Combining metabolic fingerprinting and footprinting to understand the phenotypic response of HPV16 E6 expressing cervical carcinoma cells exposed to the HIV anti-viral drug lopinavir
- 14 April 2010
- journal article
- research article
- Published by Royal Society of Chemistry (RSC) in The Analyst
- Vol. 135 (6), 1235-1244
- https://doi.org/10.1039/b923046g
Abstract
Recently, it has been reported that the anti-viral drug, lopinavir, which is currently used as a human immunodeficiency virus (HIV) protease inhibitor, could also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In this study, C33A parent control cells and HPV16 E6-transfected cells were exposed to lopinavir at concentrations ranging from 0 to 30 µM. The phenotypic response was assessed by Fourier transform infrared (FT-IR) spectroscopy directly on cells (the metabolic fingerprint) and on the cell growth medium (the metabolic footprint). Multivariate analysis of the data using both principal components analysis (PCA) and canonical variates analysis (PC-CVA) showed trends in scores plots that were related to the concentration of the drug. Inspection of the PC-CVA loadings vector revealed that the effect was not due to the drug alone and that several IR spectral regions including proteins, nucleotides and carbohydrates contributed to the separation in PC-CVA space. Finally, partial least squares regression (PLSR) could be used to predict the concentration of the drug accurately from the metabolic fingerprints and footprints, indicating a dose related phenotypic response. This study shows that the combination of metabolic fingerprinting and footprinting with appropriate chemometric analysis is a valuable approach for studying cellular responses to anti-viral drugs.Keywords
This publication has 48 references indexed in Scilit:
- Papillomavirus E6 proteinsVirology, 2009
- FT-IR microspectroscopy as a tool to assess lung cancer cells response to chemotherapyVibrational Spectroscopy, 2005
- Metabolic footprinting and systems biology: the medium is the messageNature Reviews Microbiology, 2005
- Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learningAnalytica Chimica Acta, 2004
- Applications of FT-IR spectrometry to plasma contents analysis and monitoringVibrational Spectroscopy, 2003
- Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rulesVibrational Spectroscopy, 2003
- Epidemiologic Classification of Human Papillomavirus Types Associated with Cervical CancerThe New England Journal of Medicine, 2003
- Functional Genomics via Metabolic Footprinting: Monitoring Metabolite Secretion byEscherichia coliTryptophan Metabolism Mutants Using FT–IR and Direct Injection Electrospray Mass SpectrometryComparative and Functional Genomics, 2003
- Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic NetworksComparative and Functional Genomics, 2001
- Papillomavirus infections — a major cause of human cancersBiochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1996