The rapidly expanding severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and its variants demand a continuous monitoring method through portable and wearable devices. Utilizing the rich surface chemistry and high chemical‐to‐electrical signal conversion of 2D MXene‐graphene heterostructure thin films, a field‐effect‐transistor (FET) sensor, which has a flexible substrate to be assembled onto the mask and combines with a Bluetooth system for wireless transmission is developed, to detect the influenza and SARS‐CoV‐2 viruses in air and breath. At first, the developed sensors are examined in the laboratory through direct contact with sensing targets in solution form. The results show a low limit of detection (LOD) of 1 fg mL−1for recombinant SARS‐CoV‐2 spike protein and 125 copies mL−1for inactivated influenza A (H1N1) virus with high specificity in differing recombinant SARS‐CoV‐2 spike protein and inactivated H1N1 virus. Then the sensors are tested under various simulated human breathing modes through controlled exposure to atomizer‐generated aerosols in an enclosed chamber and mask coverage. The results show the high sensitivity of the developed sensors under varying distances to the source, viral load, flow rate, and enclosed conditions. At last, clinical tests are carried out to demonstrate the robustness and potential field applications of the sensors.
Conserved Genomic Terminals of SARS-CoV-2 as Coevolving Functional Elements and Potential Therapeutic Targets
ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 40 million people worldwide, with over 1 million deaths as of October 2020 and with multiple efforts in the development and testing of antiviral drugs and vaccines under way. In order to gain insights into SARS-CoV-2 evolution and drug targets, we investigated how and to what extent the SARS-CoV-2 genome sequence differs from those of other well-characterized human and animal coronavirus genomes, as well as how polymorphic SARS-CoV-2 genomes are generally. We ultimately sought to identify features in the SARS-CoV-2 genome that may contribute to its viral replication, host pathogenicity, and vulnerabilities. Our analyses suggest the presence of unique sequence signatures in the 3′ untranslated region (3′-UTR) of betacoronavirus lineage B, which phylogenetically encompasses SARS-CoV-2 and SARS-CoV as well as multiple groups of bat and animal coronaviruses. In addition, we identified genome-wide patterns of variation across different SARS-CoV-2 strains that likely reflect the effects of selection. Finally, we provide evidence for a possible host-microRNA-mediated interaction between the 3′-UTR and human microRNA hsa-miR-1307-3p based on the results of multiple computational target prediction analyses and an assessment of similar interactions involving the influenza A H1N1 virus. This interaction also suggests a possible survival mechanism, whereby a mutation in the SARS-CoV-2 3′-UTR leads to a weakened host immune response. The potential roles of host microRNAs in SARS-CoV-2 replication and infection and the exploitation of conserved features in the 3′-UTR as therapeutic targets warrant further investigation. IMPORTANCE The coronavirus disease 2019 (COVID-19) outbreak is having a dramatic global effect on public health and the economy. As of October 2020, SARS-CoV-2 has been detected in over 189 countries, has infected over 40 million people, and is responsible for more than 1 million deaths. The genome of SARS-CoV-2 is small but complex, and its functions and interactions with human host factors are being studied extensively. The significance of our study is that, using extensive SARS-CoV-2 genome analysis techniques, we identified potential interacting human host microRNA targets that share similarity with those of influenza A virus H1N1. Our study results will allow the development of virus-host interaction models that will enhance our understanding of SARS-CoV-2 pathogenesis and motivate the exploitation of both the interacting viral and host factors as therapeutic targets.
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- Award ID(s):
- 2031819
- PAR ID:
- 10297137
- Editor(s):
- Lee, Benhur
- Date Published:
- Journal Name:
- mSphere
- Volume:
- 5
- Issue:
- 6
- ISSN:
- 2379-5042
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1128/mSphere.00754-20
