Biology subjects

Wong, L.

Publications and source records attributed to Wong, L..

23 records · Page 2Linked to original sources

bioRxiv2020

Characterisation of the genetic architecture of immune-mediated disease through informed dimension reduction

Integration of genome-wide association study (GWAS) data has been used to generate new hypotheses of biological mechanism, aetiological relationships between traits, or test causality of one factor for another. However, such approaches have typically been limited to pairwise comparisons of traits. We propose a generally applicable method, that exploits ideas from Bayesian genetic fine mapping to define a "lens" that focuses relevant variants before dimension reduction of a set of related GWAS summary statistics. We applied this technique to immune-mediated diseases, deriving 13 components which summarise the multidimensional patterns of genetic risk. Projection of independent datasets demonstrated the specificity and accuracy of our reduced dimension basis, enabled us to functionally characterise individual components, identify disease-discriminating components and suggest novel associations in rare diseases where classical GWAS approaches are challenging. Our approach summarises the genetic architectures underlying any range of aetiologically-related traits in fewer dimensions, facilitating more nuanced multidimensional comparative analyses.

genetics↗

bioRxiv2020

Inferring Disease-Associated Piwi-Interacting RNAs via Graph Attention Networks

MotivationPIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlates with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes out gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. This study aims to make preliminary attempts on bionetwork based graph mining. ResultsIn this study, we present a method based on graph attention network to identify potential and biologically significant piRNA-disease associations (PDAs), called GAPDA. The attention mechanism can calculate a hidden representation of an association in the network based on neighbor nodes and assign weights to the input to make decisions. In particular, we introduced the attention-based Graph Neural Networks to the field of bio-association prediction for the first time, and proposed an abstract network topology suitable for small samples. Specifically, we combined piRNA sequence information and disease semantic similarity with piRNA-disease association network to construct a new attribute network. In the experiment, GAPDA performed excellently in five-fold cross-validation with the AUC of 0.9038. Not only that, but it still has superior performance compared to methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research. Contactzhuhongyou@ms.xjb.ac.cn;leiwang@ms.xjb.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Alcohol Drinking Exacerbates Neural and Behavioral Pathology in the 3xTg-AD Mouse Model of Alzheimer’s Disease

Alzheimers disease (AD) is a progressive neurodegenerative disorder that represents the most common cause of dementia in the United States. Although the link between alcohol use and AD has been studied, preclinical research has potential to elucidate neurobiological mechanisms that underlie this interaction. This study was designed to test the hypothesis that non-dependent alcohol drinking exacerbates the onset and magnitude of AD-like neural and behavioral pathology. We first evaluated the impact of voluntary 24-h, 2-bottle choice home-cage alcohol drinking on the prefrontal cortex and amygdala neuroproteome in C57BL/6J mice and found a striking association between alcohol drinking and AD-like pathology. Bioinformatics identified the AD-associated proteins MAPT (Tau), amyloid beta precursor protein (APP), and presenilin-1 (PSEN-1) as the main modulators of alcohol-sensitive protein networks that included AD-related proteins that regulate energy metabolism (ATP5D, HK1, AK1, PGAM1, CKB), cytoskeletal development (BASP1, CAP1, DPYSL2 [CRMP2], ALDOA, TUBA1A, CFL2, ACTG1), cellular/oxidative stress (HSPA5, HSPA8, ENO1, ENO2), and DNA regulation (PURA, YWHAZ). To address the impact of alcohol drinking on AD, studies were conducted using 3xTg-AD mice that express human MAPT, APP, and PSEN-1 transgenes and develop AD-like brain and behavioral pathology. 3xTg-AD and wildtype mice consumed alcohol or saccharin for 4 months. Behavioral tests were administered during a 1-month alcohol free period. Alcohol intake induced AD-like behavioral pathologies in 3xTg-AD mice including impaired spatial memory in the Morris Water Maze, diminished sensorimotor gating as measured by prepulse inhibition, and exacerbated conditioned fear. Multiplex immunoassay conducted on brain lysates showed that alcohol drinking upregulated primary markers of AD pathology in 3xTg-AD mice: A{beta} 42/40 ratio in the lateral entorhinal and prefrontal cortex and total Tau expression in the lateral entorhinal cortex and amygdala at 1-month post alcohol exposure. Immunocytochemistry showed that alcohol use upregulated expression of pTau (Ser199/Ser202) in the hippocampus, which is consistent with late stage AD. According to the NIA-AA Research Framework, these results suggest that alcohol use is associated with Alzheimers pathology. Results also showed that alcohol use was associated with a general reduction in Akt/mTOR signaling via several phosphoproteins (IR, IRS1, IGF1R, PTEN, ERK, mTOR, p70S6K, RPS6) in multiple brain regions including hippocampus and entorhinal cortex. Dysregulation of Akt/mTOR phosphoproteins suggests alcohol may target this pathway in AD progression. These results suggest that nondependent alcohol drinking increases the onset and magnitude of AD-like neural and behavioral pathology in 3xTg-AD mice.

neuroscience↗

bioRxiv2019

Machine learning prediction of emesis and gastrointestinal state in ferrets

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus to duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

neuroscience↗

bioRxiv2018

Feasibility of constructing multi-dimensional genomic maps of juvenile idiopathic arthritis

BackgroundJuvenile idiopathic arthritis (JIA) is one of the most common chronic conditions of childhood. Like many common chronic human illnesses, JIA likely involves complex interactions between genes and the environment, mediated by the epigenome. Such interactions are best understood through multi-dimensional genomic maps that identify critical genetic and epigenetic components of the disease. However, constructing such maps in a cost-effective way is challenging, and this challenge is further complicated by the challenge of obtaining biospecimens from pediatric patients at time of disease diagnosis, prior to therapy, as well as the limited quantity of biospecimen that can be obtained from children,particularly those who are unwell. In this paper, we demonstrate the feasibility and utility of creating multi-dimensional genomic maps for JIA from limited sample numbers.\n\nMethodsTo accomplish our aims, we used an approach similar to that used in the ENCODE and Roadmap Epigenomics projects, which used only 2 replicates for each component of the genomic maps. We used genome-wide DNA methylation sequencing, whole genome sequencing on the Illumina 10x platform, RNA sequencing, and chromatin immunoprecipitation-sequencing for informative histone marks (H3K4me1 and H3K27ac) to construct a multi-dimensional map of JIA neutrophils, a cell we have shown to be important in the pathobiology of JIA.\n\nResultsThe epigenomes of JIA neutrophils display numerous differences from those from healthy children. DNA methylation changes, however, had only a weak effect on differential gene expression. In contrast, H3K4me1 and H3K27ac, commonly associated with enhancer functions, strongly correlated with gene expression. Furthermore, although unique/novel enhancer marks were associated with insertion-deletion events (indels) identified on whole genome sequencing, we saw no strong association between epigenetic changes and underlying genetic variation. The initiation of treatment in JIA is associated with a re-ordering of both DNA methylation and histone modifications, demonstrating the plasticity of the epigenome in this setting.\n\nConclusionsThese findings, generated from a small number of patient samples, demonstrate how multidimensional genomic studies may yield new understandings of biology of JIA and provide insight into how therapy alters gene expression patterns.

genomics↗