Zuojian Tang

Genetic inactivation of the polycomb repressive complex 2 in T cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an immature hematopoietic malignancy driven mainly by oncogenic activation of NOTCH1 signaling1. In this study we report the presence of loss-of-function mutations and deletions of EZH2 and SUZ12 genes, encoding critical components of the Polycomb Repressive Complex 2 (PRC2) complex2,3, in 25% of T-ALLs. To further study the role of the PRC2 complex in T-ALL, we used NOTCH1-induced animal models of the disease, as well as human T-ALL samples, and combined locus-specific and global analysis of NOTCH1-driven epigenetic changes. These studies demonstrated that activation of NOTCH1 specifically induces loss of the repressive mark lysine-27 tri-methylation of histone 3 (H3K27me3)4 by antagonizing the activity of the Polycomb Repressive Complex 2 (PRC2) complex. These studies demonstrate a tumor suppressor role for the PRC2 complex in human leukemia and suggest a hitherto unrecognized dynamic interplay between oncogenic NOTCH1 and PRC2 function for the regulation of gene expression and cell transformation.

Relapse-specific mutations in NT5C2 in childhood acute lymphoblastic leukemia

Relapsed childhood acute lymphoblastic leukemia (ALL) carries a poor prognosis, despite intensive retreatment, owing to intrinsic drug resistance. The biological pathways that mediate resistance are unknown. Here, we report the transcriptome profiles of matched diagnosis and relapse bone marrow specimens from ten individuals with pediatric B-lymphoblastic leukemia using RNA sequencing. Transcriptome sequencing identified 20 newly acquired, novel nonsynonymous mutations not present at initial diagnosis, with 2 individuals harboring relapse-specific mutations in the same gene, NT5C2, encoding a 5′-nucleotidase. Full-exon sequencing of NT5C2 was completed in 61 further relapse specimens, identifying additional mutations in 5 cases. Enzymatic analysis of mutant proteins showed that base substitutions conferred increased enzymatic activity and resistance to treatment with nucleoside analog therapies. Clinically, all individuals who harbored NT5C2 mutations relapsed early, within 36 months of initial diagnosis (P = 0.03). These results suggest that mutations in NT5C2 are associated with the outgrowth of drug-resistant clones in ALL.

FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells

Promoters and enhancers establish precise gene transcription patterns. The development of functional approaches for their identification in mammalian cells has been complicated by the size of these genomes. Here we report a high-throughput functional assay for directly identifying active promoter and enhancer elements called FIREWACh (Functional Identification of Regulatory Elements Within Accessible Chromatin), which we used to simultaneously assess over 80,000 DNA fragments derived from nucleosome-free regions within the chromatin of embryonic stem cells (ESCs) and identify 6,364 active regulatory elements. Many of these represent newly discovered ESC-specific enhancers, showing enriched binding-site motifs for ESC-specific transcription factors including SOX2, POU5F1 (OCT4) and KLF4. The application of FIREWACh to additional cultured cell types will facilitate functional annotation of the genome and expand our view of transcriptional network dynamics.

Synthesis, debugging, and effects of synthetic chromosome consolidation: synVI and beyond

INTRODUCTION Total synthesis of designer chromosomes and genomes is a new paradigm for the study of genetics and biological systems. The Sc2.0 project is building a designer yeast genome from scratch to test and extend the limits of our biological knowledge. Here we describe the design, rapid assembly, and characterization of synthetic chromosome VI (synVI). Further, we investigate the phenotypic, transcriptomic, and proteomic consequences associated with consolidation of three synthetic chromosomes–synVI, synIII, and synIXR—into a single poly-synthetic strain. RATIONALE A host of Sc2.0 chromosomes, including synVI, have now been constructed in discrete strains. With debugging steps, where the number of bugs scales with chromosome length, all individual synthetic chromosomes have been shown to power yeast cells to near wild-type (WT) fitness. Testing the effects of Sc2.0 chromosome consolidation to uncover possible synthetic genetic interactions and/or perturbations of native cellular networks as the number of designer changes increases is the next major step for the Sc2.0 project. RESULTS SynVI was rapidly assembled using nine sequential steps of SwAP-In (switching auxotrophies progressively by integration), yielding a ~240-kb synthetic chromosome designed to Sc2.0 specifications. We observed partial silencing of the left- and rightmost genes on synVI, each newly positioned subtelomerically relative to their locations on native VI. This result suggests that consensus core X elements of Sc2.0 universal telomere caps are insufficient to fully buffer telomere position effects. The synVI strain displayed a growth defect characterized by an increased frequency of glycerol-negative colonies. The defect mapped to a synVI design feature in the essential PRE4 gene (YFR050C), encoding the β7 subunit of the 20S proteasome. Recoding 10 codons near the 3′ end of the PRE4 open reading frame (ORF) caused a ~twofold reduction in Pre4 protein level without affecting RNA abundance. Reverting the codons to the WT sequence corrected both the Pre4 protein level and the phenotype. We hypothesize that the formation of a stem loop involving recoded codons underlies reduced Pre4 protein level. Sc2.0 chromosomes (synI to synXVI) are constructed individually in discrete strains and consolidated into poly-synthetic (poly-syn) strains by “endoreduplication intercross.” Consolidation of synVI with synthetic chromosomes III (synIII) and IXR (synIXR) yields a triple-synthetic (triple-syn) strain that is ~6% synthetic overall—with almost 70 kb deleted, including 20 tRNAs, and more than 12 kb recoded. Genome sequencing of double-synthetic (synIII synVI, synIII synIXR, synVI synIXR) and triple-syn (synIII synVI synIXR) cells indicates that suppressor mutations are not required to enable coexistence of Sc2.0 chromosomes. Phenotypic analysis revealed a slightly slower growth rate for the triple-syn strain only; the combined effect of tRNA deletions on different chromosomes might underlie this result. Transcriptome and proteome analyses indicate that cellular networks are largely unperturbed by the existence of multiple synthetic chromosomes in a single cell. However, a second bug on synVI was discovered through proteomic analysis and is associated with alteration of the HIS2 transcription start as a consequence of tRNA deletion and loxPsym site insertion. Despite extensive genetic alterations across 6% of the genome, no major global changes were detected in the poly-syn strain “omics” analyses. CONCLUSION Analyses of phenotypes, transcriptomics, and proteomics of synVI and poly-syn strains reveal, in general, WT cell properties and the existence of rare bugs resulting from genome editing. Deletion of subtelomeres can lead to gene silencing, recoding deep within an ORF can yield a translational defect, and deletion of elements such as tRNA genes can lead to a complex transcriptional output. These results underscore the complementarity of transcriptomics and proteomics to identify bugs, the consequences of designer changes in Sc2.0 chromosomes. The consolidation of Sc2.0 designer chromosomes into a single strain appears to be exceptionally well tolerated by yeast. A predictable exception to this is the deletion of tRNAs, which will be restored on a separate neochromosome to avoid synthetic lethal genetic interactions between deleted tRNA genes as additional synthetic chromosomes are introduced. Debugging synVI and characterization of poly-synthetic yeast cells. (A) The second Sc2.0 chromosome to be constructed, synVI, encodes a “bug” that causes a variable colony size, dubbed a “glycerol-negative growth-suppression defect.” (B) Synonymous changes in the essential PRE4 ORF lead to a reduced protein level, which underlies the growth defect

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

BackgroundPathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.ResultsThe results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer.ConclusionsOur study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation.ReviewersThis article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan.

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

Effects of Nickel Treatment on H3K4 Trimethylation and Gene Expression

Occupational exposure to nickel compounds has been associated with lung and nasal cancers. We have previously shown that exposure of the human lung adenocarcinoma A549 cells to NiCl(2) for 24 hr significantly increased global levels of trimethylated H3K4 (H3K4me3), a transcriptional activating mark that maps to the promoters of transcribed genes. To further understand the potential epigenetic mechanism(s) underlying nickel carcinogenesis, we performed genome-wide mapping of H3K4me3 by chromatin immunoprecipitation and direct genome sequencing (ChIP-seq) and correlated with transcriptome genome-wide mapping of RNA transcripts by massive parallel sequencing of cDNA (RNA-seq). The effect of NiCl(2) treatment on H3K4me3 peaks within 5,000 bp of transcription start sites (TSSs) on a set of genes highly induced by nickel in both A549 cells and human peripheral blood mononuclear cells were analyzed. Nickel exposure increased the level of H3K4 trimethylation in both the promoters and coding regions of several genes including CA9 and NDRG1 that were increased in expression in A549 cells. We have also compared the extent of the H3K4 trimethylation in the absence and presence of formaldehyde crosslinking and observed that crosslinking of chromatin was required to observe H3K4 trimethylation in the coding regions immediately downstream of TSSs of some nickel-induced genes including ADM and IGFBP3. This is the first genome-wide mapping of trimethylated H3K4 in the promoter and coding regions of genes induced after exposure to NiCl(2). This study may provide insights into the epigenetic mechanism(s) underlying the carcinogenicity of nickel compounds.

Combining multiple ChIP-seq peak detection systems using combinatorial fusion

BackgroundDue to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator.ResultsWe define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination.ConclusionOur method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.

Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer

BackgroundOncogenic mechanisms in small-cell lung cancer remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. Nonetheless, additional genomic approaches are warranted, due to the increasing availability of suitable small-cell lung cancer datasets. Gene co-expression network approaches are a recent and promising avenue, since they have been successful in identifying gene modules that drive phenotypic traits in several biological systems, including other cancer types.ResultsWe derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC.ConclusionsSCLC treatment has thus far been limited to chemotherapy and radiation. Our WGCNA analysis identifies SYK both as a candidate biomarker to stratify SCLC patients and as a potential therapeutic target. In summary, WGCNA represents an alternative strategy to large scale sequencing for the identification of potential oncogenic drivers, based on a systems view of signaling networks. This strategy is especially useful in cancer types where no actionable mutations have emerged.

A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.