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Biology subjects

Shuai, S.

Publications and source records attributed to Shuai, S..

6 recordsLinked to original sources

Multimodal CRISPR screens uncover DDX39B as a global repressor of A-to-I RNA editing

Adenosine-to-Inosine (A-to-I) RNA editing is a critical post-transcriptional modification that diversifies the transcriptome and influences various cellular processes. Despite its significance, the regulatory mechanisms controlling A-to-I editing remain largely unknown. In this study, we present two complementary CRISPR-based genetic screening platforms: CREDITS (CRISPR-based RNA EDITing regulator Screening), which enables genome-scale identification of editing regulators using an RNA recorder-based reporter system, and scCREDIT-seq (single-cell CRISPR-based RNA EDITing sequencing), which provides multiplexed single-cell characterization of transcriptome and editome changes for pooled perturbations on dozens of selected genes. Through screening 1,350 RNA-binding proteins, we identified a series of known and novel A-to-I editing regulators. Detailed mechanistic investigation revealed DDX39B as a global repressor of A-to-I editing, which functions by preventing double-stranded RNA accumulation through its helicase and ATPase activities. We demonstrate that targeting DDX39B significantly enhances the efficiency of RNA editing-based tools like CellREADR and LEAPER, and represents a promising strategy for anti-HDV therapy by modulating viral genome editing. These technological advances not only expand our understanding of RNA editing regulation but also provide powerful tools for exploring tissue-specific and context-dependent RNA modification mechanisms, with broad implications for therapeutic development.

molecular biology↗

Cell type-specific consequences of mosaic structural variants in hematopoietic stem and progenitor cells

The functional impact and cellular context of mosaic structural variants (mSVs) in normal tissues is understudied. Utilizing Strand-seq, we sequenced 1,133 single cell genomes from 19 human donors of increasing age, revealing a heterogeneous mSV landscape in hematopoietic stem and progenitor cells (HSPCs). While mSV clonal expansions are confined to individuals over 60, de novo mSV formation occurs consistently across age, frequently leading to megabase-scale segmental aneuploidies. Cells harboring subclonal mosaicism show evidence for increased mSV formation. To enable high-resolution cell-typing of each Strand-seq library, we generated single-cell MNase-seq reference datasets for eight distinct HSPCs. Subclonal mSVs frequently exhibit enrichment in myeloid progenitors, and single-cell multiomic analysis suggests that these mSVs result in recurrent dysregulation of pathways related to proliferation and metabolism, including Ras signaling and lipid metabolism. The comprehensive mSV landscape identified in this study implicates mSVs in cell type-specific molecular phenotypes, establishing a foundation for deciphering links between mSVs, aging, and disease risk.

genomics↗

Pan-cancer analysis reveals tumor microbiome associations with host molecular aberrations

AbstractHost-microbiome interaction is known to play a pivotal role in the cancer ecosystem, yet the associations have not been systematically investigated at the pan-cancer and the multi-omics level. Here, we evaluated nearly 10,000 samples across 32 cancer types collected from The Cancer Genome Atlas (TCGA), to investigate the association between the tumor microbiome (taxa, n=1,630) and tumor microenvironment composition (cell types, n=20), epigenome (CpG island methylation, n=30,716), transcriptome (gene expression, n=10,216) and proteome (protein expression, n=193). We identified 836,738 candidate associations between the tumor microbiome and host molecular aberrations across multiple cancers. Besides cancer-specific associations, we also revealed recurrent pan-cancer associations between microbes (Lachnoclostridium, Flammeovirga, Terrabacter and Campylobacter) and immune cells, as well as between microbes (Collimonas and Sutterella) and fibroblasts, which were further validated by cell type estimations derived from pathological images and methylation data. We also identified several potential microbe and gene/protein expression associations mediated by DNA methylation using the sequential mediation analysis. Furthermore, our survival analysis demonstrated that tumor microbes may affect the patients overall survival and progression-free survival. Finally, a user-friendly web portal, Multi-Omics and Microbiome Associations in Cancer (MOMAC) was constructed for users to explore potential host-microbe interactions in cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/536730v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@70f74eorg.highwire.dtl.DTLVardef@7c3078org.highwire.dtl.DTLVardef@152354dorg.highwire.dtl.DTLVardef@299282_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

cancer biology↗

DriverPower: Combined burden and functional impact tests for cancer driver discovery

We describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPowers background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

bioinformatics↗