Mass spectrometry metabonomics of HIV-1 sera

Abstract

Background: Metabolic complications resulting from the human immunodeficiency virus type 1 (HIV-1) and the acquired immunodeficiency syndrome (AIDS) are as common as the immune system disruption caused by the virus, but not as well known. Highly active antiretroviral therapy (HAART) used to treat HIV-1 infection exacerbates the effects HIV-1 has on the host‘s metabolism. Common metabolic complications such as insulin resistance, lipodystrophy, lactic acidosis and others contribute to morbidity and mortality during HIV/AIDS. The detection of HIV-1 related metabolic biomarkers assists in diagnosing and monitoring metabolic complications, however, limitations of the conventional methodologies used for detecting these molecules caused a paucity of data on HIV-related metabolic indicators. Metabonomics, the ability to measure multiple metabolites simultaneasously, shows promise in distinguishing HIV-1 negative and positive patients through nuclear magnetic resonance (NMR) and vibrational spectroscopy as well as mass spectrometry (MS) profiles of various biofluids. The objective of this study was to determine the abilities of ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) in the identification of serum metabolites associated with HIV and/or HAART. The study was also aimed at determining whether data from a less sensitive technique, Fourier transform infrared (FTIR) spectroscopy would lead to comparable conclusions as those derived using UPLC-MS data. Methods: Sera were collected from three experimental groups; HIV negative (n=32), HIV positive (n=29) and HIV positive patients receiving HAART (n=34). Metabolites were extracted using a conventional approach of cold methanol extraction as well as the OstroTM plate extraction technology which involved filtration by positive pressure. The filtrate was analysed in the negative and positive electrospray ionization (ESI) modes of UPLC-MS. Serum samples were also dried overnight and analysed using FTIR. Data processing and chemometric analysis was carried out using the SPSS 19.0 and MassLynx v4.1 software packages. Following extensive statistical evaluation of data, bioinformatics approaches that assisted with metabolite identification were conducted. Results: The combination of OstroTM plates and UPLC-MS produced high resolution chromatograms that showed visible differences among the serum samples of HIV negative, HIV positive and HIV positive patients receiving HAART. Linear discriminant analysis (LDA) classified experimental groups into the correct categories with great accuracy (>88%), using potential biomarkers responsible for the observed group variations. Principal component analysis (PCA) showed clear separations as well as some overlap among the three experimental groups. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) showed clear differences between two classes of samples at a time and potential biomarkers were selected from accompanying S-Plots. Hundred and twelve distinct group distinguishing metabolites detected from both ESI positive and ESI negative modes were significantly altered (p<0.05). HIV and/or HAART altered metabolites of energy, neuronal and mitochondrial processes were identified and were evident in the amino acid, carbohydrate, lipid and nucleoside/nucleotide metabolic products being detected. Antiviral drugs [mostly nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs)], antiinflammatory and anti-cancer drugs, nutrient supplements and other drugs associated with the treatment of HIV conditions also contributed to class variations. FTIR generated metabolic patterns that separated the three experimental groups on an LDA scatter plot which achieved correct (>87%) classification accuracy. The significantly altered patterns indicated hydroxyl and alkene group vibrations and these groups were present in the metabolites identified by MS. Conclusion: OstroTM plates and UPLC-MS successfully purified, detected and identified sera metabolites distinguishing HIV and/or HAART patients. The different statistical analysis methods applied in this study were in agreement and the OPLS-DA statistical tool complemented the sensitivity of UPLC-MS for the detected distinguishing metabolites. The approach employed here delivered promising findings for use in the discovery of metabolic biomarkers. Distinguishing metabolites identified could be traced to HIV-infection and/or treatment. Findings from this study corroborated with others which showed that NRTIs remain a challenge in the era of HAART toxicities, especially their dominant effect on mitochondrial dysfunction. This work therefore suggests the use of UPLC-MS in HIV disease diagnosis, prognosis, monitoring of treatment success or failure and the ability to link treatment to metabolic complications. Even though FTIR is less sensitive than UPLC-MS, it was successful in detecting metabolic patterns that corresponded to some metabolites detected by UPLC-MS. This suggests that this easier to perform technique also has potential clinical application in monitoring HIV/AIDS.