Dual RNA-Seq Enables Full-Genome Assembly of Measles Virus and Characterization of Host-Pathogen Interactions

Dual RNA-seq Analysis of Patients' Cells and Viral Genome After Measles Infection

During the infection of a host cell by an infectious agent, a series of gene expression changes occurs as a consequence of host-pathogen interactions. Unraveling this complex interplay is the key for understanding of microbial virulence and host response pathways, thus providing the basis for new molecular insights into the mechanisms of pathogenesis and the corresponding immune response. Dual RNA sequencing (dual RNA-seq) has been developed to simultaneously determine pathogen and host transcriptomes enabling both differential and coexpression analyses between the two partners as well as genome characterization in the case of RNA viruses. Here, we provide a detailed laboratory protocol and bioinformatics analysis guidelines for dual RNA-seq experiments focusing on - but not restricted to - measles virus (MeV) as a pathogen of interest. The application of dual RNA-seq technologies in MeV-infected patients can potentially provide valuable information on the structure of the viral RNA genome and on cellular innate immune responses and drive the discovery of new targets for antiviral therapy.

Regulation of host gene expression by J paramyxovirus

Paramyxoviruses are negative-sense, single-stranded RNA viruses that are associated with numerous diseases in humans and animals. J paramyxovirus (JPV) was first isolated from moribund mice (Mus musculus) with hemorrhagic lung lesions in Australia in 1972. In 2016, JPV was classified into the newly established genus Jeilongvirus. Novel jeilongviruses are being discovered worldwide in wildlife populations. However, the effects of jeilongvirus infection on host gene expression remains uncharacterized. To address this, cellular RNA from JPV-infected mouse fibroblasts was collected at 2, 4, 8, 12, 16, 24, and 48 hours post-infection (hpi) and were sequenced using single-end 75 base pairs (SE75) sequencing chemistry on an Illumina NextSeq platform. Differentially expressed genes (DEGs) between the virus-infected replicates and mock replicates at each timepoint were identified using the Tophat2-Cufflinks-Cuffdiff protocol. At 2 hpi, 11 DEGs were identified in JPV-infected cells, while 1,837 DEGs were detected at 48 hpi. A GO analysis determined that the genes at the earlier timepoints were involved in interferon responses, while there was a shift towards genes that are involved in antigen processing and presentation processes at the later timepoints. At 48 hpi, a KEGG analysis revealed that many of the DEGs detected were involved in pathways that are important for immune responses. qRT-PCR verified that Rtp4, Ifit3, Mx2, and Stat2 were all upregulated during JPV infection, while G0s2 was downregulated. After JPV infection, the expression of inflammatory and antiviral factors in mouse fibroblasts changes significantly. This study provides crucial insight into the different arms of host immunity that mediate Jeilongvirus infection. Understanding the pathogenic mechanisms of Jeilongvirus will lead to better strategies for the prevention and control of potential diseases that may arise from this group of viruses.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

Targeted Virome Sequencing Enhances Unbiased Detection and Genome Assembly of Known and Emerging Viruses-The Example of SARS-CoV-2

Targeted virome enrichment and sequencing (VirCapSeq-VERT) utilizes a pool of oligos (baits) to enrich all known—up to 2015—vertebrate-infecting viruses, increasing their detection sensitivity. The hybridisation of the baits to the target sequences can be partial, thus enabling the detection and genomic reconstruction of novel pathogens with <40% genetic diversity compared to the strains used for the baits’ design. In this study, we deploy this method in multiplexed mixes of viral extracts, and we assess its performance in the unbiased detection of DNA and RNA viruses after cDNA synthesis. We further assess its efficiency in depleting various background genomic material. Finally, as a proof-of-concept, we explore the potential usage of the method for the characterization of unknown, emerging human viruses, such as SARS-CoV-2, which may not be included in the baits’ panel. We mixed positive samples of equimolar DNA/RNA viral extracts from SARS-CoV-2, coronavirus OC43, cytomegalovirus, influenza A virus H3N2, parvovirus B19, respiratory syncytial virus, adenovirus C and coxsackievirus A16. Targeted virome enrichment was performed on a dsDNA mix, followed by sequencing on the NextSeq500 (Illumina) and the portable MinION sequencer, to evaluate its usability as a point-of-care (PoC) application. Genome mapping assembly was performed using viral reference sequences. The untargeted libraries contained less than 1% of total reads mapped on most viral genomes, while RNA viruses remained undetected. In the targeted libraries, the percentage of viral-mapped reads were substantially increased, allowing full genome assembly in most cases. Targeted virome sequencing can enrich a broad range of viruses, potentially enabling the discovery of emerging viruses.

Dual RNA-Seq of H5N1 Avian Influenza Virus and Host Cell Transcriptomes Reveals Novel Insights Into Host-Pathogen Cross Talk

H5N1 avian influenza virus (AIV) is a highly pathogenic influenza virus that poses a substantial threat to poultry production and public health. A comprehensive understanding of host-pathogen interactions for AIV requires knowledge of gene expression changes in both the pathogen and the host upon infection. We report the use of dual RNA sequencing technology to uncover trends in gene expression in H5N1 AIV and chickens (DF1 cells) during the course of infection. The expression of all viral genes increased continuously from 0 to 20 h post infection. We also identified 2,762 differentially expressed host genes during infection. Pathway analysis found that genes related to the signaling pathways of DNA replication, T cell activation, NF-kappa B signaling pathway, and RNA degradation were significantly enriched. We demonstrated that the cis-acting lncRNA MSTRG.14019.1 targeted CSE1L and may affect virus replication. This study provides a more comprehensive and detailed understanding of host-virus interactions at the RNA level during the course of H5N1 AIV infection.

Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil–lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.