Abstract
To explore the differences between the extreme SIV infection
phenotypes, nonprogression (BEN: benign) to AIDS in sooty mangabeys
(SMs) and progression to AIDS (MAL: malignant) in rhesus macaques
(RMs), we performed an integrated dual positive-negative connectivity
(DPNC) analysis of gene coexpression networks (GCN) based on publicly
available big data sets in the GEO database of NCBI. The
microarray-based gene expression data sets were generated,
respectively, from the peripheral blood of SMs and RMs at several time
points of SIV infection. Significant differences of GCN changes in DPNC
values were observed in SIV-infected SMs and RMs. There are three
groups of enriched genes or pathways (EGPs) that are associated with
three SIV infection phenotypes (BEN^+, MAL^+ and mixed BEN^+/MAL^+).
The MAL^+ phenotype in SIV-infected RMs is specifically associated with
eight EGPs, including the protein ubiquitin proteasome system, p53,
granzyme A, gramzyme B, polo-like kinase, Glucocorticoid receptor,
oxidative phosyphorylation and mitochondrial signaling. Mitochondrial
(endosymbiotic) dysfunction is solely present in RMs. Specific BEN+
pattern changes in four EGPs are identified in SIV-infected SMs,
including the pathways contributing to interferon signaling, BRCA1/DNA
damage response, PKR/INF induction and LGALS8. There are three enriched
pathways (PRR-activated IRF signaling, RIG1-like receptor and PRR
pathway) contributing to the mixed (BEN+/MAL+) phenotypes of SIV
infections in RMs and SMs, suggesting that these pathways play a dual
role in the host defense against viral infections. Further analysis of
Hub genes in these GCNs revealed that the genes LGALS8 and IL-17RA,
which positively regulate the barrier function of the gut mucosa and
the immune homeostasis with the gut microbiota (exosymbiosis), were
significantly differentially expressed in RMs and SMs. Our data suggest
that there exists an exo- (dysbiosis of the gut microbiota) and endo-
(mitochondrial dysfunction) symbiotic imbalance (EESI) in HIV/SIV
infections. Dissecting the mechanisms of the exo-endo symbiotic balance
(EESB) that maintains immune homeostasis and the EESI problems in
HIV/SIV infections may lead to a better understanding of the
pathogenesis of AIDS and the development of novel interventions for the
rational control of this disease.
Introduction
Progressive AIDS caused by the human immunodeficiency virus type 1
(HIV) and simian immunodeficiency virus (SIV) is characterized by
systemic inflammation, opportunistic infection and malignant disorders
resulting from generalized immune activation-mediated destruction of
the host defense system [[40]1–[41]3]. Although tremendous progress has
been made in the fighting against AIDS since the discovery of this
disease 1981, there is currently no effective vaccine or cure available
for AIDS today [[42]4–[43]5]. Pathogenesis, prevention, treatment and
cure of HIV-1 infection remain one of the greatest challenges in modern
medicine due to its ability to mutate very quickly, and to hide within
cells from both drugs and the immune system, which leads to persistent
viral infection/immune activation and microbial translocation, and
eventually progresses to AIDS. Understanding of the differences in
phenotypes of HIV infection may be very important for uncovering this
relationship and conquering AIDS. There are usually two basic
phenotypes [malignant (MAL) and benign (BEN)] of microbial infection
including human HIV/AIDS [[44]6]. The two extreme phenotypes of HIV and
SIV infection include slow or rapid progression to AIDS (MAL) in a
majority of the infected human population and the non-natural primate
host (i.e., rhesus macaques, RMs), and nonprogression to AIDS (BEN) in
a minority of the infected human population and the natural primate
hosts (i.e., sooty mangabeys, SMs) [[45]7]. Studies on SIV infection in
nonhuman primates (NHP) have offered promise and advantages for gaining
new insights into the pathogenesis of HIV/AIDS [[46]7–[47]14].
The nature of host-microbe relationships is critical for development of
microbial infections including AIDS [[48]15–[49]18]. Microbial
infection is an ecological and evolutionary paradigm, which is
associated with co-evolution between hosts and microbes in dynamic
ecosystems [[50]6,[51]15,[52]18]. Nonpathogenic microbiota, the major
microbial community, forms a healthy symbiotic ‘superorganism’ with the
hosts [[53]16]. There are two types of symbiosis (Sym), exosymbiosis
(e.g, microbiota) and endosymbiosis (e.g., mitochondria). It has been
suggested that the exo-endo Sym balance (EESB) highly contribute to
maintain the host homeostasis [[54]18]. From birth to death, a
symbiotic relationship has been established and maintained between the
host and a vast, complex, and dynamic consortium of microbes
[[55]16–[56]18]. Most of our microbial commensals with up to 100
trillion (10^14) microbes reside in the gastrointestinal (GI), which is
the largest mucosal lymphoid organ in the body with a very large
percentage of the immune cells [[57]18–[58]19]. GI abnormalities, such
as diarrhea, weight loss, and malnutrition, have been shown to occur in
HIV-1 and SIV-infected individuals. In the early phase of HIV/SIV
infection, disruption of the intestinal epithelial barrier is
characterized by apoptosis, changes in gene expression associated with
epithelial barrier functions as well as upregulation of inflammatory
genes [[59]19–[60]20]. The GI integrity is disturbed with concurrent
disruption of the mucosal immune system that is characterized by a
significant and substantial loss of mucosal CD4 T cells. This process
persists throughout the course of viral infection. The major
consequence of the disturbed GI integrity is the increased
translocation of microbes and their products that would normally be
present within the intestinal lumen into the lamina propria, draining
lymph nodes, and ultimately the systemic circulation. Disturbed
exosymbiosis (dysbiosis) has been observed in HIV- and SIV-infected
individuals with a disproportionate amount of Proteobacteria within the
microbiome that is a common hallmark in diseases with the involvement
of inflammation within the GI tract. In conjunction with this microbial
translocation, HIV/SIV infection is associated with increased immune
activation [[61]19–[62]21]. The disturbed mucosal community of
microorganisms is shown to be correlated with a number of markers of
disease progression, systemic inflammation, and upregulation of the
tryptophan catabolism pathway, which is a crossroad between microbes
and host [[63]22]. During the course of HIV infection, a significant
and substantial depletion of mucosal CD4 T cells occurs in conjunction
with significant declines in T helper 17 (Th17) cells. These T cells
contribute to intestinal barrier homeostasis as well as to mucosal
defense.
Based on the two extreme phenotypes (BEN and MAL) of HIV and SIV
infections [[64]7], we have proposed that HIV/SIV infection is a
two-way paradigm (BEN and MAL), not a one-way paradigm (MAL), the
conventional wisdom in medicine holding that microbial infection is a
pathogenic process [[65]17–[66]18]. The emphasis is on the antagonism
or conflict, not the symbiotic relationship. It underrates the effects
of exosymbiotic (e.g., microbiota)/endosymbiotic (e.g., mitochondria)
factors on microbial infection and hinders the ability to develop
rational interventions to cure AIDS [[67]18,[68]23]. Mitochondrial
disorders have been found to contribute to the pathogenesis and
therapeutics of HIV infection [[69]21]. Mitochondria can directly
influence the progression of AIDS, including the viral infectivity, the
course of HIV-1 infection, and the prevalence of side effects from the
primary HIV-1 therapy, highly active antiretroviral therapy (HAART)
since this organelle play a key role in the production of energy and
the induction of cellular apoptosis [[70]21]. The common features of
gene coexpression networks during HIV infection are the significant
changes in the genes with negative connectivity. Currently, the
progression of HIV/AIDS, the mechanistic connection between the BEN and
MAL phenotypes, and the imbalance between exosymbiosis and
endosymbiosis are unknown. Therefore, it remains to be elusive whether
or not there is a mechanistic connection between exosymbiosis and
endosymbiosis.
The SIV infection of non-natural hosts typically progresses to the AIDS
similar to the HIV infection. In contrast, the SIV infection of natural
hosts is typically lack of this progression, although it also displays
high-level virus loads. Comparative studies between the SIV infection
of natural and non-natural hosts can help us to get novel insights into
the pathogenesis and therapeutics of HIV/AIDS [[71]24–[72]27]. The
transcriptome-wide gene expression provided by microarray technologies
provides a valuable resource to perform this comparative analysis
[[73]28]. Differential expression analysis has revealed several
differences between the SIV infection of natural and non-natural hosts
at the gene level [[74]7,[75]11–[76]12]. However, this method ignores
the strong coexpression relationships between different genes, and
leads to the molecular mechanisms of SIV/HIV infection underlying in
gene expression data only partially exploited. Gene coexpression
network (GCN) analysis can produce a network to elucidate coexpression
patterns of hundreds of or thousands of genes at the system level, thus
provides an alternative and powerful approach for further investigating
this disease. In the GCN, each node represents a gene, and the line
indicates the coexpression relationship between two genes. Genes with
high connectivity (i.e., Hub genes) indicate that they may be
biologically important in the analyzed disease stage because highly
connected hub nodes are positioned at the centers of the network’s
architecture [[77]28]. The effectiveness of such gene coexpression
network analysis has been demonstrated in the application of
understanding molecular mechanisms of various diseases, including HIV
infection [[78]23], Alzheimer’s disease [[79]29], obesity [[80]30],
schizophrenia [[81]31], etc.
The goal of this study is to find hub genes whose expression profiles
correlate positively with the extreme phenotypes of SIV infections. We
employed the GCN analysis on the microarray-based gene expression data
from sooty mangabeys (SMs) and rhesus macaques (RMs) infected with the
same SIVsmm virus strain. As the natural host of SIVsmm, SMs are found
to be extensively different from RMs (i.e., non-natural hosts of
SIVsmm) in several aspects at the system level. Firstly, the changes of
positive and negative connectivity in SM-specific GCNs greatly differ
from those in RM-specific GCNs during SIV infection. Secondly, the
distribution of genes in the GCNs among three distinct groups varies
during the infection and presents a maximum contrary between SM- and
RM-specific GCNs at 14 days after SIV infection. Thirdly, selected Hub+
genes usually outnumbers Hub- genes in SM-specific GCNs (See
definitions of Hub+ and Hub- genes in [82]Materials and Methods), which
is contrary to RM-specific GCNs. Fourthly, interesting differences are
observed on genes with the highest connectivity in GCNs, which might be
important candidate genes for further investigating pathogenesis of SIV
infection. Integrating with resources such as Ingenuity Pathway
Analysis system, our further analysis of Hub+/Hub- genes in these GCNs
reveals that SIV-infected SMs and RMs exhibit substantial differentials
on patterns of enriched pathways, correlation between gene expression
and CD4+ T cell level, as well as immune gene and transcription factor
number in Hub genes.
Results
Data preprocessing and Analysis Design
After data preprocessing, individual probe sets of 428 (SMs) and 941
(RMs) were identified as having significantly differential expressions
based on the p-value (<0.05) of one-way ANOVA test (Benjamini-Hochberg
FDR multiple test correction) and fold-change value of gene expression.
Global differences in SM- and RM-specific GCNs during SIV Infection
Three SMs and three RMs were selected for the GCN analysis, since they
had gene expression data at all the analyzed time points of SIV
infection. With the Pearson correlation coefficient (PCC)-based method,
fourteen GCNs (7 for SMs and 7 for RMs) were constructed for SMs and
RMs at 7 time points of SIV infection (See details in [83]Materials and
Methods). The connectivity of a gene in the GCN is composed with two
components: positive connectivity and negative connectivity, which
respectively represent the numbers of positively and negatively
coexpressed genes. The positive and negative connectivity of genes in
each GCN are shown in [84]Fig 1. The gray dash line is the diagonal
line of the plot, on which genes have the same positive and negative
connectivity. At 14 days after SIV infection, SM genes tend to be
distributed around the diagonal line, while RM genes tend to be
distributed closely to the axis ([85]Fig 1E). While at 10 and 30 days
after SIV infection, SM genes tend to be distributed closely to the
axis and RM genes tend to be distributed around the diagonal line
([86]Fig 1D and 1F). These results reveal that the coexpression
relationships of genes in both SMs and RMs are varied during SIV
infection. More importantly, they also indicate that there are
extensive differences between the SIV infection of SMs and RMs at the
system level from GCNs.
Fig 1. The positive and negative connectivity of genes in SM- and RM-
specific GCNs during SIV infection.
[87]Fig 1
[88]Open in a new tab
“-5D”, “3D”, “7D”, “10D”, “14D”, “30D” and “180D” respectively
represent 5 days before SIV infection, and 3, 7, 10, 14, 30 and 180
days after SIV infection.
The differences are also exhibited with the detailed analysis of genes
in SM- and RM-specific GCNs of SIV infection. According to the positive
and negative connectivity, genes in the GCN can be grouped into three
classes: Gp (positive connectivity > negative connectivity, above
diagonal line), Ge (positive connectivity = negative connectivity, on
diagonal line) and Gn (negative connectivity > positive connectivity,
below diagonal line). The percentages of genes belonging to Gp, Ge and
Gn in SM- and RM-specific GCNs are respectively shown in [89]Fig 2A and
2B. In SM-specific GCNs, the percentage of genes belonging to Gn is
firstly increased from -5D to 14D, and then decreased from 14D to 180D.
While in RM-specific GCNs, the percentage of genes belonging to Gn is
increased and decreased more frequently during SIV infection than that
in SM-specific GCNs. HSP90AA1, a Hub gene with positive connectivity,
is specifically associated with SIV infection in RMs at three time
points (-D5, 10D and 180D). There is a Hub gene (LGALS8) with negative
connectivity that is only expressed in SIV-infected SMs at three time
points (D3, 14D and 30D).
Fig 2. Detailed information of GCNs of SIV infection.
[90]Fig 2
[91]Open in a new tab
(A) Percentages of Gn, Ge and Gp in SM-specific GCNs during SIV
infection. (B) Percentages of Gn, Ge and Gp in RM-specific GCNs during
SIV infection. (C) The ratios of the number of Hub+ and Hub- genes in
SM- and RM-specific GCNs during SIV infection. (D) The Hub+ and Hub-
genes with strongest connectivity in SM- and RM-specific GCNs during
SIV infection.
In the groups of Gp and Gn, some genes (i.e., Hub genes) are highly
connected. We selected Hub+ and Hub- genes from these two groups with
the criteria defined in Materials and Methods, respectively. The ratios
of the number of Hub+ and Hub- genes in constructed GNCs are shown in
[92]Fig 2C (the hub gene numbers are given in [93]S1 Fig). In
SM-specific GCNs, the number of the Hub+ genes is usually larger than
that of the Hub- genes. On the contrary, the number of the Hub+ genes
is usually smaller than that of the Hub- genes in RM-specific GCNs.
We further investigated genes with highest connectivity in GCNs at
different time points of SIV infection ([94]Fig 2D). In RM-specific
GCNs, we found 7 unique genes (C11ORF57, CD69, FAM46A, GBP1, HSP90AA1,
KTN1, SPCS3). There are also 7 unique genes (GBP1, GBP2, LGALS8,
PHACTR2, SLFN5, STAT1, ZCCHC2) found in SM-specific GCNs. GBP1
(guanylate-binding protein-1), which is a major interferon gamma
(IFN-γ) induced protein [[95]32], is selected for the gene with highest
connectivity in SM- and RM-specific GCNs at several time points of SIV
infection. In SMs and RMs, the expression levels of GBP1 are
respectively 5.74-fold and 21.56-fold increased at 10 days after SIV
infection compared to those before SIV infection ([96]S2A Fig). These
results indicate that GBP1 might play an important role of antiviral
effect against SIV infection in SMs and RMs. Interestingly, GBP2 is
another IFN-γ induced guanylate-binding protein, but it is only
up-regulated in SM-specific GCNs. It is found to be an essential immune
effector molecule mediating antimicrobial resistance [[97]33]. The gene
expression of GBP2 is more than 10-fold increased at the 7 and 10 days
after SIV infection when compared with that before infection ([98]S2B
Fig). STAT1 is an important transcription factor (TF) that can induce a
set of IFN-γ-regulated genes including GBP2 [[99]34]. The fold-change
of STAT1 gene expression in SMs is much higher than that in RMs at 10
days after SIV infection ([100]S2C Fig). CD69 is a biomarker of T cell
activation. The fold-change analysis of CD69 gene expression ([101]S2D
Fig) indicates that the T cell activation is much stronger in RMs than
that in SMs at 10 days after SIV infection related to that before
infection. Besides these genes, LGALS8, PHACTR2, SLFN5 and ZCCHC2,
which are significantly up-regulated in SMs, might be important genes
contributing to the BEN phenotypes of SIV/HIV infections
[[102]35–[103]37].
BEN-MAL patterns of expression phenotypes of hub genes in SIV-infected SMs
and RMs
For Hub genes in each GCN, we further performed the pathway enrichment
analysis with the IPA system. Pathways significantly enriched in the
Hub genes of GCNs at different time points of SIV infection are shown
in [104]Fig 3 (Fisher’s exact test, p-value < 0.01). Several enriched
pathways are specifically observed for SMs and RMs. Based on the gene
expression phenotypes, these pathways are classified into three
different groups: (1) the MAL^+ phenotypes that are only observed in
RMs; (2) the mixed phenotypes (MAL^+/BEN^+) present in both RMs and
SMs; and (3) the BEN^+ phenotypes that are solely present in SMs
([105]Fig 3). There are eight enriched pathways in the MAL^+ group,
including the protein ubiquitination pathway, p53, granzyme A
signaling, gramzyme B signaling, Mitotic roles of polo-like kinase,
Glucocorticoid receptor signaling, oxidative phosyphorylation and
mitochondrial dysfunction pathways [[106]38–[107]43]. Another enriched
pathway (i.e., granzyme A signaling pathway) is also observed at three
time points of SIV infection in RMs. Only two genes (HIST1HIC and PRF1)
are involved in this pathway. At the 10 and 14 days of SIV infection,
the PRF1 gene expression levels are respectively 4.31-fold and
3.13-fold down-regulated in RMs. These enriched pathways might play an
important role in the MAL phenotypes of SIV infection. Specific BEN^+
pattern changes in 4 pathways enriched in Hub genes are identified in
SIV-infected SMs. These include the pathways contributing to interferon
signaling, BRCA1/DNA damage response, PKR/INF induction and LGALS8
[[108]35,[109]44–[110]46]. These genes were significantly upregulated
at two or three time points of SIV infection. There are three enriched
pathways (PRR-activated IRF signaling, RIG1-like receptor and PRR
pathway) contributing to the mixed (BEN^+/MAL^+) phenotypes of SIV
infections in RMs and SMs, suggesting that these pathways play a dual
role in the host defense against viral infections [[111]44,
[112]47–[113]48].
Fig 3. Pathway enrichment analysis of Hub genes in GCNs.
[114]Fig 3
[115]Open in a new tab
“SR” indicates the pathway enriched in both “SM” and “RM”. “PRRs &
Bacteria and Viruses” represent the “Role of Pattern Recognition
Receptors (PRRs) in Recognition of Bacteria and Viruses”. “PKR & IFN
induction and Antiviral” represent the “Role of Protein Kinase R (PKR)
in Interferon (IFN) Induction and Antiviral Response” signaling
pathway, “BRCA1 & DNA Damage Response” indicate the “Role of the Breast
Cancer 1 (BRCA1) gene in DNA Damage Response” signaling pathway.
Of note, the ubiquitin proteasome pathway appeared to be actively
involved in SIV infection in RMs at -5, 3 and 30 days. In contrast,
they are down-regulated in SMs (1.07-fold and 1.31-fold). Eight genes
(PSMA2, PSMA3, PSMA6, USP16, USP38, USP47, UBE2V2, HSP90AA1)
contributed to the pattern change in this pathway, which might play a
critical role in the degradation of proteins involved in various
cellular processes, including inflammatory response, cell
proliferation, apoptosis, and so on. The fold-changes in expression of
these genes in SMs and RMs during SIV infection are shown in [116]Fig
4. Interestingly, we found that all these genes are rapidly and
robustly upregulated at the 10 days after SIV infection in RMs. But in
SMs, some of these genes (PSMA2, PSMA3, PSMC6 and HSP90AA1) are
slightly upregulated during SIV infection. These findings suggest that
proteasomes, the main actors in cellular proteolysis, play important
roles in the pathogenesis of HIV/SIV infections.
Fig 4.
[117]Fig 4
[118]Open in a new tab
Fold-changes in gene expression involved in the protein ubiquitination
pathway in SMs (A) and RMs (B) after SIV infection (range: 5–180 days).
Eight genes are differentially expressed, including Proteasome subunit
alpha (PSMA) type-2 (PSMA2), type-3 (PSMA3), ATPase subunit Rpt4
(PSMC6), ubiquitin specific peptidase 16 (USP16), USP38, USP47,
ubiquitin conjugating enzyme E2 variant 2 (UBE2V2) and heat shock
protein 90kDa alpha family class A member 1 (HSP90AA1). The p-values in
the RM group are statistically significant (p<0.05): PSMA2, p = 0.003;
PSMA3, p = 0.003; SMC6, p = 0.03; USP16, p = 0.004; USP38, p = 0.03;
USP47, p = 0.002; UBE2V2, p = 0.002; HSP90AA1, p = 0.006.
Differential expression of immune genes in SIV-infected SMs and RMs
Our further investigation was aimed to compare the expression patterns
of hub genes that contribute to immune activation and immune defense in
SMs and RMs during SIV infection ([119]Fig 5). In the SM-specific GCNs,
the number of Hub+ and Hub- immune defense genes is equal to or larger
than that of Hub+ and Hub- immune activation genes during SIV infection
([120]Fig 5A and 5B). But there are some exceptions in Hub+ genes of
RM-specific GCNs at 10 and 14 days of SIV infection and in Hub- genes
of RM-specific GCNs at the 7 days of SIV infection ([121]Fig 5C and
5D). These results concurred with the notion that the limited immune
activation in SIV-infected SMs is a mechanism of favoring the
preservation of CD4^+ T-cell homeostasis [[122]8].
Fig 5. Gene expression patterns of immune activation and immune defense in
SMs and RMs after SIV infection (range: 5–180 days).
[123]Fig 5
[124]Open in a new tab
(A) Hub+ genes of SM-specific GCNs. (B) Hub- genes of SM-specific GCNs.
(C) Hub+ genes of RM-specific GCNs. (D) Hub- genes of RM-specific GCNs.
Increased number of transcription factors (TFs) coded by Hub genes in RMs
In the GCN, the Hub+ and Hub- genes are usually subject to different
kinds of transcription regulations [[125]49]. We investigate the
patterns of TF number included in Hub+ and Hub- genes of SM- and
RM-specific GCNs of SIV infection ([126]Fig 6). Three TFs are included
in the Hub+ genes of SM-specific GCNs during SIV infection (except 14
days after SIV infection), but TFs are only appeared in Hub- genes of
GCNs at the 7, 10 and 14 days of SIV infection. IFI16 and STAT1 are
observed at all the analyzed time points of SIV infection, indicating
their importance in the SIV infection of SMs. IFI16, which has the
ability of detecting the bacterial DNA, also has a role in antiviral
innate immune response [[127]50]. STAT1 belongs to the Hub- gene at the
14 days of SIV infection, but belongs to the Hub+ gene at other time
points of SIV infection. This implicates that the coexpression
relationship between STAT1 and other genes might be largely changed.
STAT1 is also an important TF in the SIV infection of RM. Different
from the SMs, STAT1 is a Hub- gene in the RM-specific GCN at the 10
days after SIV infection, and a non-Hub gene in the RM-specific GCN at
the 14 days after SIV infection. SMAD5 belongs to the Hub+ gene of GCNs
at all considered time points of SIV infection. SMAD5 (SMAD family
member 5) is associated with the transforming growth factor β (TGF- β)
anti-proliferative effects [[128]51].
Fig 6. Transcription factors are differentially expressed in SMs (A) and RMs
(B) after SIV infection (range: 5–180 days).
[129]Fig 6
[130]Open in a new tab
Both Hub+ and Hub- genes are included.
Correlation between Hub genes and changes in CD4^+T cells in SIV-infected RMs
and SMs
Connectivity analysis of the SM- and RM-specific GCNs reveals that
there are extensive differences between the SIV infection of SMs and
RMs. For the highly connected genes (i.e., Hub genes), pathway
enrichment analysis shows that SMs exhibit a strong innate immune
response to SIV viral infections. We further examined if expression
patterns of Hub genes are correlated with changes in phenotypes and
numbers of CD4+T cells in SIV-infected RMs and SMs. Detailed analysis
of Hub+ and Hub- genes indicates that differential transcription
regulations of LGALS8, IL17RA and IL12A exist in SMs and RMs during SIV
infection ([131]Fig 7 and [132]S3 and [133]S4 Figs). The immune
functions of these genes are associated with the phenotypes of CD4+ T
cells [[134]52–[135]55], suggesting that LGALS8, IL17RA and IL12A may
contribute to the pathogenesis and therapeutics of HIV/SIV infection.
Further analysis of the Pearson correlations between Hub+ and Hub-
genes also reveals the differences in gene expression patterns in
SIV-infected SMs and RMs ([136]Fig 8A–8C). Further investigation of
these differences might help to understand the pathogenesis of SIV/HIV
infection.
Fig 7.
[137]Fig 7
[138]Open in a new tab
Pearson correlation between the gene expression levels of LGALS8,
IL17RA and IL12A in SMs (A & B) and RMs (C & D) after SIV infection
(range: 5–180 days). A & C: Galectin-8 (LGALS8) and interleukin 17
receptor A (IL17RA); B & D: LGALS8 and interleukin 12A (IL12A).
Fig 8. Pearson correlation between Hub+ and Hub- gene expression patterns.
[139]Fig 8
[140]Open in a new tab
(A) Hub genes in SM-specific GCNs. (B) Hub genes in RM-specific GCNs.
(C) Overlapping gene expression patterns of Hub+ and Hub- genes in SMs
and RMs after SIV infection (range: 5–180 days). The frequency of the
overlapping Hub+ genes (1–9) is significantly higher than that of the
Hub- genes (0–4).
Discussion
A number of comparative studies have reported the extreme phenotypes of
SIV infection in RMs and SMs suggesting that in addition to causing
abnormalities in the adaptive immune response, the viral infection also
has a significant impact on essential components of the innate immune
system that may further contribute to the remarkable phenotypic changes
[[141]7–[142]10, [143]24–[144]27]. In this study, we first reported the
meta-analysis of double connectivity of GCNs toward the study of
molecular mechanisms of SIV/HIV diseases by using the gene expression
data generated from the SIV-infected SMs and RMs [[145]11,[146]23].
Comparative double connectivity analysis of SM- and RM-specific GCNs at
different time points of SIV infection reveals some novel insights that
have not previously been reported.
For genes with the highest connectivity in the Groups of Gn and Gp,
several genes (LGALS8, PHACTR2, SLFN5, STAT1, ZCCHC2) are only observed
in SM-specific GCNs. Further analysis of these genes might gain more
insights of the SIV infection. It has been shown that these genes may
contribute to the host defense against various diseases including
microbial infections and cancer [[147]35–[148]37]. For example, LGALS8
(galectin 8), which has recently been found as a bactericidal lectin,
is involved in the host innate immunity [[149]35]. These genes might
have an important effect on the BEN phenotypes of SIV/HIV infections.
According to the molecular interaction information in the Ingenuity
Pathway Analysis (IPA) knowledge base [[150]56], we found the LGALS8
might be associated with the IL-17 receptor (IL17RA) and IL-12 (IL12A).
IL-17 is produced by the Th17 cell. IL-12 is a cytokine which is
crucial for differentiating naive CD4+ T cell to the Th1 pathway
[[151]55]. The gene expression of LGALS8 is significantly positive
correlated with that of IL17RA in SMs, but the gene expression of
LGALS8 is significantly negatively correlated with that of IL12A in
RMs. LGALS8 might play a role in altering the balance between Th17 and
Th1 cells in the infection of SIV disease [[152]53].
We investigated in detail three groups of genes associated with three
phenotypes (BEN^+, MAL^+ and mixed BEN^+/MAL^+) identified through a
comparative double connectivity analysis of the natural SM and
non-natural RM host transcriptomic data. Assessment of enrichment of
gene ontology terms for gene expression patterns with nominally
significant p values for each of the three phenotypes showed enrichment
in several signaling pathways. Four signaling pathways (interferon
signaling, BRCA1/DNA damage response, PKR/INF induction and LGALS8),
which may control the viral infection, are clearly associated with the
BEN^+ phenotype in SMs. Among the members of the interferon regulatory
factor (IRF) family, IRF1 is one of the central mediators of both the
innate and adaptive immune responses, which are required for antigen
processing/presentation, Th1/Th2 differentiation, and the immune
activities of natural killer (NK) cells and macrophages [[153]57]. PKR
is a key component of the IFN-associated innate antiviral defense
pathway in mammalian cells [[154]46]. BRCA1, a critical regulator of
DNA damage repair and cell survival [[155]45], might attenuate the
sequelae of SIV/HIV infections. LGALS8, a danger receptor that
restricts bacterial proliferation [[156]35], may have antiviral
activity against HIV/SIV infections. During early SIV/HIV infection,
IRF1 and related signaling pathways (RIG1-like receptor- and
PRR-mediated pathways) may play a dual role that is critical for
driving viral replication as well as eliciting antiviral responses
[[157]44, [158]47–[159]48]. Therefore, these factors can contribute to
the mixed (BEN^+/MAL^+) phenotypes of SIV infections through
differential regulatory mechanisms. Among the eight enriched genes or
pathways in the MAL^+ group [the ubiquitin proteasome system (UPS),
p53, granzyme A signaling, gramzyme B signaling, Mitotic roles of
polo-like kinase, glucocorticoid receptor signaling, oxidative
phosyphorylation and mitochondrial dysfunction], mitochondrial
signaling may be the central player that contributes to the
pathogenesis of HIV/SIV infections with coordination of the other seven
signaling partners [[160]38–[161]43].
Mitochondria is an intracellular organelle that arose from bacteria
entering a eukaryotic cell to form a symbiotic relationship. This
organelle is now recognized not only as the main intracellular source
of energy by converting the potential energy of food molecules into
ATP, but also as a major controller in many cellular pathways,
including the pathways contributing to the MAL^+ phenotype of SIV
infection, autophagy/mitophagy and apoptosis, which regulate the
turnover of organelles and proteins within cells, and of cells within
organisms, respectively [[162]58]. Proteins from HIV have been
implicated in acting on mitochondria to delete the targeted key immune
cells, which results in viral evasion of the immune system and
progresses to AIDS [[163]59]. It has been suggested that the exo- (e.g,
microbiota) and endo- (e.g., mitochondria) symbiotic balance (EESB) is
essential for health and that the exo-endo Sym imbalance plays an
important role in the pathogenesis of infectious diseases, including
HIV/AIDS [[164]17–[165]18]. There are two significant factors,
microbial translocation across the gut barrier (disturbing
exosymbiosis) and mitochondria mediated apoptosis (dysregulation of
endosymbiosis), which have contributed to the pathogenesis of AIDS
caused by HIV and SIV [[166]19–[167]21]. The GCN analyses in this study
associate the MAL^+ phenotype with an enrichment for linked to
dysfunctions of mitochondrial signaling and related pathways. The genes
LGALS8 and IL-17RA, which positively regulate the barrier function of
the gut mucosa, are significantly down-regulated in RMs when compared
to SMs within 7 to 10 days after SIV infection. Our findings concurred
with previous studies in that SIV-induced IL-17 deficiency could
promote bacterial translocation from the gut and LGALS8 could suppress
the viability of targeted bacteria [[168]60–[169]61]. It has also been
shown that lower levels of mucosal T cells secreting IL-17 are
associated with AIDS progression, dysbiosis of the gut microbiota,
increased apoptosis and higher levels of T cell activation in HIV-or
SIV-infected subjects [[170]62–[171]63]. As progression to AIDS is
dynamic, the time-series experiments with SIV-infected SMs and RMs
enable the identification of Hub genes contributing to the pathogenesis
of AIDS. HSP90AA1, a Hub gene with positive connectivity, is only
expressed in SIV-infected RMs at multiple time points. SP90AA1 has been
recently detected as a hub node in patients with HIV-associated
encephalitis [[172]64]. Another Hub gene (LGALS8) with negative
connectivity is specifically associated with SMs at the early stage (3,
14 and 30 days) of SIV infection. LGALS8 could modulate the neutrophil
function related to transendothelial migration and microbial killing
[[173]65]. It remains unclear whether these two Hub genes contribute to
modulation of EESB. Taken together, these results suggest that there is
a mechanistic link between dysbiosis of the gut microbiota
(exosymbiotic disorder) and mitochondrial dysfunction (endosymbiotic
abnormalities) in HIV/SIV infections. These findings support the notion
that the exo-endo Sym imbalance (EESI) may play an important role in
pathogenesis and management of infectious diseases, including AIDS
caused by HIV-1 and SIV. These findings also emphasize the interchange
between the organism and its ecological environments with more holistic
consideration of immune regulation. Dissecting the mechanisms of the
EESB that maintains immune homeostasis and the EESI problems in HIV/SIV
infections may lead to a better understanding of the pathogenesis of
AIDS and the development of novel interventions for the rational
control of this disease.
Materials and Methods
Ethics statement
The animal study was performed in strict accordance with the
recommendations in the Guide for the Care and Use of Laboratory Animals
of the National Institutes of Health. Our protocols were approved by
the Institutional Animal Care and Use Committee (IACUC) of Emory
University and the University of Pennsylvania (permit No. A3180-01).
All surgery was performed under anesthesia with ketamine or Telazol,
and all efforts were made to minimize suffering [[174]11]. There were
no unexpected deaths in this study [[175]11].
Animals and SIV infection
A total of 18 nonhuman primates were included in this study, of which
17 underwent full analysis. Five SMs and five RMs were inoculated with
an uncloned SIVsmm derived from an experimentally infected SM at 11
days after infection (1 ml of plasma) as described previously
[[176]11]. One RM was excluded from the analysis due to absence of
robust virus replication. In addition, eight RMs were inoculated i.v.
with SIVmac239. In the SIV-infected animals, the average durations of
infection were 12.0 ± 0.5 years for SMs, as estimated by the date of
the first SIV-positive test, and 0.8 ± 0.1 years for RMs. The sooty
manbabeys and rhesus macaques in this study were infected with SIVsmm
in two separate mixed-species cohort in Oct and Dec 2005. The current
study focused on SIV infections during the first 0.5 year. Blood
collection was performed by venipuncture. For lymph node (LN) biopsy,
animals were anesthetized with Ketamine or Telazol. LN biopsies were
taken from 2 SIVsmm-infected SMs and 2 SIVmac239-infected RMs for each
interval. All animals used in this study were housed at the Yerkes
National Primate Research Center in accordance with the regulations of
the merican Association of Accreditation of Laboratory Animal Care
standards. All animals were in single cage housing during the study as
per IACUC approval. The feeding regimens for all nonhuman primates in
this study was provision of chow twice daily as well as additional
enrichment foods at certain time intervals, and is in compliance with
the Animal Welfare Act. All animals in this study were enrolled in a
Nonhuman Primate Enrichment Program for that is locally IACUC approved
and in compliance with the Animal Welfare Act. The feeding regimens for
all nonhuman primates also is covered by a Center SOP (SOP 4.8)
describing provision of chow twice daily as well as additional
enrichment foods at certain time intervals. At the conclusion of this
study, mangabeys were re-assigned to other projects per the Yerkes
Research Animal Access Committee (RAAC). At the conclusion of this
study, rhesus macaques were re-assigned to other AIDS studies at Yerkes
and received continual veterinary monitoring, and eventual euthanasia
by pentabarbitol (100mg/kg) injection. Rhesus were anesthetized with
ketamine 10mg/kg or Telazol 4–5 mg/kg prior to euthanasia.
Microarray Dataset
We analyzed the transcriptome-wide gene expression data from several
SMs and RMs infected with the same SIV strain (i.e., SIVsmm) [[177]11].
The gene-expression profiles of monkeys at seven time points of SIV
infection (i.e., 5 days before infection, and 3, 7, 10, 14, 30 and 180
days after infection) were measured with the Affymetrix GeneChip Rhesus
Macaque Genome Arrays, which contained more than 47,000 transcripts.
The microarray dataset analyzed in this study was generated in
experimental infections of five SMs and four RMs with the same SIV
viral strain (i.e., SIVsmm) [[178]11]. The RNA samples derived from
whole blood were collected at different time points of SIVsmm infection
(5 days before infection, and 3, 7, 10, 14, 30 and 180 days after
infection). The transcriptome data in these samples were measured with
the Affymetrix GeneChip Rhesus Macaque Genome Arrays, which contains
over 47,000 transcripts. The expression values can be obtained from the
NCBI GEO database ([179]http://www.ncbi.nlm.nih.gov/geo/, accession
number [180]GSE16147). The CD4+ T cell counts on whole blood were also
measured at these time points using the flow cytometry. A detailed
description of the experiments can be found in the original paper
[[181]11].
Identification of differentially expressed genes and function annotation
Using the robust multichip average (RMA) normalized microarray data,
differentially expressed genes were determined based on the fold change
method and the p-value of one-way analysis of variance (ANOVA) model
adjusted by the Benjamini-Hochberg multiple testing correction
[[182]66]. With the criteria of fold change ≥2 and the p-value ≤
0.0008342, Bosinger and colleagues have picked up 428 probes with
differential expression during SIV infection in SMs [[183]11]. In this
study, we further identified 941 differentially expressed probes in RMs
with the same approaches (the fold change ≥2; the p-value ≤ 0.05) using
Partek Genomics Suite software v6.5 (Partek Inc). These selected probes
were mapped to gene symbols using the DAVID bioinformatics resources
[[184]67] and the Ingenuity Pathway Analysis (IPA) system [[185]56].
The functional categories of these genes were further annotated with
the DAVID bioinformatics resources, the IPA software and literature
examination. The probes without gene symbol annotation were not
included in the following analysis.
Determination of miRNA target genes and transcription factors
The annotation information from miRTarbase database (v2.4, released on
04/15/2011) [[186]68] and miRNA target predictions from TargetScan
website (v5.1, [187]http://www.targetscan.org/) [[188]69] were used to
determine whether the analyzed genes were miRNA targeting or not. The
miRTarbase is a high-quality database which collects about 4,000
experimentally validated miRNA-target interactions. For TargetScan,
only the reliable predictions (i.e., conserved gene transcripts
targeting with conserved miRNAs, and context score percentile > 50)
were considered.
The transcription factors (TFs) were obtained from the annotation
information from IPA system, Entrez gene database
([189]http://www.ncbi.nlm.nih.gov/gene/), and the TF collection in
previous literature [[190]70].
Pathway enrichment analysis
To further define the mechanistic connection between the BEN and MAL
phenotypes of SIV infections we performed the pathway enrichment
analysis using the gene expression data generated from the SIV-infected
SMs and RMs [[191]11]. The experiment was designed to compare gene
expression in SIV-infected SMs and RMs. We performed the pathway
enrichment analysis with the IPA system. With the Fisher’s exact test
method, the IPA system identified pathways which were statistically
significant to a set of genes. The significance level (i.e., the
p-value of Fisher’s exact test) indicates the likelihood that the
pathway would be indentified by random chance. In this study, the
significant pathways were defined as those with p-value ≤ 0.01 and the
number of focus genes ≥ 2.
Network reconstruction
The gene coexpression networks (GCNs) were respectively reconstructed
for SM and RM at different time points of SIV infection. In a GCN, the
nodes represent genes, and the edges connect two coexpressed genes. For
a given time point of SIV infection in SM, the Pearson correlation
coefficient method was firstly used to measure the similarity of gene
expression profiles between any pair of probes. The PCC value between
probes x and y can be computed with the following equation:
[MATH: r=∑i=1n(xi−x¯)(yi−y¯)∑i=1n(xi−x¯)2<
/msqrt>•∑i=1n(yi−y¯)2<
/msqrt> :MATH]
where n (n≥ 3) is the total number of samples at a given time points,
x[i] and y[i] are expression levels of x and y in the ith sample,
respectively, and
[MATH: x¯
:MATH]
and
[MATH: y¯
:MATH]
are means of expression levels among samples, respectively. The PCC
value can be ranged from -1.0 (completely negative correlation) to 1.0
(completely positive correlation). The statistical significance of PCC
value was then assessed using the result that the statistic
[MATH: t=r(n−2)/(1−r2) :MATH]
has a Student’s t-distribution with df = n-2 under the null hypothesis
of no correlation [[192]49,[193]71]. Finally, we connected genes with
significantly PCC values in the GCNs, and defined the weight of each
edge with the PCC value of connected two genes. For genes with two more
probes, the PCC value indicating the highest connection was chose for
the edge weight.
For a given GCN, the connectivity of a gene is usually defined as the
total number of its corresponding edges, and is consisted with two
components: positive and negative connectivity, while considering the
algebraic sign of PCC value [[194]49]. Following the method presented
in [[195]49], we defined one gene i as a Hub- gene if it satisfied the
following criteria: (1) X[(i-)] > X[(i+)]; (2) X[(i-)] > T-, where
X[(i-)] and X[(i+)] respectively represent the negative and positive
connectivity of the analyzed gene, T- is the threshold value and can be
computed with the equation: T- = + 1.4SD[x-], and SD[x-] are
the average and standard deviation of negative connectivity in the
analyzed GCN, respectively. Similarly, the Hub+ genes can be defined
with the criteria: (1) X[(i+)] > X[(i-)]; (2) X[(i+)] > T+. Since the
GCNs were constructed with the significantly expressed genes, a
slightly low factor 1.4 was set in this study for the selection of Hub
genes. According to this method, Hub genes (including the Hub- and Hub+
genes) can be obtained for each GCN.
In this study, the molecular interaction networks were also constructed
for LGALS8 and its coexpressed genes in SM- and RM-specific GCNs with
the IPA system. The IPA Knowledge Base is a high-quality database
collecting the interaction information published in previous
publications. Based on the interaction information, the IPA system
algorithmically assembles the molecular interaction networks with the
input genes and other molecules contained in the IPA Knowledge Base.
The IPA system also scores each network based on the number of genes
included in the input gene set. The network score is the negative log
of Fisher's exact test P value, which measures the probability of the
focus genes in a given network by random chance [[196]72].
Database
The Gene IDs in Entrez gene database are listed as follows: C11ORF57,
Macaca mulatta, 711168; CD69, Macaca mulatta, 717288; FAM46A, Macaca
mulatta, 693306; GBP1, Macaca mulatta, 694538; HSP90AA1, Macaca
mulatta, 708431; KTN1, Macaca mulatta, 697744; SPCS3, Macaca mulatta,
100429721; PSMA2, Macaca mulatta, 701683; PSMA3, Macaca mulatta,
700251; PSMC6, Macaca mulatta, 710822; USP16, Macaca mulatta, 706403;
USP38, Macaca mulatta, 700235; USP47, Macaca mulatta, 701660; UBE2V2,
Macaca mulatta, 714735; HSP90AA1, Macaca mulatta, 708431; LGALS8,
Macaca mulatta, 710375; IL17RA, Macaca mulatta, 709005; IL12A, Macaca
mulatta, 703205; GBP1, Cercocebus atys, 105593619; GBP2, Cercocebus
atys, 105593623; LGALS8, Cercocebus atys, 105574976; PHACTR2,
Cercocebus atys, 105600340; SLFN5, Cercocebus atys, 105589355; STAT1,
Cercocebus atys, 105579958; ZCCHC2, Cercocebus atys, 105590002; PSMA2,
Cercocebus atys, 105596930; PSMA3, Cercocebus atys, 105586681; PSMC6,
Cercocebus atys, 105586626; USP16, Cercocebus atys, 105571857; USP38,
Cercocebus atys, 105580776; USP47, Cercocebus atys, 105595701; UBE2V2,
Cercocebus atys, 105592463; HSP90AA1, Cercocebus atys, 105596717;
IL17RA, Cercocebus atys, 105591278; IL12A, Cercocebus atys, 105598171.
Supporting Information
S1 Fig. Number of Hub genes in SM- and RM-specific GCNs of SIV
infection at different time points.
(TIF)
[197]Click here for additional data file.^ (45.1KB, tif)
S2 Fig
Fold change of averaged gene expression of GBP1 (A), GBP2 (B), STAT1
(C) and CD69 (D) during SIV infection in SMs and RMs.
(TIF)
[198]Click here for additional data file.^ (64.1KB, tif)
S3 Fig. LGALS8 related molecular interaction network in SMs.
There is an interaction LGALS8 and IL17R, which positively regulate the
barrier function of the gut mucosa. LGALS8 may contribute to the
regulation of PDE6H and PDE8B.
(TIF)
[199]Click here for additional data file.^ (150.8KB, tif)
S4 Fig. A LGALS8 and IL12R-related network module in SIV-infected RMs.
The gene expression of LGALS8 is significantly negatively correlated
with that of IL12A in RMs.
(TIF)
[200]Click here for additional data file.^ (156.9KB, tif)
Data Availability
All relevant data are within the paper and its Supporting Information
files.
Funding Statement
This work was funded by grants from the Bill & Melinda Gates Foundation
through the Grant Challenges Explorations Initiative (S.H.H.), NIH
(R37-AI66998/P30-AI-504 to G.S., and RR000165/OD011132 to the Yerkes
National Primate Research Center and the Emory Center for AIDS
Research), National Natural Science Foundation of China
(30971642)(Y.H.Z.) and Natural Science Foundation of Hubei Province of
China (2009CDA161)(Y.H.Z.). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of
the manuscript.
References