Epigenetic suppression of interferon lambda receptor expression leads to enhanced HuNoV replication in vitro, bioRxiv, 2019-01-17

Human norovirus (HuNoV) is the main cause of gastroenteritis worldwide yet no therapeutics are currently available. Here, we utilize a human norovirus replicon in human gastric tumor (HGT) cells to identify host factors involved in promoting or inhibiting HuNoV replication. We observed that an IFN-cured population of replicon-harboring HGT cells (HGT-cured) was enhanced in their ability to replicate transfected HuNoV RNA compared to parental HGT cells, suggesting that differential gene expression in HGT-cured cells created an environment favouring norovirus replication. Microarray analysis was used to identify genes differentially regulated in HGT-NV and HGT-cured compared to parental HGT cells. We found that the IFN lambda receptor alpha (IFNLR1) expression was highly reduced in HGT-NV and HGT-cured cells. All three cell lines responded to exogenous IFN-β by inducing interferon stimulated genes (ISGs), however, HGT-NV and HGT-cured failed to respond to exogenous IFN-λ. Inhibition of DNA methyltransferase activity with 5-aza-2'-deoxycytidine partially reactivated IFNLR1 expression in HGT-NV and IFN-cured cells suggesting that host adaptation occurred via epigenetic reprogramming. In line with this, ectopic expression of the IFN-λ receptor alpha rescued HGT-NV and HGT-cured cells response to IFN-λ. We conclude that type III IFN is important in inhibiting HuNoV replication in vitro and that the loss of IFNLR1 enhances replication of HuNoV. This study unravels for the first time epigenetic reprogramming of the interferon lambda receptor as a new mechanism of cellular adaptation during long-term RNA virus replication and shows that an endogenous level of interferon lambda signalling is able to control human norovirus replication.

biorxiv microbiology 0-100-users 2019

Evolving super stimuli for real neurons using deep generative networks, bioRxiv, 2019-01-17

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.

biorxiv neuroscience 200-500-users 2019

phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes Supplementary Information, bioRxiv, 2019-01-17

The SSU rRNA gene is the key marker in molecular ecology for all domains of life, but is largely absent from metagenome-assembled genomes that often are the only resource available for environmental microbes. Here we present phyloFlash, a pipeline to overcome this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based binning of full metagenomic assemblies. We show that a cleanup of artifacts is pivotal even with a curated reference database. With such a filtered database, the general-purpose mapper BBmap extracts SSU rRNA reads five times faster than the rRNA-specialized tool SortMeRNA with similar sensitivity and higher selectivity on simulated metagenomes. Reference-based targeted assemblers yielded either highly fragmented assemblies or high levels of chimerism, so we employ the general-purpose genomic assembler SPAdes. Our optimized implementation is independent of reference database composition and has satisfactory levels of chimera formation. Using the phyloFlash workflow we could recover the first complete genomes of several enigmatic taxa, including Marinamargulisbacteria from surface ocean seawater. phyloFlash quickly processes Illumina (meta)genomic data, is straightforward to use, even as part of high-throughput quality control, and has user-friendly output reports. The software is available at httpsgithub.comHRGVphyloFlash (GPL3 license) and is documented with an online manual.

biorxiv bioinformatics 0-100-users 2019

BEHST genomic set enrichment analysis enhanced through integration of chromatin long-range interactions, bioRxiv, 2019-01-16

Transforming data from genome-scale assays into knowledge of affected molecular functions and pathways is a key challenge in biomedical research. Using vocabularies of functional terms and databases annotating genes with these terms, pathway enrichment methods can identify terms enriched in a gene list. With data that can refer to intergenic regions, however, one must first connect the regions to the terms, which are usually annotated only to genes. To make these connections, existing pathway enrichment approaches apply unwarranted assumptions such as annotating non-coding regions with the terms from adjacent genes. We developed a computational method that instead links genomic regions to annotations using data on long-range chromatin interactions. Our method, Biological Enrichment of Hidden Sequence Targets (BEHST), finds Gene Ontology (GO) terms enriched in genomic regions more precisely and accurately than existing methods. We demonstrate BEHST's ability to retrieve more pertinent and less ambiguous GO terms associated with results of in vivo mouse enhancer screens or enhancer RNA assays for multiple tissue types. BEHST will accelerate the discovery of affected pathways mediated through long-range interactions that explain non-coding hits in genome-wide association study (GWAS) or genome editing screens. BEHST is free software with a command-line interface for Linux or macOS and a web interface (httpbehst.hoffmanlab.org).

biorxiv bioinformatics 100-200-users 2019

Killer whale genomes reveal a complex history of recurrent admixture and vicariance Supplementary Materials, bioRxiv, 2019-01-16

Reconstruction of the demographic and evolutionary history of populations assuming a consensus tree-like relationship can mask more complex scenarios, which are prevalent in nature. An emerging genomic toolset, which has been most comprehensively harnessed in the reconstruction of human evolutionary history, enables molecular ecologists to elucidate complex population histories. Killer whales have limited extrinsic barriers to dispersal and have radiated globally, and are therefore a good candidate model for the application of such tools. Here, we analyse a global dataset of killer whale genomes in a rare attempt to elucidate global population structure in a non-human species. We identify a pattern of genetic homogenisation at lower latitudes and the greatest differentiation at high latitudes, even between currently sympatric lineages. The processes underlying the major axis of structure include high drift at the edge of species' range, likely associated with founder effects and allelic surfing during post-glacial range expansion. Divergence between Antarctic and non-Antarctic lineages is further driven by ancestry segments with up to four-fold older coalescence time than the genome-wide average; relicts of a previous vicariance during an earlier glacial cycle. Our study further underpins that episodic gene flow is ubiquitous in natural populations, and can occur across great distances and after substantial periods of isolation between populations. Thus, understanding the evolutionary history of a species requires comprehensive geographic sampling and genome-wide data to sample the variation in ancestry within individuals.

biorxiv evolutionary-biology 100-200-users 2019

Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers Supplementary tables, bioRxiv, 2019-01-16

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via mapping procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

biorxiv bioinformatics 100-200-users 2019