Ketamine Use for Palliative Care in the Austere Environment: Is Ketamine the Path Forward for Palliative Care.

The goal of palliative care is to focus on the holistic needs of the patient and their family versus the pathology of the patient's diagnosis to reduce the stress of illness. U.S. servicemembers deployed to austere environments worldwide have significantly less access to palliative care than in military treatment facilities in the U.S. Preparation for future conflicts introduces the concept of prolonged medical management for an environment where urgent casualty evacuation is impossible. Ketamine is currently widely used for analgesia and anesthesia in the care of military service members and its use has increased in combat zones of Iraq and Afghanistan due to the favorable preservation of respiratory function, minimal changes in hemodynamics, and lower pain scores compared to opioids. Ketamine acts as a non-competitive antagonist on N-methyl-D aspartate (NMDA) receptors. Its anesthesia and analgesic effects are complex and include both presynaptic and postsynaptic neurons in brain and spinal cord. The use of palliative care to minimize suffering should not be withheld due to the logistical boundaries of austere military environments or lack of guidelines for recommended use. The use of ketamine for palliative care is a new clinical management strategy to provide both sedation and pain management for an acute pain crisis or comfort measures for the terminally ill. This makes ketamine an attractive consideration for palliative care when managing critically wounded patients for an extended time.

Positive effects of public breeding on US rice yields under future climate scenarios.

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.

Bioinformatic analysis of human ZPR1 gene pathogenic exome mutations.

Advanced sequencing technologies enable rapid detection of sequence variants, aiming to uncover the molecular foundations of human genetic disorders. The challenge lies in interpreting the influence of new exome variants that lead to diverse phenotypes. Our study introduces a detailed, multi-tiered method for assessing the impact of novel variants, particularly focusing on the zinc finger protein 1 (ZPR1) gene. Herein, we employed a combination of variant effect predictors, protein stability analyses, and the American College of Medical Genetics and Association of Molecular Pathology (ACMG/AMP) guidelines. Our structural analysis pinpoints specific amino acid residues in the ZPR1 zinc finger domains that are sensitive to changes, distinguishing between benign and disease-causing coding variants using rigorous in silico tools. We examined 223 germline ZPR1 exome variants, uncovering significant ethnic disparities in the frequency of heterozygous harmful ZPR1 variants, ranging from 0.04% in the Ashkenazi Jewish population to 0.34% in African/African Americans. Additionally, the discovery of three homozygous carriers in European and South Asian groups suggests a higher occurrence of ZPR1 variants in these demographics, meriting further exploration. This research provides insights into the prevalence and implications of amino acid substitutions in the ZPR1 protein.

Overview of the Knowledge Management Center for Illuminating the Druggable Genome.

The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.

Genetic loci regulating the concentrations of anthocyanins and proanthocyanidins in the pericarps of purple and red rice.

The pigmented flavonoids, anthocyanins and proanthocyanidins, have health promoting properties. Previous work determined that the genes Pb and Rc turn on and off the biosynthesis of anthocyanins (purple) and proanthocyanidins (red), respectively. Not yet known is how the concentrations of these pigmented flavonoids are regulated in grain pericarps. Quantitative trait locus (QTL) analysis in a population of rice (Oryza sativa L.) F5 recombinant inbred lines from white pericarp "IR36ae" x red+purple pericarp "242" revealed three QTLs associated with grain concentrations of anthocyanins (TAC) or proanthocyanidins (PA). Both TAC and PA independently mapped to a 1.5 Mb QTL region on chromosome 3 between RM3400 (at 15.8 Mb) and RM15123 (17.3 Mb), named qPR3. Across 2 years, qPR3 explained 36.3% of variance in TAC and 35.8% in PA variance not attributable to Pb or Rc. The qPR3 region encompasses Kala3, a MYB transcription factor previously known to regulate purple grain characteristics. Study of PbPbRcrc progeny showed that TAC of RcRc near isogenic lines (NILs) was 2.1-4.5x that of rcrc. Similarly, study of PbPbRcRc NILs, which had 70% higher PA than pbpbRcRc NILs, revealed a mutual enhancement, not a trade-off between these compounds that share precursors. This suggests that Pb and Rc upregulate genes in a shared pathway as they activate TAC and PA synthesis, respectively. This study provides molecular markers for facilitating marker-assisted selection of qPR3, qPR5, and qPR7 to enhance grain concentrations of pigmented flavonoids and documented that stacking Rc and Pb genes further increases both flavonoid compounds.

SARS-CoV-2 variant identification using a genome tiling array and genotyping probes.

With over 5.5 million deaths worldwide attributed to the respiratory disease COVID-19 caused by the novel coronavirus SARS-CoV-2, it is essential that continued efforts be made to track the evolution and spread of the virus globally. The authors previously presented a rapid and cost-effective method to sequence the entire SARS-CoV-2 genome with 95% coverage and 99.9% accuracy. This method is advantageous for identifying and tracking variants in the SARS-CoV-2 genome compared with traditional short-read sequencing methods which can be time-consuming and costly. Herein, the addition of genotyping probes to a DNA chip that targets known SARS-CoV-2 variants is presented. The incorporation of genotyping probe sets along with the advent of a moving average filter improved the sequencing coverage and accuracy of the SARS-CoV-2 genome.

Throughout the COVID-19 pandemic the virus known as SARS-CoV-2 has continued to mutate and evolve. It is imperative to continue to track these mutations and where the virus has traveled to best inform healthcare practices and global strategies to combat the virus. The authors previously developed a method to investigate 95% of this viral genome with 99.9% accuracy that was more cost-effective and less time-consuming than previous methods. In this work, specific markers were added to the technology to allow tracking of mutations in the virus that have already been documented. In doing so, the accuracy and how much of the viral genome can be sequenced was improved.

Pharos 2023: an integrated resource for the understudied human proteome.

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

Spatial Stochastic Model of the Pre-B Cell Receptor.

Survival and proliferation of immature B lymphocytes requires expression and tonic signaling of the pre-B cell receptor (pre-BCR). This low level, ligand-independent signaling is likely achieved through frequent, but short-lived, homo interactions. Tonic signaling is also central in the pathology of precursor B acute lymphoblastic leukemia (B-ALL). In order to understand how repeated, transient events can lead to sustained signaling and to assess the impact of receptor accumulation induced by the membrane landscape, we developed a spatial stochastic model of receptor aggregation and downstream signaling events. Our rule- and agent-based model builds on previous mature BCR signaling models and incorporates novel parameters derived from single particle tracking of pre-BCR on surfaces of two different B-ALL cell lines, 697 and Nalm6. Live cell tracking of receptors on the two cell lines revealed characteristic differences in their dimer dissociation rates and diffusion coefficients. We report here that these differences affect pre-BCR aggregation and consequent signal initiation events. Receptors on Nalm6 cells, which have a lower off-rate and lower diffusion coefficient, more frequently form higher order oligomers than pre-BCR on 697 cells, resulting in higher levels of downstream phosphorylation in the Nalm6 cell line.

Navigating rice seedling cold resilience: QTL mapping in two inbred line populations and the search for genes.

Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches. Within the QTL regions, 25 cold-tolerant candidate genes were identified based on genomic differences between the cold-tolerant and cold-sensitive parents. Of those genes, 20% coded for receptor-like kinases potentially involved in signal transduction of cold tolerance responses; 16% coded for transcription factors or factors potentially involved in regulating cold tolerance response effector genes; and 64% coded for protein chaperons or enzymes potentially serving as cold tolerance effector proteins. Most of the 25 genes were cold temperature regulated and had deleterious nucleotide variants in the cold-sensitive parent, which might contribute to its cold-sensitive phenotype.

Identification of quantitative trait loci for tillering, root, and shoot biomass at the maximum tillering stage in rice.

Tillering and plant biomass are key determinants of rice crop productivity. Tillering at the vegetative stage is associated with weed competition, nutrient uptake, and methane emissions. However, little information is available on quantitative trait loci (QTLs) associated with tiller number (qTN), root biomass (qRB), and shoot biomass (qSB) at the active tillering stage which occurs approximately 6 weeks after planting. Here, we mapped tiller and biomass QTLs with ~ 250 recombinant inbred lines derived from a 'Francis' by 'Rondo' cross using data collected at the maximum tillering stage from two years of greenhouse study, and further compared these QTLs with those mapped at the harvest stage from a field study. Across these three studies, we discovered six qTNs, two qRBs, and three qSBs. Multiple linear regression further indicated that qTN1-2, qTN3-3, qTN4-1, qRB3-1, and qRB5-1 were significant at the maximum tillering stage while qTN3-2 was detected only at the harvest stage. Moreover, qTN3-1 was consistently significant across different developmental stages and growing environments. The genes identified from the peak target qTN regions included a carotenoid metabolism enzyme, a MYB transcription factor, a CBS domain-containing protein, a SAC3/GANP family protein, a TIFY motif containing protein, and an ABC transporter protein. Two genes in the qRB peak target regions included an expressed protein and a WRKY gene. This knowledge of the QTLs, associated markers, candidate genes, and germplasm resources with high TN, RB and SB is of value to rice cultivar improvement programs.

Phenotypic Variation and the Impact of Admixture in the Oryza rufipogon Species Complex (ORSC).

Crop wild relatives represent valuable reservoirs of variation for breeding, but their populations are threatened in natural habitats, are sparsely represented in genebanks, and most are poorly characterized. The focus of this study is the Oryza rufipogon species complex (ORSC), wild progenitor of Asian rice (Oryza sativa L.). The ORSC comprises perennial, annual and intermediate forms which were historically designated as O. rufipogon, O. nivara, and O. sativa f. spontanea (or Oryza spp., an annual form of mixed O. rufipogon/O. nivara and O. sativa ancestry), respectively, based on non-standardized morphological, geographical, and/or ecologically-based species definitions and boundaries. Here, a collection of 240 diverse ORSC accessions, characterized by genotyping-by-sequencing (113,739 SNPs), was phenotyped for 44 traits associated with plant, panicle, and seed morphology in the screenhouse at the International Rice Research Institute, Philippines. These traits included heritable phenotypes often recorded as characterization data by genebanks. Over 100 of these ORSC accessions were also phenotyped in the greenhouse for 18 traits in Stuttgart, Arkansas, and 16 traits in Ithaca, New York, United States. We implemented a Bayesian Gaussian mixture model to infer accession groups from a subset of these phenotypic data and ascertained three phenotype-based group assignments. We used concordance between the genotypic subpopulations and these phenotype-based groups to identify a suite of phenotypic traits that could reliably differentiate the ORSC populations, whether measured in tropical or temperate regions. The traits provide insight into plant morphology, life history (perenniality versus annuality) and mating habit (self- versus cross-pollinated), and are largely consistent with genebank species designations. One phenotypic group contains predominantly O. rufipogon accessions characterized as perennial and largely out-crossing and one contains predominantly O. nivara accessions characterized as annual and largely inbreeding. From these groups, 42 "core" O. rufipogon and 25 "core" O. nivara accessions were identified for domestication studies. The third group, comprising 20% of our collection, has the most accessions identified as Oryza spp. (51.2%) and levels of O. sativa admixture accounting for more than 50% of the genome. This third group is potentially useful as a "pre-breeding" pool for breeders attempting to incorporate novel variation into elite breeding lines.

Assessment of Rice Sheath Blight Resistance Including Associations with Plant Architecture, as Revealed by Genome-Wide Association Studies.

BACKGROUND: Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement. Many ShB-QTL are associated with plant architectural traits detrimental to yield, including tall plants, late maturity, or open canopy from few or procumbent tillers, which confound detection of physiological resistance. RESULTS: To identify QTL for ShB resistance, 417 accessions from the Rice Diversity Panel 1 (RDP1), developed for association mapping studies, were evaluated for ShB resistance, plant height and days to heading in inoculated field plots in Arkansas, USA (AR) and Nanning, China (NC). Inoculated greenhouse-grown plants were used to evaluate ShB using a seedling-stage method to eliminate effects from height or maturity, and tiller (TN) and panicle number (PN) per plant. Potted plants were used to evaluate the RDP1 for TN and PN. Genome-wide association (GWA) mapping with over 3.4 million SNPs identified 21 targeted SNP markers associated with ShB which tagged 18 ShB-QTL not associated with undesirable plant architecture traits. Ten SNPs were associated with ShB among accessions of the Indica subspecies, ten among Japonica subspecies accessions, and one among all RDP1 accessions. Across the 18 ShB QTL, only qShB4-1 was not previously reported in biparental mapping studies and qShB9 was not reported in the GWA ShB studies. All 14 PN QTL overlapped with TN QTL, with 15 total TN QTL identified. Allele effects at the five TN QTL co-located with ShB QTL indicated that increased TN does not inevitably increase disease development; in fact, for four ShB QTL that overlapped TN QTL, the alleles increasing resistance were associated with increased TN and PN, suggesting a desirable coupling of alleles at linked genes. CONCLUSIONS: Nineteen accessions identified as containing the most SNP alleles associated with ShB resistance for each subpopulation were resistant in both AR and NC field trials. Rice breeders can utilize these accessions and SNPs to develop cultivars with enhanced ShB resistance along with increased TN and PN for improved yield potential.

Elevated SARS-CoV-2 in peripheral blood and increased COVID-19 severity in American Indians/Alaska Natives.

Epidemiological data across the United States show health disparities in COVID-19 infection, hospitalization, and mortality by race/ethnicity. While the association between elevated SARS-CoV-2 viral loads (VLs) (i.e. upper respiratory tract (URT) and peripheral blood (PB)) and increased COVID-19 severity has been reported, data remain largely unavailable for some disproportionately impacted racial/ethnic groups, particularly for American Indian or Alaska Native (AI/AN) populations. As such, we determined the relationship between SARS-CoV-2 VL dynamics and disease severity in a diverse cohort of hospitalized patients. Results presented here are for study participants (n = 94, ages 21-88 years) enrolled in a prospective observational study between May and October 2020 who had SARS-CoV-2 viral clades 20A, C, and G. Based on self-reported race/ethnicity and sample size distribution, the cohort was stratified into two groups: (AI/AN, n = 43) and all other races/ethnicities combined (non-AI/AN, n = 51). SARS-CoV-2 VLs were quantified in the URT and PB on days 0-3, 6, 9, and 14. The strongest predictor of severe COVID-19 in the study population was the mean VL in PB (OR = 3.34; P = 2.00 × 10-4). The AI/AN group had the following: (1) comparable co-morbidities and admission laboratory values, yet more severe COVID-19 (OR = 4.81; P = 0.014); (2) a 2.1 longer duration of hospital stay (P = 0.023); and (3) higher initial and cumulative PB VLs during severe disease (P = 0.025). Moreover, self-reported race/ethnicity as AI/AN was the strongest predictor of elevated PB VLs (ß = 1.08; P = 6.00 × 10-4) and detection of SARS-CoV-2 in PB (hazard ratio = 3.58; P = 0.004). The findings presented here suggest a strong relationship between PB VL (magnitude and frequency) and severe COVID-19, particularly for the AI/AN group.

Modeling the Cluster Size Distribution of Vascular Endothelial Growth Factor (VEGF) Receptors.

We previously developed a method of defining receptor clusters in the membrane based on mutual distance and applied it to a set of transmission microscopy images of vascular endothelial growth factor receptors. An optimal length parameter was identified, resulting in cluster identification and a procedure that assigned a geometric shape to each cluster. We showed that the observed particle distribution results were consistent with the random placement of receptors within the clusters and, to a lesser extent, the random placement of the clusters on the cell membrane. Here, we develop and validate a stochastic model of clustering, based on a hypothesis of preexisting domains that have a high affinity for receptors. The proximate objective is to clarify the mechanism behind cluster formation and to estimate the effect on signaling. Receptor-enriched domains may significantly impact signaling pathways that rely on ligand-induced dimerization of receptors. We define a simple statistical model, based on the preexisting domain hypothesis, to predict the probability distribution of cluster sizes. The process yielded sets of parameter values that can readily be used in dynamical calculations as the estimates of the quantitative characteristics of the clustering domains.

LT1, an ONT long-read-based assembly scaffolded with Hi-C data and polished with short reads.

We present LT1, the first high-quality human reference genome from the Baltic States. LT1 is a female de novo human reference genome assembly, constructed using 57× nanopore long reads and polished using 47× short paired-end reads. We utilized 72 GB of Hi-C chromosomal mapping data for scaffolding, to maximize assembly contiguity and accuracy. The contig assembly of LT1 was 2.73 Gbp in length, comprising 4490 contigs with an NG50 value of 12.0 Mbp. After scaffolding with Hi-C data and manual curation, the final assembly has an NG50 value of 137 Mbp and 4699 scaffolds. Assessment of gene prediction quality using Benchmarking Universal Single-Copy Orthologs (BUSCO) identified 89.3% of the single-copy orthologous genes included in the benchmark. Detailed characterization of LT1 suggests it has 73,744 predicted transcripts, 4.2 million autosomal SNPs, 974,616 short indels, and 12,079 large structural variants. These data may be used as a benchmark for further in-depth genomic analyses of Baltic populations.

MicroRNA Analysis of Human Stroke Brain Tissue Resected during Decompressive Craniectomy/Stroke-Ectomy Surgery.

BACKGROUND: Signaling pathways mediated by microRNAs (miRNAs) have been identified as one of the mechanisms that regulate stroke progression and recovery. Recent investigations using stroke patient blood and cerebrospinal fluid (CSF) demonstrated disease-specific alterations in miRNA expression. In this study, for the first time, we investigated miRNA expression signatures in freshly removed human stroke brain tissue. METHODS: Human brain samples were obtained during craniectomy and brain tissue resection in severe stroke patients with life-threatening brain swelling. The tissue samples were subjected to histopathological and immunofluorescence microscopy evaluation, next generation miRNA sequencing (NGS), and bioinformatic analysis. RESULTS: miRNA NGS analysis detected 34 miRNAs with significantly aberrant expression in stroke tissue, as compared to non-stroke samples. Of these miRNAs, 19 were previously identified in stroke patient blood and CSF, while dysregulation of 15 miRNAs was newly detected in this study. miRNA direct target gene analysis and bioinformatics approach demonstrated a strong association of the identified miRNAs with stroke-related biological processes and signaling pathways. CONCLUSIONS: Dysregulated miRNAs detected in our study could be regarded as potential candidates for biomarkers and/or targets for therapeutic intervention. The results described herein further our understanding of the molecular basis of stroke and provide valuable information for the future functional studies in the experimental models of stroke.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks.

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Detecting SARS-CoV-2 and its variant strains with a full genome tiling array.

Coronavirus disease 2019 pandemic is the most damaging pandemic in recent human history. Rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and variant strains is paramount for recovery from this pandemic. Conventional SARS-CoV-2 tests interrogate only limited regions of the whole SARS-CoV-2 genome, which are subjected to low specificity and miss the opportunity of detecting variant strains. In this work, we developed the first SARS-CoV-2 tiling array that captures the entire SARS-CoV-2 genome at single nucleotide resolution and offers the opportunity to detect point mutations. A thorough bioinformatics protocol of two base calling methods has been developed to accompany this array. To demonstrate the effectiveness of the tiling array, we genotyped all genomic positions of eight SARS-CoV-2 samples. Using high-throughput sequencing as the benchmark, we show that the tiling array had a genome-wide accuracy of at least 99.5%. From the tiling array analysis results, we identified the D614G mutation in the spike protein in four of the eight samples, suggesting the widespread distribution of this variant at the early stage of the outbreak in the United States. Two additional nonsynonymous mutations were identified in one sample in the nucleocapsid protein (P13L and S197L), which may complicate future vaccine development. With around $5 per array, supreme accuracy, and an ultrafast bioinformatics protocol, the SARS-CoV-2 tiling array makes an invaluable toolkit for combating current and future pandemics. Our SARS-CoV-2 tilting array is currently utilized by Molecular Vision, a CLIA-certified lab for SARS-CoV-2 diagnosis.

SARS-CoV-2 Variant Identification Using a Genome Tiling Array and Genotyping Probes.

With over three million deaths worldwide attributed to the respiratory disease COVID-19 caused by the novel coronavirus SARS-CoV-2, it is essential that continued efforts be made to track the evolution and spread of the virus globally. We previously presented a rapid and cost-effective method to sequence the entire SARS-CoV-2 genome with 95% coverage and 99.9% accuracy. This method is advantageous for identifying and tracking variants in the SARS-CoV-2 genome when compared to traditional short read sequencing methods which can be time consuming and costly. Herein we present the addition of genotyping probes to our DNA chip which target known SARS-CoV-2 variants. The incorporation of the genotyping probe sets along with the advent of a moving average filter have improved our sequencing coverage and accuracy of the SARS-CoV-2 genome.

Highly Accurate Chip-Based Resequencing of SARS-CoV-2 Clinical Samples.

SARS-CoV-2 has infected over 128 million people worldwide, and until a vaccine is developed and widely disseminated, vigilant testing and contact tracing are the most effective ways to slow the spread of COVID-19. Typical clinical testing only confirms the presence or absence of the virus, but rather, a simple and rapid testing procedure that sequences the entire genome would be impactful and allow for tracing the spread of the virus and variants, as well as the appearance of new variants. However, traditional short read sequencing methods are time consuming and expensive. Herein, we describe a tiled genome array that we developed for rapid and inexpensive full viral genome resequencing, and we have applied our SARS-CoV-2-specific genome tiling array to rapidly and accurately resequence the viral genome from eight clinical samples. We have resequenced eight samples acquired from patients in Wyoming that tested positive for SARS-CoV-2. We were ultimately able to sequence over 95% of the genome of each sample with greater than 99.9% average accuracy.