Biology subjects

Guan, L.

Publications and source records attributed to Guan, L..

3 recordsLinked to original sources

bioRxiv2018

Inhibition of Hsp70 suppresses neuronal hyperexcitability and attenuates seizures by enhancing A-type potassium currents

The heat shock protein 70 (Hsp70) is upregulated in response to stress and has been implicated as a stress marker in temporal lobe epilepsy (TLE). However, whether Hsp70 plays a pathologic or protective role in TLE remains unclear. Here we report that Hsp70 exerts an unexpected deleterious role in kainic acid (KA)-induced seizures, and inhibition of Hsp70 suppresses neuronal hyperexcitability and attenuates both acute and chronic seizures via enhancing A-type potassium currents primarily formed by Kv4 -subunits and auxiliary KChIPs. Proteosomal degradation of Kv4-KChIP4a channel complexes is enhanced by Hsp70, which can be reversed by the Hsp70 inhibitors, 2-phenylethynesulfonamide (PES) and VER-155008 (VER). In cultured hippocampal neurons, either PES or VER can increase A-type Kv4 current to suppress neuronal hyperexcitability. Mechanistically, Hsp70-CHIP complexes directly bind to the N-terminus of auxiliary KChIP4a and target Kv4-KChIP4a complexes to the proteasome. Our findings reveal a previously unrecognized role of Hsp70 in mediating degradation of Kv4-KChIP4a complexes and regulating neuronal excitability, thus highlighting a therapeutic potential for hyperexcitability-related neurological disorders through Hsp70 inhibition.

neuroscience↗

bioRxiv2017

Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.\n\nAuthor SummaryRecently, the cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). CyTOF enables a significant increase in the ability to monitor 50 or more cellular markers for millions of cells at the single-cell level. Initial studies with CyTOF focused on few samples, in which expert manual discovery of cell types were acceptable. As the technology matures, it is now feasible to collect more samples, which enables systematic studies of cell types across multiple samples. However, the statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. Furthermore, it was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. In this work, we present a scalable analysis pipeline that is grounded in strong statistical foundation. Partition-Assisted Clustering (PAC) offers fast and accurate clustering and Multiple Alignments of Networks (MAN) utilizes network structures learned from each homogeneous cluster to organize the data into data-set level clusters. PAC-MAN thus enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of similarly large or larger datasets.

bioinformatics↗

bioRxiv2017

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis acting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

genetics↗