Biology subjects

Kwok, D.

Publications and source records attributed to Kwok, D..

4 recordsLinked to original sources

bioRxiv2021

The PD-1 checkpoint receptor maintains tolerance of self-reactive CD8 T cell in skin

Peripheral tolerance is thought to result from anergy or deletion of self-reactive T cells shortly after antigen encounter. However, the frequent occurrences of immune-related Adverse Events (irAEs) following checkpoint inhibitor (CPI) treatment suggest a hypothesis that immunologically healthy individuals have self-reactive effector T cells that are kept in a non-pathogenic state through checkpoint receptor-mediated suppression, instead of anergy or deletion. We expressed self-antigens in healthy skin and found that antigen-specific CD8 T cells infiltrated the tissue, but remained tolerant, despite having a transcriptional program that resembled effector T cells found after CPIs. These self-reactive PD-1+ CD8 T cells drove IFN{gamma}-dependent increases in PD-L1 on skin myeloid cells. Blockade of PD-1 or PD-1/CTLA-4 led to post-transcriptional upregulation of effector proteins by antigen-specific CD8 T cells and elimination of antigen-expressing epithelial cells, resulting in localized tissue pathology with features of human cutaneous irAEs. This data supports the hypothesis that myeloid cells in healthy skin prevent pathology from self-reactive effector CD8 T cells through the PD-1/PD-L1 pathway.

immunology↗

bioRxiv2020

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

The success of transcriptome-wide association studies (TWAS) has led to substantial research towards improving its core component of genetically regulated expression (GReX). GReX links expression information with phenotype by serving as both the outcome of genotype-based expression models and the predictor for downstream association testing. In this work, we demonstrate that current linear models of GReX inadvertently combine two separable steps of machine learning - feature selection and aggregation - which can be independently replaced to improve overall power. We show that the monolithic approach of GReX limits the adaptability of TWAS methodology and practice, especially given low expression heritability.

bioinformatics↗

bioRxiv2020

kTWAS: integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a category of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast, kernel methods such as SKAT model genotypic and phenotypic variance using various kernel functions that capture genetic similarity between subjects, allowing non-linear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning, and feature modeling. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS and kernel-based methods. In this work we developed a novel method called kTWAS that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in WTCCC genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

bioinformatics↗

bioRxiv2020

De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or haplotypes. However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

bioinformatics↗