Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks.

Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.

A resampling strategy for studying robustness in virus detection pipelines.

Next-generation sequencing is regularly used to identify viral sequences in DNA or RNA samples of infected hosts. A major step of most pipelines for virus detection is to map sequence reads against known virus genomes. Due to small differences between the sequences of related viruses, and due to several biological or technical errors, mapping underlies uncertainties. As a consequence, the resulting list of detected viruses can lack robustness. A new approach for generating artificial sequencing reads together with a strategy of resampling from the original findings is proposed that can help to assess the robustness of the originally identified list of viruses. From the original mapping result in form of a SAM file, a set of statistical distributions are derived. These are used in the resampling pipeline to generate new artificial reads which are again mapped versus the reference genomes. By summarizing the resampling procedure, the analyst receives information about whether the presence of a particular virus in the sample gains or losses evidence, and thus about the robustness of the original mapping list but also that of individual viruses in this list. To judge robustness, several indicators are derived from the resampling procedure such as the correlation between original and resampling read counts, or the statistical detection of outliers in the differences of read counts. Additionally, graphical illustrations of read count shifts via Sankey diagrams are provided. To demonstrate the use of the new approach, the resampling approach is applied to three real-world data samples, one of them with laboratory-confirmed Influenza sequences, and to artificially generated data where virus sequences have been spiked into the sequencing data of a host. By applying the resampling pipeline, several viruses drop from the original list while new viruses emerge, showing robustness of those viruses that remain in the list. The evaluation of the new approach shows that the resampling approach is helpful to analyze the viral content of a biological sample, to rate the robustness of original findings and to better show the overall distribution of findings. The method is also applicable to other virus detection pipelines based on read mapping.

Sequential MAVS and MyD88/TRIF signaling triggers anti-viral responses of tick-borne encephalitis virus-infected murine astrocytes.

Tick-borne encephalitis virus (TBEV), a member of the Flaviviridae family, is typically transmitted upon tick bite and can cause meningitis and encephalitis in humans. In TBEV-infected mice, mitochondrial antiviral-signaling protein (MAVS), the downstream adaptor of retinoic acid-inducible gene-I (RIG-I)-like receptor (RLR) signaling, is needed to induce early type I interferon (IFN) responses and to confer protection. To characterize the brain-resident cell subset that produces protective IFN-ß in TBEV-infected mice, we isolated neurons, astrocytes, and microglia from mice and exposed these cell types to TBEV in vitro. Under such conditions, neurons showed the highest percentage of infected cells, whereas astrocytes and microglia were infected to a lesser extent. In the supernatant (SN) of infected neurons, IFN-ß was not detectable, while infected astrocytes showed high and microglia low IFN-ß expression. Transcriptome analyses of astrocytes implied that MAVS signaling was needed early after TBEV infection. Accordingly, MAVS-deficient astrocytes showed enhanced TBEV infection and significantly reduced early IFN-ß responses. Nevertheless, at later time points, moderate amounts of IFN-ß were detected in the SN of infected MAVS-deficient astrocytes. Transcriptome analyses indicated that MAVS deficiency negatively affected the induction of early anti-viral responses, which resulted in significantly increased TBEV replication. Treatment with MyD88 and TRIF inhibiting peptides reduced only late IFN-ß responses of TBEV-infected WT astrocytes and blocked entirely IFN-ß responses of infected MAVS-deficient astrocytes. Thus, upon TBEV exposure of brain-resident cells, astrocytes are important IFN-ß producers showing biphasic IFN-ß induction that initially depends on MAVS and later on MyD88/TRIF signaling.

MyD88 signaling by neurons induces chemokines that recruit protective leukocytes to the virus-infected CNS.

Viral encephalitis initiates a series of immunological events in the brain that can lead to brain damage and death. Astrocytes express IFN-ß in response to neurotropic infection, whereas activated microglia produce proinflammatory cytokines and accumulate at sites of infection. Here, we observed that neurotropic vesicular stomatitis virus (VSV) infection causes recruitment of leukocytes into the central nervous system (CNS), which requires MyD88, an adaptor of Toll-like receptor and interleukin-1 receptor signaling. Infiltrating leukocytes, and in particular CD8+ T cells, protected against lethal VSV infection of the CNS. Reconstitution of MyD88, specifically in neurons, restored chemokine production in the olfactory bulb as well as leukocyte recruitment into the infected CNS and enhanced survival. Comparative analysis of the translatome of neurons and astrocytes verified neurons as the critical source of chemokines, which regulated leukocyte infiltration of the infected brain and affected survival.

Measuring reproducibility of virus metagenomics analyses using bootstrap samples from FASTQ-files.

MOTIVATION: High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings. RESULTS: The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals. AVAILABILITY AND IMPLEMENTATION: We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.