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. 2017 Mar 3;13(3):e1006256.
doi: 10.1371/journal.ppat.1006256. eCollection 2017 Mar.

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

Zoi E Sychev  1   2 , Alex Hu  3 , Terri A DiMaio  2 , Anthony Gitter  4 , Nathan D Camp  3 , William S Noble  3 , Alejandro Wolf-Yadlin  3 , Michael Lagunoff  2
Affiliations

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

Zoi E Sychev et al. PLoS Pathog. .

Abstract

Kaposi's Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi's Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells.

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Conflict of interest statement

The authors have declared that no competing interests exist

Figures

Fig 1
Fig 1. Phosphoproteome and Proteome Profiling of endothelial cells infected with KSHV.
A.) Overview of the workflow of proteomics and phosphoproteomics sample preparation and data collection. TIME cells were infected with KSHV derived from BCBL-1 cells and harvested 48 hpi, labeled with iTRAQ and used for tyrosine phospho-proteomic analysis, global phosphoproteomic analysis, including serine and threonine phosphopeptides, and global proteomic analysis using LC-MS/MS. Biological Replicate (BR), Immunoprecipitation (IP), phosphotyrosine, phosphoserine, phosphothreonine (pY/pS/pT), 1-dimesional or 2- dimensional High Pressure Liquid Chromatography (1D HPLC, 2D HPLC) Liquid Chromatography Mass Spectrometry (LC-MS/MS). (B.) Table showing the number of proteins detected and the upregulated and downregulated proteins in the proteome as well as the specific phosphopeptides detected from the phosphoproteome following KSHV using Comet peptide search algorithm with a FDR 5% and p < .05. (C.) KEGG pathway analysis of the upregulated hits of the phospho and proteome analysis. (D and E.) Scatter plots demonstrating changes in relative abundance for the peptides detected in the phosphoproteome (D) and total proteome (E) following KSHV 48 hpi. The dotted lines represent the significance cut off where the points to the right of the red dotted line are up significantly upregulated hits and points to the left of the blue dotted line are significantly downregulated hits.
Fig 2
Fig 2. Transcription factor (TF) enrichment analysis.
(A) Total mRNAs identified in the endothelial cells and the numbers upregulated and downregulated following infection with KSHV using a negative binomial distribution with an FDR% of 1 (B.) Schematic of RNAseq and TF enrichment analysis. Three biological replicates of KSHV infected endothelial cells harvested at 48 hpi for RNAseq of mRNA were analyzed. TF-specific binding motifs identified upstream of transcription start sites and differential expression p-values between mock and KSHV-infected cells from RNASeq data were used for the motif enrichment hypothesis tests. Colored arrows represent transcription start sites. Red and blue lines represent downregulated and upregulated transcripts, respectively. Triangles represent transcription factors binding upstream of transcription start site. (C.) List of significant TFs that are predicted to be altered by KSHV during latency with a p < .05 that were used for the Steiner forest analysis.
Fig 3
Fig 3. Steiner forest prediction of pathways activated by KSHV infection.
(A.) List of the top KEGG pathways predicted to be altered by KSHV infection of endothelial cells from Steiner forest network and KEGG pathways database analysis (complete list in S1 Table). (B.) The bar graph on each node displays a signed version of the prizes used as input for the Steiner forest analysis. Each bar from left to right indicates the proteomic, phosphoproteomic (p-Proteomic), and TF scores. Positive scores (above the horizontal line) indicate the protein had higher intensity or the TF’s target genes were more highly expressed in KSHV infection than mock infection. Negative scores indicate that KSHV infection decreases intensity or lowers activity. Node color indicates whether the protein primarily upregulated (blue) or downregulated in response to viral infection (green). Node shape indicates the largest score for that protein: proteomic (square), phosphoproteomic (circle), TF (octagon), or Steiner node with no score (triangle). Latency-related KSHV genes are shown as purple diamonds. Steiner nodes are shown in gray. Edge thickness indicates the fraction of Steiner forest networks that contain the edge when the algorithm is run multiple times. (C.) Proteins with bold borders are peroxisome pathway members: SCP2, PRDX5, ACSL3, MLYCD, AGPS, EHHADH, PEX19 and two predicted Steiner nodes, PEX12 and PEX5. Their direct neighbors in our KSHV network are displayed as well to show their relationships. A Full Steiner forest is shown in figure S4 Fig.
Fig 4
Fig 4. KSHV latently infected endothelial cells induces peroxisome formation.
(A.) Flow cytometry of mock- and KSHV- infected TIME cells (TIMECs) harvested at 48 hpi, fixed and stained with antibody to ABCD3, a peroxisome marker. (B.) Geometric mean fold change of KSHV over mock at 48hpi for 3 experiments as in panel A, p < 0.05 student’s t-test. (C.) Flow cytometry of mock- and KSHV- infected TIMECs, harvested at 96 hpi, fixed and stained as in panel A. (D.) Geometric mean Fold change of KSHV over mock at 96 hpi for 3 experiments as in panel C, p < .05 student’s t-test. (E.) Flow cytometry of mock- and KSHV- infected primary human dermal microvascular endothelial cells (hDMVECs) harvested at 96 hpi, fixed and stained as in A. (F.) Geometric mean fold change of KSHV over mock at 96 hpi for 3 experiments as in E, p < .05 student’s t-test. (G.) Flow cytometry of mock- and KSHV- infected lymphatic endothelial cells (LECs) harvested at 96 hpi, fixed and stained as in A. (H.) Geometric mean fold change of KSHV over mock at 96 hpi for 3 experiments as in panel B, p < .05 student’s t-test. All the data are represented as mean +/- SEM and were analyzed using FlowJo software. (I) Representative confocal images of Mock and KSHV infected TIME cells at 96 hpi stained with antibody to ABCD3 and DAPI to identify the nuclei. (J) Quantification of number of peroxisomes per cell in three biological replicates of Mock and KSHV-infected cells stained as in panel I, analyzed using student’s t-test p < 0.0001.
Fig 5
Fig 5. KSHV latency locus is sufficient to induce a peroxisome marker in endothelial cells.
(A.) Flow cytometry of mock-, KSHV-UV irradiated and KSHV- infected TIME cells harvested at 96 hpi, fixed and stained with antibody to ABCD3. (B.) Geometric mean fold change of KSHV, KSHV-UV-irradiated over mock at 96 hpi for 3 experiments as in A, p < .05 student’s t -test. (C.) Flow cytometry of AdGFP or AdKLAR (KSHV latency-associated region in a gutted adenovirus) infected TIME cells harvested at 96 hpi, fixed and stained with antibody to ABCD3. (D.) Geometric mean fold change of AdGFP over AdKLAR at 96 hpi p < .05 student’s t-test. All the data are represented as mean +/- SEM and were analyzed using FlowJo software. (E.) Western blot analysis of TIME cells mock infected or infected with AdGFP-, or AdKLAR stained with antibodies to GFP or LANA.
Fig 6
Fig 6. KSHV latently infected endothelial cells require peroxisome proteins.
(A.) Overview of Essential Fatty Acids (EFA) and peroxisome pathway. Numbers indicate genes altered by KSHV (time post infection in black and fold change in red) as identified by RNA-seq in orange (PLA2G4A), a previously published metabolomics screen [19] in blue and proteomic screen in red. siRNA treatments of ABCD3 and ACOX1 for panel B are indicated in blocked red sign. (B.) TIME cells were transfected with a control siRNA (siSCRB) or siRNA to ABCD3 or ACOX1. siABCD3 and siACOX1 treatments lead to greater than 70% reduction in ABCD3 and ACOX1 expression as determined by qRT-PCR normalized to the housekeeping genes GAPDH and HPRT. (C.) TIME cells were transfected with siRNAs as in panel B and 24 later were Mock- or KSHV-infected. 96 hpi (120 hours post transfection) cells were harvested and % cell death was measured using Trypan blue stain. In parallel, cells were treated with 20 μM QVD, a pan-caspase inhibitor. Data shown is from three independent experiments. Student’s t-test (D.) Data shows the average fold change in % dead cells over control siRNA transfected cells from three independent experiments from panel C. (E.) IncuCyte microscopy images identifying dead cell nuclei (YOYO-1) for Mock- and KSHV-infected cells transfected with siSCRB, siABCD3 or siACOX1 at 96 hpi. Essen software was used to identify cell nuclei by size and fluorescent intensity, with background subtracted. YOYO-1 positive nuclei are in fluorescent green. All the data are represented as mean +/- SEM.
Fig 7
Fig 7. Workflow of the systems biology data integration analysis and Schematic of the metabolism of VLCFs in the peroxisome.
Experimental conditions of mock and KSHV infected TIME cells were processed using proteomics techniques and in parallel transcriptomics analysis. The RNA-seq data was used for TF prediction and then TFs were used as the link to generate the predicted protein-protein interaction Steiner forest network. Analysis of the Steiner forest network was done using KEGG pathway analysis and followed by experimental validation of peroxisome biogenesis and mediated lipid metabolism.
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