Structure and transcription of integrated HPV DNA in vulvar carcinomas.

HPV infections are associated with a fraction of vulvar cancers. Through hybridization capture and DNA sequencing, HPV DNA was detected in five of thirteen vulvar cancers. HPV16 DNA was integrated into human DNA in three of the five. The insertions were in introns of human NCKAP1, C5orf67, and LRP1B. Integrations in NCKAP1 and C5orf67 were flanked by short direct repeats in the human DNA, consistent with HPV DNA insertions at sites of abortive, staggered, endonucleolytic incisions. The insertion in C5orf67 was present as a 36 kbp, human-HPV-hetero-catemeric DNA as either an extrachromosomal circle or a tandem repeat within the human genome. The human circularization/repeat junction was defined at single nucleotide resolution. The integrated viral DNA segments all retained an intact upstream regulatory region and the adjacent viral E6 and E7 oncogenes. RNA sequencing revealed that the only HPV genes consistently transcribed from the integrated viral DNAs were E7 and E6*I. The other two HPV DNA+ tumors had coinfections, but no evidence for integration. HPV-positive and HPV-negative vulvar cancers exhibited contrasting human, global gene expression patterns partially overlapping with previously observed differences between HPV-positive and HPV-negative cervical and oropharyngeal cancers. A substantial fraction of the differentially expressed genes involved immune system function. Thus, transcription and HPV DNA integration in vulvar cancers resemble those in other HPV-positive cancers. This study emphasizes the power of hybridization capture coupled with DNA and RNA sequencing to identify a broad spectrum of HPV types, determine human genome integration status of viral DNAs, and elucidate their structures.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution.

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

De novo detection of somatic variants in long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, possibly leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), copy-number alterations (CNAs), and gene fusions to reconstruct the tumor clonal heterogeneity. For SNV calling, LongSom distinguishes somatic SNVs from germline polymorphisms by reannotating marker gene expression-based cell types using called variants and applying strict filters. Applying LongSom to ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against single-cell and bulk panel DNA-seq data and could not be detected with short-read (SR) scRNA-seq. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo SNVs, CNAs, and fusions detection, thus enabling the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector.

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival.

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Multiplexed mRNA assembly into ribonucleoprotein particles plays an operon-like role in the control of yeast cell physiology.

Prokaryotes utilize polycistronic messages (operons) to co-translate proteins involved in the same biological processes. Whether eukaryotes achieve similar regulation by selectively assembling and translating monocistronic messages derived from different chromosomes is unknown. We employed transcript-specific RNA pulldowns and RNA-seq/RT-PCR to identify yeast mRNAs that co-precipitate as ribonucleoprotein (RNP) complexes. Consistent with the hypothesis of eukaryotic RNA operons, mRNAs encoding components of the mating pathway, heat shock proteins, and mitochondrial outer membrane proteins multiplex in trans, forming discrete messenger ribonucleoprotein (mRNP) complexes (called transperons). Chromatin capture and allele tagging experiments reveal that genes encoding multiplexed mRNAs physically interact; thus, RNA assembly may result from co-regulated gene expression. Transperon assembly and function depends upon histone H4, and its depletion leads to defects in RNA multiplexing, decreased pheromone responsiveness and mating, and increased heat shock sensitivity. We propose that intergenic associations and non-canonical histone H4 functions contribute to transperon formation in eukaryotic cells and regulate cell physiology.

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

PURPOSE: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. METHODS: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

von Willebrand factor D and EGF domains is an evolutionarily conserved and required feature of blastemas capable of multitissue appendage regeneration.

Regenerative ability varies tremendously across species. A common feature of regeneration of appendages such as limbs, fins, antlers, and tails is the formation of a blastema-a transient structure that houses a pool of progenitor cells that can regenerate the missing tissue. We have identified the expression of von Willebrand factor D and EGF domains (vwde) as a common feature of blastemas capable of regenerating limbs and fins in a variety of highly regenerative species, including axolotl (Ambystoma mexicanum), lungfish (Lepidosiren paradoxa), and Polpyterus (Polypterus senegalus). Further, vwde expression is tightly linked to the ability to regenerate appendages in Xenopus laevis. Functional experiments demonstrate a requirement for vwde in regeneration and indicate that Vwde is a potent growth factor in the blastema. These data identify a key role for vwde in regenerating blastemas and underscore the power of an evolutionarily informed approach for identifying conserved genetic components of regeneration.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods.

BACKGROUND: Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly. RESULTS: We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes. CONCLUSION: The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Transcriptomic landscape of the blastema niche in regenerating adult axolotl limbs at single-cell resolution.

Regeneration of complex multi-tissue structures, such as limbs, requires the coordinated effort of multiple cell types. In axolotl limb regeneration, the wound epidermis and blastema have been extensively studied via histology, grafting, and bulk-tissue RNA-sequencing. However, defining the contributions of these tissues is hindered due to limited information regarding the molecular identity of the cell types in regenerating limbs. Here we report unbiased single-cell RNA-sequencing on over 25,000 cells from axolotl limbs and identify a plethora of cellular diversity within epidermal, mesenchymal, and hematopoietic lineages in homeostatic and regenerating limbs. We identify regeneration-induced genes, develop putative trajectories for blastema cell differentiation, and propose the molecular identity of fibroblast-like blastema progenitor cells. This work will enable application of molecular techniques to assess the contribution of these populations to limb regeneration. Overall, these data allow for establishment of a putative framework for adult axolotl limb regeneration.

SynerClust: a highly scalable, synteny-aware orthologue clustering tool.

Accurate orthologue identification is a vital component of bacterial comparative genomic studies, but many popular sequence-similarity-based approaches do not scale well to the large numbers of genomes that are now generated routinely. Furthermore, most approaches do not take gene synteny into account, which is useful information for disentangling paralogues. Here, we present SynerClust, a user-friendly synteny-aware tool based on synergy that can process thousands of genomes. SynerClust was designed to analyse genomes with high levels of local synteny, particularly prokaryotes, which have operon structure. SynerClust's run-time is optimized by selecting cluster representatives at each node in the phylogeny; thus, avoiding the need for exhaustive pairwise similarity searches. In benchmarking against Roary, Hieranoid2, PanX and Reciprocal Best Hit, SynerClust was able to more completely identify sets of core genes for datasets that included diverse strains, while using substantially less memory, and with scalability comparable to the fastest tools. Due to its scalability, ease of installation and use, and suitability for a variety of computing environments, orthogroup clustering using SynerClust will enable many large-scale prokaryotic comparative genomics efforts.

Erratum: The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation.

This corrects the article DOI: 10.1038/nature24029.

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation.

Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU-NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces.

Advances in Decoding Axolotl Limb Regeneration.

Humans and other mammals are limited in their natural abilities to regenerate lost body parts. By contrast, many salamanders are highly regenerative and can spontaneously replace lost limbs even as adults. Because salamander limbs are anatomically similar to human limbs, knowing how they regenerate should provide important clues for regenerative medicine. Although interest in understanding the mechanics of this process has never wavered, until recently researchers have been vexed by seemingly impenetrable logistics of working with these creatures at a molecular level. Chief among the problems has been the very large size of salamander genomes, and not a single salamander genome has been fully sequenced to date. Recently the enormous gap in sequence information has been bridged by approaches that leverage mRNA as the starting point. Together with functional experimentation, these data are rapidly enabling researchers to finally uncover the molecular mechanisms underpinning the astonishing biological process of limb regeneration.

Loggerhead sea turtle embryos (Caretta caretta) regulate expression of stress response and developmental genes when exposed to a biologically realistic heat stress.

Oviparous reptile embryos are expected to breach their critical thermal maxima if temperatures reach those predicted under current climate change models due to the lack of the maternal buffering processes and parental care. Heat-shock proteins (HSPs) are integral in the molecular response to thermal stress, and their expression is heritable, but the roles of other candidate families such as the heat-shock factors (HSFs) have not been determined in reptiles. Here, we subject embryonic sea turtles (Caretta caretta) to a biologically realistic thermal stress and employ de novo transcriptomic profiling of brain tissue to investigate the underlying molecular response. From a reference transcriptome of 302 293 transcripts, 179 were identified as differentially expressed between treatments. As anticipated, genes enriched in the heat-shock treatment were primarily associated with the Hsp families, or were genes whose products play similar protein editing and chaperone functions (e.g. bag3, MYOC and serpinh1). Unexpectedly, genes encoding the HSFs were not significantly upregulated under thermal stress, indicating their presence in unstressed cells in an inactive state. Genes that were downregulated under thermal stress were less well functionally defined but were associated with stress response, development and cellular organization, suggesting that developmental processes may be compromised at realistically high temperatures. These results confirm that genes from the Hsp families play vital roles in the thermal tolerance of developing reptile embryos and, in addition with a number of other genes, should be targets for evaluating the capacity of oviparous reptiles to respond adaptively to the effects of climate change.

A Tissue-Mapped Axolotl De Novo Transcriptome Enables Identification of Limb Regeneration Factors.

Mammals have extremely limited regenerative capabilities; however, axolotls are profoundly regenerative and can replace entire limbs. The mechanisms underlying limb regeneration remain poorly understood, partly because the enormous and incompletely sequenced genomes of axolotls have hindered the study of genes facilitating regeneration. We assembled and annotated a de novo transcriptome using RNA-sequencing profiles for a broad spectrum of tissues that is estimated to have near-complete sequence information for 88% of axolotl genes. We devised expression analyses that identified the axolotl orthologs of cirbp and kazald1 as highly expressed and enriched in blastemas. Using morpholino anti-sense oligonucleotides, we find evidence that cirbp plays a cytoprotective role during limb regeneration whereas manipulation of kazald1 expression disrupts regeneration. Our transcriptome and annotation resources greatly complement previous transcriptomic studies and will be a valuable resource for future research in regenerative biology.

The liver transcriptome of suckermouth armoured catfish (Pterygoplichthys anisitsi, Loricariidae): Identification of expansions in defensome gene families.

Pterygoplichthys is a genus of related suckermouth armoured catfishes native to South America, which have invaded tropical and subtropical regions worldwide. Physiological features, including an augmented resistance to organic xenobiotics, may have aided their settlement in foreign habitats. The liver transcriptome of Pterygoplichthys anisitsi was sequenced and used to characterize the diversity of mRNAs potentially involved in the responses to natural and anthropogenic chemicals. In total, 66,642 transcripts were assembled. Among the identified defensome genes, cytochromes P450 (CYP) were the most abundant, followed by sulfotransferases (SULT), nuclear receptors (NR) and ATP binding cassette transporters (ABC). A novel expansion in the CYP2Y subfamily was identified, as well as an independent expansion of the CYP2AAs. Two expansions were also observed among SULT1. Thirty-two transcripts were classified into twelve subfamilies of NR, while 21 encoded ABC transporters. The diversity of defensome transcripts sequenced herein could contribute to this species' resistance to organic xenobiotics.

A Regression-Based Analysis of Ribosome-Profiling Data Reveals a Conserved Complexity to Mammalian Translation.

A fundamental goal of genomics is to identify the complete set of expressed proteins. Automated annotation strategies rely on assumptions about protein-coding sequences (CDSs), e.g., they are conserved, do not overlap, and exceed a minimum length. However, an increasing number of newly discovered proteins violate these rules. Here we present an experimental and analytical framework, based on ribosome profiling and linear regression, for systematic identification and quantification of translation. Application of this approach to lipopolysaccharide-stimulated mouse dendritic cells and HCMV-infected human fibroblasts identifies thousands of novel CDSs, including micropeptides and variants of known proteins, that bear the hallmarks of canonical translation and exhibit translation levels and dynamics comparable to that of annotated CDSs. Remarkably, many translation events are identified in both mouse and human cells even when the peptide sequence is not conserved. Our work thus reveals an unexpected complexity to mammalian translation suited to provide both conserved regulatory or protein-based functions.