Metagenomic Analysis Reveals Previously Undescribed Bat Coronavirus Strains in Eswatini.

We investigated the prevalence of coronaviruses in 44 bats from four families in northeastern Eswatini using high-throughput sequencing of fecal samples. We found evidence of coronaviruses in 18% of the bats. We recovered full or near-full-length genomes from two bat species: Chaerephon pumilus and Afronycteris nana, as well as additional coronavirus genome fragments from C. pumilus, Epomophorus wahlbergi, Mops condylurus, and Scotophilus dinganii. All bats from which we detected coronaviruses were captured leaving buildings or near human settlements, demonstrating the importance of continued surveillance of coronaviruses in bats to better understand the prevalence, diversity, and potential risks for spillover.

Author Correction: Longitudinal assessment of tumor development using cancer avatars derived from genetically engineered pluripotent stem cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Longitudinal assessment of tumor development using cancer avatars derived from genetically engineered pluripotent stem cells.

Many cellular models aimed at elucidating cancer biology do not recapitulate pathobiology including tumor heterogeneity, an inherent feature of cancer that underlies treatment resistance. Here we introduce a cancer modeling paradigm using genetically engineered human pluripotent stem cells (hiPSCs) that captures authentic cancer pathobiology. Orthotopic engraftment of the neural progenitor cells derived from hiPSCs that have been genome-edited to contain tumor-associated genetic driver mutations revealed by The Cancer Genome Atlas project for glioblastoma (GBM) results in formation of high-grade gliomas. Similar to patient-derived GBM, these models harbor inter-tumor heterogeneity resembling different GBM molecular subtypes, intra-tumor heterogeneity, and extrachromosomal DNA amplification. Re-engraftment of these primary tumor neurospheres generates secondary tumors with features characteristic of patient samples and present mutation-dependent patterns of tumor evolution. These cancer avatar models provide a platform for comprehensive longitudinal assessment of human tumor development as governed by molecular subtype mutations and lineage-restricted differentiation.

Exploring the landscape of focal amplifications in cancer using AmpliconArchitect.

Focal oncogene amplification and rearrangements drive tumor growth and evolution in multiple cancer types. We present AmpliconArchitect (AA), a tool to reconstruct the fine structure of focally amplified regions using whole genome sequencing (WGS) and validate it extensively on multiple simulated and real datasets, across a wide range of coverage and copy numbers. Analysis of AA-reconstructed amplicons in a pan-cancer dataset reveals many novel properties of copy number amplifications in cancer. These findings support a model in which focal amplifications arise due to the formation and replication of extrachromosomal DNA. Applying AA to 68 viral-mediated cancer samples, we identify a large fraction of amplicons with specific structural signatures suggestive of hybrid, human-viral extrachromosomal DNA. AA reconstruction, integrated with metaphase fluorescence in situ hybridization (FISH) and PacBio sequencing on the cell-line UPCI:SCC090 confirm the extrachromosomal origin and fine structure of a Forkhead box E1 (FOXE1)-containing hybrid amplicon.

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus.

Aligning sequences for phylogenetic analysis (multiple sequence alignment; MSA) is an important, but increasingly computationally expensive step with the recent surge in DNA sequence data. Much of this sequence data is publicly available, but can be extremely fragmentary (i.e., a combination of full genomes and genomic fragments), which can compound the computational issues related to MSA. Traditionally, alignments are produced with automated algorithms and then checked and/or corrected "by eye" prior to phylogenetic inference. However, this manual curation is inefficient at the data scales required of modern phylogenetics and results in alignments that are not reproducible. Recently, methods have been developed for fully automating alignments of large data sets, but it is unclear if these methods produce alignments that result in compatible phylogenies when compared to more traditional alignment approaches that combined automated and manual methods. Here we use approximately 33,000 publicly available sequences from the hepatitis B virus (HBV), a globally distributed and rapidly evolving virus, to compare different alignment approaches. Using one data set comprised exclusively of whole genomes and a second that also included sequence fragments, we compared three MSA methods: (1) a purely automated approach using traditional software, (2) an automated approach including by eye manual editing, and (3) more recent fully automated approaches. To understand how these methods affect phylogenetic results, we compared resulting tree topologies based on these different alignment methods using multiple metrics. We further determined if the monophyly of existing HBV genotypes was supported in phylogenies estimated from each alignment type and under different statistical support thresholds. Traditional and fully automated alignments produced similar HBV phylogenies. Although there was variability between branch support thresholds, allowing lower support thresholds tended to result in more differences among trees. Therefore, differences between the trees could be best explained by phylogenetic uncertainty unrelated to the MSA method used. Nevertheless, automated alignment approaches did not require human intervention and were therefore considerably less time-intensive than traditional approaches. Because of this, we conclude that fully automated algorithms for MSA are fully compatible with older methods even in extremely difficult to align data sets. Additionally, we found that most HBV diagnostic genotypes did not correspond to evolutionarily-sound groups, regardless of alignment type and support threshold. This suggests there may be errors in genotype classification in the database or that HBV genotypes may need a revision.

ViFi: accurate detection of viral integration and mRNA fusion reveals indiscriminate and unregulated transcription in proximal genomic regions in cervical cancer.

The integration of viral sequences into the host genome is an important driver of tumorigenesis in many viral mediated cancers, notably cervical cancer and hepatocellular carcinoma. We present ViFi, a computational method that combines phylogenetic methods with reference-based read mapping to detect viral integrations. In contrast with read-based reference mapping approaches, ViFi is faster, and shows high precision and sensitivity on both simulated and biological data, even when the integrated virus is a novel strain or highly mutated. We applied ViFi to matched genomic and mRNA data from 68 cervical cancer samples from TCGA and found high concordance between the two. Surprisingly, viral integration resulted in a dramatic transcriptional upregulation in all proximal elements, including LINEs and LTRs that are not normally transcribed. This upregulation is highly correlated with the presence of a viral gene fused with a downstream human element. Moreover, genomic rearrangements suggest the formation of apparent circular extrachromosomal (ecDNA) human-viral structures. Our results suggest the presence of apparent small circular fusion viral/human ecDNA, which correlates with indiscriminate and unregulated expression of proximal genomic elements, potentially contributing to the pathogenesis of HPV-associated cervical cancers. ViFi is available at https://github.com/namphuon/ViFi.

Ultra-large alignments using phylogeny-aware profiles.

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp .

iBioSim: a tool for the analysis and design of genetic circuits.

SUMMARY: iBioSim is a tool that supports learning of genetic circuit models, efficient abstraction-based analysis of these models and the design of synthetic genetic circuits. iBioSim includes project management features and a graphical user interface that facilitate the development and maintenance of genetic circuit models as well as both experimental and simulation data records. AVAILABILITY: iBioSim is available for download for Windows, Linux, and MacOS at http://www.async.ece.utah.edu/iBioSim/ CONTACT: myers@ece.utah.edu.