Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies

Engineering a NanoBiT biosensor for detecting angiotensin-converting enzyme-2 (hACE2) interaction with SARS-CoV-2 spike protein and screening the inhibitors to block hACE2 and spike interaction

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is facilitated by its trimeric surface spike protein, which binds to the human angiotensin-converting enzyme 2 (hACE2) receptor. This critical interaction facilitates viral entry and is a primary target for therapeutic intervention against COVID-19. However, it is difficult to fully optimize viral infection using existing protein-protein interaction methods. Herein, we introduce a nano-luciferase binary technology (NanoBiT)-based pseudoviral sensor designed to stimulate the dynamics of viral infection in both living cells and animals. Infection progression can be dynamically visualized via a rapid increase in luminescence within 3 h using an in vivo imaging system (IVIS). Inhibition of viral infection by baicalein and baicalin was evaluated using a NanoBiT-based pseudoviral sensor. These results indicate that the inhibitory efficacy of baicalein was strengthened by targeting the spike protein, whereas baicalin targeted the hACE2 protein. Additionally, under optimized conditions, baicalein and baicalin provided a synergistic combination to inhibit pseudoviral infection. Live bioluminescence imaging was used to evaluate the in vivo effects of baicalein and baicalin treatment on LgBiT-hACE2 mice infected with the BA.2-SmBiT spike pseudovirus. This innovative bioluminescent system functions as a sensitive and early-stage quantitative viral transduction in vitro and in vivo. This platform provides novel opportunities for studying the molecular biology of animal models.

Evaluating storage conditions and enhancement strategies on viral biomarker recovery for WBE applications

Wastewater-based epidemiology (WBE) is a valuable disease surveillance tool. However, little is known on how factors such as transportation, storage, and wastewater characteristics influence the accuracy of the quantification methods. Hence, this study investigated the impact of storage temperatures and physicochemical characteristics of wastewater on SARS-CoV-2 and influenza A stability using droplet digital PCR. Additionally, strategies to enhance viral recovery were explored. Municipal influent wastewater stored between ±25 and -80 °C was assessed for a period of 84 days to determine viral degradation. Degradation up to 94.1% of influenza A and SARS-CoV-2 was observed in all samples with the highest at ±25 °C. Viral degradation was correlated to the changes in wastewater physicochemical characteristics. The low degradation observed of SARS-CoV-2 in the spiked pellets were indicative of viral adhesion to wastewater solids, which correlated with changes in pH. Ultrasonication frequencies ranging from 4 to 16 kHz, increased SARS-CoV-2 concentrations in the supernatant between 3.30 and 35.65%, indicating viral RNA attachment to wastewater solids. These results highlight the importance of additional pretreatment methods for maximizing RNA recovery from wastewater samples. Based on these findings, it was deduced that wastewater preservation studies are essential, and pretreatment should be included in the WBE methodology.

SARS-CoV-2 Orphan Gene ORF10 Contributes to More Severe COVID-19 Disease

The orphan gene of SARS-CoV-2, ORF10, is the least studied gene in the virus responsible for the COVID-19 pandemic. Recent experimentation indicated ORF10 expression moderates innate immunity in vitro. However, whether ORF10 affects COVID-19 in humans remained unknown. We determine that the ORF10 sequence is identical to the Wuhan-Hu-1 ancestral haplotype in 95% of genomes across five variants of concern (VOC). Four ORF10 variants are associated with less virulent clinical outcomes in the human host: three of these affect ORF10 protein structure, one affects ORF10 RNA structural dynamics. RNA-Seq data from 2070 samples from diverse human cells and tissues reveals ORF10 accumulation is conditionally discordant from that of other SARS-CoV-2 transcripts. Expression of ORF10 in A549 and HEK293 cells perturbs immune-related gene expression networks, alters expression of the majority of mitochondrially-encoded genes of oxidative respiration, and leads to large shifts in levels of 14 newly-identified transcripts. We conclude ORF10 contributes to more severe COVID-19 clinical outcomes in the human host.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.