Deep indel mutagenesis reveals the impact of insertions and deletions on protein stability and function
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ABSTRACT:
PROVIDER: PRJNA1021220 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26184100_1.fastq.gz | Fastqsanger.gz | |||
| SRR26184100_2.fastq.gz | Fastqsanger.gz | |||
| SRR26184101_1.fastq.gz | Fastqsanger.gz | |||
| SRR26184101_2.fastq.gz | Fastqsanger.gz | |||
| SRR26184102_1.fastq.gz | Fastqsanger.gz |
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Project description:Amino acid insertions and deletions (indels) are an abundant class of genetic variants. However, compared to substitutions, the effects of indels are not well understood and poorly predicted. Here we address this shortcoming by performing deep indel mutagenesis (DIM) of structurally diverse proteins. Indel tolerance is strikingly different to substitution tolerance and varies extensively both between different proteins and within different regions of the same protein. Although state of the art variant effect predictors perform poorly on indels, we show that both experimentally-measured and computationally-predicted substitution scores can be repurposed as good indel variant effect predictors by incorporating information on protein secondary structures. Quantifying the effects of indels on protein-protein interactions reveals that insertions can be an important class of gain-of-function variants. Our results provide an overview of the impact of indels on proteins and a method to predict their effects genome-wide.
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Project description:Insertions and deletions (indels) are common sources of structural variation, and insertions originating from spontaneous DNA lesions are frequent in cancer. We developed a highly sensitive assay (Indel-Seq) to monitor rearrangements in human cells at the TRIM37 acceptor locus that reports indels stemming from experimentallyinduced and spontaneous genome instability. Templated insertions, which derive from sequences genome-wide, require contact between donor and acceptor loci, homologous recombination, and are stimulated by DNA end-processing. Insertions are facilitated by transcription and involve a DNA/RNA hybrid intermediate. Indel-Seq reveals that insertions are generated via multiple pathways. The broken acceptor site anneals with a resected DNA break or invades the displaced strand of a transcription bubble or R-loop followed by DNA synthesis, displacement and then ligation by nonhomologous end-joining. Our studies identify transcription-coupled insertions as a critical source of spontaneous genome instability that are distinct from cut-and-paste events
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Project description:Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >10^6 reads corresponding to >10^4 insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors. Solid tumors in mice were generated by somatic transposon mutagenesis with a PiggyBac transposon system. Insertion sites of transposons in 11 tumors and 6 non-cancerous tail controls were determined by Illumina high-throughput sequencing. Insertions were determined both on 5' and 3' sides of the transposon (PB5 and PB3, respectively). Quantitative analysis of read numbers revealed enrichment of certain insertions in tumors, but not in controls, and these enriched insertions identify candidate cancer genes.
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Project description:Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >10^6 reads corresponding to >10^4 insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors.
2013-08-28 | GSE46210 | GEO
Project description:A missense mutation of collagen type VIII alpha 2 chain (COL8A2) gene leads to early onset Fuchs’ endothelial corneal dystrophy (FECD), which can cause blindness through progressive loss of corneal endothelial cells. We established a novel procedure for achieving structural and functional rescue of the post-mitotic corneal endothelium without surgery, using CRISPR/Cas9-based postnatal gene editing in a mouse model of FECD. A single intraocular injection of an adenovirus encoding both the Cas9 gene and guide RNA (Ad-Cas9-Col8a2gRNA) at a titer below the cytotoxic threshold, efficiently knocked down COL8A2 protein expression in corneal endothelial cells, prevented endothelial cell loss, and rescued corneal endothelium pumping function in adult Col8a2 mutant mice. Here, to determine the indel rate in mouse corneal endothelium, we performed deep sequencing of PCR products (including the target site) amplified from genomic DNA of corneal endothelium. We found that the indel rate was 23.7 ± 4.5% in mouse corneal endothelium. Most insertions were 1bp insertions (19.8 ± 4.0% in total reads), while 2bp deletions were the most frequent (1.0 ± 0.3% in total reads). We moreover found that A or T insertion was predominant, with the proportion of A:T:G:C being 48.7 : 44.6 : 1.8 : 4.9.
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Project description:Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.
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