Jeffrey Rosenfeld

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Introduction Plummeting sequencing costs have led to a great increase in the number of personal genomes. Interpreting the large number of variants in them, particularly in noncoding regions, is a current challenge. This is especially the case for somatic variants in cancer genomes, a large proportion of which are noncoding. Prioritization of candidate noncoding cancer drivers based on patterns of selection. (Step 1) Filter somatic variants to exclude 1000 Genomes polymorphisms; (2) retain variants in noncoding annotations; (3) retain those in “sensitive” regions; (4) prioritize those disrupting a transcription-factor binding motif and (5) residing near the center of a biological network; (6) prioritize ones in annotation blocks mutated in multiple cancer samples. Methods We investigated patterns of selection in DNA elements from the ENCODE project using the full spectrum of variants from 1092 individuals in the 1000 Genomes Project (Phase 1), including single-nucleotide variants (SNVs), short insertions and deletions (indels), and structural variants (SVs). Although we analyzed broad functional annotations, such as all transcription-factor binding sites, we focused more on highly specific categories such as distal binding sites of factor ZNF274. The greater statistical power of the Phase 1 data set compared with earlier ones allowed us to differentiate the selective constraints on these categories. We also used connectivity information between elements from protein-protein-interaction and regulatory networks. We integrated all the information on selection to develop a workflow (FunSeq) to prioritize personal-genome variants on the basis of their deleterious impact. As a proof of principle, we experimentally validated and characterized a few candidate variants. Results We identified a specific subgroup of noncoding categories with almost as much selective constraint as coding genes: “ultrasensitive” regions. We also uncovered a number of clear patterns of selection. Elements more consistently active across tissues and both maternal and paternal alleles (in terms of allele-specific activity) are under stronger selection. Variants disruptive because of mechanistic effects on transcription-factor binding (i.e. “motif-breakers”) are selected against. Higher network connectivity (i.e. for hubs) is associated with higher constraint. Additionally, many hub promoters and regulatory elements show evidence of recent positive selection. Overall, indels and SVs follow the same pattern as SNVs; however, there are notable exceptions. For instance, enhancers are enriched for SVs formed by nonallelic homologous recombination. We integrated these patterns of selection into the FunSeq prioritization workflow and applied it to cancer variants, because they present a strong contrast to inherited polymorphisms. In particular, application to ~90 cancer genomes (breast, prostate and medulloblastoma) reveals nearly a hundred candidate noncoding drivers. Discussion Our approach can be readily used to prioritize variants in cancer and is immediately applicable in a precision-medicine context. It can be further improved by incorporation of larger-scale population sequencing, better annotations, and expression data from large cohorts. Identifying Important Identifiers Each of us has millions of sequence variations in our genomes. Signatures of purifying or negative selection should help identify which of those variations is functionally important. Khurana et al. (1235587) used sequence polymorphisms from 1092 humans across 14 populations to identify patterns of selection, especially in noncoding regulatory regions. Noncoding regions under very strong negative selection included binding sites of some chromatin and general transcription factors (TFs) and core motifs of some important TF families. Positive selection in TF binding sites tended to occur in network hub promoters. Many recurrent somatic cancer variants occurred in noncoding regulatory regions and thus might indicate mutations that drive cancer. Regions under strong selection in the human genome identify noncoding regulatory elements with possible roles in disease. Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study.

High-throughput RNA sequencing (RNA-seq) greatly expands the potential for genomics discoveries, but the wide variety of platforms, protocols and performance capabilitites has created the need for comprehensive reference data. Here we describe the Association of Biomolecular Resource Facilities next-generation sequencing (ABRF-NGS) study on RNA-seq. We carried out replicate experiments across 15 laboratory sites using reference RNA standards to test four protocols (poly-A–selected, ribo-depleted, size-selected and degraded) on five sequencing platforms (Illumina HiSeq, Life Technologies PGM and Proton, Pacific Biosciences RS and Roche 454). The results show high intraplatform (Spearman rank R > 0.86) and inter-platform (R > 0.83) concordance for expression measures across the deep-count platforms, but highly variable efficiency and cost for splice junction and variant detection between all platforms. For intact RNA, gene expression profiles from rRNA-depletion and poly-A enrichment are similar. In addition, rRNA depletion enables effective analysis of degraded RNA samples. This study provides a broad foundation for cross-platform standardization, evaluation and improvement of RNA-seq.

Implication of a Rare Deletion at Distal 16p11.2 in Schizophrenia

CONTEXTnLarge genomic copy number variations have been implicated as strong risk factors for schizophrenia. However, the rarity of these events has created challenges for the identification of further pathogenic loci, and extremely large samples are required to provide convincing replication.nnnOBJECTIVEnTo detect novel copy number variations that increase the susceptibility to schizophrenia by using 2 ethnically homogeneous discovery cohorts and replication in large samples.nnnDESIGNnGenetic association study of microarray data.nnnSETTINGnSamples of DNA were collected at 9 sites from different countries.nnnPARTICIPANTSnTwo discovery cohorts consisted of 790 cases with schizophrenia and schizoaffective disorder and 1347 controls of Ashkenazi Jewish descent and 662 parent-offspring trios from Bulgaria, of which the offspring had schizophrenia or schizoaffective disorder. Replication data sets consisted of 12,398 cases and 17,945 controls.nnnMAIN OUTCOME MEASURESnStatistically increased rate of specific copy number variations in cases vs controls.nnnRESULTSnOne novel locus was implicated: a deletion at distal 16p11.2, which does not overlap the proximal 16p11.2 locus previously reported in schizophrenia and autism. Deletions at this locus were found in 13 of 13,850 cases (0.094%) and 3 of 19,954 controls (0.015%) (odds ratio, 6.25 [95% CI, 1.78-21.93]; P = .001, Fisher exact test).nnnCONCLUSIONSnDeletions at distal 16p11.2 have been previously implicated in developmental delay and obesity. The region contains 9 genes, several of which are implicated in neurological diseases, regulation of body weight, and glucose homeostasis. A telomeric extension of the deletion, observed in about half the cases but no controls, potentially implicates an additional 8 genes. Our findings add a new locus to the list of copy number variations that increase the risk for development of schizophrenia.

Implications for health and disease in the genetic signature of the Ashkenazi Jewish population.

BackgroundRelatively small, reproductively isolated populations with reduced genetic diversity may have advantages for genomewide association mapping in disease genetics. The Ashkenazi Jewish population represents a unique population for study based on its recent (< 1,000 year) history of a limited number of founders, population bottlenecks and tradition of marriage within the community. We genotyped more than 1,300 Ashkenazi Jewish healthy volunteers from the Hebrew University Genetic Resource with the Illumina HumanOmni1-Quad platform. Comparison of the genotyping data with that of neighboring European and Asian populations enabled the Ashkenazi Jewish-specific component of the variance to be characterized with respect to disease-relevant alleles and pathways.ResultsUsing clustering, principal components, and pairwise genetic distance as converging approaches, we identified an Ashkenazi Jewish-specific genetic signature that differentiated these subjects from both European and Middle Eastern samples. Most notably, gene ontology analysis of the Ashkenazi Jewish genetic signature revealed an enrichment of genes functioning in transepithelial chloride transport, such as CFTR, and in equilibrioception, potentially shedding light on cystic fibrosis, Usher syndrome and other diseases over-represented in the Ashkenazi Jewish population. Results also impact risk profiles for autoimmune and metabolic disorders in this population. Finally, residual intra-Ashkenazi population structure was minimal, primarily determined by class 1 MHC alleles, and not related to host country of origin.ConclusionsThe Ashkenazi Jewish population is of potential utility in disease-mapping studies due to its relative homogeneity and distinct genomic signature. Results suggest that Ashkenazi-associated disease genes may be components of population-specific genomic differences in key functional pathways.

Novel multi-nucleotide polymorphisms in the human genome characterized by whole genome and exome sequencing

Genomic sequence comparisons between individuals are usually restricted to the analysis of single nucleotide polymorphisms (SNPs). While the interrogation of SNPs is efficient, they are not the only form of divergence between genomes. In this report, we expand the scope of polymorphism detection by investigating the occurrence of double nucleotide polymorphisms (DNPs) and triple nucleotide polymorphisms (TNPs), in which two or three consecutive nucleotides are altered compared to the reference sequence. We have found such DNPs and TNPs throughout two complete genomes and eight exomes. Within exons, these novel polymorphisms are over-represented amongst protein-altering variants; nearly all DNPs and TNPs result in a change in amino acid sequence and, in some cases, two adjacent amino acids are changed. DNPs and TNPs represent a potentially important new source of genetic variation which may underlie human disease and they should be included in future medical genetics studies. As a confirmation of the damaging nature of xNPs, we have identified changes in the exome of a glioblastoma cell line that are important in glioblastoma pathogenesis. We have found a TNP causing a single amino acid change in LAMC2 and a TNP causing a truncation of HUWE1.

The impact of read length on quantification of differentially expressed genes and splice junction detection

BackgroundThe initial next-generation sequencing technologies produced reads of 25 or 36 bp, and only from a single-end of the library sequence. Currently, it is possible to reliably produce 300 bp paired-end sequences for RNA expression analysis. While read lengths have consistently increased, people have assumed that longer reads are more informative and that paired-end reads produce better results than single-end reads. We used paired-end 101 bp reads and trimmed them to simulate different read lengths, and also separated the pairs to produce single-end reads. For each read length and paired status, we evaluated differential expression levels between two standard samples and compared the results to those obtained by qPCR.ResultsWe found that, with the exception of 25 bp reads, there is little difference for the detection of differential expression regardless of the read length. Once single-end reads are at a length of 50 bp, the results do not change substantially for any level up to, and including, 100 bp paired-end. However, splice junction detection significantly improves as the read length increases with 100 bp paired-end showing the best performance. We performed the same analysis on two ENCODE samples and found consistent results confirming that our conclusions have broad application.ConclusionsA researcher could save substantial resources by using 50 bp single-end reads for differential expression analysis instead of using longer reads. However, splicing detection is unquestionably improved by paired-end and longer reads. Therefore, an appropriate read length should be used based on the final goal of the study.

Patient-Derived Xenograft Models of Non-Small Cell Lung Cancer and Their Potential Utility in Personalized Medicine

Traditional preclinical studies of cancer therapeutics have relied on the use of established human cell lines that have been adapted to grow in the laboratory and, therefore, may deviate from the cancer they were meant to represent. With the emphasis of cancer drug development shifting from non-specific cytotoxic agents to rationally designed molecularly targeted therapies or immunotherapy comes the need for better models with predictive value regarding therapeutic activity and response in clinical trials. Recently, the diversity and accessibility of immunodeficient mouse strains has greatly enhanced the production and utility of patient-derived xenograft (PDX) models for many tumor types, including non-small cell lung cancer (NSCLC). Combined with next-generation sequencing, NSCLC PDX mouse models offer an exciting tool for drug development and for studying targeted therapies while utilizing patient samples with the hope of eventually aiding in clinical decision-making. Here, we describe NSCLC PDX mouse models generated by us and others, their ability to reflect the parental tumors’ histomorphological characteristics, as well as the effect of clonal selection and evolution on maintaining genomic integrity in low-passage PDXs compared to the donor tissue. We also raise vital questions regarding the practical utility of PDX and humanized PDX models in predicting patient response to therapy and make recommendations for addressing those questions. Once collaborations and standardized xenotransplantation and data management methods are established, NSCLC PDX mouse models have the potential to be universal and invaluable as a preclinical tool that guides clinical trials and standard therapeutic decisions.

High throughput sequencing analysis of Trypanosoma brucei DRBD3/PTB1-bound mRNAs.

Trypanosomes are early-branched eukaryotes that show an unusual dependence on post-transcriptional mechanisms to regulate gene expression. RNA-binding proteins are crucial in controlling mRNA fate in these organisms, but their RNA substrates remain largely unknown. Here we have analyzed on a global scale the mRNAs associated with the Trypanosoma brucei RNA-binding protein DRBD3/PTB1, by capturing ribonucleoprotein complexes using UV cross-linking and subsequent immunoprecipitation. DRBD3/PTB1 associates with many transcripts encoding ribosomal proteins and translation factors. Consequently, silencing of DRBD3/PTB1 expression altered the protein synthesis rate. DRBD3/PTB1 also binds to mRNAs encoding the enzymes required to obtain energy through the oxidation of proline to succinate. We hypothesize that DRBD3/PTB1 is a key player in RNA regulon-based gene control influencing protein synthesis in trypanosomes.

An essential domain of an early-diverged RNA polymerase II functions to accurately decode a primitive chromatin landscape

Abstract A unique feature of RNA polymerase II (RNA pol II) is its long C-terminal extension, called the carboxy-terminal domain (CTD). The well-studied eukaryotes possess a tandemly repeated 7-amino-acid sequence, called the canonical CTD, which orchestrates various steps in mRNA synthesis. Many eukaryotes possess a CTD devoid of repeats, appropriately called a non-canonical CTD, which performs completely unknown functions. Trypanosoma brucei, the etiologic agent of African Sleeping Sickness, deploys an RNA pol II that contains a non-canonical CTD to accomplish an unusual transcriptional program; all protein-coding genes are transcribed as part of a polygenic precursor mRNA (pre-mRNA) that is initiated within a several-kilobase-long region, called the transcription start site (TSS), which is upstream of the first protein-coding gene in the polygenic array. In this report, we show that the non-canonical CTD of T. brucei RNA pol II is important for normal protein-coding gene expression, likely directing RNA pol II to the TSSs within the genome. Our work reveals the presence of a primordial CTD code within eukarya and indicates that proper recognition of the chromatin landscape is a central function of this RNA pol II-distinguishing domain.

Insect genome content phylogeny and functional annotation of core insect genomes

Twenty-one fully sequenced and well annotated insect genomes were examined for genome content in a phylogenetic context. Gene presence/absence matrices and phylogenetic trees were constructed using several phylogenetic criteria. The role of e-value on phylogenetic analysis and genome content characterization is examined using scaled e-value cutoffs and a single linkage clustering approach to orthology determination. Previous studies have focused on the role of gene loss in terminals in the insect tree of life. The present study examines several common ancestral nodes in the insect tree. We suggest that the common ancestors of major insect groups like Diptera, Hymenoptera, Hemiptera and Holometabola experience more gene gain than gene loss. This suggests that as major insect groups arose, their genomic repertoire expanded through gene duplication (segmental duplications), followed by contraction by gene loss in specific terminal lineages. In addition, we examine the functional significance of the loss and gain of genes in the divergence of some of the major insect groups.