Biology subjects

Fu, A. Q.

Publications and source records attributed to Fu, A. Q..

5 recordsLinked to original sources

bioRxiv2022

Types of cis- and trans-gene regulation of expression quantitative trait loci across human tissues

Expression quantitative trait loci (eQTLs) have been identified for most genes in the human genome across tissues and cell types. While most of the eQTLs are near the associated genes, some can be far away or on different chromosomes, with the regulatory mechanisms largely unknown. Here, we study cis- and trans-regulation by eQTLs on protein-coding genes and long noncoding RNAs (lncRNAs) across nearly 50 tissues and cell types. Specifically, we constructed trios consisting of an eQTL, its cis-gene and trans-gene and inferred the regulatory relationships with causal network inference. We identify multiple types of regulatory networks for trios: across all the tissues, more than half of the trios are inferred to be conditionally independent, where the two genes are conditionally independent given the genotype of the eQTL (gene 1 [<-] eQTL [->] gene 2). Around 1.5% of the trios are inferred to be mediation (eQTL [->] mediator [->] target), around 1.3% fully connected among the three nodes, and just a handful v-structures (eQTL [->] gene 1 [<-] gene 2). Unexpectedly, across the tissues, on average more than half of the mediation trios have the trans-gene as the mediator. Most of the mediators (cis and trans) are tissue specific, and cis-gene mediators are significantly enriched for protein-coding genes, whereas trans-gene mediators have a similar distribution of protein-coding genes and lncRNAs to the whole genome.

genomics↗

bioRxiv2020

Intrathecal catheter implantation decreases cerebrospinal fluid dynamics in cynomolgus monkeys

A detailed understanding of the CSF dynamics is essential for testing and evaluation of intrathecal drug delivery. Preclinical work using large-animal models (e.g., monkeys, dogs and sheep) has great utility for defining spinal drug distribution/pharmacokinetics and provide an important tool for defining safety. In this study, we investigated the impact of catheter implantation in the sub-dural space on CSF flow dynamics in Cynomolgus monkeys. Magnetic resonance imaging (MRI) was performed before and after catheter implantation to quantify the differences based on catheter placement location in the cervical compared to the lumbar spine. Several geometric and hydrodynamic parameters were calculated based on the 3D segmentation and flow analysis. Hagen-Poiseuille equation was used to investigate the impact of catheter implantation on flow reduction and hydraulic resistance. A linear mixed-effects model was used in this study to investigate if there is a statistically significant difference between cervical and lumbar implantation, or between two MRI time points. Results showed that geometric parameters did not change statistically across MRI measurement time points and did not depend on catheter location. However, catheter insertion did have a significant impact on the hydrodynamic parameters and the effect was greater with the cervical implantation. CSF flow rate decreased up to 54.7% when the catheter located in the cervical region. The maximum flow rate reduction in the lumbar implantation group was 21%. Overall, lumbar catheter implantation disrupted CSF dynamics to a lesser degree than cervical catheter implantation and this effect remained up to two weeks post-catheter implantation

pharmacology and toxicology↗

bioRxiv2019

Systematic identification of A-to-I editing associated regulators from multiple human cancers

A-to-I editing is the most common editing type in human that is catalyzed by ADAR family members (ADARs), ADAR1 and ADAR2. Millions of A-to-I editing sites have been discovered recently, however, the regulation mechanisms of the RNA editing process are still not clear. Here we developed a two-step logistic regression model to identify genes that are potentially involved in RNA editing process in four human cancers. The first step by classifying the editing sites into different categories assists the analysis at the second step. In the first step, ADAR1 was identified as the enzyme that associated with the majority of the A-to-I editing sites. Thus, ADAR1 was taken as a control gene in the second step to identify genes that have a stronger effect on editing sites than ADAR1. In addition, the detectable interferons and their receptors were used as covariates in the both steps to account for potential association caused by interferons. Using our advanced method, we successfully found a set of genes that were significantly positively or negatively associated (PA or NA) with specific sets of RNA editing sites. We highlighted two genes, SRSF5 and MIR22HG which were supported by multiple evidences. Most PA and NA genes were unique to each cancer, and only a few shared across two cancers. Pathway enrichment analysis showed that the PA genes from the four cancer types were enriched in Immune System, while the NA genes were enriched in two pathways: Metabolism of RNA, and Metabolism. The functional similarity of the PA and NA genes across all the four cancers indicates that even though most of the editing associated genes were unique to each cancer, they may impact on editing process through common pathways. Interestingly, the PA genes from kidney cancer were enriched for survival-associated genes while the NA genes were depleted of these genes, indicating that the PA genes may play more important roles in kidney cancer development.

bioinformatics↗

bioRxiv2018

Imputation of single-cell gene expression with an autoencoder neural network

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.\n\nMethodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.\n\nResultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.\n\nConclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

bioinformatics↗

bioRxiv2017

Learning causal biological networks with generalized Mendelian randomization

Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i.e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when many phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in classical algorithms for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms existing general-purpose network inference methods and other PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci.

bioinformatics↗