African ZIKV lineage fails to sustain infectivity in an in vitro mimetic urban cycle

Zika virus (ZIKV) is an arbovirus maintained in nature in two distinct cycles of transmission: urban and sylvatic. Each cycle includes specific vertebrate and invertebrate hosts, and through alternate infections, a conserved consensus sequence is maintained that might vary depending on the cycle. The current study aimed to investigate the ability of ZIKVAF and ZIKVBR to maintain an infectious cycle by alternating passages in cells mimicking the urban (UC) and semi-sylvatic (SC) cycles. The complete genome of the original inoculum and the last passages for each cycle were sequenced by Sanger. Ten passages were performed, as planned, for ZIKVBR UC, ZIKVAF SC, and ZIKVBR SC. ZIKVBR SC showed significant variation in viral titers along the passages, suggesting that the virus is not well adapted to the non-human primate host. ZIKVAF passage in UC was abrogated in the third passage, showing the inability of the African lineage to sustain cycles in human cells, suggesting a low capacity to establish an urban cycle. Several mutations were found in both strains along the passages, but not occurring at equivalent positions. Further studies are needed to elucidate whether any of these specific mutations affect viral fitness. ZIKV strains behave differently in artificial transmission cycles in vitro: Brazilian ZIKV was able to establish urban and semi-sylvatic cycles in vitro. African ZIKV proved unable to cycle among human and mosquito cells and is compatible only with the semi-sylvatic cycle. The main mutations arose in the NS2A region after artificial transmission cycles for both ZIKV strains but not at equivalent positions.

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies

Background

The genome of SARS-CoV-2 is susceptible to mutations during viral replication due to the errors generated by RNA-dependent RNA polymerases. These mutations enable the SARS-CoV-2 to evolve into new strains. Viral quasispecies emerge from de novo mutations that occur in individual patients. In combination, these sets of viral mutations provide distinct genetic fingerprints that reveal the patterns of transmission and have utility in contact tracing.

Methods

Leveraging thousands of sequenced SARS-CoV-2 genomes, we performed a viral pangenome analysis to identify conserved genomic sequences. We used a rapid and highly efficient computational approach that relies on k-mers, short tracts of sequence, instead of conventional sequence alignment. Using this method, we annotated viral mutation signatures that were associated with specific strains. Based on these highly conserved viral sequences, we developed a rapid and highly scalable targeted sequencing assay to identify mutations, detect quasispecies variants, and identify mutation signatures from patients. These results were compared to the pangenome genetic fingerprints.

Results

We built a k-mer index for thousands of SARS-CoV-2 genomes and identified conserved genomics regions and landscape of mutations across thousands of virus genomes. We delineated mutation profiles spanning common genetic fingerprints (the combination of mutations in a viral assembly) and a combination of mutations that appear in only a small number of patients. We developed a targeted sequencing assay by selecting primers from the conserved viral genome regions to flank frequent mutations. Using a cohort of 100 SARS-CoV-2 clinical samples, we identified genetic fingerprints consisting of strain-specific mutations seen across populations and de novo quasispecies mutations localized to individual infections. We compared the mutation profiles of viral samples undergoing analysis with the features of the pangenome.

Conclusions

We conducted an analysis for viral mutation profiles that provide the basis of genetic fingerprints. Our study linked pangenome analysis with targeted deep sequenced SARS-CoV-2 clinical samples. We identified quasispecies mutations occurring within individual patients and determined their general prevalence when compared to over 70,000 other strains. Analysis of these genetic fingerprints may provide a way of conducting molecular contact tracing.

Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity

The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.

The impact of oxidative stress damage induced by the environmental stressors on COVID-19

The ongoing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a substantial stressor that is greatly impacting environmental sustainability. Besides, the different pre-existing environmental stressors and coronavirus disease-2019 (COVID-19)-related stressors are further worsening the effects of the viral disease by inducing the generation of oxidative stress. The generated oxidative stress results in nucleic acid damage associated with viral mutations, that could potentially reduce the effectiveness of COVID-19 management, including the vaccine approach. The current review is aimed to overview the impact of the oxidative stress damage induced by various environmental stressors on COVID-19. The available data regarding the COVID-19-related stressors and the effects of oxidative stress damage induced by the chronic stress, exposure to free radicals, and malnutrition are also analyzed to showcase the promising options, which could be investigated further for sustainable control of the pandemic.