Full-length isoform concatenation sequencing to resolve cancer transcriptome complexity.

BACKGROUND: Cancers exhibit complex transcriptomes with aberrant splicing that induces isoform-level differential expression compared to non-diseased tissues. Transcriptomic profiling using short-read sequencing has utility in providing a cost-effective approach for evaluating isoform expression, although short-read assembly displays limitations in the accurate inference of full-length transcripts. Long-read RNA sequencing (Iso-Seq), using the Pacific Biosciences (PacBio) platform, can overcome such limitations by providing full-length isoform sequence resolution which requires no read assembly and represents native expressed transcripts. A constraint of the Iso-Seq protocol is due to fewer reads output per instrument run, which, as an example, can consequently affect the detection of lowly expressed transcripts. To address these deficiencies, we developed a concatenation workflow, PacBio Full-Length Isoform Concatemer Sequencing (PB_FLIC-Seq), designed to increase the number of unique, sequenced PacBio long-reads thereby improving overall detection of unique isoforms. In addition, we anticipate that the increase in read depth will help improve the detection of moderate to low-level expressed isoforms. RESULTS: In sequencing a commercial reference (Spike-In RNA Variants; SIRV) with known isoform complexity we demonstrated a 3.4-fold increase in read output per run and improved SIRV recall when using the PB_FLIC-Seq method compared to the same samples processed with the Iso-Seq protocol. We applied this protocol to a translational cancer case, also demonstrating the utility of the PB_FLIC-Seq method for identifying differential full-length isoform expression in a pediatric diffuse midline glioma compared to its adjacent non-malignant tissue. Our data analysis revealed increased expression of extracellular matrix (ECM) genes within the tumor sample, including an isoform of the Secreted Protein Acidic and Cysteine Rich (SPARC) gene that was expressed 11,676-fold higher than in the adjacent non-malignant tissue. Finally, by using the PB_FLIC-Seq method, we detected several cancer-specific novel isoforms. CONCLUSION: This work describes a concatenation-based methodology for increasing the number of sequenced full-length isoform reads on the PacBio platform, yielding improved discovery of expressed isoforms. We applied this workflow to profile the transcriptome of a pediatric diffuse midline glioma and adjacent non-malignant tissue. Our findings of cancer-specific novel isoform expression further highlight the importance of long-read sequencing for characterization of complex tumor transcriptomes.

Pacific Biosciences Fusion and Long Isoform Pipeline for Cancer Transcriptome-Based Resolution of Isoform Complexity.

Genomic profiling using short-read sequencing has utility in detecting disease-associated variation in both DNA and RNA. However, given the frequent occurrence of structural variation in cancer, molecular profiling using long-read sequencing improves the resolution of such events. For example, the Pacific Biosciences long-read RNA-sequencing (Iso-Seq) transcriptome protocol provides full-length isoform characterization, discernment of allelic phasing, and isoform discovery, and identifies expressed fusion partners. The Pacific Biosciences Fusion and Long Isoform Pipeline (PB_FLIP) incorporates a suite of RNA-sequencing software analysis tools and scripts to identify expressed fusion partners and isoforms. In addition, sequencing of a commercial reference (Spike-In RNA Variants) with known isoform complexity was performed and demonstrated high recall of the Iso-Seq and PB_FLIP workflow to benchmark our protocol and analysis performance. This study describes the utility of Iso-Seq and PB_FLIP analysis in improving deconvolution of complex structural variants and isoform detection within an institutional pediatric and adolescent/young adult translational cancer research cohort. The exemplar case studies demonstrate that Iso-Seq and PB_FLIP discover novel expressed fusion partners, resolve complex intragenic alterations, and discriminate between allele-specific expression profiles.

Murine cytomegalovirus promotes renal allograft inflammation via Th1/17 cells and IL-17A.

Human cytomegalovirus (HCMV) infection is associated with renal allograft failure. Allograft damage in animal models is accelerated by CMV-induced T helper 17 (Th17) cell infiltrates. However, the mechanisms whereby CMV promotes Th17 cell-mediated pathological organ inflammation are uncharacterized. Here we demonstrate that murine CMV (MCMV)-induced intragraft Th17 cells have a Th1/17 phenotype co-expressing IFN-γ and/or TNF-α, but only a minority of these cells are MCMV specific. Instead, MCMV promotes intragraft expression of CCL20 and CXCL10, which are associated with recruitment of CCR6+ CXCR3+ Th17 cells. MCMV also enhances Th17 cell infiltrates after ischemia-reperfusion injury, independent of allogeneic responses. Pharmacologic inhibition of the Th17 cell signature cytokine, IL-17A, ameliorates MCMV-associated allograft damage without increasing intragraft viral loads or reducing MCMV-specific Th1 cell infiltrates. Clinically, HCMV DNAemia is associated with higher serum IL-17A among renal transplant patients with acute rejection, linking HCMV reactivation with Th17 cell cytokine expression. In summary, CMV promotes allograft damage via cytokine-mediated Th1/17 cell recruitment, which may be pharmacologically targeted to mitigate graft injury while preserving antiviral T cell immunity.

Discovery of clinically relevant fusions in pediatric cancer.

BACKGROUND: Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. RESULTS: Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient's medical record, both known and novel fusions provided medically meaningful information. CONCLUSIONS: The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Defining the AHR-regulated transcriptome in NK cells reveals gene expression programs relevant to development and function.

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that regulates cellular processes in cancer and immunity, including innate immune cell development and effector function. However, the transcriptional repertoire through which AHR mediates these effects remains largely unexplored. To elucidate the transcriptional elements directly regulated by AHR in natural killer (NK) cells, we performed RNA and chromatin immunoprecipitation sequencing on NK cells exposed to AHR agonist or antagonist. We show that mature peripheral blood NK cells lack AHR, but its expression is induced by Stat3 during interleukin-21-driven activation and proliferation, coincident with increased NCAM1 (CD56) expression resulting in a CD56bright phenotype. Compared with control conditions, NK cells expanded in the presence of the AHR antagonist, StemRegenin-1, were unaffected in proliferation or cytotoxicity, had no increase in NCAM1 transcription, and maintained the CD56dim phenotype. However, it showed altered expression of 1004 genes including those strongly associated with signaling pathways. In contrast, NK cells expanded in the presence of the AHR agonist, kynurenine, showed decreased cytotoxicity and altered expression of 97 genes including those strongly associated with oxidative stress and cellular metabolism. By overlaying these differentially expressed genes with AHR chromatin binding, we identified 160 genes directly regulated by AHR, including hallmark AHR targets AHRR and CYP1B1 and known regulators of phenotype, development, metabolism, and function such as NCAM1, KIT, NQO1, and TXN. In summary, we define the AHR transcriptome in NK cells, propose a model of AHR and Stat3 coregulation, and identify potential pathways that may be targeted to overcome AHR-mediated immune suppression.

Evidence of pioneer factor activity of an oncogenic fusion transcription factor.

Recent characterizations of pioneer transcription factors provide insights into their structures and patterns of chromatin recognition associated with their roles in cell fate commitment and transformation. Intersecting with these basic science concepts, identification of pioneer factors (PFs) fused together as driver translocations in childhood cancers raises questions of whether these fusions retain the fundamental ability to invade repressed chromatin, consistent with their monomeric PF constituents. This study defines the cellular and chromatin localization of PAX3-FOXO1, an oncogenic driver of childhood rhabdomyosarcoma (RMS), derived from a fusion of PFs. To quantitatively define its chromatin-targeting functions and capacity to drive epigenetic reprogramming, we developed a ChIP-seq workflow with per-cell normalization (pc-ChIP-seq). Our quantitative localization studies address structural variation in RMS genomes and reveal insights into inactive chromatin localization of PAX3-FOXO1. Taken together, our studies are consistent with pioneer function for a driver oncoprotein in RMS, with repressed chromatin binding and nucleosome-motif targeting.

Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population.

BACKGROUND: The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of "silent" genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking. RESULTS: We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population. CONCLUSIONS: These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a "Structural Predictivity Index" (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.

Samovar: Single-Sample Mosaic Single-Nucleotide Variant Calling with Linked Reads.

Linked-read sequencing enables greatly improves haplotype assembly over standard paired-end analysis. The detection of mosaic single-nucleotide variants benefits from haplotype assembly when the model is informed by the mapping between constituent reads and linked reads. Samovar evaluates haplotype-discordant reads identified through linked-read sequencing, thus enabling phasing and mosaic variant detection across the entire genome. Samovar trains a random forest model to score candidate sites using a dataset that considers read quality, phasing, and linked-read characteristics. Samovar calls mosaic single-nucleotide variants (SNVs) within a single sample with accuracy comparable with what previously required trios or matched tumor/normal pairs and outperforms single-sample mosaic variant callers at minor allele frequency 5%-50% with at least 30X coverage. Samovar finds somatic variants in both tumor and normal whole-genome sequencing from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.

AR-13, a Celecoxib Derivative, Directly Kills Francisella In Vitro and Aids Clearance and Mouse Survival In Vivo.

Francisella tularensis (F. tularensis) is the causative agent of tularemia and is classified as a Tier 1 select agent. No licensed vaccine is currently available in the United States and treatment of tularemia is confined to few antibiotics. In this study, we demonstrate that AR-13, a derivative of the cyclooxygenase-2 inhibitor celecoxib, exhibits direct in vitro bactericidal killing activity against Francisella including a type A strain of F. tularensis (SchuS4) and the live vaccine strain (LVS), as well as toward the intracellular proliferation of LVS in macrophages, without causing significant host cell toxicity. Identification of an AR-13-resistant isolate indicates that this compound has an intracellular target(s) and that efflux pumps can mediate AR-13 resistance. In the mouse model of tularemia, AR-13 treatment protected 50% of the mice from lethal LVS infection and prolonged survival time from a lethal dose of F. tularensis SchuS4. Combination of AR-13 with a sub-optimal dose of gentamicin protected 60% of F. tularensis SchuS4-infected mice from death. Taken together, these data support the translational potential of AR-13 as a lead compound for the further development of new anti-Francisella agents.

Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics.

While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of these data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/.