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Summary Programmed cell death pathways are triggered by various stresses or stimuli, including viral infections. The mechanism underlying the regulation of these pathways upon Influenza A virus (IAV) infection is not well characterized. We report that a cytosolic DNA sensor IFI16 is essential for the activation of programmed cell death pathways in IAV infected cells. We have identified that IFI16 functions as an RNA sensor for the influenza A virus by interacting with genomic RNA. The activation of IFI16 triggers the production of type I, III interferons, and also pro-inflammatory cytokines via the STING-TBK1 and Pro-caspase-1 signaling axis, thereby promoting cell death (apoptosis and pyroptosis in IAV infected cells). On the contrary, IFI16 knockdown cells showed reduced inflammatory responses and also prevented cell mortality during IAV infection. Collectively, these results demonstrate the pivotal role of IFI16-mediated IAV sensing and its essential role in activating programmed cell death pathways.

Dengue virus (DENV) is a mosquito‐borne flavivirus that causes frequent outbreaks in tropical countries. Due to the four different serotypes and ever‐mutating RNA genome, it is challenging to develop efficient therapeutics. Early diagnosis is crucial to prevent the severe form of dengue, leading to mortality. In the past decade, rapid advancement in the high throughput sequencing technologies has shed light on the crucial regulating role of non‐coding RNAs (ncRNAs), also known as the “dark matter” of the genome, in various pathological processes. In addition to the human host ncRNAs like microRNAs and circular RNAs, DENV also produces ncRNAs such as subgenomic flaviviral RNAs that can modulate the virus life cycle and regulate disease outcomes. This review outlines the advances in understanding the interplay between the human host and DENV ncRNAs, their regulation of the innate immune system of the host, and the prospects of the ncRNAs in clinical applications such as dengue diagnosis and promising therapeutics.

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The Coronavirus disease 2019 (COVID-19) pandemic, caused by rapidly evolving variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2), continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome data sets. The robust 7-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome data set of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality.