Browsing by Author "Sapoval, Nicolae"

Now showing 1 - 8 of 8
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    Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater
    (Springer Nature, 2024) Liu, Yunxi; Sapoval, Nicolae; Gallego-García, Pilar; Tomás, Laura; Posada, David; Treangen, Todd J.; Stadler, Lauren B.
    Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
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    Current progress and open challenges for applying deep learning across the biosciences
    (Springer Nature, 2022) Sapoval, Nicolae; Aghazadeh, Amirali; Nute, Michael G.; Antunes, Dinler A.; Balaji, Advait; Baraniuk, Richard; Barberan, C.J.; Dannenfelser, Ruth; Dun, Chen; Edrisi, Mohammadamin; Elworth, R.A. Leo; Kille, Bryce; Kyrillidis, Anastasios; Nakhleh, Luay; Wolfe, Cameron R.; Yan, Zhi; Yao, Vicky; Treangen, Todd J.
    Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
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    Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
    (Springer Nature, 2023) Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G.; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.
    As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).
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    Infectious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Exhaled Aerosols and Efficacy of Masks During Early Mild Infection
    (Oxford University Press, 2022) Adenaiye, Oluwasanmi O.; Lai, Jianyu; Bueno de Mesquita, P. Jacob; Hong, Filbert; Youssefi, Somayeh; German, Jennifer; Tai, S.H. Sheldon; Albert, Barbara; Schanz, Maria; Weston, Stuart; Hang, Jun; Fung, Christian; Chung, Hye Kyung; Coleman, Kristen K.; Sapoval, Nicolae; Treangen, Todd; Berry, Irina Maljkovic; Mullins, Kristin; Frieman, Matthew; Ma, Tianzhou; Milton, Donald K.; University of Maryland StopCOVID Research Group
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemiology implicates airborne transmission; aerosol infectiousness and impacts of masks and variants on aerosol shedding are not well understood.We recruited coronavirus disease 2019 (COVID-19) cases to give blood, saliva, mid-turbinate and fomite (phone) swabs, and 30-minute breath samples while vocalizing into a Gesundheit-II, with and without masks at up to 2 visits 2 days apart. We quantified and sequenced viral RNA, cultured virus, and assayed serum samples for anti-spike and anti-receptor binding domain antibodies.We enrolled 49 seronegative cases (mean days post onset 3.8 ± 2.1), May 2020 through April 2021. We detected SARS-CoV-2 RNA in 36% of fine (≤5 µm), 26% of coarse (>5 µm) aerosols, and 52% of fomite samples overall and in all samples from 4 alpha variant cases. Masks reduced viral RNA by 48% (95% confidence interval [CI], 3 to 72%) in fine and by 77% (95% CI, 51 to 89%) in coarse aerosols; cloth and surgical masks were not significantly different. The alpha variant was associated with a 43-fold (95% CI, 6.6- to 280-fold) increase in fine aerosol viral RNA, compared with earlier viruses, that remained a significant 18-fold (95% CI, 3.4- to 92-fold) increase adjusting for viral RNA in saliva, swabs, and other potential confounders. Two fine aerosol samples, collected while participants wore masks, were culture-positive.SARS-CoV-2 is evolving toward more efficient aerosol generation and loose-fitting masks provide significant but only modest source control. Therefore, until vaccination rates are very high, continued layered controls and tight-fitting masks and respirators will be necessary.
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    Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters
    (Oxford University Press, 2022) Gallego-García, Pilar; Varela, Nair; Estévez-Gómez, Nuria; De Chiara, Loretta; Fernández-Silva, Iria; Valverde, Diana; Sapoval, Nicolae; Treangen, Todd J; Regueiro, Benito; Cabrera-Alvargonzález, Jorge Julio; del Campo, Víctor; Pérez, Sonia; Posada, David
    A detailed understanding of how and when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced forty-nine SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be one to two viral particles for sample pairs whose donor–recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.
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    Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens
    (Springer Nature, 2024) Wang, Michael X.; Lou, Esther G.; Sapoval, Nicolae; Kim, Eddie; Kalvapalle, Prashant; Kille, Bryce; Elworth, R. A. Leo; Liu, Yunxi; Fu, Yilei; Stadler, Lauren B.; Treangen, Todd J.
    Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.
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    Role of miR-2392 in driving SARS-CoV-2 infection
    (Elsevier, 2021) McDonald, J. Tyson; Enguita, Francisco J.; Taylor, Deanne; Griffin, Robert J.; Priebe, Waldemar; Emmett, Mark R.; Sajadi, Mohammad M.; Harris, Anthony D.; Clement, Jean; Dybas, Joseph M.; Aykin-Burns, Nukhet; Guarnieri, Joseph W.; Singh, Larry N.; Grabham, Peter; Baylin, Stephen B.; Yousey, Aliza; Pearson, Andrea N.; Corry, Peter M.; Saravia-Butler, Amanda; Aunins, Thomas R.; Sharma, Sadhana; Nagpal, Prashant; Meydan, Cem; Foox, Jonathan; Mozsary, Christopher; Cerqueira, Bianca; Zaksas, Viktorija; Singh, Urminder; Wurtele, Eve Syrkin; Costes, Sylvain V.; Davanzo, Gustavo Gastão; Galeano, Diego; Paccanaro, Alberto; Meinig, Suzanne L.; Hagan, Robert S.; Bowman, Natalie M.; Wallet, Shannon M.; Maile, Robert; Wolfgang, Matthew C.; Hagan, Robert S.; Mock, Jason R.; Bowman, Natalie M.; Torres-Castillo, Jose L.; Love, Miriya K.; Meinig, Suzanne L.; Lovell, Will; Rice, Colleen; Mitchem, Olivia; Burgess, Dominique; Suggs, Jessica; Jacobs, Jordan; Wolfgang, Matthew C.; Altinok, Selin; Sapoval, Nicolae; Treangen, Todd J.; Moraes-Vieira, Pedro M.; Vanderburg, Charles; Wallace, Douglas C.; Schisler, Jonathan C.; Mason, Christopher E.; Chatterjee, Anushree; Meller, Robert; Beheshti, Afshin
    MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provide an exciting avenue toward antiviral therapeutics. From patient transcriptomic data, we determined that a circulating miRNA, miR-2392, is directly involved with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia, as well as promoting many symptoms associated with coronavirus disease 2019 (COVID-19) infection. We demonstrate that miR-2392 is present in the blood and urine of patients positive for COVID-19 but is not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters, and may potentially inhibit a COVID-19 disease state in humans.
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    SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission
    (Cold Spring Harbor Laboratory Press, 2021) Sapoval, Nicolae; Mahmoud, Medhat; Jochum, Michael D.; Liu, Yunxi; Elworth, R. A. Leo; Wang, Qi; Albin, Dreycey; Ogilvie, Huw A.; Lee, Michael D.; Villapol, Sonia; Hernandez, Kyle M.; Berry, Irina Maljkovic; Foox, Jonathan; Beheshti, Afshin; Ternus, Krista; Aagaard, Kjersti M.; Posada, David; Mason, Christopher E.; Sedlazeck, Fritz J.; Treangen, Todd J.
    The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.