RNase L reprograms translation by widespread mRNA turnover escaped by antiviral mRNAs, bioRxiv, 2018-12-05
SUMMARYIn response to foreign and endogenous double-stranded RNA (dsRNA), protein kinase R (PKR) and ribonuclease L (RNase L) reprogram translation in mammalian cells. PKR inhibits translation initiation through eIF2α phosphorylation, which triggers stress granule (SG) formation and promotes translation of stress responsive mRNAs. The mechanisms of RNase L-driven translation repression, its contribution to SG assembly, and its regulation of dsRNA stress-induced mRNAs are unknown. We demonstrate that RNase L drives translational shut-off in response to dsRNA by promoting widespread turnover of mRNAs. This alters stress granule assembly and reprograms translation by only allowing for the translation of mRNAs resistant to RNase L degradation, including numerous antiviral mRNAs such as IFN-β. Individual cells differentially activate dsRNA responses revealing variation that can affect cellular outcomes. This identifies bulk mRNA degradation and the resistance of antiviral mRNAs as the mechanism by which RNaseL reprograms translation in response to dsRNA.
biorxiv immunology 0-100-users 2018Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species, bioRxiv, 2018-11-30
AbstractAccurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.
biorxiv bioinformatics 0-100-users 2018Climate or disturbance temperate forest structural change and carbon sink potential, bioRxiv, 2018-11-27
ABSTRACTAnticipating forest responses to changing climate and disturbance regimes requires understanding long-term successional processes and aggregating these local processes into global relevance. Estimates of existing forest structure and biomass are improving globally; however, vegetation models continue to show substantial spread in predictions of future land carbon uptake and the roles of forest structural change and demography are increasingly being recognized as important. To identify mechanisms that drive change in tree size, density, and carbon, we need a better understanding of forest structural trajectories and the factors that affect those trajectories. Here we reveal a coherent, cyclic pattern of structural change in temperate forests, as predicted by successional theory, and identify significant sensitivity to climatic precipitation and temperature anomalies using large datasets and empirical modeling. For example, in the eastern US above average temperature (+1°C) was associated with a 27% (−0.4±0.1 Mg C ha-1 yr-1) reduction in productivity attributed to higher rates of disease (+23%), weather disturbance (+57%), and sapling mortality. Projections of future vegetative carbon sink potential suggests biomass would be lowest on managed lands (72±2 Mg C ha-1) and highest when larger trees survive in undisturbed conditions (153±21 Mg C ha-1). Overall, the indirect effects of disturbance and mortality were considerably larger than the direct effects of climate on productivity when predicting future vegetative carbon sinks. Results provide robust comparisons for global vegetation models, and valuable projections for management and carbon mitigation efforts.
biorxiv ecology 0-100-users 2018Quantitative PCR provides a simple and accessible method for quantitative microbiome profiling, bioRxiv, 2018-11-27
AbstractThe use of relative next generation sequencing (NGS) abundance data can lead to misinterpretations of microbial community structures as the increase of one taxon leads to concurrent decrease of the other(s). To overcome compositionality, we provide a quantitative NGS solution, which is achieved by adjusting the relative 16S rRNA gene amplicon NGS data with quantitative PCR (qPCR-based) total bacterial counts. By comparing the enumeration of dominant bacterial groups on different taxonomic levels in human fecal samples using taxon-specific 16S rRNA gene-targeted qPCR we show that quantitative NGS is able to estimate absolute bacterial abundances accurately. We also observed a higher degree of correspondence in the estimated microbe-metabolite relationship when quantitative NGS was applied. Being conceptually and methodologically analogous to amplicon-based NGS, our qPCR-based method can be readily incorporated into the standard, high-throughput NGS sample processing pipeline for more accurate description of interactions within and between the microbes and host.
biorxiv microbiology 0-100-users 2018Comparative assessment of long-read error-correction software applied to RNA-sequencing data, bioRxiv, 2018-11-23
AbstractMotivationLong-read sequencing technologies offer promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However these technologies are currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames, and the creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error-correction of RNA-sequencing long reads remain limited.ResultsIn this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error-correction metrics but also the effect of correction on gene families, isoform diversity, bias towards the major isoform, and splice site detection. We find that long read error-correction tools that were originally developed for DNA are also suitable for the correction of RNA-sequencing data, especially in terms of increasing base-pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error-correction tools should be used, depending on the application type.Benchmarking software<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgitlab.comleoislLR_EC_analyser>httpsgitlab.comleoislLR_EC_analyser<jatsext-link>
biorxiv bioinformatics 0-100-users 2018Nuclei multiplexing with barcoded antibodies for single-nucleus genomics, bioRxiv, 2018-11-23
AbstractSingle-nucleus RNA-Seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate, including frozen clinical samples. This opens the way, in principle, to large studies, such as those required for human genetics, clinical trials, or precise cell atlases of large organs. However, such applications are currently limited by batch effects, sequential processing, and costs. To address these challenges, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies against the nuclear pore complex to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled in multiplex with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter the recovered transcriptome profiles. We further developed demuxEM, a novel computational tool that robustly detects inter-sample nucleus multiplets and assigns singlets to their samples of origin by antibody barcodes, and validated its accuracy using gender-specific gene expression, species-mixing and natural genetic variation. Nucleus hashing significantly reduces cost per nucleus, recovering up to about 5 times as many single nuclei per microfluidc channel. Our approach provides a robust technique for diverse studies including tissue atlases of isogenic model organisms or from a single larger human organ, multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.
biorxiv genomics 0-100-users 2018