Integration of RT-LAMP and Microfluidic Technology for Detection of SARS-CoV-2 in Wastewater as an Advanced Point-of-care Platform

Development of lab-on-a-chip (LOC) system based on integration of reverse transcription loop-mediated isothermal amplification (RT-LAMP) and microfluidic technology is expected to speed up SARS-CoV-2 diagnostics allowing early intervention. In the current work, reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) and RT-LAMP assays were performed on extracted RNA of 7 wastewater samples from COVID-19 hotspots. RT{square}LAMP assay was also performed on wastewater samples without RNA extraction. Current detection of SARS-CoV-2 is mainly by RT-qPCR of ORF (ORF1ab) and N genes so we targeted both to find the best surrogate marker for SARS-CoV-2 detection. We also performed RT-LAMP with/without RNA extraction inside microfluidic device to target both genes. Positivity rates of RT-qPCR and RT-LAMP performed on extracted RNA were 100.0% (7/7) and 85.7% (6/7), respectively. RT-qPCR results revealed that all 7 wastewater samples were positive for N gene (Ct range 37-39), and negative for ORF1ab, suggesting that N gene could be used as a surrogate marker for detection of SARS-CoV-2. RT-LAMP of N and ORF (ORF1a) genes performed on wastewater samples without RNA extraction indicated that all 7 samples remains pink (negative). The color remains pink in all microchannels except microchannels which subjected to RT-LAMP for targeting N region after RNA extraction (yellow color) in 6 out of 7 samples. This study shows that SARS-CoV-2 was successfully detected from wastewater samples using RT-LAMP in microfluidic chips.

Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach

OBJECTIVE@#To predict immunogenic promiscuous T cell epitopes from the polyprotein of the Zika virus using a range of bioinformatics tools. To date, no epitope data are available for the Zika virus in the IEDB database.@*METHODS@#We retrieved nearly 54 full length polyprotein sequences of the Zika virus from the NCBI database belonging to different outbreaks. A consensus sequence was then used to predict the promiscuous T cell epitopes that bind MHC 1 and MHC II alleles using PorPred1 and ProPred immunoinformatic algorithms respectively. The antigenicity predicted score was also calculated for each predicted epitope using the VaxiJen 2.0 tool.@*RESULTS@#By using ProPred1, 23 antigenic epitopes for HLA class I and 48 antigenic epitopes for HLA class II were predicted from the consensus polyprotein sequence of Zika virus. The greatest number of MHC class I binding epitopes were projected within the NS5 (21%), followed by Envelope (17%). For MHC class II, greatest number of predicted epitopes were in NS5 (19%) followed by the Envelope, NS1 and NS2 (17% each). A variety of epitopes with good binding affinity, promiscuity and antigenicity were predicted for both the HLA classes.@*CONCLUSION@#The predicted conserved promiscuous T-cell epitopes examined in this study were reported for the first time and will contribute to the imminent design of Zika virus vaccine candidates, which will be able to induce a broad range of immune responses in a heterogeneous HLA population. However, our results can be verified and employed in future efficacious vaccine formulations only after successful experimental studies.

Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach

Objective To predict immunogenic promiscuous T cell epitopes from the polyprotein of the Zika virus using a range of bioinformatics tools. To date, no epitope data are available for the Zika virus in the IEDB database. Methods We retrieved nearly 54 full length polyprotein sequences of the Zika virus from the NCBI database belonging to different outbreaks. A consensus sequence was then used to predict the promiscuous T cell epitopes that bind MHC 1 and MHC II alleles using PorPred1 and ProPred immunoinformatic algorithms respectively. The antigenicity predicted score was also calculated for each predicted epitope using the VaxiJen 2.0 tool. Results By using ProPred1, 23 antigenic epitopes for HLA class I and 48 antigenic epitopes for HLA class II were predicted from the consensus polyprotein sequence of Zika virus. The greatest number of MHC class I binding epitopes were projected within the NS5 (21%), followed by Envelope (17%). For MHC class II, greatest number of predicted epitopes were in NS5 (19%) followed by the Envelope, NS1 and NS2 (17% each). A variety of epitopes with good binding affinity, promiscuity and antigenicity were predicted for both the HLA classes. Conclusion The predicted conserved promiscuous T-cell epitopes examined in this study were reported for the first time and will contribute to the imminent design of Zika virus vaccine candidates, which will be able to induce a broad range of immune responses in a heterogeneous HLA population. However, our results can be verified and employed in future efficacious vaccine formulations only after successful experimental studies.