| Literature DB >> 22142483 |
Felipe T Lee-Montiel1, Kelly A Reynolds, Mark R Riley.
Abstract
BACKGROUND: In a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and time-consuming, making point-of-care detection a challenge. Faster, more sensitive, highly specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular-based sensing, may offer a precise way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of cells by tracking changes in absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles.Entities:
Year: 2011 PMID: 22142483 PMCID: PMC3260089 DOI: 10.1186/1754-1611-5-16
Source DB: PubMed Journal: J Biol Eng ISSN: 1754-1611 Impact factor: 4.355
Figure 1Schematic representation of viral detection method using cell culture and FTIR spectroscopy (not to scale).
Figure 2Microscopy of BGMK cells attached on ZnSe crystal (actin filaments, vinculin and nuclei) and bright field image of BGMK cells. a) Confocal image of BGMK cells adhered to a ZnSe crystal. Actin (red), Vinculin (green) and cell nuclei (blue) are shown. b) Bright microscopy image of BGMK cells adhered to a ZnSe crystal.
Figure 3Poliovirus prediction models comparing the estimated PFU and predicted PFU at 1.5 - 8 h.p.i. Each point represent the predicted number of virus by the model for each sample exposed to different viral titers, × axis represent the estimated number of virus used.The green lines indicate a 1:1 regression model.
Comparison of poliovirus prediction models using different infrared regions and virus titers
| Method | IR region used | h.p.i | Virus titers (PFU) | Latent variables | R2 | RMSEC | RMSECV |
|---|---|---|---|---|---|---|---|
| 650 - 1650 cm-1 | 1.5 | 0, 102 - 104 | 4 | 0.910 | 0.4225 | 0.7093 | |
| 650 - 1650 cm-1 | 4 | 0, 101, 103, 104 | 3 | 0.891 | 0.5225 | 0.8900 | |
| 650 - 1650 cm-1 | 6 | 0, 101, 102, 104 | 6 | 0.868 | 0.4914 | 0.8800 | |
| 650 - 1650 cm-1 | 8 | 0 - 104 | 7 | 0.911 | 0.4290 | 0.8471 | |
| 650 - 3600 cm-1 | 8 | 0 - 104 | 7 | 0.819 | 0.6480 | 0.9187 | |
| 650 - 1650 cm-1 | 8 | 0 - 103 | 7 | 0.917 | 0.3298 | 0.5726 | |
| iPLS 9 regions | 8 | 0 - 103 | 7 | 0.903 | 0.3577 | 0.5581 | |
| iPLS 9 regions | 8 | 0, 102 - 104 | 7 | 0.944 | 0.4019 | 0.6628 | |
| iPLS 9 regions | 8 | 0 - 102 | 7 | 0.964 | 0.1640 | 0.3163 | |
| 650-1650 cm-1 | 12 | 0 - 103 | 7 | 0.716 | 0.5963 | 1.2866 | |
Table shows root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV) and correlation coefficient for each model.
Figure 4FTIR spectra showing changes in absorbance when cells are infected with poliovirus. Spectra in the wavelength region of 650 - 3600 cmshow the absorbance of BGMK cells infected with different PV1 titers 10- 10PFU/ml at 8 h.p.i. Uninfected cells served as a control. Nine regions were chosen by the PLS model as the most informative for detecting changes in cell components following virus infection. The different colors represent the mean spectra of the samples.
Figure 5Regression analysis for cells infected with PV1 at 8 h.p.i. This model uses 7 latent variables. The regression uses a log scale and 0 - 10PFU/ml in the 650 - 1600 cmwavenumber region.
Figure 6Average peak absorbance values compared to uninfected control for different virus titers at 8 h.p.i. Error bars show standard error. Note that 1654 cm-1 was used to normalize the data and therefore shows no change.
Observed changes in relative peak absorbance values with corresponding biomolecules
| Peak wavenumber (cm-1) | Corresponding biomolecules | Observed changes in cell absorbance following PV1 infection relative to uninfected control cells |
|---|---|---|
| (Broad) cis-C-H out-of-plane bend | General increase | |
| C2' endo/anti (B-form helix) conformation and left handed helix DNA (Z form) | Gradual increase corresponding to increased PV1 titer | |
| Phosphodiester stretching bands (from absorbance due to collagen and glycogen) | Decrease; trend is less clear at higher PV1 titers | |
| Symmetric stretching vibration PO2 due to RNA and DNA | General increase | |
| Symmetric phosphate stretching modes or ν(PO2 −) symmetric. Phosphate stretching modes originate from the phosphodiester groups in nucleic acids. | Sharp increase in BGMK cells infected with 101 PFU/ml titer followed by a decrease in the 102 PFU/ml infected cells then a gradual height incremental with the higher titers. | |
| Stretching vibrations of hydrogen bonding | Virus titer 102 PFU/ml shows a decrease | |
| Amide III, stretching PO2−asymmetric (phosphate I) | Decrease in the peak height for the cells infected with 102 and 103 PFU/ml; higher titers shown an increase | |
| Collagen related, amide III band components of proteins | General increase | |
| Asymmetric CH3 bending modes of the methyl groups of proteins | General decrease | |
| Lipids region (CH2 symmetric, symmetric stretching vibration of CH3 of acyl chains, stretching C-H and asymmetric stretching vibration of CH3 of acyl chains respectively) | Decrease for infection titers 102 - 103 PFU/ml | |
| Stretching N-H symmetric | General increase | |
| OH stretching (associated) | General increase; increase gradually less from 101 - 106 PFU/ml titers | |
CPE Analysis for Poliovirus at a virus titer of 107 PFU/ml.
| Sample of Known Titer (PFU/ml) | Dilution Factor | Estimated PFU/ml | |
|---|---|---|---|
| -6 | 7000000 | 2000000 | |
| -5 | 550000 | 50000 | |
| -4 | 380000 | 40000 | |
| -4 | 35000 | 5000 | |
| -3 | 39500 | 2500 | |
| -3 | 4000 | 0 | |
| -2 | 3300 | 100 | |
| -1 | 5 | 5 | |
Figure 7Hours Post-Infection vs. Root Mean Square Error of Cross Validation. Graph shows the corresponding RMSECV for the experiments using 1.5, 4, 6 and 8 h.p.i. The lowest RMSECV value was achieved using an infection time of 8 h.p.i.,which corresponds to the best prediction model.