Analysis of networks of host proteins in the early time points following HIV transduction

Abstract

Background: Utilization 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. Results: In 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. Conclusion: A 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.

An Autoantigen Atlas from Human Lung HFL1 Cells Offers Clues to Neurological and Diverse Autoimmune Manifestations of COVID-19

“…7 ). In addition, altered proteins are found in other viral infections, including porcine reproductive and respiratory syndrome virus ( 75 ), H5N1 avian influenza viruses ( 76 , 77 ), Japanese encephalitis virus ( 54 ), Rift Valley fever virus ( 78 ), Hepatitis B virus ( 79 ), HIV ( 80 – 82 ), Herpes Simplex virus ( 83 ), and Epstein-Barr virus infection ( Fig. 7B and STRING ontology analysis).…”

HIV silencing and cell survival signatures in infected T cell reservoirs

“…Module 5 also included higher levels in HIV-DNA + cells of G3BP2 , a stress granule factor in a gene family that has been implicated in cytoplasmic sequestration and translational inhibition of HIV mRNAs 45 . mRNA-processing factors in module 28 included higher levels in HIV-DNA + cells of PRRC2A —a reader of N 6 -methyladenosine RNA modifications that can be induced by HIV infection in vitro 46 —and the splicing regulator SRPK. Among the additional 26 genes, we noted that module 28 included USP19 and LRRFIP2 , which can inhibit apoptosis 47 or pyroptosis 48 and were higher in HIV-DNA + cells, and TLN1 49 , which is required for antigen-driven T cell proliferation mediated through immunological synapses 49 and was also higher in HIV-DNA + cells.…”

An Autoantigen Atlas From Human Lung HFL1 Cells Offers Clues to Neurological and Diverse Autoimmune Manifestations of COVID-19

“…20 of these hijacked nuclear proteins are identified by DS-affinity in our study ( Figure 7 ). In addition, altered proteins are found in other viral infections, including porcine reproductive and respiratory syndrome virus ( 275 ), H5N1 avian influenza viruses ( 276 , 277 ), Japanese encephalitis virus ( 254 ), Rift Valley fever virus ( 278 ), Hepatitis B virus ( 279 ), HIV ( 280 – 282 ), Herpes Simplex virus ( 283 ), and Epstein-Barr virus infection ( Figure 7B and STRING ontology analysis). In some cases, viral infections may have both enhancing and protective effects on autoimmunity in type 1 diabetes ( 284 ).…”