Integrative gene network analysis identifies key signatures, intrinsic networks and host factors for influenza virus A infections

Human long noncoding RNA, VILMIR, is induced by major respiratory viral infections and modulates the host interferon response

Long noncoding RNAs (lncRNAs) are a newer class of noncoding transcripts identified as key regulators of biological processes. Here we aimed to identify novel lncRNA targets that play critical roles in major human respiratory viral infections by systematically mining large-scale transcriptomic datasets. Using bulk RNA-sequencing (RNA-seq) analysis, we identified a previously uncharacterized lncRNA, named virus inducible lncRNA modulator of interferon response (VILMIR), that was consistently upregulated after in vitro influenza infection across multiple human epithelial cell lines and influenza A virus subtypes. VILMIR was also upregulated after SARS-CoV-2 and RSV infections in vitro. We experimentally confirmed the response of VILMIR to influenza infection and interferon-beta (IFN-β) treatment in the A549 human epithelial cell line and found the expression of VILMIR was robustly induced by IFN-β treatment in a dose and time-specific manner. Single cell RNA-seq analysis of bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients uncovered that VILMIR was upregulated across various cell types including at least five immune cells. The upregulation of VILMIR in immune cells was further confirmed in the human T cell and monocyte cell lines, SUP-T1 and THP-1, after IFN-β treatment. Finally, we found that knockdown of VILMIR expression reduced the magnitude of host transcriptional responses to IFN-β treatment in A549 cells. Together, our results show that VILMIR is a novel interferon-stimulated gene (ISG) that regulates the host interferon response and may be a potential therapeutic target for human respiratory viral infections upon further mechanistic investigation.

Post-genomic era in agriculture and veterinary science: successful and proposed application of genetic targeting technologies

Gene editing tools have become an indispensable part of research into the fundamental aspects of cell biology. With a vast body of literature having been generated based on next generation sequencing technologies, keeping track of this ever-growing body of information remains challenging. This necessitates the translation of genomic data into tangible applications. In order to address this objective, the generated Next Generation Sequencing (NGS) data forms the basis for targeted genome editing strategies, employing known enzymes of various cellular machinery, in generating organisms with specifically selected phenotypes. This review focuses primarily on CRISPR/Cas9 technology in the context of its advantages over Zinc finger proteins (ZNF) and Transcription activator-like effector nucleases (TALEN) and meganucleases mutagenesis strategies, for use in agricultural and veterinary applications. This review will describe the application of CRISPR/Cas9 in creating modified organisms with custom-made properties, without the undesired non-targeted effects associated with virus vector vaccines and bioactive molecules produced in bacterial systems. Examples of the successful and unsuccessful applications of this technology to plants, animals and microorganisms are provided, as well as an in-depth look into possible future trends and applications in vaccine development, disease resistance and enhanced phenotypic traits will be discussed.

Nardosinone and aurantio-obtusin, two medicine food homology natural compounds, are anti-influenza agents as indicated by transcriptome signature reversion

BACKGROUND

Medicine food homology (MFH) refers to food that can be used as medicine, and compounds isolated from MFH materials are valuable in novel drug discovery due to their good safety. Transcriptome signature reversion (TSR) is an attractive method for discovering drugs through transcriptional reverse matching; namely, the changes in transcriptional signatures induced by compounds are matched to a certain disease. This strategy can be used to discover anti-influenza agents among MFH natural compounds.

PURPOSE

MFH natural compounds with anti-influenza activities were identified through analyses of the reversal in the expression of multiple informative genes followed by in vitro evaluation of the cytopathic effect (CPE) caused by influenza infection and relative quantification of the nucleoprotein (NP) gene in viral RNA (vRNA). The combined effect of active compounds was determined through network-based separation score prediction followed by quantification of the viral hemagglutinin (HA) level.

METHODS

The transcriptome profiles of 4 lung or airway cell lines infected with 7 influenza virus strains were analyzed by robust rank aggregation (RRA) to identify informative genes in the signature of influenza virus infection. The identified informative genes were then matched to a transcriptomic profile library of MFH natural compounds. The anti-influenza activities of MFH natural compounds with negative enrichment scores (ESs) were evaluated in vitro using a CPE assay and relative quantification of the NP gene in the vRNA in the supernatant and cytoplasm to identify anti-influenza agents. The effects of combinations of active compounds were analyzed using network-based calculations followed by confirmation through bioassays for quantifying the viral HA levels.

RESULTS

Among the 159 MFH natural compounds, 54 compounds had negative ESs, as determined through TSR, and the anti-influenza activities of nardosinone and aurantio-obtusin were confirmed by bioassays. The half-maximal effective concentrations (EC₅₀) of nardosinone and aurantio-obtusin were 4.3-84.4 μM and 31.9-113.6 μM, respectively. The separation score between the informative genes with expression that was negatively regulated by nardosinone and aurantio-obtusin in the human protein-protein interaction (PPI) network was calculated to be 0.10, which indicated that the two compounds potentially exert a synergistic effect, and this effect was confirmed by the finding that the combination indexes (CIs) were calculated to equal 0.86 at inhibition level of 50% and 0.44 at inhibition level of 90%.

CONCLUSION

The TSR analysis and in vitro evaluation identified nardosinone and aurantio-obtusin as anti-influenza agents. Their antiviral activities were exerted by reversing the expression of multiple informative genes of the host cells. The separation analysis between the informative genes that were reversely regulated by nardosinone and aurantio-obtusin indicated that their combination may exert a synergistic effect, which was confirmed in vitro.

Common and species-specific molecular signatures, networks, and regulators of influenza virus infection in mice, ferrets, and humans

Molecular responses to influenza A virus (IAV) infections vary between mammalian species. To identify conserved and species-specific molecular responses, we perform a comparative study of transcriptomic data derived from blood cells, primary epithelial cells, and lung tissues collected from IAV-infected humans, ferrets, and mice. The molecular responses in the human host have unique functions such as antigen processing that are not observed in mice or ferrets. Highly conserved gene coexpression modules across the three species are enriched for IAV infection-induced pathways including cell cycle and interferon (IFN) signaling. TDRD7 is predicted as an IFN-inducible host factor that is up-regulated upon IAV infection in the three species. TDRD7 is required for antiviral IFN response, potentially modulating IFN signaling via the JAK/STAT/IRF9 pathway. Identification of the common and species-specific molecular signatures, networks, and regulators of IAV infection provides insights into host-defense mechanisms and will facilitate the development of novel therapeutic interventions against IAV infection.

Identification of Critical Genes and Pathways for Influenza A Virus Infections via Bioinformatics Analysis

Influenza A virus (IAV) requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system but also provides further targets for antiviral drug development. To uncover critical pathways and potential targets of influenza infection, we assembled a large amount of data from 8 RNA sequencing studies of IAV infection for integrative network analysis. Weighted gene co-expression network analysis (WGCNA) was performed to investigate modules and genes correlated with the time course of infection and/or multiplicity of infection (MOI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and pathways of the genes in 5 significant modules. Top hub genes were identified using the cytoHubba plugin in the protein interaction network. The correlation between expression levels of 7 top hub genes and time course or MOI was displayed and validated, including BCL2L13, PLSCR1, ARID5A, LMO2, NDRG4, HAP1, and CARD10. Dysregulated expression of these genes potently impacted the development of IAV infection through modulating IAV-related biological processes and pathways. This study provides further insights into the underlying molecular mechanisms and potential targets in IAV infection.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Application of the CRISPR/Cas9 System to Study Regulation Pathways of the Cellular Immune Response to Influenza Virus

Influenza A virus (IAV) causes a respiratory infection that affects millions of people of different age groups and can lead to acute respiratory distress syndrome. Currently, host genes, receptors, and other cellular components critical for IAV replication are actively studied. One of the most convenient and accessible genome-editing tools to facilitate these studies is the CRISPR/Cas9 system. This tool allows for regulating the expression of both viral and host cell genes to enhance or impair viral entry and replication. This review considers the effect of the genome editing system on specific target genes in cells (human and chicken) in terms of subsequent changes in the influenza virus life cycle and the efficiency of virus particle production.

Latest Advances of Virology Research Using CRISPR/Cas9-Based Gene-Editing Technology and Its Application to Vaccine Development

In recent years, the CRISPR/Cas9-based gene-editing techniques have been well developed and applied widely in several aspects of research in the biological sciences, in many species, including humans, animals, plants, and even in viruses. Modification of the viral genome is crucial for revealing gene function, virus pathogenesis, gene therapy, genetic engineering, and vaccine development. Herein, we have provided a brief review of the different technologies for the modification of the viral genomes. Particularly, we have focused on the recently developed CRISPR/Cas9-based gene-editing system, detailing its origin, functional principles, and touching on its latest achievements in virology research and applications in vaccine development, especially in large DNA viruses of humans and animals. Future prospects of CRISPR/Cas9-based gene-editing technology in virology research, including the potential shortcomings, are also discussed.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Insulin resistance is characterized by a defective response (“resistance”) to normal insulin concentrations to uptake the glucose present in the blood, and is the underlying condition that leads to type 2 diabetes (T2D) and increases the risk of cardiovascular disease. It is estimated that 25–33% of the US population are insulin resistant enough to be at risk of serious clinical consequences. For more than a decade, large population studies have tried to discover the genes that participate in the development of insulin resistance, but without much success. It is now increasingly clear that the complex genetic nature of insulin resistance requires novel approaches centered in patient specific cellular models. To fill this gap, we have generated an induced pluripotent stem cell (iPSC) library from individuals with accurate measurements of insulin sensitivity, and performed gene expression and key driver analyses. Our work demonstrates that iPSCs can be used as a revolutionary technology to model insulin resistance and to discover key genetic drivers. Moreover, they can develop our basic knowledge of the disease, and are ultimately expected to increase the therapeutic targets to treat insulin resistance and type 2 diabetes.

Characterization of the hepatic transcriptome following phenobarbital induction in mice with AIP

Acute Intermittent Porphyria (AIP), an autosomal dominant hepatic disorder, results from hydroxymethylbilane synthase (HMBS) mutations that decrease the encoded enzymatic activity, thereby predisposing patients to life-threatening acute neurovisceral attacks. The ~1% penetrance of AIP suggests that other genetic factors modulate the onset and severity of the acute attacks. Here, we characterized the hepatic transcriptomic response to phenobarbital (PB) administration in AIP mice, which mimics the biochemical attacks of AIP. At baseline, the mRNA profiles of 14,138 hepatic genes prior to treatment were remarkably similar between AIP and the congenic wild-type (WT) mice. After PB treatment (~120 mg/kg x 3d), 1347 and 1120 genes in AIP mice and 422 and 404 genes in WT mice were uniquely up- and down-regulated, respectively, at a False Discovery Rate < 0.05. As expected, the ALAS1 expression increased 4.5-fold and 15.9-fold in the WT and AIP mice, respectively. ALA-dehydrogenase also was induced ~1.7-fold in PB-induced AIP mice, but was unchanged in PB-induced WT mice. There was no statistically significant difference in the overall expression of 155 hepatic cytochrome P450 enzymes, although Cyp2c40, Cyp2c68, Cyp2c69, Mgst3 were upregulated only in PB-induced AIP mice (>1.9-fold) and Cyp21a1 was upregulated only in PB-induced WT mice (>9-fold). Notably, the genes differentially expressed in induced AIP mice were enriched in circadian rhythm, mitochondria biogenesis and electron transport, suggesting these pathways were involved in AIP mice responding to PB treatment. These results advance our understanding of the hepatic metabolic changes in PB-induced AIP mice and have implications in the pathogenesis of AIP acute attacks.