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Andersen P, Barksdale S, Barclay RA, Smith N, Fernandes J, Besse K, Goldfarb D, Barbero R, Dunlap R, Jones-Roe T, Kelly R, Miao S, Ruhunusiri C, Munns A, Mosavi S, Sanson L, Munns D, Sahoo S, Swahn O, Hull K, White D, Kolb K, Noroozi F, Seelam J, Patnaik A, Lepene B. Magnetic hydrogel particles improve nanopore sequencing of SARS-CoV-2 and other respiratory viruses. Sci Rep 2023; 13:2163. [PMID: 36750714 PMCID: PMC9903261 DOI: 10.1038/s41598-023-29206-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 01/31/2023] [Indexed: 02/09/2023] Open
Abstract
Presented here is a magnetic hydrogel particle enabled workflow for capturing and concentrating SARS-CoV-2 from diagnostic remnant swab samples that significantly improves sequencing results using the Oxford Nanopore Technologies MinION sequencing platform. Our approach utilizes a novel affinity-based magnetic hydrogel particle, circumventing low input sample volumes and allowing for both rapid manual and automated high throughput workflows that are compatible with Nanopore sequencing. This approach enhances standard RNA extraction protocols, providing up to 40 × improvements in viral mapped reads, and improves sequencing coverage by 20-80% from lower titer diagnostic remnant samples. Furthermore, we demonstrate that this approach works for contrived influenza virus and respiratory syncytial virus samples, suggesting that it can be used to identify and improve sequencing results of multiple viruses in VTM samples. These methods can be performed manually or on a KingFisher automation platform.
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Affiliation(s)
- P Andersen
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA.
| | - S Barksdale
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - R A Barclay
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - N Smith
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - J Fernandes
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - K Besse
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - D Goldfarb
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - R Barbero
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - R Dunlap
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - T Jones-Roe
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - R Kelly
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - S Miao
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - C Ruhunusiri
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - A Munns
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - S Mosavi
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - L Sanson
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - D Munns
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - S Sahoo
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - O Swahn
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - K Hull
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - D White
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - K Kolb
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - F Noroozi
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - J Seelam
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - A Patnaik
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA
| | - B Lepene
- Ceres Nanosciences, Inc., Manassas, VA, 20110, USA.
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Bell KJ, Gray R, Munns D, Petocz P, Howard G, Colagiuri S, Brand-Miller JC. Estimating insulin demand for protein-containing foods using the food insulin index. Eur J Clin Nutr 2014; 68:1055-9. [PMID: 25005674 DOI: 10.1038/ejcn.2014.126] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 04/18/2014] [Accepted: 05/20/2014] [Indexed: 11/09/2022]
Abstract
BACKGROUND/OBJECTIVE The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both carbohydrate counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. SUBJECTS/METHODS A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with carbohydrate counting. Postprandial glycemia was measured in capillary blood glucose samples at 15-30 min intervals over 3 h. Researchers and participants were blinded to treatment. RESULTS Compared with carbohydrate counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2 vs 6.5±0.2 mmol/l, P=0.003) and the mean change in blood glucose level (-0.7±0.2 vs 0.1±0.2 mmol/l, P=0.001). Peak blood glucose was reached earlier using the FII algorithm than using carbohydrate counting (34±5 vs 56±7 min, P=0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs carbohydrate counting, respectively, P=0.155). CONCLUSIONS In adults with type 1 diabetes, compared with carbohydrate counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.
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Affiliation(s)
- K J Bell
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
| | - R Gray
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - D Munns
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - P Petocz
- Department of Statistics, Macquarie University, Sydney, NSW, Australia
| | - G Howard
- Sydney Insulin Pump Clinic, Sydney, NSW, Australia
| | - S Colagiuri
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
| | - J C Brand-Miller
- Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia
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