Abstract
BackgroundUtilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible.ResultsIn this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle.ConclusionA relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry.
Highlights
Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis
Our aim was to analyze the proteomic landscape of the early stage of Human immunodeficiency virus (HIV)-1 based lentiviral vector transduction
293 T cells were infected with Vesicular stomatitis virus (VSV)-G pseudotyped Human Immunodeficiency Virus type 1 (HIV1) vector, and 0, 4 and 12 h post-infection, cell lysates were harvested
Summary
Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Utilization of state-of the art proteomics methods can generate thousands of data points, and extensive information on proteins present in the sample can be obtained. Despite the high amount of data available, it is sometimes difficult to acquire relevant biological information, in which case sophisticated analytical methods and capable software are needed [3]. New concepts on network analysis are emerging helping the understanding of biological complexity [12], in most cases, only the presence or absence of the protein is considered, the available quantitative data can hardly be incorporated into the network analyses. The replication cycle of human immunodeficiency virus-1 (HIV-1) is a complex, multi-step, and highly regulated process. Due to the multiple processes involved, the replication cycle has been
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