Daniel Bottomly

Oncogenic CSF3R mutations in chronic neutrophilic leukemia and atypical CML

BACKGROUND The molecular causes of many hematologic cancers remain unclear. Among these cancers are chronic neutrophilic leukemia (CNL) and atypical (BCR-ABL1-negative) chronic myeloid leukemia (CML), both of which are diagnosed on the basis of neoplastic expansion of granulocytic cells and exclusion of genetic drivers that are known to occur in other myeloproliferative neoplasms and myeloproliferative-myelodysplastic overlap neoplasms. METHODS To identify potential genetic drivers in these disorders, we used an integrated approach of deep sequencing coupled with the screening of primary leukemia cells obtained from patients with CNL or atypical CML against panels of tyrosine kinase-specific small interfering RNAs or small-molecule kinase inhibitors. We validated candidate oncogenes using in vitro transformation assays, and drug sensitivities were validated with the use of assays of primary-cell colonies. RESULTS We identified activating mutations in the gene encoding the receptor for colony-stimulating factor 3 (CSF3R) in 16 of 27 patients (59%) with CNL or atypical CML. These mutations segregate within two distinct regions of CSF3R and lead to preferential downstream kinase signaling through SRC family-TNK2 or JAK kinases and differential sensitivity to kinase inhibitors. A patient with CNL carrying a JAK-activating CSF3R mutation had marked clinical improvement after the administration of the JAK1/2 inhibitor ruxolitinib. CONCLUSIONS Mutations in CSF3R are common in patients with CNL or atypical CML and represent a potentially useful criterion for diagnosing these neoplasms. (Funded by the Leukemia and Lymphoma Society and others.).

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, ‘digital mRNA counting’ is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.

Identification of β-catenin binding regions in colon cancer cells using ChIP-Seq

Deregulation of the Wnt/β-catenin signaling pathway is a hallmark of colon cancer. Mutations in the adenomatous polyposis coli (APC) gene occur in the vast majority of colorectal cancers and are an initiating event in cellular transformation. Cells harboring mutant APC contain elevated levels of the β-catenin transcription coactivator in the nucleus which leads to abnormal expression of genes controlled by β-catenin/T-cell factor 4 (TCF4) complexes. Here, we use chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) to identify β-catenin binding regions in HCT116 human colon cancer cells. We localized 2168 β-catenin enriched regions using a concordance approach for integrating the output from multiple peak alignment algorithms. Motif discovery algorithms found a core TCF4 motif (T/A–T/A–C–A–A–A–G), an extended TCF4 motif (A/T/G–C/G–T/A–T/A–C–A–A–A–G) and an AP-1 motif (T–G–A–C/T–T–C–A) to be significantly represented in β-catenin enriched regions. Furthermore, 417 regions contained both TCF4 and AP-1 motifs. Genes associated with TCF4 and AP-1 motifs bound β-catenin, TCF4 and c-Jun in vivo and were activated by Wnt signaling and serum growth factors. Our work provides evidence that Wnt/β-catenin and mitogen signaling pathways intersect directly to regulate a defined set of target genes.

Modeling Host Genetic Regulation of Influenza Pathogenesis in the Collaborative Cross

Genetic variation contributes to host responses and outcomes following infection by influenza A virus or other viral infections. Yet narrow windows of disease symptoms and confounding environmental factors have made it difficult to identify polymorphic genes that contribute to differential disease outcomes in human populations. Therefore, to control for these confounding environmental variables in a system that models the levels of genetic diversity found in outbred populations such as humans, we used incipient lines of the highly genetically diverse Collaborative Cross (CC) recombinant inbred (RI) panel (the pre-CC population) to study how genetic variation impacts influenza associated disease across a genetically diverse population. A wide range of variation in influenza disease related phenotypes including virus replication, virus-induced inflammation, and weight loss was observed. Many of the disease associated phenotypes were correlated, with viral replication and virus-induced inflammation being predictors of virus-induced weight loss. Despite these correlations, pre-CC mice with unique and novel disease phenotype combinations were observed. We also identified sets of transcripts (modules) that were correlated with aspects of disease. In order to identify how host genetic polymorphisms contribute to the observed variation in disease, we conducted quantitative trait loci (QTL) mapping. We identified several QTL contributing to specific aspects of the host response including virus-induced weight loss, titer, pulmonary edema, neutrophil recruitment to the airways, and transcriptional expression. Existing whole-genome sequence data was applied to identify high priority candidate genes within QTL regions. A key host response QTL was located at the site of the known anti-influenza Mx1 gene. We sequenced the coding regions of Mx1 in the eight CC founder strains, and identified a novel Mx1 allele that showed reduced ability to inhibit viral replication, while maintaining protection from weight loss.

Utilizing RNA-Seq data for de novo coexpression network inference

MOTIVATION RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks. RESULTS The RNA-Seq coexpression network displayed scale-free, hierarchical network structure. We detected transcripts groups (modules) with correlated profiles; modules overlap distinct ontology categories. Neuroanatomical data from the Allen Brain Atlas reveal several modules with spatial colocalization. The network was compared with microarray-derived networks; correlations from RNA-Seq data were higher, likely because greater sensitivity and dynamic range. Higher correlations result in higher network connectivity, heterogeneity and centrality. For transcripts present across platforms, network structure appeared largely preserved. From this study, we present the first RNA-Seq data de novo network inference.

Expression quantitative trait loci for extreme host response to influenza A in pre-collaborative cross mice

Outbreaks of influenza occur on a yearly basis, causing a wide range of symptoms across the human population. Although evidence exists that the host response to influenza infection is influenced by genetic differences in the host, this has not been studied in a system with genetic diversity mirroring that of the human population. Here we used mice from 44 influenza-infected pre-Collaborative Cross lines determined to have extreme phenotypes with regard to the host response to influenza A virus infection. Global transcriptome profiling identified 2671 transcripts that were significantly differentially expressed between mice that showed a severe (“high”) and mild (“low”) response to infection. Expression quantitative trait loci mapping was performed on those transcripts that were differentially expressed because of differences in host response phenotype to identify putative regulatory regions potentially controlling their expression. Twenty-one significant expression quantitative trait loci were identified, which allowed direct examination of genes associated with regulation of host response to infection. To perform initial validation of our findings, quantitative polymerase chain reaction was performed in the infected founder strains, and we were able to confirm or partially confirm more than 70% of those tested. In addition, we explored putative causal and reactive (downstream) relationships between the significantly regulated genes and others in the high or low response groups using structural equation modeling. By using systems approaches and a genetically diverse population, we were able to develop a novel framework for identifying the underlying biological subnetworks under host genetic control during influenza virus infection.

Androgen Receptor Promotes Ligand-Independent Prostate Cancer Progression through c-Myc Upregulation

The androgen receptor (AR) is the principal therapeutic target in prostate cancer. For the past 70 years, androgen deprivation therapy (ADT) has been the major therapeutic focus. However, some patients do not benefit, and those tumors that do initially respond to ADT eventually progress. One recently described mechanism of such an effect is growth and survival-promoting effects of the AR that are exerted independently of the AR ligands, testosterone and dihydrotestosterone. However, specific ligand-independent AR target genes that account for this effect were not well characterized. We show here that c-Myc, which is a key mediator of ligand-independent prostate cancer growth, is a key ligand-independent AR target gene. Using microarray analysis, we found that c-Myc and AR expression levels strongly correlated with each other in tumors from patients with castration-resistant prostate cancer (CRPC) progressing despite ADT. We confirmed that AR directly regulates c-Myc transcription in a ligand-independent manner, that AR and c-Myc suppression reduces ligand-independent prostate cancer cell growth, and that ectopic expression of c-Myc attenuates the anti-growth effects of AR suppression. Importantly, treatment with the bromodomain inhibitor JQ1 suppressed c-Myc function and suppressed ligand-independent prostate cancer cell survival. Our results define a new link between two critical proteins in prostate cancer – AR and c-Myc – and demonstrate the potential of AR and c-Myc-directed therapies to improve prostate cancer control.

Genes, Behavior, and Next-Generation RNA Sequencing

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs

BackgroundAllelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.ResultsWe sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 – 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.ConclusionComparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.

A DNA methylation microarray-based study identifies ERG as a gene commonly methylated in prostate cancer

DNA methylation of promoter regions is a common event in prostate cancer, one of the most common cancers in men worldwide. Because prior reports demonstrating that DNA methylation is important in prostate cancer studied a limited number of genes, we systematically quantified the DNA methylation status of 1505 CpG dinucleotides for 807 genes in 78 paraffin-embedded prostate cancer samples and three normal prostate samples. The ERG gene, commonly repressed in prostate cells in the absence of an oncogenic fusion to the TMPRSS2 gene, was one of the most commonly methylated genes, occurring in 74% of prostate cancer specimens. In an independent group of patient samples, we confirmed that ERG DNA methylation was common, occurring in 57% of specimens, and cancer-specific. The ERG promoter is marked by repressive chromatin marks mediated by polycomb proteins in both normal prostate cells and prostate cancer cells, which may explain ERG’s predisposition to DNA methylation and the fact that tumors with ERG DNA methylation were more methylated, in general. These results demonstrate that bead arrays offer a high-throughput method to discover novel genes with promoter DNA methylation such as ERG, whose measurement may improve our ability to more accurately detect prostate cancer.