enriched at synapses. In the adult hippocampus, activity-dependent secretion of netrin-1 by neurons potentiates glutamatergic synapse function, and is critical for long-term potentiation, an experimental cellular model of learning and memory. Here, we assessed the impact of neuronal expression of netrin-1 in the adult brain on behavior using tests of learning and memory. We show that adult mice exhibit impaired spatial memory following conditional deletion of netrin-1 from glutamatergic neurons in the hippocampus and neocortex. Further, we provide evidence that mice with conditional deletion of netrin-1 do not display aberrant anxiety-like phenotypes and show a reduction in self-grooming behaviour. These findings reveal a critical role for netrin-1 expressed by neurons in the regulation of spatial memory formation.

neuroscience↗

bioRxiv2018

GSAP modulates γ-secretase specificity by inducing conformational change in PS1

The mechanism by which GSAP ({gamma}-secretase activating protein) regulates {gamma}-secretase activity has not yet been elucidated. Here, we show that knockout of GSAP in cultured cells directly reduces {gamma}-secretase activity for A{beta} production, but not for Notch1 cleavage, suggesting that GSAP may induce a conformational change contributing to the specificity of {gamma}-secretase. Furthermore, using an active site directed photoprobe with double cross-linking moieties, we demonstrate that GSAP modifies the orientation and/or distance of PS1-NTF and PS1-CTF, a region containing the active site of {gamma}-secretase. This work offers insight into how GSAP regulates {gamma}-secretase specificity.

neuroscience↗

bioRxiv2018

Machine Learning Reveals Modules of Economic Behavior from Foraging Mice

Complex ethological behaviors could be constructed from modules that are discrete functional units of behavior with a genetic basis. Here, we test this idea for foraging, and develop a paradigm to dissect foraging patterns in mice. We uncover discrete behavioral modules linked to round trip excursions from the home. Machine learning reveals 59 modules across different genetic backgrounds and ages. Different modules develop at different ages and are linked to different aspects of economic behavior, including memory, reward, risk and effort responses. Crosses of distant mouse strains reveal that parental and genetic effects shape foraging differently, and parental effects grow stronger with age. Specific behavioral modules, genes and pathways are found to be sensitive to parental effects. One candidate gene, Magel2, is linked to Prader-Willi Syndrome and shaped the expression of discrete modules in an age-dependent manner. Our results reveal building blocks for normal and abnormal economic behavior patterns.\n\nHIGHLIGHTSO_LIIdentification of 59 economic behavior modules underlying foraging\nC_LIO_LIDiscrete modules are linked to memory, reward, risk and effort responses\nC_LIO_LIGenetic and parental effects shape foraging by changing module expression\nC_LIO_LIMagel2, a Prader-Willi Syndrome gene, affects specific modules at specific ages\nC_LI

neuroscience↗

bioRxiv2018

A Highly Efficient and Faithful MDS Patient-Derived Xenotransplantation Model for Pre-Clinical Studies

Comprehensive preclinical studies of Myelodysplastic Syndromes (MDS) have been elusive due to limited ability of MDS stem cells to engraft current immunodeficient murine hosts. We developed a novel MDS patient-derived xenotransplantation model in cytokine-humanized immunodeficient \"MISTRG\" mice that for the first time provides efficient and faithful disease representation across all MDS subtypes. MISTRG MDS patient-derived xenografts (PDX) reproduce patients' dysplastic morphology with multi-lineage representation, including erythro- and megakaryopoiesis. MISTRG MDS-PDX replicate the original sample's genetic complexity and can be propagated via serial transplantation. MISTRG MDS-PDX demonstrate the cytotoxic and differentiation potential of targeted therapeutics providing superior readouts of drug mechanism of action and therapeutic efficacy. Physiologic humanization of the hematopoietic stem cell niche proves critical to MDS stem cell propagation and function in vivo. The MISTRG MDS-PDX model opens novel avenues of research and long-awaited opportunities in MDS research.

cancer biology↗

bioRxiv2017

Cardiac directed differentiation using small molecule Wnt modulation at single-cell resolution

Differentiation into diverse cell lineages requires the orchestration of gene regulatory networks guiding diverse cell fate choices. Utilizing human pluripotent stem cells, we measured expression dynamics of 17,718 genes from 43,168 cells across five time points over a thirty day time-course of in vitro cardiac-directed differentiation. Unsupervised clustering and lineage prediction algorithms were used to map fate choices and transcriptional networks underlying cardiac differentiation. We leveraged this resource to identify strategies for controlling in vitro differentiation as it occurs in vivo. HOPX, a non-DNA binding homeodomain protein essential for heart development in vivo was identified as dys-regulated in in vitro derived cardiomyocytes. Utilizing genetic gain and loss of function approaches, we dissect the transcriptional complexity of the HOPX locus and identify the requirement of hypertrophic signaling for HOPX transcription in hPSC-derived cardiomyocytes. This work provides a single cell dissection of the transcriptional landscape of cardiac differentiation for broad applications of stem cells in cardiovascular biology.

developmental biology↗

bioRxiv2017

Allelic Differentiation Of Complex Trait Loci Across Human Populations

How genetic variation contributes to phenotypic variation is a central question in genetics. Association signals for a complex trait are found throughout the majority of the genome suggesting much of the genome is under some degree of genetic constraint. Here, we develop a intraspecific population genetics approach to define a measure of population structure for each single nucleotide polymorphism (SNP). Using this approach, we test for evidence of stabilizing selection at complex traits and pleiotropic loci arising from the evolutionary history of 47 complex traits and common diseases. Our approach allowed us to identify traits and regions under stabilizing selection towards both global and subpopulation optima. Strongest depletion of allelic diversity was found at disease loci, indicating stabilizing selection has acted on these phenotypes in all subpopulations. Pleiotropic loci predominantly displayed evidence of stabilizing selection, often contributed to multiple disease risks, and sometimes also affected non-disease traits such as height. Risk alleles at pleiotropic disease loci displayed a more consistent direction of effect than expected by chance suggesting that stabilizing selection acting on pleiotropic loci is amplified through multiple disease phenotypes.

evolutionary biology↗

bioRxiv2016

The human functional genome defined by genetic diversity

Large scale efforts to sequence whole human genomes provide extensive data on the non-coding portion of the genome. We used variation information from 11,257 human genomes to describe the spectrum of sequence conservation in the population. We established the genome-wide variability for each nucleotide in the context of the surrounding sequence in order to identify departure from expectation at the population level (context-dependent conservation). We characterized the population diversity for functional elements in the genome and identified the coordination of conserved sequences of distal and cis enhancers, chromatin marks, promoters, coding and intronic regions. The most context-dependent conserved regions of the genome are associated with unique functional annotations and a genomic organization that spreads up to one megabase. Importantly, these regions are enriched by over 100-fold of non-coding pathogenic variants. This analysis of human genetic diversity thus provides a detailed view of sequence conservation, functional constraint and genomic organization of the human genome. Specifically, it identifies highly conserved non-coding sequences that are not captured by analysis of interspecies conservation and are greatly enriched in disease variants.

genomics↗