C-to-G editing generates double-strand breaks causing deletion, transversion and translocation.

Base editors (BEs) introduce base substitutions without double-strand DNA cleavage. Besides precise substitutions, BEs generate low-frequency 'stochastic' byproducts through unclear mechanisms. Here, we performed in-depth outcome profiling and genetic dissection, revealing that C-to-G BEs (CGBEs) generate substantial amounts of intermediate double-strand breaks (DSBs), which are at the centre of several byproducts. Imperfect DSB end-joining leads to small deletions via end-resection, templated insertions or aberrant transversions during end fill-in. Chromosomal translocations were detected between the editing target and off-targets of Cas9/deaminase origin. Genetic screenings of DNA repair factors disclosed a central role of abasic site processing in DSB formation. Shielding of abasic sites by the suicide enzyme HMCES reduced CGBE-initiated DSBs, providing an effective way to minimize DSB-triggered events without affecting substitutions. This work demonstrates that CGBEs can initiate deleterious intermediate DSBs and therefore require careful consideration for therapeutic applications, and that HMCES-aided CGBEs hold promise as safer tools.

Mesoscale DNA feature in antibody-coding sequence facilitates somatic hypermutation.

Somatic hypermutation (SHM), initiated by activation-induced cytidine deaminase (AID), generates mutations in the antibody-coding sequence to allow affinity maturation. Why these mutations intrinsically focus on the three nonconsecutive complementarity-determining regions (CDRs) remains enigmatic. Here, we found that predisposition mutagenesis depends on the single-strand (ss) DNA substrate flexibility determined by the mesoscale sequence surrounding AID deaminase motifs. Mesoscale DNA sequences containing flexible pyrimidine-pyrimidine bases bind effectively to the positively charged surface patches of AID, resulting in preferential deamination activities. The CDR hypermutability is mimicable in in vitro deaminase assays and is evolutionarily conserved among species using SHM as a major diversification strategy. We demonstrated that mesoscale sequence alterations tune the in vivo mutability and promote mutations in an otherwise cold region in mice. Our results show a non-coding role of antibody-coding sequence in directing hypermutation, paving the way for the synthetic design of humanized animal models for optimal antibody discovery and explaining the AID mutagenesis pattern in lymphoma.

DNA repair mechanisms that promote insertion-deletion events during immunoglobulin gene diversification.

Insertions and deletions (indels) are low-frequency deleterious genomic DNA alterations. Despite their rarity, indels are common, and insertions leading to long complementarity-determining region 3 (CDR3) are vital for antigen-binding functions in broadly neutralizing and polyreactive antibodies targeting viruses. Because of challenges in detecting indels, the mechanism that generates indels during immunoglobulin diversification processes remains poorly understood. We carried out ultra-deep profiling of indels and systematically dissected the underlying mechanisms using passenger-immunoglobulin mouse models. We found that activation-induced cytidine deaminase-dependent ±1-base pair (bp) indels are the most prevalent indel events, biasing deleterious outcomes, whereas longer in-frame indels, especially insertions that can extend the CDR3 length, are rare outcomes. The ±1-bp indels are channeled by base excision repair, but longer indels require additional DNA-processing factors. Ectopic expression of a DNA exonuclease or perturbation of the balance of DNA polymerases can increase the frequency of longer indels, thus paving the way for models that can generate antibodies with long CDR3. Our study reveals the mechanisms that generate beneficial and deleterious indels during the process of antibody somatic hypermutation and has implications in understanding the detrimental genomic alterations in various conditions, including tumorigenesis.

REV7 is required for processing AID initiated DNA lesions in activated B cells.

Activation-induced cytidine deaminase (AID) initiates both antibody class switch recombination (CSR) and somatic hypermutation (SHM) in antibody diversification. DNA double-strand break response (DSBR) factors promote rearrangement in CSR, while translesion synthesis (TLS) polymerases generate mutations in SHM. REV7, a component of TLS polymerase zeta, is also a downstream effector of 53BP1-RIF1 DSBR pathway. Here, we study the multi-functions of REV7 and find that REV7 is required for the B cell survival upon AID-deamination, which is independent of its roles in DSBR, G2/M transition or REV1-mediated TLS. The cell death in REV7-deficient activated B cells can be fully rescued by AID-deficiency in vivo. We further identify that REV7-depedent TLS across UNG-processed apurinic/apyrimidinic sites is required for cell survival upon AID/APOBEC deamination. This study dissects the multiple roles of Rev7 in antibody diversification, and discovers that TLS is not only required for sequence diversification but also B cell survival upon AID-initiated lesions.

Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method.

The high mortality rate of pancreatic cancer makes it one of the most studied diseases among all cancer types. Many researches have been conducted to understand the mechanism underlying its emergence and pathogenesis of this disease. Here, by using minimum-redundancy-maximum-relevance (mRMR) method, we studied a set of transcriptome data of pancreatic cancer. As we gradually added features to achieve the most accurate classification results of Jackknife, a gene set of 9 genes was identified. They were NHS, SCML2, LAMC2, S100P, COL17A1, AMIGO2, PTPRR, KPNA7 and KCNN4. Through STRING 2.0 protein-protein interactions (PPIs) analysis, 40 proteins were identified in the shortest paths between genes in the gene set, 30 of them passed the permutation test, which indicated they were hubs in the background network. Those genes in the protein-protein interaction network were enriched to 37 functional modules, such as: negative regulation of transcription from RNA polymerase II promoter, negative regulation of ERK1 and ERK2 cascade and BMP signaling pathway. Our study indicated new mechanism of pancreatic cancer, suggesting potential therapeutic targets for further study.

cisASE: a likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data.

MOTIVATION: Allele-specific expression (ASE) is a useful way to identify cis-acting regulatory variation, which provides opportunities to develop new therapeutic strategies that activate beneficial alleles or silence mutated alleles at specific loci. However, multiple problems hinder the identification of ASE in next-generation sequencing (NGS) data. RESULTS: We developed cisASE, a likelihood-based method for detecting ASE on single nucleotide variant (SNV), exon and gene levels from sequencing data without requiring phasing or parental information. cisASE uses matched DNA-seq data to control technical bias and copy number variation (CNV) in putative cis-regulated ASE identification. Compared with state-of-the-art methods, cisASE exhibits significantly increased accuracy and speed. cisASE works moderately well for datasets without DNA-seq and thus is widely applicable. By applying cisASE to real datasets, we identified specific ASE characteristics in normal and cancer tissues, thus indicating that cisASE has potential for wide applications in cancer genomics. AVAILABILITY AND IMPLEMENTATION: cisASE is freely available at http://lifecenter.sgst.cn/cisASE CONTACT: biosinodx@gmail.com or yxli@sibs.ac.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Hyperlipidemia-associated gene variations and expression patterns revealed by whole-genome and transcriptome sequencing of rabbit models.

The rabbit (Oryctolagus cuniculus) is an important experimental animal for studying human diseases, such as hypercholesterolemia and atherosclerosis. Despite this, genetic information and RNA expression profiling of laboratory rabbits are lacking. Here, we characterized the whole-genome variants of three breeds of the most popular experimental rabbits, New Zealand White (NZW), Japanese White (JW) and Watanabe heritable hyperlipidemic (WHHL) rabbits. Although the genetic diversity of WHHL rabbits was relatively low, they accumulated a large proportion of high-frequency deleterious mutations due to the small population size. Some of the deleterious mutations were associated with the pathophysiology of WHHL rabbits in addition to the LDLR deficiency. Furthermore, we conducted transcriptome sequencing of different organs of both WHHL and cholesterol-rich diet (Chol)-fed NZW rabbits. We found that gene expression profiles of the two rabbit models were essentially similar in the aorta, even though they exhibited different types of hypercholesterolemia. In contrast, Chol-fed rabbits, but not WHHL rabbits, exhibited pronounced inflammatory responses and abnormal lipid metabolism in the liver. These results provide valuable insights into identifying therapeutic targets of hypercholesterolemia and atherosclerosis with rabbit models.

Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis.

Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocellular carcinoma (HCC) is the main type of liver cancer. We applied a machine learning approach with maximum-relevance-minimum-redundancy (mRMR) algorithm followed by incremental feature selection (IFS) to a set of microarray data generated from 43 tumor and 52 nontumor samples. With the machine learning approach, we identified 117 gene probes that could optimally separate tumor and nontumor samples. These genes not only include known HCC-relevant genes such as MT1X, BMI1, and CAP2, but also include cancer genes that were not found previously to be closely related to HCC, such as TACSTD2. Then, we constructed a molecular interaction network based on the protein-protein interaction (PPI) data from the STRING database and identified 187 genes on the shortest paths among the genes identified with the machine learning approach. Network analysis reveals new potential roles of ubiquitin C in the pathogenesis of HCC. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we showed that the identified subnetwork is significantly enriched in biological processes related to cell death. These results bring new insights of understanding the process of HCC.