Molecular diagnosis of COVID-19 primarily relies on the detection of RNA of the SARS-CoV-2 virus, the causative infectious agent of the pandemic. Reverse transcription polymerase chain reaction (RT-PCR) enables sensitive detection of specific sequences of genes that encode the RNA dependent RNA polymerase (RdRP), nucleocapsid (N), envelope (E), and spike (S) proteins of the virus. Although RT-PCR tests have been widely used and many alternative assays have been developed, the current testing capacity and availability cannot meet the unprecedented global demands for rapid, reliable, and widely accessible molecular diagnosis. Challenges remain throughout the entire analytical process, from the collection and treatment of specimens to the amplification and detection of viral RNA and the validation of clinical sensitivity and specificity. We highlight the main issues surrounding molecular diagnosis of COVID-19, including false negatives from the detection of viral RNA, temporal variations of viral loads, selection and treatment of specimens, and limiting factors in detecting viral proteins. We discuss critical research needs, such as improvements in RT-PCR, development of alternative nucleic acid amplification techniques, incorporating CRISPR technology for point-of-care (POC) applications, validation of POC tests, and sequencing of viral RNA and its mutations. Improved assays are also needed for environmental surveillance or wastewater-based epidemiology, which gauges infection on the community level through analyses of viral components in the community's wastewater. Public health surveillance benefits from large-scale analyses of antibodies in serum, although the current serological tests do not quantify neutralizing antibodies. Further advances in analytical technology and research through multidisciplinary collaboration will contribute to the development of mitigation strategies, therapeutics, and vaccines. Lessons learned from molecular diagnosis of COVID-19 are valuable for better preparedness in response to other infectious diseases.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) protein systems revolutionize genome engineering and advance analytical chemistry and diagnostic technology.

Chronic hepatitis B virus (HBV) infection is a major public health problem. New treatment approaches are needed because current treatments do not target covalently closed circular DNA (cccDNA), the template for HBV replication, and rarely clear the virus. We harnessed adeno-associated virus (AAV) vectors and CRISPR- Staphylococcus aureus ( Sa )Cas9 to edit the HBV genome in liver-humanized FRG mice chronically infected with HBV and receiving entecavir. Gene editing was detected in livers of five of eight HBV-specific AAV- Sa Cas9-treated mice, but not control mice, and mice with detectable HBV gene editing showed higher levels of Sa Cas9 delivery to HBV + human hepatocytes than those without gene editing. HBV-specific AAV- Sa Cas9 therapy significantly improved survival of human hepatocytes, showed a trend toward decreasing total liver HBV DNA and cccDNA, and was well tolerated. This work provides evidence for the feasibility and safety of in vivo gene editing for chronic HBV infections, and it suggests that with further optimization, this approach may offer a plausible way to treat or even cure chronic HBV infections.

Systems biology provides opportunities to fully understand the genes and pathways in disease pathogenesis. We used literature knowledge and unbiased multiple data meta-analysis paradigms to analyze microarray datasets across different mouse strains and acute allergic asthma models. Our combined gene-driven and pathway-driven strategies generated a stringent signature list totaling 933 genes with 41% (440) asthma-annotated genes and 59% (493) ignorome genes, not previously associated with asthma. Within the list, we identified inflammation, circadian rhythm, lung-specific insult response, stem cell proliferation domains, hubs, peripheral genes, and super-connectors that link the biological domains (Il6, Il1ß, Cd4, Cd44, Stat1, Traf6, Rela, Cadm1, Nr3c1, Prkcd, Vwf, Erbb2). In conclusion, this novel bioinformatics approach will be a powerful strategy for clinical and across species data analysis that allows for the validation of experimental models and might lead to the discovery of novel mechanistic insights in asthma.