Biology subjects

Su, Z.

Publications and source records attributed to Su, Z..

8 recordsLinked to original sources

bioRxiv2026

Temozolomide Induces Aberrant RNA Alkylation and Widespread Translational Repression

Temozolomide (TMZ) is a frontline alkylating chemotherapy, yet its direct impact on RNA modification and global translation dynamics remains poorly understood. Here, we demonstrate that TMZ induces pervasive RNA alkylation causing severe translational impairment. TMZ directly deposits aberrant methyl groups onto single-stranded mRNA in vitro, creating physical lesions that lower translational efficiency. In glioblastoma cells, acute TMZ exposure triggers a rapid, widespread accumulation of m7G on cellular RNAs, leading to the significant attenuation of global protein synthesis. Nanopore direct RNA sequencing identified distinct guanine-specific error signatures and sequence context preferences associated with TMZ-induced damage. Using a quantitative yeast spike-in ribosome profiling strategy, we mapped this translational repression at transcript-level, revealing a global downregulation of translational efficiency. This widespread repression disproportionately targets highly interconnected networks essential for cellular proliferation, specifically chromosome organization. We show that the severity of this translational repression is driven by a transcript's coding guanine density, stability and translation initiation speed. Together, our findings suggest that TMZ-induced alkylation targets stable, highly translated, guanine-rich transcripts. This establishes aberrant RNA methylation and subsequent translational arrest as a potential mechanism of temozolomide cytotoxicity.

biochemistry↗

bioRxiv2018

Deciphering epigenomic code for cell differentiation using deep learning

Epigenomic markers, such as histone modifications, play important roles in cell fate determination and type maintenance during cell differentiation. Although genomic sequence plays a crucial role in establishing the unique epigenome in each cell type produced during cell differentiation, little is known about the sequence determinants that lead to the unique epigenomes of the cells. Here, using a dataset of six histone markers measured in four human CD4+ T cell types produced at different stages of T cell development, we showed that two types of highly accurate deep convolutional neural networks (CNNs) constructed for each cell type and for each histone marker are a powerful strategy to uncover the sequence determinants of the various histone modification patterns in difference cell types. We found that sequence motifs learned by the CNN models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Our results suggest that both the unique histone modification patterns in each cell type and the different patterns of the same histone marker in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of shared few motifs learned in the different cell models reflect the lineage relationships of the cells, while the level of few shared motifs learned in different histone marker models reflect their functional relationships. Furthermore, using these models, we can predict the importance of the learned motifs and their interactions in determining specific histone marker patterns in the cell types.

bioinformatics↗

bioRxiv2018

New genetic variants associated with major adverse cardiovascular events in patients with acute coronary syndromes and treated with clopidogrel and aspirin

ImportanceAlthough a few studies have reported the effects of several polymorphisms on major adverse cardiovascular events (MACE) in patients with acute coronary syndromes (ACS) and those undergoing percutaneous coronary intervention (PCI), these genotypes account for only a small fraction of the variation and evidence is insufficient. This study aims to identify new genetic variants associated with MACE by large-scale sequencing data.\n\nObjectiveTo identify the genetic variants that caused MACE.\n\nDesignAll patients in this study were allocated to dual antiplatelet therapy for up to 12 months and have the follow-up duration of 18 months.\n\nSettingA two-stage association study was performed.\n\nParticipantsWe evaluated the associations of genetic variants and MACE in 1961 patients with ACS undergoing PCI (2009-2012), including high-depth whole exome sequencing of 168 patients in the discovery cohort and high-depth targeted sequencing of 1793 patients in the replication cohort.\n\nMain Outcomes and MeasureThe primary clinical efficacy endpoint was the major adverse cardiovascular events (MACE) composite endpoint, including cardiovascular death, myocardial infarction (MI), stroke (CT or MR scan confirmed) and repeated revascularization (RR).\n\nResultsWe discovered and confirmed six new genotypes associated with MACE in patients with ACS. Of which, rs17064642 at MYOM2 increased the risk of MACE (hazard ratio [HR] 2.76; P = 2.95 x 10-9) and reached genome-wide significance. The other five suggestive variants were KRTAP10-4 (rs201441480), WDR24 (rs11640115), ECHS1 (rs140410716), AGAP3 (rs75750968) and NECAB1 (rs74569896). Notably, the expressions of MYOM2 and ECHS1 are down-regulated in both animal models and patients with phenotypes related to MACE. Importantly, we developed the first superior classifier for predicting MACE and achieved high predictive accuracy (0.809).\n\nConclusions and RelevanceWe identified six new genotypes associated with MACE and developed a superior classifier for predicting MACE. Our findings shed light on the pathogenesis of cardiovascular outcomes and may help clinician to make decision on the therapeutic intervention for ACS patients.\n\nTrial RegistrationThis study has been registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn, Registration number: ChiCTR-OCH-11001198).

genetics↗

bioRxiv2018

BicGO: a new biclustering algorithm based on global optimization

Recognizing complicated biclusters submerged in large scale datasets (matrix) has been being a highly challenging problem. We introduce a biclustering algorithm BicGO consisting of two separate strategies which can be selectively used by users. The BicGO which was developed based on global optimization can be implemented by iteratively answering if a real number belongs to a given interval. Tested on various simulated datasets in which most complicated and most general trend-preserved biclusters were submerged, BicGO almost always extracted all the actual bicluters with accuracy close to 100%, while on real datasets, it also achieved an incredible superiority over all the salient tools compared in this article. As far as we know, the BicGO is the first tool capable of identifying any complicated (e.g., constant, shift, scale, shift-scale, order-preserved, trend-preserved, etc), any shapes (narrow or broad) of biclusters with overlaps allowed. In addition, it is also highly parsimonious in the usage of computing resources. The BicGO is available at https://www.dropbox.com/s/hsj3j96rekoks5n/BicGO.zip?dl=0 for free download.

bioinformatics↗

bioRxiv2018

Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

The availability of a large volume of chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets for various transcription factors (TF) has provided an unprecedented opportunity to identify all functional TF binding motifs clustered in the enhancers in genomes. However, the progress has been largely hindered by the lack of a highly efficient and accurate tool that is fast enough to find not only the target motifs, but also cooperative motifs contained in very large ChIP-seq datasets with a binding peak length of typical enhancers ([~] 1,000 bp). To circumvent this hurdle, we herein present an ultra-fast and highly accurate motif-finding algorithm, ProSampler, with automatic motif length detection. ProSampler first identifies significant k-mers in the dataset and combines highly similar significant k-mers to form preliminary motifs. ProSampler then merges preliminary motifs with subtle similarity using a novel graph-based Gibbs sampler to find core motifs. Finally, ProSampler extends the core motifs by applying a two-proportion z-test to the flanking positions to identify motifs longer than k. As the number of preliminary motifs is much smaller than that of k-mers in a dataset, we greatly reduce the search space of the Gibbs sampler compared with conventional ones. By storing flanking sequences in a hash table, we avoid extensive IO and the necessity of examining all lengths of motifs in an interval. When evaluated on both synthetic and real ChIP-seq datasets, ProSampler runs orders of magnitude faster than the fastest existing tools while more accurately discovering primary motifs as well as cooperative motifs than do the best existing tools. Using ProSampler, we revealed previously unknown complex motif occurrence patterns in large ChIP-seq datasets, thereby providing insights into the mechanisms of cooperative TF binding for gene transcriptional regulation. Therefore, by allowing fast and accurate mining of the entire ChIP-seq datasets, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

bioinformatics↗

bioRxiv2018

Myricetin Attenuates LPS-induced Inflammation in RAW 264.7 Macrophages and Mouse Models

BackgroundMyricetin has been demonstrated to inhibit inflammation in a variety of diseases, but little is known about its characters in acute lung injury (ALI). In this study, we aimed to investigate the protective effects of myricetin on inflammation in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells and a LPS-induced lung injury model.\n\nMethodsSpecifically, we investigated its effects on lung edema and histological damage by lung W/D weight ratio, HE staining and Evans Blue dye. Then macrophage activation was detected by evaluating the TNF-, IL-6 and IL-1{beta} mRNA and protein iNOS and COX-2. Myricetin was used to detect the impact on the inflammatory responses in LPS-induced RAW264.7 cells with the same manners in mouse model. Finally, NF-{kappa}B and MAPK signaling pathways were investigated with Western blot assay in LPS-induced RAW264.7 cells.\n\nResultsMyricetin significantly inhibited the production of the pro-inflammatory cytokines in vitro and in vivo. The in vivo experiments showed that pretreatment with Myricetin markedly attenuated the development of pulmonary edema, histological severities and macrophage activation in mice with ALI. The underlying mechanisms were further demonstrated in vitro that myricetin exerted an anti-inflammatory effect through suppressing the NF-{kappa}B p65 and AKT activation in NF-{kappa}B pathway and JNK, p-ERK and p38 in mitogen-activated protein kinases signaling pathway.\n\nConclusionMyricetin alleviated ALI by inhibiting macrophage activation, and inhibited inflammation in vitro and in vivo. It may be a potential therapeutic candidate for the prevention of inflammatory diseases.

cell biology↗

bioRxiv2017

Unexpected CRISPR off-target mutation pattern in vivo are not typicallygermline-like

To the EditorSchaefer et al.1 (referred to as Study_1) recently presented the provocative conclusion that CRISPR-Cas9 nuclease can induce many unexpected off-target mutations across the genome that arise from the sites with poor homology to the gRNA. As Wilson et al.2 pointed out, however, the selection of a co-housed mouse as the control is insufficient to attribute the observed mutation differences between the CRISPR-treated mice and control mice. Therefore, the causes of these mutations need to be further investigated. In 2015, Iyer et al.3 (referred to as Study_2) used Cas9 and a pair of sgRNAs to mutate the Ar gene in vivo and off-target mutations were investigated by comparison the control mice and the offspring of the modified mice. After analyzing the whole genome sequencing (WGS) of the offspring and the control mice, they claimed that off-target mutations are rare from CRISPR-Cas9 engineering. Notably, their study only focused on indel off-target mutations. We re-analyzed the WGS data of these two studies and detected both single nucleotide variants (SNVs) and indel mutations.

genetics↗

bioRxiv2017

Global Gene Repression By Dicer-Independent tRNA Fragments

tRNA derived RNA fragments (tRFs) is an emerging group of small RNAs as abundant as miRNAs, and yet their roles are not well understood. Here, we focus on endogenous tRFs (18-22 bases) derived from 3 end of human mature tRNAs (tRF-3) and their functions in gene repression. tRF-3 levels increase upon parental tRNA over-expression or tRNA induction by c-Myc oncogene activation. Elevated tRF-3 levels lead to repression of target genes with a sequence complementary to the tRF-3 in the 3 UTR. The tRF-3-mediated repression is Dicer-independent, Argonaute-dependent and the targets are recognized by 5 seed sequence rules similar to miRNAs. Furthermore, tRF-3s associate with GW proteins in P-bodies. RNA-seq identifies the endogenous target genes of tRF-3s that are specifically repressed upon tRF-3 induction. Overall, our analysis shows Dicer-independent tRF-3s, generated upon tRNA upregulation such as c-Myc overexpression, regulate gene expression globally through Argounate via seed sequence matches.

molecular biology↗