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Biology subjects

Yuan, B.

Publications and source records attributed to Yuan, B..

60 records · Page 4Linked to original sources
bioRxiv2020

Structural and biochemical characterization of nsp12-nsp7-nsp8 core polymerase complex from COVID-19 virus

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) has caused huge number of human deaths. Currently, there are no specific drugs or vaccines available for this virus. The viral polymerase is a promising antiviral target. However, the structure of COVID-19 virus polymerase is yet unknown. Here, we describe the near-atomic resolution structure of its core polymerase complex, consisting of nsp12 catalytic subunit and nsp7-nsp8 cofactors. This structure highly resembles the counterpart of SARS-CoV with conserved motifs for all viral RNA-dependent RNA polymerases, and suggests the mechanism for activation by cofactors. Biochemical studies revealed reduced activity of the core polymerase complex and lower thermostability of individual subunits of COVID-19 virus as compared to that of SARS-CoV. These findings provide important insights into RNA synthesis by coronavirus polymerase and indicate a well adaptation of COVID-19 virus towards humans with relatively lower body temperatures than the natural bat hosts.

microbiology↗
bioRxiv2020

Long-read Individual-molecule Sequencing Reveals CRISPR-induced Genetic Heterogeneity in Human ESCs

Accurately quantifying the genetic heterogeneity of a cell population is essential to understanding of biological systems. We develop a universal method to label individual DNA molecules for analyzing diverse types of rare genetic variants, with frequency as low as 4x10-5, using short- or long-read sequencing. It enables base-resolution haplotype-resolved quantitative characterization of rare variants. It provides the first quantitative evidence of persistent nonrandom large deletions and insertions following DNA repair of double-strand breaks induced by CRISPR-Cas9 in human pluripotent stem cells.

molecular biology↗
bioRxiv2020

Inhibitory effects of Akt on neuronal FoxO4 activation after subarachnoid hemorrhage in vivo and in vitro

Secondary brain injury following subarachnoid hemorrhage (SAH) is the critical contributor to the mortality of SAH patients. The underlying mechanisms are poorly understood. In this study, we utilized a mice model of SAH to investigate whether FoxO4 is related to the brain injury after SAH and identified its upstream regulator Akt. Experimental SAH was induced in adult male mice by prechiasmatic cistern injection. Brain FoxO4 protein levels in cytoplasm and nucleaus were examined in the sham-operated controls, and in mice 1h, 6h, 12h, 24h, 3d, and 5d after SAH induction. The Akt inhibitor LY294002 was administered by intracerebroventricular infusion to determine its effects on FoxO4. Moreover, the expression of FoxO4 was also investigated in neurons incubated with hemoglobin in vitro, which was also dertermined after inhibition of Akt. FoxO4 protein expression in the nuclei increased remarkably after SAH. The Akt inhibitor LY294002 induced more FoxO4 nuclear localization after SAH in vivo and in vitro. Our results suggest the activation of FoxO4 after SAH and which was inhibited by the increased phosphorylated Akt (p-Akt).

neuroscience↗
bioRxiv2019

Integrated Sequencing & Array Comparative Genomic Hybridization in Familial Parkinson’s Disease

BackgroundParkinsons disease (PD) is a genetically heterogeneous condition; both single nucleotide variants (SNVs) and copy number variants (CNVs) are important genetic risk factors. We examined the utility of combining exome sequencing and genome-wide array-based comparative genomic hybridization (aCGH) for identification of PD genetic risk factors. MethodsWe performed exome sequencing on 110 subjects with PD and a positive family history; 99 subjects were also evaluated using genome-wide aCGH. We interrogated exome sequencing and array comparative genomic hybridization data for pathogenic SNVs and CNVs at Mendelian PD gene loci. SNVs were confirmed via Sanger sequencing. CNVs were confirmed with custom-designed high-density aCGH, droplet digital PCR, and breakpoint sequencing. ResultsUsing exome sequencing, we discovered individuals with known pathogenic single nucleotide variants in GBA (p.E365K, p.T408M, p.N409S, p.L483P) and LRRK2 (p.R1441G and p.G2019S). Two subjects were each double heterozygotes for variants in GBA and LRRK2. Based on aCGH, we additionally discovered cases with an SNCA duplication and heterozygous intragenic GBA deletion. Five additional subjects harbored both SNVs (p.N52fs, p.T240M, p.P437L, p.W453*) and likely disrupting CNVs at the PARK2 locus, consistent with compound heterozygosity. In nearly all cases, breakpoint sequencing revealed microhomology, a mutational signature consistent with CNV formation due to DNA replication errors. ConclusionsIntegrated exome sequencing and aCGH yielded a genetic diagnosis in 19.3% of our familial PD cohort. Our analyses highlight potential mechanisms for SNCA and PARK2 CNV formation, uncover multilocus pathogenic variation, and identify novel SNVs and CNVs for further investigation as potential PD risk alleles.

genetics↗
bioRxiv2019

Interpretable Machine Learning for Perturbation Biology

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides an informative data resource for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in an enormously complex multi-dimensional solution space and to mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of dynamic cell biological processes with a machine learning framework, implemented in Tensorflow. We tested the modeling framework on a perturbation-response dataset for a melanoma cell line after drug treatments. The models can be efficiently trained to accurately describe cellular behavior, as tested by cross-validation. Even though completely data-driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The main predictive application is the identification of combinatorial candidates for cancer therapy. The approach is readily applicable to a wide range of kinetic models of cell biology.

systems biology↗
bioRxiv2017

Identification of methylation and hydroxymethylation haplotype blocks as distal regulatory elements aids in deconvolution of heterogeneous brain tissues

5-Hydroxymethylcytosine (5hmC) is an oxidation product of 5-methylcytosine (5mC), and adjacent CpG sites in the mammalian genome can be co-methylated and co-hydroxymethylated due to the processivity of DNMT and TET enzymes. We applied TAB-seq and oxBS-seq to selectively detect 5hmC and 5mC, respectively, at base resolution in the mouse cortex, olfactory bulb and cerebellum tissues. We found that majority of the called 5hmC CpG sites frequently had 5mC modification simultaneously and enriched in gene body regions of neuron development related genes in brain tissues. These results supported a prominent role of oxidizing 5mC to 5hmC as new epigenetic mark. Strikingly, by a systematic search of regions that show highly coordinated methylation and hydroxymethylation (MHBs and hMHBs), we found that MHBs significantly overlapped with hMHBs in gene body regions which further supported that oxidized 5mC to 5hmC co-ordinately in a subset of cells within heterogeneous brain tissues. Consistently, using a metric called methylation haplotype load, we defined a subset of 1,361 tissue-specific MHBs and 3,818 shared MHBs which were predominantly regulatory elements, and aids in deconvolution of heterogeneous brain tissues. Our results provide new insights into the role of co-ordinately oxidized 5mC to 5hmC as distal regulatory elements may involve in regulating tissue identity.

genetics↗