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Abstract Multiple omics analyzes of Vaccinia virus (VACV) infection have defined molecular characteristics of poxvirus biology. However, little is known about the monkeypox (mpox) virus (MPXV) in humans, which has a different disease manifestation despite its high sequence similarity to VACV. Here, we perform an in-depth multi-omics analysis of the transcriptome, proteome, and phosphoproteome signatures of MPXV-infected primary human fibroblasts to gain insights into the virus-host interplay. In addition to expected perturbations of immune-related pathways, we uncover regulation of the HIPPO and TGF-β pathways. We identify dynamic phosphorylation of both host and viral proteins, which suggests that MAPKs are key regulators of differential phosphorylation in MPXV-infected cells. Among the viral proteins, we find dynamic phosphorylation of H5 that influenced the binding of H5 to dsDNA. Our extensive dataset highlights signaling events and hotspots perturbed by MPXV, extending the current knowledge on poxviruses. We use integrated pathway analysis and drug-target prediction approaches to identify potential drug targets that affect virus growth. Functionally, we exemplify the utility of this approach by identifying inhibitors of MTOR, CHUK/IKBKB, and splicing factor kinases with potent antiviral efficacy against MPXV and VACV.

Background: The intensified search for low-threshold herbal anti-viral drugs would be of great advantage in prevention of early stages of infection. Since the SARS-CoV-2 Omicron variant has prevailed in western countries, the course has only been mild, but there are still no widely available drugs that can alleviate or shorten disease progression and counteract the development of Long-COVID. This study aimed to investigate the antiviral effects of a CO2-extract from Petasites hybridus (Ze 339). Methods: We analyzed the infection and replication rate of SARS-CoV-2 in primary normal human bronchial epithelial cells (NHBEs) using a GFP-expressing version of the wild-type SARS-CoV-2 virus and live cell imaging. Upon infection with a clinical isolate of the Omicron variant, viral RNA content was quantified, and plaque assays were performed. In addition, the human transcriptome was analyzed after 4- and 24-hours post infection using whole genome microarrays. Results: Ze 339 had a protective effect on primary airway epithelial cells during SARS-CoV-2 infection and impeded SARS-CoV-2 infection and replication in NHBE. Notably, Ze 339 inhibited the expression of infection-induced IFNA10 by 8.6-fold (p

In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole-genome sequencing (WGS) analysis, with a specific focus on its implications in oncology. Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI’s emergence as a potent ally in processing and analyzing the voluminous genomic data generated. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the importance of ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more successful personalized and targeted treatments.