Gene expression profiles and protein–protein interaction network analysis in AIDS patients with HIV-associated encephalitis and dementia

Figures & data

Figure 1 Scheme of the brain tissue analysis from AIDS patients with and without apparent features of HIVE-induced dementia using the Affymetrix microarray platform.

Note: Brain tissue sample preparation (A). Microarray data processing (B, C) is performed to identify DEGs and construct the PPI network.
Abbreviations: HIVE, HIV-associated encephalitis; DEGs, differentially expressed genes; PPI, protein-protein interaction.

Figure 2 MA-plots of Affymetrix microarrays (n=4) plotted with common pseudo-array reference and represented each gene with a dot for AIDS patients with (A and B) and without (C and D) HIVE-induced dementia.

Notes: Loess line and horizontal axis at M=0 are depicted in red and blue, respectively. The x- and y-axis are on a binary logarithmic (log₂) scale.
Abbreviations: AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis; IQR, interquartile range.

Figure 3 Box-plots of raw (A) and RMA-normalized (B) data from four replicate Affymetrix arrays.

Note: The y-axis is on a binary logarithmic scale.
Abbreviation: RMA, robust multiarray average.

Figure 4 “Induced” graphs of the enriched GO categories for GO:0005886 (A), GO:0044459GO (B), and GO:0000166 (C) terms with highest counts of DEGs in samples from AIDS patients with and without HIVE-induced dementia.

Abbreviations: GO, gene ontology; DEGs, differentially expressed genes; AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis.

Table 1 Top ten significantly enriched GO terms with high counts of DEGs in samples from AIDS patients with and without HIVE-induced dementia

Term	Category	Description	Count	P-value
GO:0005886	CC	Plasma membrane	380	4.54E−07
GO:0044459	CC	Plasma membrane part	262	1.78E−11
GO:0000166	MF	Nucleotide binding	246	7.66E−07
GO:0043228	CC	Nonmembrane-bounded organelle	243	0.009174
GO:0043232	CC	Intracellular nonmembrane-bounded organelle	243	0.009174
GO:0017076	MF	Purine nucleotide binding	216	6.24E−07
GO:0032553	MF	Ribonucleotide binding	211	1.97E−07
GO:0032555	MF	Purine ribonucleotide binding	211	1.97E−07
GO:0001883	MF	Purine nucleoside binding	180	8.27E−06
GO:0001882	MF	Nucleoside binding	180	1.23E−05

Abbreviations: GO, gene ontology; DEGs, differentially expressed genes; AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis; CC, cellular compartment; MF, molecular function.

Table 2 Top ten enriched KEGG pathways of DEGs with low P-values in samples of AIDS patients with and without HIVE-induced dementia

Pathway	Genes	P-value
ErbB signaling pathway	25	7.18E−06
MAPK signaling pathway	53	7.40E−06
Focal adhesion	42	2.31E−05
Endocytosis	39	3.47E−05
Epithelial cell signaling	20	5.34E−05
Gap junction	23	1.08E−04
Phosphatidylinositol signaling	20	1.85E−04
Colorectal cancer	20	0.001031
ECM–receptor interaction	20	0.001031
Neurotrophin signaling pathway	26	0.001098

Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis; ErbB, erythroblastic leukemia viral oncogene; MAPK, mitogen-activated protein kinase; ECM, extracellular matrix.

Figure 5 PPI network constructed from the list of DEGs on the basis of human interactome using spring embedded layout.

Notes: The red and blue nodes indicate the highly expressed DEGs with minimal (P-value ≥1.33E−10) and maximal (P-value ≤0.006) P-values. The ellipse and rectangular node shapes indicate the DEGs with low (log₂ FC ≥−2.54) and high (log₂ FC ≤3.22) log₂ FC values. Nodes with high degree values (hubs) are depicted using large node shape feature by creating pseudo-exponential gradient mapping.
Abbreviations: PPI, protein–protein interaction; DEGs, differentially expressed genes; FC, fold change; min, minimum; max, maximum.

Figure 6 Subnetworks identified from the local PPI network using unweighted force-directed layout.

Notes: The red and blue nodes indicate the highly expressed DEGs with minimal (P-value ≥1.33E−10) and maximal (P-value ≤0.006) P-values. The ellipse and rectangular node shapes indicate the DEGs with low (log₂ FC ≥−2.54) and high (log₂ FC ≤3.22) log₂ FC values. Nodes with high degree values (hubs) are depicted using large node shape feature by creating pseudo-exponential gradient mapping.
Abbreviations: PPI, protein–protein interaction; DEGs, differentially expressed genes; FC, fold change.

Table 3 Statistics for top six subnetworks identified by MCODE method in PPI network for AIDS patients with and without HIVE-induced dementia

Subnetwork	Score	Proteins	Interactions
1	8.34	35	292
2	6.27	22	138
3	3.26	27	88
4	2.43	28	68
5	2.32	28	65
6	2.12	26	55

Abbreviations: MCODE, molecular complex detection; PPI, protein–protein interaction; AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis.

Table 4 Top 15 hub nodes identified in PPI network for DEGs from samples of AIDS patients with and without HIVE-induced dementia

ID	Name	P-value	log2 FC	Degree
HSP90AA1	Heat-shock protein alpha, class A member 1	0.003639	0.454711	139
FN1	Fibronectin 1	5.74E−06	−1.07768	138
SUMO1	SMT3 suppressor of mif two 3 homolog 1	2.14E−04	0.489265	122
TUBB	Tubulin, beta	8.60E−04	−0.38154	83
HSP90AB1	Heat-shock protein alpha, class B member 1	8.31E−04	0.390789	82
NEDD8	Neural precursor cell 8	0.00373	0.301632	76
FYN	FYN oncogene related to SRC, FGR, YES	9.45E−04	−0.36713	74
PRKDC	DNA-activated, catalytic polypeptide	0.002548	0.31338	68
CALM1	Calmodulin 1 (phosphorylase kinase, delta)	0.001049	0.373122	67
ABL1	c-abl oncogene 1, receptor tyrosine kinase	0.005356	−0.28995	62
MAPK14	Mitogen-activated protein kinase 14	0.003497	0.321649	58
CSNK1E	Casein kinase 1, epsilon	0.00417	−0.29183	57
YBX1	Y box binding protein 1	1.86E−06	−1.3169	57
DDX3X	DEAD box polypeptide 3, X-linked	0.001003	−0.49537	56
RPA1	Replication protein A1, 70 kDa	6.28E−04	0.400196	56

Abbreviations: PPI, protein–protein interactions; DEGs, differentially expressed genes; AIDS, acquired immunodeficiency virus syndrome; HIV, human immunodeficiency virus; HIVE, HIV-associated encephalitis; FC, fold change; SMT3, ubiquitin-like protein of the SUMO family; SUMO, small ubiqutin-like modifier; FYN, proto-oncogene tyrosine-protein kinase Fyn; SRC, proto-oncogene tyrosine-protein kinase Src; FGR, tyrosine-protein kinase Fgr; YES, tyrosine-protein kinase Yes; DEAD, D-E-A-D amino acid sequence (asp-glu-ala-asp).

Figure 7 Total percentage of hubs identified for six subnetworks to detect main DEGs associated with different cellular processes.

Abbreviation: DEGs, differentially expressed genes.

Table S1 R script to define and analyze differentially expressed genes (DEGs) from four Affymetrix microarrays

#####################

####### script.R ####

#####################

## How to run this script:

# source(“script.R”)

## (A) RMA normalization

# Loads required libraries

library(affy); library(limma); library(gcrma)

# Import expression raw data and stores them as AffyBatch object

data <- ReadAffy()

# Normalizes the data with ‘rma’ function and assigns them to exprSet object

eset_rma <- rma (data)

# Create box plots for raw data and normalized data

pdf (file = “raw_boxplot.pdf ”)

boxplot (data, col = “gray”, main = “Raw Data”); dev.off()

pdf (file = “rma_boxplot.pdf ”);

boxplot(data.frame(exprs(eset_rma)), col = “gray”, main = “RMA Normalized Data”); dev.off()

## (B) DEG analysis for RMA data

# Creates appropriate design matrix

design <- model.matrix (~0 + factor (c(1,1,2,2)))

# Assigns nicer column names

colnames (design) <- c(“Gl”, “G2”)

# Creates appropriate contrast matrix for pairwise comparisons

contrast.matrix <- makeContrasts (G2-Gl, Gl-G2, levels = design)

# Fits a linear model for each gene based on the given series of arrays

fit <- lmFit(eset_rma, design)

# Computes estimated coefficients and standard errors for a given set of contrasts

fit2 <- contrasts.fit(fit, contrast.matrix)

# Computes moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage

fit2 <- eBayes (fit2)

rma_deg <- topTable(fit2, coef = 1, adjust = “fdr”, sort.by = “B”, number = 50000)

rma_deg_result <- rma_deg[rma_deg_result$adj.P.Val < 0.05,]

# Exports all expression values to tab delimited text file

write.table(rma_deg_result, “rma_deg_result.xls”, col.names = NA, quote= FALSE, sep = “\t”)

## (C) Create Venn diagram for RMA data

rma_venn <- decideTests(fit2,p.value = 0.05)

pdf(file = “rma_venn.pdf ”); vennDiagram(rma_venn); dev.off()

## (D) DEG annotations

# Verify annotation platform for expression values to tab delimited text file

eset_rma@annotation

# Loads required libraries

library (annotate); library (hgu95av2.db)

# List objects availble in annotation package.

is (“package:hgu95av2.db”)

# Extract feature names from normalized dataeset

ID <- featureNames(eset_rma)

# Check gene symbol, name, and ensembl gene IDs

Symbol <- getSYMBOL(ID, “hgu95av2.db”)

Name <- as.character(lookUp(ID, “hgu95av2.db”, “GENENAME”, “P.Value”))

Ensembl <- as.character(lookUp(ID, “hgu95av2.db”, “ENSEMBL”))

# Create hyperlink for ensembl genome browser

Ensembl <- ifelse(Ensembl=“NA”,NA,

paste (“<a href=’http://useast.ensembl.org/Gene/Summary?g=”,

Ensembl,“’>”, Ensembl, “</a>”, sep=””))

# Make temporary data frame with all IDs

tmp_rma <- data.frame(ID = ID, Symbol = Symbol, Name = Name,

Ensembl = Ensembl, stringsAsFactors = F)

# Make “NA” characters for temporary data frame

tmp_rma[tmp_rma=“NA”] <- NA

# Exports all expression values to tab delimited text file

write.table(tmp_rma, file = “annot_rma.xls”, row.names = F, sep = “\t”)

# Merge annotations and expression data values

all <-merge(tmp_rma, rma_deg_result, by.x = “ID”, by.y= “ID”, all= T)

# Exports all expression values to tab delimited text file

write.table(all, file = “annot_deg_rma_fmal.xls”, col.names = NA, sep = “\t”)