Biology subjects

Hui, S.

Publications and source records attributed to Hui, S..

3 recordsLinked to original sources

bioRxiv2018

Structural Basis of Broad Ebolavirus Neutralization by a Human Survivor Antibody

The structural features that govern broad-spectrum activity of broadly neutralizing, anti-ebolavirus antibodies (Abs) are currently unknown. Here we describe the first structure of a broadly neutralizing human Ab, ADI-15946, in complex with cleaved Ebola virus glycoprotein (EBOV GPCL). We find that ADI-15946 employs structural mimicry of a conserved interaction between the GP core and the glycan cap {beta}17-{beta}18 loop to inhibit infection. Both endosomal proteolysis of EBOV GP and binding of monoclonal Ab (mAb) FVM09 displace this loop, increase exposure of ADI-15946s conserved epitope and potentiate neutralization. Our work also illuminated the determinants of ADI-15946s reduced activity against Sudan virus (SUDV), and enabled rational, structure-guided engineering to enhance binding and neutralization against SUDV while retaining the parental breadth of activity.\n\nOne Sentence SummaryThe first crystal structure of a broadly active antibody against surface glycoproteins of ebolaviruses identifies a highly conserved epitope beneath the glycan cap and highlights the molecular requirements for broad ebolavirus neutralization.

immunology↗

bioRxiv2018

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.

genetics↗

bioRxiv2016

netDx: Patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.

bioinformatics↗