# Infectomic Analysis of Gene Expression Profiles of

Authors: Ambrose Jong; Chun-Hua Wu; Wensheng Zhou; Han-Min Chen; Sheng-He Huang
Journal: Journal of Biomedicine and Biotechnology
 (2008)

---

## Abstract

In order to dissect the pathogenesis ofCryptococcus neoformans meningoencephalitis, a genomic survey of the changes in gene expression of human brain microvascular endothelial cells infected by C. neoformans was carried out in a time-course study. Principal component analysis (PCA) revealed significant fluctuations in the expression levels of different groups of genes during the pathogen-host interaction. Self-organizing map (SOM) analysis revealed that most genes were up- or downregulated 2 folds or more at least at one time point during the pathogen-host engagement. The microarray data were validated by Western blot analysis of a group of genes, including β-actin, Bcl-x, CD47, Bax, Bad, and Bcl-2. Hierarchical cluster profile showed that 61 out of 66 listed interferon genes were changed at least at one time point. Similarly, the active responses in expression of MHC genes were detected at all stages of the interaction. Taken together, our infectomic approaches suggest that the host cells significantly change the gene profiles and also actively participate in immunoregulations of the central nervous system (CNS) during C. neoformans infection.


---

## Body

## 1. Introduction

The major challenge posed by infectious diseases is to
holistically and integratively understand the fundamental
issues of how infectious
agents and human hosts interact during
microbial infection. Host-microbe interactions in the pathogenesis of
infectious diseases are dynamic and complex processes [1] which result in changes in whole genome expression
profiles of microbial pathogens and their hosts. A new discipline called
infectomics [1] became established when the recently developed high-throughput
omic approaches and computational tools were combined with the conventional
approaches for the study of infectious diseases. Infectomes are detailed maps
of microbial infections, and the availability of whole genomes of many living
organisms paves the way for their holistic and integrative study. Infectomics
can be defined as the study of infectomes, which are encoded by genomes of
microbes and their hosts. Microarray has
been a powerful tool to monitor infectomes in microorganisms and their host responses
during microbial infection.Infection byCryptococcus neoformans has increased considerably over the past few years [2–4]. Dehydrated haploid yeast or basidiospore of C. neoformans is the usual form of
inhalation  [3, 5]. The organisms are likely to spread hematogeneously to
extrapulmonary tissues and show a remarkable propensity in spreading to the
brain and meninges, where life-threatening meningoencephalitis develops  [2, 6, 7]. In order to
cause meningoencephalitis, C. neoformans must penetrate the blood-brain barrier
(BBB), which is a barrier between blood circulation and the brain parenchyma.
BBB mainly consists of brain microvascular endothelial cells (BMECs), which are
responsible for maintaining the biochemical homeostasis within the central
nervous system (CNS) [8–10]. BMEC has been
established as an in vitro cell culture model for dissecting the underlying
mechanism(s) whereby C. neoformans crosses the BBB. We have recently
demonstrated that C. neoformans are able
to alter the cytoskeleton of human brain microvascular endothelial cells (HBMECs)
[11]. We have also
identified and characterized a C. neoformans capsule gene, CPS1 [12]. This
demonstrated that CPS1 encodes hyaluronic acid synthase. The above
information suggested that C. neoformans hyaluronic acid (HA) plays a
role as an adhesion molecule during the yeast entry. It also suggested that host cell factors are
required for C. neoformans HA-binding and the pathogen entry into HBMEC
[12].Like many other pathogens,C. neoformans may manipulate the host system to facilitate its
invasion. The investigation of virulence of the pathogen C. neoformans and the study of the responses from HBMEC are equally
important in the understanding
of the complex invasion process. A more comprehensive knowledge of the
interplay between the host and microbial pathogen at the levels of genome
expression profiles is central to the understanding of the pathogenesis of
infectious diseases. In order to dissect
the pathogenesis of this disease, we have combined the infectomic approach with
the in vitro model of the BBB to monitor gene expression profiles of HBMECs
infected with C. neoformans. These studies provide global and useful
information for building a comprehensive framework to interpret C.
neoformans pathogenic processes.


## 2. Materials and Methods

### 2.1. Cultures of Yeasts and Human Brain Microvascular Endothelial Cells (HBMECs)

C. neoformans strain B3501 was used for this
study [11].
Yeast cells were grown aerobically at 30°C in the rich YPD broth
containing 1% yeast extracts, 2% peptone, and 2% dextrose. Cells were harvested at early log phase,
washed with PBS, and resuspended in Ham-F12/M199 (1:1; v:v) and 5% heat
inactivated fetal bovine serum (FBS) (experimental medium).The
HBMEC culture was prepared as described previously [11, 13]. Briefly, the HBMEC cultures were maintained
in RPMI 1640 medium including 10% FBS and 10% NuSerum (BD Biosciences, Bedford, MA, USA) at 37°C in a humid atmosphere of 5% CO2 as described above. For the preparation of interactive cultures for microarray
analysis, the HBMEC were grown in collagen-coated 24 well tissue culture plates
(Costar Corp, Cambridge, MA, USA) until confluency.  An inoculum of 106 yeast cells in
1 mL experimental medium was added. C.
neoformans B3501 was incubated
with HBMEC at 37°C and harvested at 0, 4, 8, 12, 16, 20, 24 hours. One-tenth of cell pellets were saved for
Western blots and the rest were subjected to the RNA extraction for making the probes.


### 2.2. Preparation of the Biotinylated cRNA  for Microarray

Total RNA was
preparedusing TRIZOL reagent (Invitrogen, Calif, USA), and
subjectedto isolation of poly(A)+ RNA using the Oligotex-dT30
mRNA purification kit (TaKaRa Shuzo Co., Kyoto, Japan) according to
the manufacturer's instructions. The biotinylated cRNA probe was prepared using
the RNA Transcript labeling kit (Enzo Biochem, Farmingdale, NY, USA) according
to the manufacturer's instructions. The
quality of the probe was first examined with 1% agarose gel, showing the bands
between 0.5∼1.5 kb, peaking at around 0.75 kb. An aliquot of biotinylated cRNA
probes was then examined by the Affymetirx test chips. Internal controls, G3PDH and β-actin, on the test
chip were measured to evaluate the biotinylated probes. Only high-quality cRNA
probes were used to hybridize with DNA microarray HU95A chips (12,809 gene
spots in ~1.64 cm2 filters). After hybridization, the HP GeneArray scanner was used to analyze the
patterns of gene expression.


### 2.3. Microarray Analysis

Biotinylated
cRNA probes were
prepared from 10μg of poly(A)+ RNA. Hybridization and fluorescence
detection were performed according to the manufacturer's instructions.
Images
were analyzed with GeneSpring, Genetrix softwares. Three clustering approaches were used in this
study as follows. (a) Hierarchical clustering where the data points were
organized in a phylogenetic tree in which the branch lengths represent the
degree of similarity between the values.
(b) Self-organizing maps (SOMs)
that was a nonhierarchical clustering approach.
Using this algorithm, gene expression data were transformed into vectors
or coordinates in an n-dimensional space, where n equals the number of
variables or time points. (c)
Principal-component analysis (PCA) that was used to obtain a simplified
visualization of entire datasets.


### 2.4. Western Blot Analysis

Protein concentration was determined by Bio-Rad protein
assay and equal amounts of protein were used from different time point samples.
SDS˜-PAGE sample buffer (50 mM Tris-HCl pH 6.8, 10%   β-mercaptoethanol, 2% SDS˜, 0.1% bromophenol blue, 10% glycerol) was added
to the samples and they were boiled in a water bath for 10 minutes. 2 μg of total cell extracts were separated on
homogeneous 12.5% PhastGel (Phastsystem, Amersham Pharmacia Biotech, NJ, USA)
with SDS buffer strips according to manufacturers instructions with subsequent
transfer to PVDF membrane for 40 minutes.
The membrane was blocked by 0.5% blocking solution (skim milk-based),
incubated with anti-Bcl-2 antibody, anti-Bad antibody, anti-Bcl-x antibody,
anti-Bax antibody (Transduction Laboratories, Calif, USA), anti-CD47 antibody (Lab
Vision, Calif, USA), or anti-β-actin antibody
(Chemicon Interna-tional, Calif) at 25°C followed by incubation with
peroxidase-coupled secondary antibody (Kirkegaard Perry Laboratories, Md, USA)
and detected by the ECL-enhanced chemiluminescent system (Boehringer Mannheim,
Germany).


## 2.1. Cultures of Yeasts and Human Brain Microvascular Endothelial Cells (HBMECs)

C. neoformans strain B3501 was used for this
study [11].
Yeast cells were grown aerobically at 30°C in the rich YPD broth
containing 1% yeast extracts, 2% peptone, and 2% dextrose. Cells were harvested at early log phase,
washed with PBS, and resuspended in Ham-F12/M199 (1:1; v:v) and 5% heat
inactivated fetal bovine serum (FBS) (experimental medium).The
HBMEC culture was prepared as described previously [11, 13]. Briefly, the HBMEC cultures were maintained
in RPMI 1640 medium including 10% FBS and 10% NuSerum (BD Biosciences, Bedford, MA, USA) at 37°C in a humid atmosphere of 5% CO2 as described above. For the preparation of interactive cultures for microarray
analysis, the HBMEC were grown in collagen-coated 24 well tissue culture plates
(Costar Corp, Cambridge, MA, USA) until confluency.  An inoculum of 106 yeast cells in
1 mL experimental medium was added. C.
neoformans B3501 was incubated
with HBMEC at 37°C and harvested at 0, 4, 8, 12, 16, 20, 24 hours. One-tenth of cell pellets were saved for
Western blots and the rest were subjected to the RNA extraction for making the probes.


## 2.2. Preparation of the Biotinylated cRNA  for Microarray

Total RNA was
preparedusing TRIZOL reagent (Invitrogen, Calif, USA), and
subjectedto isolation of poly(A)+ RNA using the Oligotex-dT30
mRNA purification kit (TaKaRa Shuzo Co., Kyoto, Japan) according to
the manufacturer's instructions. The biotinylated cRNA probe was prepared using
the RNA Transcript labeling kit (Enzo Biochem, Farmingdale, NY, USA) according
to the manufacturer's instructions. The
quality of the probe was first examined with 1% agarose gel, showing the bands
between 0.5∼1.5 kb, peaking at around 0.75 kb. An aliquot of biotinylated cRNA
probes was then examined by the Affymetirx test chips. Internal controls, G3PDH and β-actin, on the test
chip were measured to evaluate the biotinylated probes. Only high-quality cRNA
probes were used to hybridize with DNA microarray HU95A chips (12,809 gene
spots in ~1.64 cm2 filters). After hybridization, the HP GeneArray scanner was used to analyze the
patterns of gene expression.


## 2.3. Microarray Analysis

Biotinylated
cRNA probes were
prepared from 10μg of poly(A)+ RNA. Hybridization and fluorescence
detection were performed according to the manufacturer's instructions.
Images
were analyzed with GeneSpring, Genetrix softwares. Three clustering approaches were used in this
study as follows. (a) Hierarchical clustering where the data points were
organized in a phylogenetic tree in which the branch lengths represent the
degree of similarity between the values.
(b) Self-organizing maps (SOMs)
that was a nonhierarchical clustering approach.
Using this algorithm, gene expression data were transformed into vectors
or coordinates in an n-dimensional space, where n equals the number of
variables or time points. (c)
Principal-component analysis (PCA) that was used to obtain a simplified
visualization of entire datasets.


## 2.4. Western Blot Analysis

Protein concentration was determined by Bio-Rad protein
assay and equal amounts of protein were used from different time point samples.
SDS˜-PAGE sample buffer (50 mM Tris-HCl pH 6.8, 10%   β-mercaptoethanol, 2% SDS˜, 0.1% bromophenol blue, 10% glycerol) was added
to the samples and they were boiled in a water bath for 10 minutes. 2 μg of total cell extracts were separated on
homogeneous 12.5% PhastGel (Phastsystem, Amersham Pharmacia Biotech, NJ, USA)
with SDS buffer strips according to manufacturers instructions with subsequent
transfer to PVDF membrane for 40 minutes.
The membrane was blocked by 0.5% blocking solution (skim milk-based),
incubated with anti-Bcl-2 antibody, anti-Bad antibody, anti-Bcl-x antibody,
anti-Bax antibody (Transduction Laboratories, Calif, USA), anti-CD47 antibody (Lab
Vision, Calif, USA), or anti-β-actin antibody
(Chemicon Interna-tional, Calif) at 25°C followed by incubation with
peroxidase-coupled secondary antibody (Kirkegaard Perry Laboratories, Md, USA)
and detected by the ECL-enhanced chemiluminescent system (Boehringer Mannheim,
Germany).


## 3. Results

### 3.1. Kinetics of Gene Expression of HBMEC Showing the Progression of Gene Profile Changes duringC. neoformans Infection

Microbial
invasion is a complex and dynamic process. We performed a time-course study to
examine the gene expression profiles in HBMEC duringC. neoformans infection. We
selected C. neoformans strain B3501
for this study because the genomic sequence of this strain has been completed.
In addition, we have used this strain for in vitro adhesion and transcytosis studies on HBMEC [11, 13]. It is an encapsulated strain with moderate
adhesion activity to HBMEC, which was used as the in vitro model of BBB. A spectrum of 24 hours would allow us to
evaluate its effect on HBMEC. Poly(A)+ RNAs derived from the incubated HBMEC at 0, 4, 8, 12, 16, 20, or 24
hours after B3501 incubation were subjected to the RNA extraction and the
preparation of the biotinylated cRNA probe. The prepared probes at different
time points were hybridized with biochip, individually. We used an
Affymetric HU95A microarray chip harboring 12,559 human clones, facilitating
efficient detection of changes in gene expression in the host cells.
The expression levels were measured and analyzed by the GeneChip program. In a hierarchical clustering, the closet pair
of expression values is grouped and the data points are organized in a
phylogenetic tree in which the branch lengths represent the degree of
similarity between the values. The mRNA
profiles of HBMEC, assessed at 0, 4, 8, 12, 16, 20, and 24 hours after C. neoformans incubation, are shown in
Figure 1.Figure 1
Gene expression profiles duringC. neoformans infection;   microarray analysis of mRNA levels was
assessed at 0, 4, 8, 12, 16, 20, and 24 hours after C. neoformans incubation. Time points are represented
by columns, and genes in rows. Red,
green, and blue represent the
higher, equal, and lower mRNA level relative to that of zero time point.  A hierarchical clustering analysis of
genes with expression levels that changed during C. neoformans and HBMEC
interactions was performed. The distance
among infectomic profiles of HBMEC is shown. Each lane represents a result form a gene chip. Genes with high intensity reading are shown
in bright colors; genes with low intensity reading are in dim colors (grey) so
they can be distinguished during analysis.These 12,559 gene
patterns were classified into two major clusters (Clusters 0–12 hours and 16–24
hours) by using the GeneSpring software.  Rapid changes in gene
profiles at an early time point are followed by a gradual alteration until the 12-hour
time point. Of
interest, the gene patterns of the last three time points were similar, yet
more distal related to the early gradual changes of gene profiles (0 to 12
hours). At these time points (16, 20, 24
hours), the gene profile seems to reach a plateau. The HBMEC is a homogeneous
cell culture. The changes in the gene expression profiles are not due to the
heterogeneity of cell cultures. The perturbation of gene expression profiles is
most likely due to the presence ofC.
neoformans.  Overall, the hierarchical clustering analysis of
the mRNA levels reveals a sequential change in HBMEC infected with C. neoformans. The overall profile was altered more
prominently at the initial stage of the pathogen-host interaction and
subsequently persistent expression.


### 3.2. Comparison of Gene Expression Profiles from the
Microarray and Protein Levels from Western Blot Analyses

To determine the validity of results obtained by the
microarrayanalysis, some genes were subjected to Western
analysis. These genes includedβ-actin,
Bcl-x, CD47, Bax, Bad, and Bcl-2 (see Figure 2). They were chosen for blot analysis because
their protein levels were variable and the affinity of commercially available
antibodies. For example, β-actin
mRNA and protein levels are maintained in a constant level at all time points,
whereas CD47 mRNA and protein level increase to the 8 hour and then decline
back to the normal levels. In general, the protein levels of these genes were
expressed in varied degrees, and the protein expression profiles were
comparable between microarray and protein analyses (see Figure 2). The
results validated the competency of the microarray analysis for detection of
changes in gene expression in HBMEC during C. neoformans infection.Comparison of results obtained with
microarray and protein blot analysis for changes in protein levels duringC. neoformans infection. Protein levels
at various time points (top panel)
relative to the mRNA levels in microarray analysis (bottom panel) are
shown for the following genes: (a) β-actin, (b) Bcl-x, (c) CD47, (d) Bax, (e) Bad, and (f)
Bcl-2. The relative scales of the mRNA
expression are indicated on the y-axis.
(a)(b)(c)(d)(e)(f)


### 3.3. Principal Component Analysis (PCA) of HBMEC Gene Profiles duringC. neoformans Infection

In order to obtain a
global grouping of gene profiles, we examined the PCA grouping of mRNA from
HBMEC. PCA was a useful linear approach for obtaining a simplified
visualization of entire datasets, without losing experimental information
(variance). PCA allowed the dimension of
complex data to be reduced and the most relevant features of a given dataset
(transcriptome) to be highlighted. It
was useful for our kinetic studies because it was possible to describe trends
that were, otherwise, irretrievably by a direct examination of the entire dataset. The gene fluctuation could be classified into
7 major groups. The major group (PCA
group 1) contained 4155 genes (33.08% of total analyzed genes) and the PCA
group 2 contained 3129 genes (24.92% of total analyzed genes) (see Figure3). Thus, these two groups represented more than
50% of HBMEC mRNAs. The gene profile of
PCA group 1 showed a very slightly increase at 8 and 12 hours, and then a rapid
decline. On the other hand, the PCA
group 2 showed a gradual decline till 8 hours and then bounced back to near the
original levels. These two profiles were
nearly a mirror image. The results
suggested that HBMEC altered its mRNA levels or adjusted its physiological
status in response
to the pathogen invasion.Figure 3
PCA of HBMEC profiles afterC. neoformans infection; microarray data were analyzed by
the GeneSpring software to classify the gene expression patterns. Seven major gene profiles were obtained as
shown in different color. The major
groups (PCA groups no.1 and no.2) are indicated in the graph, which contain
more than 50% of total analyzed genes.


### 3.4. Gene Expression Profiles at Different Time Points in HBMEC Analyzed by
the Self-Organizing Maps (SOM)

The interpretation of
system complexity is the most challenging task of biology in this century. The analysis of complexity in biological
systems might start from a simplified representation of static gene networks
and then move to an increasingly well-defined and integrated description of
biological phenomena, bearing in mind that only dynamic
networks will explain reality adequately. An example of a nonhierarchical clustering method is SOM. As SOM solves difficult
high-dimensional and nonlinear problems. Using
this algorithm, gene expression data are transformed into vectors or
coordinates in ann-dimensional space, where n equals the number of variables
or time points. Gene profiles of HBMEC
postinfection at 7 time points (0, 4, 8, 12, 16, 20, 24 hours) were analyzed by
SOM (7×6) (see Figure 4). Upper bound
(4.0), normal (1.0), and lower bound (0.0) were shown. The dynamic of gene expression during
pathogen-host interaction can be clearly observed (see Figure 4). The use of SOM for grouping of different
profiles greatly facilitates further analysis.Figure 4
Infectomic profiles ofC. neoformans-infected HBEMC using SOM analysis. Gene profiles of HBMEC postinfection at 7 time points (0, 4, 8, 12, 16, 20, 24 hours) (x-axis) were analyzed by SOM (7 x 6).  Upper bound (4.0), normal (1.0), and lower bound (0.0) were shown (y-axis).


### 3.5. Persistently Upregulated and Down-Regulated Genes duringC.
neoformans Infection

Despites significant fluctuation of gene
expression profiles in most HBMEC genes in response toC. neoformans infection, there were some genes that were upregulated or downregulated
persistently during 24 hours period. One
example of the upregulated genes was the cytochrome P450 gene which was
continuously upregulated through the course of infection. A similar gene profile could be found and
grouped, for example, genes that had a correlation at least 0.95 to the
expression profile of cytochrome P450 were defined as upregulated genes (see Figure 5(a)). In the same manner, downregulated genes were grouped, in which genes
that had a correlation at least 0.95 to the expression profile of  troponin I type 3 gene TNNI3 (see Figure 5(b)).The upregulated and downregulated gene
profiles of HBMEC duringC. neoformans infection. (a) Upregulated genes (left chart): cytochrome P450 gene was continuously upregulated
during the course of infection. Genes
that have correlation at least 0.95 to the expression profile of cytochrome
P450 are grouped and defined as upregulated genes. (b) Downregulated genes (right chart):  TNNI3 gene was continuously downregulated
during the course of infection. Genes
that have correlation at least 0.95 to the expression profile of TNNI3 are
grouped and defined as downregulated genes.
(a)(b)


### 3.6. Induced Fluctuation of Interferons and MHC Group Genes duringC. neoformans Infection

We have performed the hierarchical and
gene tree analyses to test whether the interferon genes and MHC-related genes
were fluctuation duringC. neoformans infection. The group of
interferon-related genes (66 genes) was clustered based on their expression
level (see Figure 6). Most genes were
fluctuated during the course of infection, suggesting interferon played a role
in the innate immune response to C. neoformans. Many infectious
microbes induced expression of chemokines and adhesion molecules in human
endothelial cells.  Previous studies of
the expression of IL-8, INF-γ-inducible protein-10 (IP-10), MCP-1, and the
leukocyte ligand ICAM-1 in primary HUVEC revealed that C. neoformans had the ability to interfere with inflammatory
signaling in human endothelial cells, and suggested that C. neoformans might induce leukocyte activation and trafficking in
the infected host (HUVEC) [5, 6]. Our results showed that IL-1 increased slightly, while
MCP-1 did not change significantly. No changes in IP-10 and ICAM-1 expressions
were observed in this chip. The TNF-α
profile was also increased, though quite late, during the infection. Similarly,
the expression of a group of MHC genes including 29 of those in MHC II class
was fluctuated (see Figure 7). Although
some genes were listed but not expressed, our results suggested that
endothelial cells contributed to the host immune response to C. neoformans infection. Taken together, HBMEC is not a professional
immune cell; however, the fluctuation in the expression of interferons and MHC
genes suggests that the innate immune systems are activated during the pathogen
invasion.Figure 6
Hierarchical cluster of interferon 66 gene tree. Different expression of 66 interferon-related
genes were analyzed by Gene Tree program.
The expression of more than two thirds (red and blue) was changed,
and about one third of the genes were not expressed (green).Figure 7
Gene tree of MHC genes fluctuated duringC. neoformans infection: expression profiles changes in HBMEC are
observed. A subgroup of the profile with
MHC class genes was selected and subjected to Gene Tree analysis using
GeneSpringTM software. The
alternations of gene expression in this group suggested that innate immune
contributes to C. neoformans infection.


## 3.1. Kinetics of Gene Expression of HBMEC Showing the Progression of Gene Profile Changes duringC. neoformans Infection

Microbial
invasion is a complex and dynamic process. We performed a time-course study to
examine the gene expression profiles in HBMEC duringC. neoformans infection. We
selected C. neoformans strain B3501
for this study because the genomic sequence of this strain has been completed.
In addition, we have used this strain for in vitro adhesion and transcytosis studies on HBMEC [11, 13]. It is an encapsulated strain with moderate
adhesion activity to HBMEC, which was used as the in vitro model of BBB. A spectrum of 24 hours would allow us to
evaluate its effect on HBMEC. Poly(A)+ RNAs derived from the incubated HBMEC at 0, 4, 8, 12, 16, 20, or 24
hours after B3501 incubation were subjected to the RNA extraction and the
preparation of the biotinylated cRNA probe. The prepared probes at different
time points were hybridized with biochip, individually. We used an
Affymetric HU95A microarray chip harboring 12,559 human clones, facilitating
efficient detection of changes in gene expression in the host cells.
The expression levels were measured and analyzed by the GeneChip program. In a hierarchical clustering, the closet pair
of expression values is grouped and the data points are organized in a
phylogenetic tree in which the branch lengths represent the degree of
similarity between the values. The mRNA
profiles of HBMEC, assessed at 0, 4, 8, 12, 16, 20, and 24 hours after C. neoformans incubation, are shown in
Figure 1.Figure 1
Gene expression profiles duringC. neoformans infection;   microarray analysis of mRNA levels was
assessed at 0, 4, 8, 12, 16, 20, and 24 hours after C. neoformans incubation. Time points are represented
by columns, and genes in rows. Red,
green, and blue represent the
higher, equal, and lower mRNA level relative to that of zero time point.  A hierarchical clustering analysis of
genes with expression levels that changed during C. neoformans and HBMEC
interactions was performed. The distance
among infectomic profiles of HBMEC is shown. Each lane represents a result form a gene chip. Genes with high intensity reading are shown
in bright colors; genes with low intensity reading are in dim colors (grey) so
they can be distinguished during analysis.These 12,559 gene
patterns were classified into two major clusters (Clusters 0–12 hours and 16–24
hours) by using the GeneSpring software.  Rapid changes in gene
profiles at an early time point are followed by a gradual alteration until the 12-hour
time point. Of
interest, the gene patterns of the last three time points were similar, yet
more distal related to the early gradual changes of gene profiles (0 to 12
hours). At these time points (16, 20, 24
hours), the gene profile seems to reach a plateau. The HBMEC is a homogeneous
cell culture. The changes in the gene expression profiles are not due to the
heterogeneity of cell cultures. The perturbation of gene expression profiles is
most likely due to the presence ofC.
neoformans.  Overall, the hierarchical clustering analysis of
the mRNA levels reveals a sequential change in HBMEC infected with C. neoformans. The overall profile was altered more
prominently at the initial stage of the pathogen-host interaction and
subsequently persistent expression.


## 3.2. Comparison of Gene Expression Profiles from the
Microarray and Protein Levels from Western Blot Analyses

To determine the validity of results obtained by the
microarrayanalysis, some genes were subjected to Western
analysis. These genes includedβ-actin,
Bcl-x, CD47, Bax, Bad, and Bcl-2 (see Figure 2). They were chosen for blot analysis because
their protein levels were variable and the affinity of commercially available
antibodies. For example, β-actin
mRNA and protein levels are maintained in a constant level at all time points,
whereas CD47 mRNA and protein level increase to the 8 hour and then decline
back to the normal levels. In general, the protein levels of these genes were
expressed in varied degrees, and the protein expression profiles were
comparable between microarray and protein analyses (see Figure 2). The
results validated the competency of the microarray analysis for detection of
changes in gene expression in HBMEC during C. neoformans infection.Comparison of results obtained with
microarray and protein blot analysis for changes in protein levels duringC. neoformans infection. Protein levels
at various time points (top panel)
relative to the mRNA levels in microarray analysis (bottom panel) are
shown for the following genes: (a) β-actin, (b) Bcl-x, (c) CD47, (d) Bax, (e) Bad, and (f)
Bcl-2. The relative scales of the mRNA
expression are indicated on the y-axis.
(a)(b)(c)(d)(e)(f)


## 3.3. Principal Component Analysis (PCA) of HBMEC Gene Profiles duringC. neoformans Infection

In order to obtain a
global grouping of gene profiles, we examined the PCA grouping of mRNA from
HBMEC. PCA was a useful linear approach for obtaining a simplified
visualization of entire datasets, without losing experimental information
(variance). PCA allowed the dimension of
complex data to be reduced and the most relevant features of a given dataset
(transcriptome) to be highlighted. It
was useful for our kinetic studies because it was possible to describe trends
that were, otherwise, irretrievably by a direct examination of the entire dataset. The gene fluctuation could be classified into
7 major groups. The major group (PCA
group 1) contained 4155 genes (33.08% of total analyzed genes) and the PCA
group 2 contained 3129 genes (24.92% of total analyzed genes) (see Figure3). Thus, these two groups represented more than
50% of HBMEC mRNAs. The gene profile of
PCA group 1 showed a very slightly increase at 8 and 12 hours, and then a rapid
decline. On the other hand, the PCA
group 2 showed a gradual decline till 8 hours and then bounced back to near the
original levels. These two profiles were
nearly a mirror image. The results
suggested that HBMEC altered its mRNA levels or adjusted its physiological
status in response
to the pathogen invasion.Figure 3
PCA of HBMEC profiles afterC. neoformans infection; microarray data were analyzed by
the GeneSpring software to classify the gene expression patterns. Seven major gene profiles were obtained as
shown in different color. The major
groups (PCA groups no.1 and no.2) are indicated in the graph, which contain
more than 50% of total analyzed genes.


## 3.4. Gene Expression Profiles at Different Time Points in HBMEC Analyzed by
the Self-Organizing Maps (SOM)

The interpretation of
system complexity is the most challenging task of biology in this century. The analysis of complexity in biological
systems might start from a simplified representation of static gene networks
and then move to an increasingly well-defined and integrated description of
biological phenomena, bearing in mind that only dynamic
networks will explain reality adequately. An example of a nonhierarchical clustering method is SOM. As SOM solves difficult
high-dimensional and nonlinear problems. Using
this algorithm, gene expression data are transformed into vectors or
coordinates in ann-dimensional space, where n equals the number of variables
or time points. Gene profiles of HBMEC
postinfection at 7 time points (0, 4, 8, 12, 16, 20, 24 hours) were analyzed by
SOM (7×6) (see Figure 4). Upper bound
(4.0), normal (1.0), and lower bound (0.0) were shown. The dynamic of gene expression during
pathogen-host interaction can be clearly observed (see Figure 4). The use of SOM for grouping of different
profiles greatly facilitates further analysis.Figure 4
Infectomic profiles ofC. neoformans-infected HBEMC using SOM analysis. Gene profiles of HBMEC postinfection at 7 time points (0, 4, 8, 12, 16, 20, 24 hours) (x-axis) were analyzed by SOM (7 x 6).  Upper bound (4.0), normal (1.0), and lower bound (0.0) were shown (y-axis).


## 3.5. Persistently Upregulated and Down-Regulated Genes duringC.
neoformans Infection

Despites significant fluctuation of gene
expression profiles in most HBMEC genes in response toC. neoformans infection, there were some genes that were upregulated or downregulated
persistently during 24 hours period. One
example of the upregulated genes was the cytochrome P450 gene which was
continuously upregulated through the course of infection. A similar gene profile could be found and
grouped, for example, genes that had a correlation at least 0.95 to the
expression profile of cytochrome P450 were defined as upregulated genes (see Figure 5(a)). In the same manner, downregulated genes were grouped, in which genes
that had a correlation at least 0.95 to the expression profile of  troponin I type 3 gene TNNI3 (see Figure 5(b)).The upregulated and downregulated gene
profiles of HBMEC duringC. neoformans infection. (a) Upregulated genes (left chart): cytochrome P450 gene was continuously upregulated
during the course of infection. Genes
that have correlation at least 0.95 to the expression profile of cytochrome
P450 are grouped and defined as upregulated genes. (b) Downregulated genes (right chart):  TNNI3 gene was continuously downregulated
during the course of infection. Genes
that have correlation at least 0.95 to the expression profile of TNNI3 are
grouped and defined as downregulated genes.
(a)(b)


## 3.6. Induced Fluctuation of Interferons and MHC Group Genes duringC. neoformans Infection

We have performed the hierarchical and
gene tree analyses to test whether the interferon genes and MHC-related genes
were fluctuation duringC. neoformans infection. The group of
interferon-related genes (66 genes) was clustered based on their expression
level (see Figure 6). Most genes were
fluctuated during the course of infection, suggesting interferon played a role
in the innate immune response to C. neoformans. Many infectious
microbes induced expression of chemokines and adhesion molecules in human
endothelial cells.  Previous studies of
the expression of IL-8, INF-γ-inducible protein-10 (IP-10), MCP-1, and the
leukocyte ligand ICAM-1 in primary HUVEC revealed that C. neoformans had the ability to interfere with inflammatory
signaling in human endothelial cells, and suggested that C. neoformans might induce leukocyte activation and trafficking in
the infected host (HUVEC) [5, 6]. Our results showed that IL-1 increased slightly, while
MCP-1 did not change significantly. No changes in IP-10 and ICAM-1 expressions
were observed in this chip. The TNF-α
profile was also increased, though quite late, during the infection. Similarly,
the expression of a group of MHC genes including 29 of those in MHC II class
was fluctuated (see Figure 7). Although
some genes were listed but not expressed, our results suggested that
endothelial cells contributed to the host immune response to C. neoformans infection. Taken together, HBMEC is not a professional
immune cell; however, the fluctuation in the expression of interferons and MHC
genes suggests that the innate immune systems are activated during the pathogen
invasion.Figure 6
Hierarchical cluster of interferon 66 gene tree. Different expression of 66 interferon-related
genes were analyzed by Gene Tree program.
The expression of more than two thirds (red and blue) was changed,
and about one third of the genes were not expressed (green).Figure 7
Gene tree of MHC genes fluctuated duringC. neoformans infection: expression profiles changes in HBMEC are
observed. A subgroup of the profile with
MHC class genes was selected and subjected to Gene Tree analysis using
GeneSpringTM software. The
alternations of gene expression in this group suggested that innate immune
contributes to C. neoformans infection.


## 4. Discussion

The current work demonstrates the use of cDNA
microarray-based infectomic approach to characterize transcriptomes in HBMEC
infected with the meningitic pathogenC. neoformans. Like
most of meningitic pathogens, the penetration across the BBB that is
constituted by BMEC is required for the pathogenesis of the CNS infection
caused by C. neoformans [6, 14]. Our study represents the first investigation of the
holistic transcriptional response of the in vitro BBB cell system in response to C. neoformans infection. A total of 12,559 human genes have been analyzed in this
study, and distinct alterations in HBMEC gene expression have been
observed at 4, 8, 12, 16, 20, and 24 hours of infection. Significant
changes in the transcriptional infectomes were observed in HBMEC infected with C. neoformans. This approach was used to
define the infectomic profiles of HBMEC infected with C. neoformans for
a series of time points, thus, evaluating the dynamic changes in gene
expression profiles and inflammatory factors in the host response to this
fungal pathogen. The data were analyzed
with several clustering analyses. In a
hierarchical clustering analysis, the gene expression patterns at different
time points displayed a progressive change of gene profiles. The first three incubated time points were
clustered, and showed more drastic changes at initial engagement between C.
neoformans and HBMEC (4 hours). The
gene partners of the latter time points (16, 20, 24 hours) were clustered
together, indicating the alternations reached to a plateau. Microarray analysis has been used for
monitoring host gene expression profiles from other pathogen invasion studies,
such as HIV-1. Many of the results were
to compare the data pre- and post- infections. Our studies revealed
that pathogen invasions are multifaceted and dynamics. A time point study is
necessary to monitor the alterations. In a nonhierarchical analysis SOM, the
two-dimensional space map displayed different expression profiles form 12,559
genes into 42 groups. One interesting
finding defined by PCA is that one group of genes with graduate reduction in
expression (~33% of total genes) accompanied with another group of genes showing gradually
increased expression profile (~25% of total genes). The alternative changes in the two
major groups (1 and 2) are shown as a mirror image (see Figure 3). The
biological mechanism and significance of the expression profile changes in the
pathogenesis of C. neoformans infection remain to be defined.Most interestingly, the group of interferon-related
genes (66 genes) was clustered based on their expression level. The expression of most genes fluctuated
during the course of infection, suggesting that interferons play a role in the
innate immune response to the pathogen. The expression of 29 MHC II class also
fluctuated. Though some genes were listed
but not expressed, the results suggested that endothelial cells contributed to
the host immune response toC. neoformans infection.  Many infectious microorganisms induced
expression of chemokines and adhesion molecules in human endothelial cells. Studies
of the gene expression in primary HUVEC revealed that C. neoformans had the ability to interfere with inflammatory
signaling in human endothelial cells, and suggested that C. neoformans may later induce leukocyte activation and trafficking
in the infected host (HUVEC) [15, 16].  Our results
also showed that expression of interferon-related genes and MHC group genes are
changed during C. neoformans infection. Collectively, the fluctuation
in the expression of innate-related genes suggests that C. neoformans,
like other microbial pathogens, is able to induce expression of chemokines and
other innate genes.The BBB has been considered an immunologically inactive
organ as there are few antigen-presenting cells present in the CNS and the
presence of the tight junction was thought to prevent the entry of immune cells
from the peripheral circulations into the CNS [8]. However,
increasing number of studies indicate that the endothelium of the BBB
constitutes a dynamic and immunoactive interface between the blood and the CNS
that can be modulated by endogenous factors such as bradykinin and cytokines, as
well as exogenous factors including meningitic pathogens and their products [17].  Alterations in
the BBB function are critical for the development of CNS infection including
cryptococcal meningoencephalitis. It has
been learnt about the immune response to cryptococcal infection from both in
vitro and in vivo studies with the focus on immune cells [6, 18]. The immune response to C.
neoformans infection was observed as one of the most interesting
changes.


## 5. Conclusion

In summary, the current infectomic
studies with a genome survey ofC. neoformans-induced
host response in HBMEC provide global information for the pathogenesis of the
CNS infection caused by this fungus. Similar conclusions were reached when
analyzing the microarray data with two complementary approaches, PCA and SOM. However,
PCA works well in problem
spaces that are linearly
separable. SOM
solves difficult high-dimensional and nonlinear problems. Most
importantly, our
study demonstrates for the first time that the BBB can actively participate in
immune response to cryptococcal infection by regulating the expression of
interferons, MHC and cytokines. It suggests that the BBB is not only an
impermeable cellular barrier but also constitutes an immunoactive interface between the
circulatory system and the CNS and that can be modulated by meningitic pathogens such
as C. neoformans. Further insight
into the role of the BBB in the pathogenesis of microbial meningitis and
immunoregulation of the host defense against meningitic pathogens will gain a
global view of the CNS infection and offer exciting prospects for advances in
the prevention and therapy of this disease.



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*Source: 375620-2008-02-13.xml*