Cyclin-Dependent Kinases 8 and 19 Regulate Host Cell Metabolism during Dengue Virus Serotype 2 Infection

Dengue virus pathogenesis and host molecular machineries

Dengue viruses (DENV) are positive-stranded RNA viruses belonging to the Flaviviridae family. DENV is the causative agent of dengue, the most rapidly spreading viral disease transmitted by mosquitoes. Each year, millions of people contract the virus through bites from infected female mosquitoes of the Aedes species. In the majority of individuals, the infection is asymptomatic, and the immune system successfully manages to control virus replication within a few days. Symptomatic individuals may present with a mild fever (Dengue fever or DF) that may or may not progress to a more critical disease termed Dengue hemorrhagic fever (DHF) or the fatal Dengue shock syndrome (DSS). In the absence of a universally accepted prophylactic vaccine or therapeutic drug, treatment is mostly restricted to supportive measures. Similar to many other viruses that induce acute illness, DENV has developed several ways to modulate host metabolism to create an environment conducive to genome replication and the dissemination of viral progeny. To search for new therapeutic options, understanding the underlying host-virus regulatory system involved in various biological processes of the viral life cycle is essential. This review aims to summarize the complex interaction between DENV and the host cellular machinery, comprising regulatory mechanisms at various molecular levels such as epigenetic modulation of the host genome, transcription of host genes, translation of viral and host mRNAs, post-transcriptional regulation of the host transcriptome, post-translational regulation of viral proteins, and pathways involved in protein degradation.

Multiple receptor tyrosine kinases regulate dengue infection of hepatocytes

Introduction

Dengue is an arboviral disease causing severe illness in over 500,000 people each year. Currently, there is no way to constrain dengue in the clinic. Host kinase regulators of dengue virus (DENV) infection have the potential to be disrupted by existing therapeutics to prevent infection and/or disease progression.

Methods

To evaluate kinase regulation of DENV infection, we performed kinase regression (KiR), a machine learning approach that predicts kinase regulators of infection using existing drug-target information and a small drug screen. We infected hepatocytes with DENV in vitro in the presence of a panel of 38 kinase inhibitors then quantified the effect of each inhibitor on infection rate. We employed elastic net regularization on these data to obtain predictions of which of 291 kinases are regulating DENV infection.

Results

Thirty-six kinases were predicted to have a functional role. Intriguingly, seven of the predicted kinases - EPH receptor A4 (EPHA4), EPH receptor B3 (EPHB3), EPH receptor B4 (EPHB4), erb-b2 receptor tyrosine kinase 2 (ERBB2), fibroblast growth factor receptor 2 (FGFR2), Insulin like growth factor 1 receptor (IGF1R), and ret proto-oncogene (RET) - belong to the receptor tyrosine kinase (RTK) family, which are already therapeutic targets in the clinic. We demonstrate that predicted RTKs are expressed at higher levels in DENV infected cells. Knockdown of EPHB4, ERBB2, FGFR2, or IGF1R reduces DENV infection in hepatocytes. Finally, we observe differential temporal induction of ERBB2 and IGF1R following DENV infection, highlighting their unique roles in regulating DENV.

Discussion

Collectively, our findings underscore the significance of multiple RTKs in DENV infection and advocate further exploration of RTK-oriented interventions against dengue.

CDK8 and CDK19: positive regulators of signal-induced transcription and negative regulators of Mediator complex proteins

We have conducted a detailed transcriptomic, proteomic and phosphoproteomic analysis of CDK8 and its paralog CDK19, alternative enzymatic components of the kinase module associated with transcriptional Mediator complex and implicated in development and diseases. This analysis was performed using genetic modifications of CDK8 and CDK19, selective CDK8/19 small molecule kinase inhibitors and a potent CDK8/19 PROTAC degrader. CDK8/19 inhibition in cells exposed to serum or to agonists of NFκB or protein kinase C (PKC) reduced the induction of signal-responsive genes, indicating a pleiotropic role of Mediator kinases in signal-induced transcriptional reprogramming. CDK8/19 inhibition under basal conditions initially downregulated a small group of genes, most of which were inducible by serum or PKC stimulation. Prolonged CDK8/19 inhibition or mutagenesis upregulated a larger gene set, along with a post-transcriptional increase in the proteins comprising the core Mediator complex and its kinase module. Regulation of both RNA and protein expression required CDK8/19 kinase activities but both enzymes protected their binding partner cyclin C from proteolytic degradation in a kinase-independent manner. Analysis of isogenic cell populations expressing CDK8, CDK19 or their kinase-inactive mutants revealed that CDK8 and CDK19 have the same qualitative effects on protein phosphorylation and gene expression at the RNA and protein levels, whereas differential effects of CDK8 versus CDK19 knockouts were attributable to quantitative differences in their expression and activity rather than different functions.

Inhibition of Cyclin-Dependent Kinases 8/19 Restricts Bacterial and Virus-Induced Inflammatory Responses in Monocytes

Hyperactivation of the immune system remains a dramatic, life-threatening complication of viral and bacterial infections, particularly during pneumonia. Therapeutic approaches to counteract local and systemic outbreaks of cytokine storm and to prevent tissue damage remain limited. Cyclin-dependent kinases 8 and 19 (CDK8/19) potentiate transcriptional responses to the altered microenvironment, but CDK8/19 potential in immunoregulation is not fully understood. In the present study, we investigated how a selective CDK8/19 inhibitor, Senexin B, impacts the immunogenic profiles of monocytic cells stimulated using influenza virus H1N1 or bacterial lipopolysaccharides. Senexin B was able to prevent the induction of gene expression of proinflammatory cytokines in THP1 and U937 cell lines and in human peripheral blood-derived mononuclear cells. Moreover, Senexin B substantially reduced functional manifestations of inflammation, including clustering and chemokine-dependent migration of THP1 monocytes and human pulmonary fibroblasts (HPF).

Integrated clinical and metabolomic analysis of dengue infection shows molecular signatures associated with host-pathogen interaction in different phases of the disease

PURPOSE

Dengue is a mosquito vector-borne disease caused by the dengue virus, which affects 125 million people globally. The disease causes considerable morbidity. The disease, based on symptoms, is classified into three characteristic phases, which can further lead to complications in the second phase. Molecular signatures that are associated with the three phases have not been well characterized. We performed an integrated clinical and metabolomic analysis of our patient cohort and compared it with omics data from the literature to identify signatures unique to the different phases.

METHODS

The dengue patients are recruited by clinicians after standard-of-care diagnostic tests and evaluation of symptoms. Blood from the patients was collected. NS1 antigen, IgM, IgG antibodies, and cytokines in serum were analyzed using ELISA. Targeted metabolomics was performed using LC-MS triple quad. The results were compared with analyzed transcriptomic data from the GEO database and metabolomic data sets from the literature.

RESULTS

The dengue patients displayed characteristic features of the disease, including elevated NS1 levels. TNF-α was found to be elevated in all three phases compared to healthy controls. The metabolic pathways were found to be deregulated compared to healthy controls only in phases I and II of dengue patients. The pathways represent viral replication and host response mediated pathways. The major pathways include nucleotide metabolism of various amino acids and fatty acids, biotin, etc. CONCLUSION: The results show elevated TNF-α and metabolites that are characteristic of viral infection and host response. IL10 and IFN-γ were not significant, consistent with the absence of any complications.

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.

Dengue fever is a mosquito borne viral disease caused by a single stranded RNA virus with four serotypes. DENV infection can cause various diseases, such as breakbone fever, haemorrhagic fever, and shock syndrome. As one of the most viral diseases leading to incidence rate and mortality in animal arthropods, Dengue fever has become an increasingly serious global health threat. However, the pathogenesis of dengue fever has not been fully elucidated. In this study, we used bioinformatics analysis to identify potential biomarkers associated with dengue fever and elucidate their underlying mechanisms. Finally, we predicted that IFI44L and IFI6 might be potential biomarkers of DENV infection. This finding provides a promising target for the treatment of dengue fever to a certain extent. In addition, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) network were implemented to analyze the key differentially expressed genes after DENV infection, and the related mechanisms were illuminated by this study.