- NSF-PAR ID:
- Elkins, Christopher A.
- Date Published:
- Journal Name:
- Applied and Environmental Microbiology
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a high mutation rate and many variants have emerged in the last 2 years, including Alpha, Beta, Delta, Gamma and Omicron. Studies showed that the host-genome similarity (HGS) of SARS-CoV-2 is higher than SARS-CoV and the HGS of open reading frame (ORF) in coronavirus genome is closely related to suppression of innate immunity. Many works have shown that ORF 6 and ORF 8 of SARS-CoV-2 play an important role in suppressing IFN-β signaling pathway in vivo. However, the relation between HGS and the adaption of SARS-CoV-2 variants is still not clear. This work investigates HGS of SARS-CoV-2 variants based on a dataset containing more than 40,000 viral genomes. The relation between HGS of viral ORFs and the suppression of antivirus response is studied. The results show that ORF 7b, ORF 6 and ORF 8 are the top 3 genes with the highest HGS. In the past 2 years, the HGS values of ORF 8 and ORF 7B of SARS-CoV-2 have increased greatly. A remarkable correlation is discovered between HGS and inhibition of antivirus response of immune system, which suggests that the similarity between coronavirus and host gnome may be an indicator of the suppression of innate immunity. Among the five variants (Alpha, Beta, Delta, Gamma and Omicron), Delta has the highest HGS and Omicron has the lowest HGS. This finding implies that the high HGS in Delta variant may indicate further suppression of host innate immunity. However, the relatively low HGS of Omicron is still a puzzle. By comparing the mutations in genomes of Alpha, Delta and Omicron variants, a commonly shared mutation ACT > ATT is identified in high-HGS strain populations. The high HGS mutations among the three variants are quite different. This finding strongly suggests that mutations in high HGS strains are different in different variants. Only a few common mutations survive, which may play important role in improving the adaptability of SARS-CoV-2. However, the mechanism for how the mutations help SARS-CoV-2 escape immunity is still unclear. HGS analysis is a new method to study virus–host interaction and may provide a way to understand the rapid mutation and adaption of SARS-CoV-2.more » « less
Despite widespread interest in next-generation sequencing (NGS), the adoption of personalized clinical genomics and mutation profiling of cancer specimens is lagging, in part because of technical limitations. Tumors are genetically heterogeneous and often contain normal/stromal cells, features that lead to low-abundance somatic mutations that generate ambiguous results or reside below NGS detection limits, thus hindering the clinical sensitivity/specificity standards of mutation calling. We applied COLD-PCR (coamplification at lower denaturation temperature PCR), a PCR methodology that selectively enriches variants, to improve the detection of unknown mutations before NGS-based amplicon resequencing.
We used both COLD-PCR and conventional PCR (for comparison) to amplify serially diluted mutation-containing cell-line DNA diluted into wild-type DNA, as well as DNA from lung adenocarcinoma and colorectal cancer samples. After amplification of TP53 (tumor protein p53), KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), IDH1 [isocitrate dehydrogenase 1 (NADP+), soluble], and EGFR (epidermal growth factor receptor) gene regions, PCR products were pooled for library preparation, bar-coded, and sequenced on the Illumina HiSeq 2000.
In agreement with recent findings, sequencing errors by conventional targeted-amplicon approaches dictated a mutation-detection limit of approximately 1%–2%. Conversely, COLD-PCR amplicons enriched mutations above the error-related noise, enabling reliable identification of mutation abundances of approximately 0.04%. Sequencing depth was not a large factor in the identification of COLD-PCR–enriched mutations. For the clinical samples, several missense mutations were not called with conventional amplicons, yet they were clearly detectable with COLD-PCR amplicons. Tumor heterogeneity for the TP53 gene was apparent.
As cancer care shifts toward personalized intervention based on each patient's unique genetic abnormalities and tumor genome, we anticipate that COLD-PCR combined with NGS will elucidate the role of mutations in tumor progression, enabling NGS-based analysis of diverse clinical specimens within clinical practice.
Mostafa, Heba H. (Ed.)ABSTRACT SARS-CoV-2 variants of concern (VOCs) continue to pose a public health threat which necessitates a real-time monitoring strategy to complement whole genome sequencing. Thus, we investigated the efficacy of competitive probe RT-qPCR assays for six mutation sites identified in SARS-CoV-2 VOCs and, after validating the assays with synthetic RNA, performed these assays on positive saliva samples. When compared with whole genome sequence results, the SΔ69-70 and ORF1aΔ3675-3677 assays demonstrated 93.60 and 68.00% accuracy, respectively. The SNP assays (K417T, E484K, E484Q, L452R) demonstrated 99.20, 96.40, 99.60, and 96.80% accuracies, respectively. Lastly, we screened 345 positive saliva samples from 7 to 22 December 2021 using Omicron-specific mutation assays and were able to quickly identify rapid spread of Omicron in Upstate South Carolina. Our workflow demonstrates a novel approach for low-cost, real-time population screening of VOCs. IMPORTANCE SARS-CoV-2 variants of concern and their many sublineages can be characterized by mutations present within their genetic sequences. These mutations can provide selective advantages such as increased transmissibility and antibody evasion, which influences public health recommendations such as mask mandates, quarantine requirements, and treatment regimens. Our RT-qPCR workflow allows for strain identification of SARS-CoV-2 positive saliva samples by targeting common mutation sites shared between variants of concern and detecting single nucleotides present at the targeted location. This differential diagnostic system can quickly and effectively identify a wide array of SARS-CoV-2 strains, which can provide more informed public health surveillance strategies in the future.more » « less
Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths–Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung–Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation “bands” persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a “toy” mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations.more » « less
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