Virome Data Explorer: A web resource to longitudinally explore respiratory viral infections, their interactions with other pathogens and host transcriptomic changes in over 100 people.

Viral respiratory infections are an important public health concern due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial coinfections. To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. We integrated the molecular characterization of 800+ nasopharyngeal samples with clinical data from 104 participants. Transcriptomic data enabled the identification of respiratory pathogens in nasopharyngeal samples, the characterization of markers of immune response, the identification of signatures associated with symptom severity, individual viruses, and bacterial coinfections. Specific results include a rapid restoration of baseline conditions after infection, significant transcriptomic differences between symptomatic and asymptomatic infections, and qualitatively similar responses across different viruses. We created an interactive computational resource (Virome Data Explorer) to facilitate access to the data and visualization of analytical results.

Benchmarking of microbiome detection tools on RNA-seq synthetic databases according to diverse conditions.

Motivation: Here, we performed a benchmarking analysis of five tools for microbe sequence detection using transcriptomics data (Kraken2, MetaPhlAn2, PathSeq, DRAC and Pandora). We built a synthetic database mimicking real-world structure with tuned conditions accounting for microbe species prevalence, base calling quality and sequence length. Sensitivity and positive predictive value (PPV) parameters, as well as computational requirements, were used for tool ranking. Results: GATK PathSeq showed the highest sensitivity on average and across all scenarios considered. However, the main drawback of this tool was its slowness. Kraken2 was the fastest tool and displayed the second-best sensitivity, though with large variance depending on the species to be classified. There was no significant difference for the other three algorithms sensitivity. The sensitivity of MetaPhlAn2 and Pandora was affected by sequence number and DRAC by sequence quality and length. Results from this study support the use of Kraken2 for routine microbiome profiling based on its competitive sensitivity and runtime performance. Nonetheless, we strongly endorse to complement it by combining with MetaPhlAn2 for thorough taxonomic analyses. Availability and implementation: https://github.com/fjuradorueda/MIME/ and https://github.com/lola4/DRAC/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Identification of relevant genetic alterations in cancer using topological data analysis.

Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy.

Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.

Clonal evolution of glioblastoma under therapy.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. To better understand how GBM evolves, we analyzed longitudinal genomic and transcriptomic data from 114 patients. The analysis shows a highly branched evolutionary pattern in which 63% of patients experience expression-based subtype changes. The branching pattern, together with estimates of evolutionary rate, suggests that relapse-associated clones typically existed years before diagnosis. Fifteen percent of tumors present hypermutation at relapse in highly expressed genes, with a clear mutational signature. We find that 11% of recurrence tumors harbor mutations in LTBP4, which encodes a protein binding to TGF-ß. Silencing LTBP4 in GBM cells leads to suppression of TGF-ß activity and decreased cell proliferation. In recurrent GBM with wild-type IDH1, high LTBP4 expression is associated with worse prognosis, highlighting the TGF-ß pathway as a potential therapeutic target in GBM.

De novo transcriptome reconstruction and annotation of the Egyptian rousette bat.

BACKGROUND: The Egyptian Rousette bat (Rousettus aegyptiacus), a common fruit bat species found throughout Africa and the Middle East, was recently identified as a natural reservoir host of Marburg virus. With Ebola virus, Marburg virus is a member of the family Filoviridae that causes severe hemorrhagic fever disease in humans and nonhuman primates, but results in little to no pathological consequences in bats. Understanding host-pathogen interactions within reservoir host species and how it differs from hosts that experience severe disease is an important aspect of evaluating viral pathogenesis and developing novel therapeutics and methods of prevention. RESULTS: Progress in studying bat reservoir host responses to virus infection is hampered by the lack of host-specific reagents required for immunological studies. In order to establish a basis for the design of reagents, we sequenced, assembled, and annotated the R. aegyptiacus transcriptome. We performed de novo transcriptome assembly using deep RNA sequencing data from 11 distinct tissues from one male and one female bat. We observed high similarity between this transcriptome and those available from other bat species. Gene expression analysis demonstrated clustering of expression profiles by tissue, where we also identified enrichment of tissue-specific gene ontology terms. In addition, we identified and experimentally validated the expression of novel coding transcripts that may be specific to this species. CONCLUSION: We comprehensively characterized the R. aegyptiacus transcriptome de novo. This transcriptome will be an important resource for understanding bat immunology, physiology, disease pathogenesis, and virus transmission.

Designing and Interpreting Limiting Dilution Assays: General Principles and Applications to the Latent Reservoir for Human Immunodeficiency Virus-1.

Limiting dilution assays are widely used in infectious disease research. These assays are crucial for current human immunodeficiency virus (HIV)-1 cure research in particular. In this study, we offer new tools to help investigators design and analyze dilution assays based on their specific research needs. Limiting dilution assays are commonly used to measure the extent of infection, and in the context of HIV they represent an essential tool for studying latency and potential curative strategies. Yet standard assay designs may not discern whether an intervention reduces an already miniscule latent infection. This review addresses challenges arising in this setting and in the general use of dilution assays. We illustrate the major statistical method for estimating frequency of infectious units from assay results, and we offer an online tool for computing this estimate. We recommend a procedure for customizing assay design to achieve desired sensitivity and precision goals, subject to experimental constraints. We consider experiments in which no viral outgrowth is observed and explain how using alternatives to viral outgrowth may make measurement of HIV latency more efficient. Finally, we discuss how biological complications, such as probabilistic growth of small infections, alter interpretations of experimental results.

RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity.

We have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3' regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3' regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.

Transcriptome reconstruction and annotation of cynomolgus and African green monkey.

BACKGROUND: Non-human primates (NHPs) and humans share major biological mechanisms, functions, and responses due to their close evolutionary relationship and, as such, provide ideal animal models to study human diseases. RNA expression in NHPs provides specific signatures that are informative of disease mechanisms and therapeutic modes of action. Unlike the human transcriptome, the transcriptomes of major NHP animal models are yet to be comprehensively annotated. RESULTS: In this manuscript, employing deep RNA sequencing of seven tissue samples, we characterize the transcriptomes of two commonly used NHP animal models: Cynomolgus macaque (Macaca fascicularis) and African green monkey (Chlorocebus aethiops). We present the Multi-Species Annotation (MSA) pipeline that leverages well-annotated primate species and annotates 99.8% of reconstructed transcripts. We elucidate tissue-specific expression profiles and report 13 experimentally validated novel transcripts in these NHP animal models. CONCLUSION: We report comprehensively annotated transcriptomes of two non-human primates, which we have made publically available on a customized UCSC Genome Browser interface. The MSA pipeline is also freely available.

BRAF mutations in hairy-cell leukemia.

BACKGROUND: Hairy-cell leukemia (HCL) is a well-defined clinicopathological entity whose underlying genetic lesion is still obscure. METHODS: We searched for HCL-associated mutations by performing massively parallel sequencing of the whole exome of leukemic and matched normal cells purified from the peripheral blood of an index patient with HCL. Findings were validated by Sanger sequencing in 47 additional patients with HCL. RESULTS: Whole-exome sequencing identified five missense somatic clonal mutations that were confirmed on Sanger sequencing, including a heterozygous mutation in BRAF that results in the BRAF V600E variant protein. Since BRAF V600E is oncogenic in other tumors, further analyses were focused on this genetic lesion. The same BRAF mutation was noted in all the other 47 patients with HCL who were evaluated by means of Sanger sequencing. None of the 195 patients with other peripheral B-cell lymphomas or leukemias who were evaluated carried the BRAF V600E variant, including 38 patients with splenic marginal-zone lymphomas or unclassifiable splenic lymphomas or leukemias. In immunohistologic and Western blot studies, HCL cells expressed phosphorylated MEK and ERK (the downstream targets of the BRAF kinase), indicating a constitutive activation of the RAF-MEK-ERK mitogen-activated protein kinase pathway in HCL. In vitro incubation of BRAF-mutated primary leukemic hairy cells from 5 patients with PLX-4720, a specific inhibitor of active BRAF, led to a marked decrease in phosphorylated ERK and MEK. CONCLUSIONS; The BRAF V600E mutation was present in all patients with HCL who were evaluated. This finding may have implications for the pathogenesis, diagnosis, and targeted therapy of HCL. (Funded by Associazione Italiana per la Ricerca sul Cancro and others.).