Abstract
Background
The interaction between HIV-1 and host immune cells, particularly
macrophages, is crucial in understanding viral persistence and
pathogenesis. This study aims to explore the impact of HIV-1 infection
on macrophage microRNA (miRNA) expression profiles using a systems
biology approach to uncover the potential role of miRNAs in modulating
macrophage functionality and identify key miRNA targets that may serve
as therapeutic avenues.
Methods
PMA-differentiated THP-1 cells were used to model macrophage infection
with HIV-1. A custom miRNA microarray was performed to identify
dysregulated miRNAs following infection. miRTarBase was utilized for
miRNA target identification, revealing gene targets associated with the
dysregulated miRNAs. A protein-protein interaction (PPI) map of miRNA
targets and their first interactors was constructed, with key nodes
identified based on a calculated disease score, which considered
degree, betweenness centrality, average shortest path length, and
clustering coefficient. Gene Ontology molecular function analysis was
also conducted on the identified targets.
Results
The miRNA microarray identified 23 dysregulated miRNAs in
HIV-1-infected macrophages, with 8 upregulated and 15 downregulated.
Among these, the top 10 dysregulated miRNAs targeted over 2000 unique
genes. PPI analysis revealed key nodes in the upregulated miRNA
network, including APP, MYC, ESR2, RAF1, and HIST1H4A, while ZRANB1,
HSPA8, TGOLN2, HSPA5, and BRD4 were prominent in the downregulated
miRNA network. Notably, KRAS, CUL3, TP53, ESR1, and PARP1 were
influenced by both upregulated and downregulated miRNAs. Gene Ontology
analysis indicated that the targeted genes were involved in processes
such as protein and RNA binding, ATPase activity, and ribosomal
function.
Conclusions
HIV-1 infection induces significant dysregulation of miRNAs in
macrophages, impacting a wide array of gene targets and molecular
functions. These findings suggest that miRNA-mediated regulation may
play a crucial role in HIV-1 pathogenesis within macrophages and
present potential targets for miRNA-based therapeutic strategies.
Keywords: HIV-1, MiRNA, Microarray, Gene ontology, Biological networks,
Molecular function, Pathogenesis
Graphical Abstract
[35]graphic file with name ga1.jpg
[36]Open in a new tab
1. Introduction
HIV-1 infection remains a significant global health challenge,
affecting approximately 38 million people worldwide despite advances in
antiretroviral therapy (ART) [37][1], [38][2], [39][3], [40][4]. While
current treatments have transformed HIV from a fatal disease to a
manageable chronic condition, ART cannot eliminate the virus completely
from infected individuals [41][5], [42][6]. This is largely due to the
virus's ability to establish persistent reservoirs in host cells,
particularly macrophages and resting CD4^+ T cells, which evade immune
detection and drug targeting. These reservoirs contain integrated
proviral DNA that can reactivate upon treatment interruption,
necessitating lifelong therapy [43][7], [44][8], [45][9]. The economic
burden of continuous treatment, emergence of drug resistance, and
long-term side effects of ART further highlight the urgent need for
novel therapeutic approaches [46][10], [47][11]. Understanding the
molecular mechanisms underlying HIV-1 pathogenesis, latency
establishment, and viral persistence is therefore critical for
developing strategies aimed at HIV remission or cure [48][12],
[49][13].
The human genome was once thought to be a blueprint, all of which
contained instructions solely dedicated to coding proteins. However,
the Human Genome Project and subsequent findings revealed that only a
tiny fraction of our 3 billion base pairs, fewer than 20,000 genes and
encode proteins [50][14], [51][15], [52][16]. This surprising
revelation underscored the complexity of genetic regulation,
highlighting the roles of non-coding regions in gene expression and
cellular function. Among these non-coding elements, microRNAs (miRNAs)
have emerged as critical regulators of gene expression, influencing an
array of biological processes and disease mechanisms [53][17].
MicroRNAs are small, non-coding RNA molecules, typically 21–25
nucleotides in length, that function by binding to complementary
sequences on target mRNAs, leading to translational repression or mRNA
degradation [54][18], [55][19]. This post-transcriptional regulation
enables miRNAs to fine-tune the expression of numerous genes, thereby
orchestrating complex cellular processes and maintaining homeostasis
[56][20], [57][21]. Recent studies have signified the role of miRNAs in
immune cell function and differentiation [58][22], [59][23], [60][24],
[61][25], [62][26]. Macrophages, key players in the innate immune
response, are highly plastic cells capable of adopting various
functional states in response to microenvironmental signals [63][27],
[64][28], [65][29]. This plasticity is crucial for their roles in
pathogen inflammatory response and clearance, with miRNAs playing a
notable role in regulating these macrophage functions [66][30],
[67][31], [68][32]. However, various pathological conditions, including
cancer, neurodegenerative disease and viral infections, are associated
with dysregulation of microRNAs in various cell types, with macrophages
being no exception [69][33], [70][34], [71][35], [72][36], [73][37],
[74][38], [75][39], [76][40], [77][41]. In particular, viral infection
of macrophages can be little enigmas as they employ dexterous
strategies to evade host immune responses and the miRNA dysregulation
can be attributed to both virus-induced modifications of the cellular
environment and host antiviral defences [78][42], [79][43], [80][44],
[81][45].
The relationship between microRNAs and viral infections is particularly
relevant to viral pathogenesis, as microRNAs are widely recognized as
key regulators of gene expression in macrophages during numerous viral
infections [82][46]. Specifically in HIV-1 infection, recent studies
have sparingly established that miRNAs play a crucial role in the
immunopathogenesis by modulating immune activation, inflammatory
pathways, and viral control [83][47], [84][48], [85][49]. Specific
miRNA expression patterns have been associated with the ability of
certain individuals to spontaneously control viral replication without
therapy, highlighting their regulatory influence on host antiviral
responses [86][50]. Moreover, miRNA dysregulation has been linked to
immune cell senescence and chronic inflammation, both contributing to
HIV-1 disease progression, with expression profiles varying depending
on the timing and efficacy of antiretroviral therapy [87][51]. While
some miRNAs like miR-28, miR-125b, miR-150, and miR-155 restrict HIV-1
replication in macrophages, the virus has evolved mechanisms to
manipulate host miRNA pathways, such as viral protein Vpu inducing
miR-25 and miR-93 to suppress host restriction factors [88][52],
[89][53].
The microRNA-mediated regulatory effects in macrophages are
particularly relevant given that HIV-1, the retrovirus responsible for
a global pandemic, has evolved sophisticated mechanisms to circumvent
host immunity [90][54], [91][55]. HIV-1 targets CD4 cells and has
adapted to use macrophages as viral reservoirs. While most T cells are
depleted upon HIV-1 infection, macrophages are resistant to
virus-mediated killing and survive to form reservoirs that contribute
to viral recrudescence [92][56], [93][57], [94][58]. Recent evidence
supports the capability of macrophages in transmitting HIV to other
macrophages or T cells via cell–cell contact, thereby efficiently
spreading the virus [95][59], [96][60]. The formation of latent
cellular reservoirs remains a major hurdle in preventing HIV-1
eradication, and the complex mechanisms by which macrophages contribute
to HIV-1 persistence are still largely unknown [97][61], [98][62].
While it is partly recognized that HIV-1 regulates numerous pathways in
macrophages upon infection, including those involved in immune
response, apoptosis, and cellular metabolism, the mechanisms still
require further understanding [99][63], [100][64]. Given the nature of
miRNAs, it is plausible that they represent a crucial ‘missing link’ in
HIV-1-macrophage biology, potentially explaining some of the observed
pathway dysregulations. But, thus far, the extent and implications of
miRNA dysregulation in HIV-1-infected macrophages remain poorly
understood [101][65], [102][66], [103][67]. Macrophages, representing
integral cells of the immune system taking part in both innate and
adaptive immune responses, demand more understanding of miRNA
expression profiles upon HIV-1 infection.
In the current study, we explored the dynamics of miRNAs following
HIV-1 infection in PMA-differentiated THP-1 cells, a well-established
in vitro model for macrophages [104][68]. Subsequently, in silico
database mining and network analysis predicted hub target proteins and
the altered pathways in HIV-1 infected macrophages with the goal of
identifying potential therapeutic targets and better understanding the
role of miRNAs in HIV-1 pathogenesis. With the recent approval of
RNAi-based therapeutics by the FDA, understanding miRNA significance
might open new avenues for HIV-1 therapy. The targeted modulation of
specific miRNAs represents a promising frontier in developing novel
strategies to control HIV replication and address viral reservoir
persistence.
2. Materials and methods
2.1. Cell culture and differentiation
The human monocyte/macrophage type cell line THP-1 cells (ATCC:
TIB-202™, ATCC, Manassas, VA, USA) were cultured in RPMI 1640 medium
supplemented with 10 % fetal bovine serum (FBS) (Gibco, USA) and 1 %
penicillin-streptomycin (Sigma-Aldrich, St. Louis, MO, USA), 20 mM
HEPES (Gibco, USA), 1 mM Sodium pyruvate (Gibco, USA). Cells were
maintained in a humidified incubator at 37°C with 5 % CO[2]. The cells
were passaged when they reached approximately 80 % confluency.
For differentiation into macrophages, THP-1 cells were seeded in tissue
culture plates at a density of 1 × 10^6 cells per well. Cells were then
treated with 20 nM phorbol 12-myristate 13-acetate (PMA) for 24 hours.
Following treatment, the PMA-containing medium was removed, and cells
were washed twice with phosphate-buffered saline (PBS). Fresh RPMI 1640
medium supplemented with 10 % FBS was then added, and the cells were
allowed to rest for an additional 24 hours before further experiments.
2.2. Viral strain and stock expansion
The macrophage-tropic Indian primary isolate HIV-1[VB028] (R5, Subtype
C), deposited in the viral bank of the Division of Virology,
ICMR-National Institute of Translational Virology and AIDS Research,
Pune, was employed for the study. The virus was propagated using the
phytohemagglutinin-P (PHA-P) activated Peripheral blood mononuclear
cells (PBMCs). PBMCs were separated from donor blood samples through
density gradient centrifugation utilizing Histopaque (Sigma-Aldrich,
USA). Following separation, the PBMCs were treated with 5 μg/mL PHA-P
(Sigma-Aldrich, USA) in RPMI 1640 culture medium (Gibco, USA) for its
activation. The culture medium contained 10 % fetal bovine serum (FBS),
antibiotics (50 U/mL penicillin and 50 mg/mL streptomycin), and was
supplemented with 5 U/mL interleukin-2 (IL-2). The HIV-1 p24 antigen
detection assay (Abcam, Cambridge, UK) was employed to quantify the
infectivity of the viral stocks prepared using activated PBMCs. The
TZM-bl cell line (HeLa-derived recombinant cell with HIV-1 LTR-driven
luciferase reporter) was used for viral titration, and the
Spearman-Karber method was utilized to calculate the 50 % tissue
culture infective dose (TCID[50]), as previously described [105][69],
[106][70], [107][71].
2.3. HIV-1 infection of THP-1 macrophage
THP-1 monocytes were differentiated into macrophages, as described
earlier. THP-1 macrophages 1 × 10^6 well were infected with
HIV-1[VB028]at a TCID[50] of 400. The plates were maintained at 37°C,
5 % CO[2] for 48 hours. Mock-infected controls were treated identically
but without virus. Assay duplicate was used to confirm the viral
infection using primers against HIV-1 Gag gene in qRT-PCR.
2.4. miRNA microarray
THP-1 macrophages were infected with HIV-1 for 48 h, with mock-infected
cells serving as controls. Mock infection was performed by exposing
cells to RPMI 1640 medium containing 10 % FBS. The dysregulation of
cellular miRNAs involved in HIV-1-induced inflammation was investigated
using custom-made microarray PCR plates specific for human inflammatory
pathways (YAHS-205ZD:339325, Qiagen, Hilden, Germany).
Total miRNA was extracted using the mirVana™ miRNA Isolation Kit as per
the manufacturer’s protocol (Invitrogen, Waltham, MA, USA: AM1560).
cDNA synthesis was performed using the miRCURY LNA RT Kit (Qiagen,
Hilden, Germany) with 10 ng RNA as the template. The synthesized cDNA
served as a template for SYBR green-based quantitative PCR (qPCR) using
the miScript LNA SYBR Green PCR kit (Qiagen, Hilden, Germany). qPCR
reactions were conducted under the following conditions: initial
denaturation at 95°C for 2 min, followed by 40 cycles of 95°C for 10 s
and 56°C for 60 s. All experiments were performed with three
independent biological replicates, with RNA isolated separately from
each replicate of both HIV-1 infected and mock-infected conditions.
Data analysis was performed using the GeneGlobe data analysis tool from
Qiagen ([108]https://geneglobe.qiagen.com/in accessed on 17 December
2023). For differential expression analysis, miRNAs with a fold change
threshold of 2.0 (upregulation) or −2.0 (downregulation) and a p-value
less than 0.05 were considered statistically significant and selected
for further analysis.
2.5. miRNA target identification
For miRNA target identification, we utilized miRTarBase
([109]https://mirtarbase.cuhk.edu.cn: version 9.0), an experimentally
validated microRNA-target interactions database. The top 5 upregulated
and top 5 downregulated miRNAs identified from our microarray analysis
were used as query inputs. Target genes for these miRNAs were retrieved
from the database and downloaded for further analysis.
2.6. Protein-protein interaction
To elucidate the potential functional impact caused by affecting the
identified miRNA targets, protein-protein interaction (PPI) analysis
was performed. The miRNA target data from miRTarBase was cleaned and
organized, and all identified target genes were compiled into a master
list. Any gene that is the target of either up or down miRNA or both
upregulated and downregulated miRNAs are also screened and separated.
Protein-protein interaction (PPI) data were retrieved from the BIOGRID
database ([110]https://thebiogrid.org/: version 4.4.229). From the data
set, unique human interactions were filtered after removing any
redundant interactions detected from multiple sources. The miRNA
targets were queried against the BIOGRID database to screen the
interaction of miRNA targets with different human interactors.
Subsequently, the protein-protein interactions with miRNA targets along
with their first-degree interactors, those directly affected by these
targets, were screened and used for the preparation of further
interaction network.
2.7. Cytoscape network analysis
The interaction of miRNA targets was prepared using Cytoscape
([111]https://cytoscape.org/: version 3.10.1). The network of
upregulated, downregulated, or both miRNA targets, along with their
first interactors, were extracted from combined miRNA network and
analysed separately. Network was visualized through Cytoscape, and
network analysis was done through an inbuilt network analyser tool. For
each node, a set of network parameters was calculated to characterize
its importance within the network. These parameters included degree,
betweenness centrality, closeness centrality, clustering coefficient,
average shortest path length, eccentricity, neighbourhood connectivity,
number of directed and undirected edges, radiality, stress, and
topological coefficient. The key regulatory nodes and structural
features within the miRNA-mediated interaction network were identified.
2.8. Gene ontology and pathway enrichment analysis
In order to predict the role of these miRNA targets in modulating
normal functions, the Cytoscape app GOlorize
([112]https://apps.cytoscape.org/apps/golorize) was used in conjunction
with BiNGO ([113]https://apps.cytoscape.org/apps/bingo) to predict Gene
Ontology (GO) for molecular functions. This method uses a graph layout
algorithm and finds overrepresented GO categories in a network while
using it in conjunction with BiNGO [114][72]. The network of miRNA
target genes and their first-degree interactors was imported into
Cytoscape, and BiNGO was used to analyze overrepresented GO molecular
function terms using a hypergeometric statistical test with Benjamini &
Hochberg FDR correction using Homo sapiens. A significance level of
0.05 was used to select overrepresented terms after correction, and
overrepresented terms were identified and represented. The resulting
network was visualized using Cytoscape version 3.10.1. Further, Pathway
enrichment analysis was conducted using the Database for Annotation,
Visualization, and Integrated Discovery (DAVID, v2024q2) to investigate
the biological significance of differentially expressed genes
identified in this study. Gene identifiers were submitted to DAVID, and
enrichment was performed using three databases: KEGG, Reactome, and
WikiPathways. Significantly enriched pathways were identified with the
p-value.
3. Results
Building on our understanding of the complex interplay between HIV-1
and host immune responses and shedding light on the molecular
mechanisms underlying HIV-1-induced miRNA dysregulation, we conducted a
series of experiments to elucidate the effects of HIV-1 infection on
miRNA expression in macrophages.
3.1. HIV-1 infection of macrophages induces differential expression of
inflammatory response-associated miRNAs
To explore the impact of HIV-1 infection on miRNA expression in
macrophages, we employed a custom panel focusing on miRNAs involved in
human inflammatory responses. This targeted approach allowed us to hone
in on a specific subset of miRNAs that are likely to play crucial roles
in the host immune response. The miRNA assay was normalized using a
factor calculated based on multiple reference miRNAs included in the
panel, ensuring accuracy in the fold-change calculations, which were
performed using the GeneGlobe data analysis tool. The differential
expression of miRNAs (represented as fold changes) is summarized in
[115]Table 1.
Table 1.
The differential expression of miRNA in Macrophages upon HIV-1
Infection.
Sl. No Position miRNA ID Fold Regulation p-Value
1 A01 hsa-let−7a−5p −1.59 0.089901
2 A02 hsa-let−7b−5p −1.34 0.003203
3 A03 hsa-let−7c−5p −1.56 0.00178
4 A04 hsa-let−7d−5p −1.48 0.002133
5 A05 hsa-let−7e−5p −1.63 0.001579
6 A06 hsa-let-7f−5p −1.56 0.001805
7 A07 hsa-let-7g−5p −3.56 0.001253
8 A08 hsa-let−7i−5p −1.05 0.030642
9 A09 hsa-miR−101–3p −1.58 0.001702
10 A10 hsa-miR−106b−5p −1.29 0.004025
11 A11 hsa-miR−125a−5p −1.25 0.004698
12 A12 hsa-miR−125b−5p 8.30 0.000019
13 B01 hsa-miR−128–3p 1.43 0.00229
14 B02 hsa-miR−130a−3p 1.70 0.003478
15 B03 hsa-miR−130b−3p 1.88 0.000507
16 B04 hsa-miR−1324 1.80 0.000611
17 B05 hsa-miR−144–3p 6.82 0.000023
18 B06 hsa-miR−145–5p 3.19 0.000865
19 B07 hsa-miR−15a−5p −2.40 0.036067
20 B08 hsa-miR−15b−5p −2.72 0.001367
21 B09 hsa-miR−16–5p −1.50 0.002051
22 B10 hsa-miR−17–5p 1.04 0.383563
23 B11 hsa-miR−181a−5p 1.72 0.000766
24 B12 hsa-miR−181b−5p 2.27 0.007459
25 C01 hsa-miR−181c−5p 1.80 0.000611
26 C02 hsa-miR−181d−5p −2.62 0.045591
27 C03 hsa-miR−186–5p 1.52 0.001512
28 C04 hsa-miR−195–5p −2.18 0.000851
29 C05 hsa-miR−19a−3p −1.29 0.003897
30 C06 hsa-miR−19b−3p −1.25 0.004655
31 C07 hsa-miR−202–3p 5.85 0.041074
32 C08 hsa-miR−20a−5p 1.05 0.479648
33 C09 hsa-miR−20b−5p 1.28 0.006225
34 C10 hsa-miR−21–5p −1.65 0.001527
35 C11 hsa-miR−211–5p −4.72 0.009209
36 C12 hsa-miR−23a−3p −1.52 0.001936
37 D01 hsa-miR−23b−3p −1.57 0.00176
38 D02 hsa-miR−29a−3p 1.40 0.002706
39 D03 hsa-miR−29b−3p −2.44 0.001336
40 D04 hsa-miR−29c−3p 1.01 0.103692
41 D05 hsa-miR−300 9.69 0.003237
42 D06 hsa-miR−301a−3p −1.09 0.015615
43 D07 hsa-miR−301b−3p 1.16 0.025928
44 D08 hsa-miR−302a−3p 1.80 0.000611
45 D09 hsa-miR−302b−3p 1.80 0.000611
46 D10 hsa-miR−302c−3p 1.80 0.000611
47 D11 hsa-miR−30a−5p 1.33 0.004133
48 D12 hsa-miR−30b−5p −1.61 0.001634
49 E01 hsa-miR−30c−5p −2.09 0.000910
50 E02 hsa-miR−30d−5p 1.38 0.003115
51 E03 hsa-miR−30e−5p −1.15 0.008853
52 E04 hsa-miR−340–5p −1.64 0.001540
53 E05 hsa-miR−34a−5p 1.36 0.003538
54 E06 hsa-miR−34c−5p 7.49 0.000021
55 E07 hsa-miR−372–3p 1.80 0.000611
56 E08 hsa-miR−373–3p 1.80 0.000611
57 E09 hsa-miR−374a−5p −2.59 0.008858
58 E10 hsa-miR−381–3p 1.80 0.000611
59 E11 hsa-miR−410–3p −4.63 0.048088
60 E12 hsa-miR−424–5p −2.88 0.006601
61 F01 hsa-miR−449a 1.80 0.000611
62 F02 hsa-miR−449b−5p 1.80 0.000611
63 F03 hsa-miR−454–3p −3.79 0.006141
64 F04 hsa-miR−497–5p −1.01 0.061146
65 F05 hsa-miR−511–5p 1.80 0.000611
66 F06 hsa-miR−513b−5p 1.80 0.000611
67 F07 hsa-miR−519c−3p 1.80 0.000611
68 F08 hsa-miR−519d−3p 1.80 0.000611
69 F09 hsa-miR−520d−3p 11.11 0.032702
70 F10 hsa-miR−520e−3p 1.80 0.000611
71 F11 hsa-miR−524–5p 1.80 0.000611
72 F12 hsa-miR−543 −3.00 0.045975
73 G01 hsa-miR−545–3p −7.43 0.036831
74 G02 hsa-miR−548c−3p 1.80 0.000611
75 G03 hsa-miR−548d−3p −1.30 0.003851
76 G04 hsa-miR−548e−3p 1.80 0.000611
77 G05 hsa-miR−590–5p −1.22 0.005523
78 G06 hsa-miR−607 1.80 0.000611
79 G07 hsa-miR−655–3p 1.80 0.000611
80 G08 hsa-miR−656–3p 1.80 0.000611
81 G09 hsa-miR−875–3p 1.80 0.000611
82 G10 hsa-miR−9–5p −3.25 0.046856
83 G11 hsa-miR−93–5p 1.42 0.002448
84 G12 hsa-miR−98–5p −1.33 0.003387
[116]Open in a new tab
Bold denotes downregulation and upregulation with statistical
significance.
Our analysis revealed that 23 out of 84 miRNAs were dysregulated in
macrophages following HIV-1 infection. Among these, 8 miRNAs were
upregulated, and 15 were downregulated, with a cut-off of at least a
2-fold change applied to define significant dysregulation.
Visualization of the differential expression data was conducted through
a heat map. The heat map distinctly categorized the miRNAs into two
groups: those with reduced expression (depicted in green) and those
with elevated expression (shown in red) in response to HIV-1 infection.
The intensity of these colours was directly proportional to the
magnitude of the expression changes ([117]Fig. 1).
Fig. 1.
[118]Fig. 1
[119]Open in a new tab
Heat map of miRNA expression in HIV-1 infectedvsuninfected macrophages.
Expression levels of 84 miRNAs associated with the human inflammatory
response pathway were analyzed using a focused miRNA microarray assay.
Red indicates upregulation, green indicates downregulation, with color
intensity corresponding to magnitude of change. The data derived from
three independent experimental replicates to maintain precision of the
results. 23 miRNAs showed differential expression between control and
HIV-1 infected samples with statistical significance. Differential
expression of 8 miRNAs were found to be elevated and 15 were suppressed
in HIV-1 infected cells. The analysis used a cut-off of at least 2-fold
changes.
While the heat map clearly illustrates the polarized expression
patterns of miRNAs between the HIV-1-infected and mock-infected
macrophages, with distinct differences in expression levels, the
scatter plot provides a detailed view of the magnitude of miRNA
expression differences between the control group and the HIV-1-infected
group (Group 1), plotted on a logarithmic scale ([120]Fig. 2A). Each
point on the scatter plot represents the expression of a specific
miRNA, comparing the control (X-axis) to the HIV-1-infected group
(Y-axis). Additionally, the volcano plot ([121]Fig. 2B) highlights the
statistical significance of these expression changes by plotting the
p-value against the magnitude of fold change. This representation
underscores the statistical reliability of the data derived from three
independent replicates and enhances the reproducibility and precision
of the results. The use of these visualizations ensures a robust
selection of miRNAs for further study, emphasizing both the degree of
dysregulation and the statistical significance of the observed changes.
Fig. 2.
[122]Fig. 2
[123]Open in a new tab
Differential miRNA expression in HIV-1 infected macrophages compared to
uninfected controls. (A) Scatter plot analysis of miRNA expression. The
plot compares log10 transformed expression values between HIV-1
infected and uninfected control macrophages. Each point represents an
individual miRNA. The central diagonal line indicates no change in
expression, while the outer lines represent 2-fold up- or
down-regulation thresholds. Green points indicate downregulated miRNAs,
black points show unchanged expression, and red points represent
upregulated miRNAs in HIV-1 infected macrophages. (B) Volcano plot
depicting statistical significance and magnitude of miRNA expression
changes. The x-axis shows Log2(Group 1/Control Group) fold change, and
the y-axis represents -Log10(p-value). Vertical lines denote 2-fold up-
and down-regulation thresholds, while the horizontal line indicates the
p-value significance threshold. Red points represent statistically
significant upregulated miRNAs, green points indicate statistically
significant downregulated miRNAs, and black points represent miRNAs
with non-significant changes or less than 2-fold change.
3.2. miRTarBase analysis mapped 10 most significantly dysregulated miRNAs to
over 2000 experimentally validated unique gene targets
To assess the potential functional consequences of the differentially
expressed miRNAs in HIV-1-infected macrophages, we performed target
identification analysis using miRTarBase (version 9.0), a comprehensive
database of experimentally validated miRNA-target interactions. We
focused on the 5 most upregulated and 5 most downregulated miRNAs out
of the 23 dysregulated miRNAs identified in this study. For the
upregulated miRNAs, we identified 1358 target entries, corresponding to
1258 unique genes. The miRNA with the highest number of target entries
was hsa-miR-125b-5p, with 463 targets, followed by hsa-miR-520d-3p (435
targets), hsa-miR-144–3p (204 targets), hsa-miR-34c-5p (144 targets),
and hsa-miR-300 (112 targets). In the case of the downregulated miRNAs,
we identified 1212 target entries corresponding to 1122 unique targets.
The miRNA with the highest number of target entries was hsa-miR-454–3p,
with 373 targets, followed by hsa-miR-211–5p (330 targets),
hsa-miR-410–3p (245 targets), hsa-miR-545–3p (153 targets), and
hsa-miR-543 (111 targets). Combining the targets of both upregulated
and downregulated miRNAs, we identified a total of 2570 target entries,
corresponding to 2159 unique targets. The presence of 221 duplicate
entries across the dataset suggests that several genes are targeted by
multiple dysregulated miRNAs. The complete list of these targets is
available as [124]Supplementary Data (Combined Analysis: Sheet 1–4).
3.3. BIOGRID protein-protein interaction analysis reveals extensive network
influenced by HIV-1-induced miRNA dysregulation
To further elucidate the impact of HIV-1-induced miRNA dysregulation on
cellular processes, we conducted a protein-protein interaction (PPI)
analysis using the BIOGRID database. Out of 893,638 unique human
interactions, 283,053 (31.7 %) involved interactions with the targets
of the studied miRNAs (n = 10). Notably, 28,626 of these interactions
were first-degree interactors of targets that were also directly
regulated by the 10 most dysregulated miRNAs identified in our study
([125]Supplementary Data - Combined Analysis: Sheet 5–8). These
findings suggest that HIV-1-induced miRNA dysregulation may exert
wide-ranging effects on cellular functions, potentially influencing a
substantial portion of the human interactome.
3.4. Network analysis identified key regulatory nodes in HIV-1-induced miRNA
target network
We performed a detailed network analysis using Cytoscape to visualize
and interpret the protein-protein interactions among the miRNA targets
and their first interactors. This analysis revealed a complex and
highly interconnected network of interactions. The most highly
connected node in this network was ZRANB1, a target of downregulated
miRNAs, with a degree of 4172. This was followed by MYC (degree: 3189)
and KRAS (degree: 2919), both of which were targets of upregulated
miRNAs. Other key nodes identified included CUL3 (degree: 2798), TP53
(degree: 2721), APP (degree: 2638), and ESR1 (degree: 2479).
Interestingly, several nodes such as KRAS, TP53, ESR1, CUL3, PARP1, and
GSK3B were found to be targets of both upregulated and downregulated
miRNAs. This dual targeting suggests a complex regulatory landscape
where these genes are subject to multifaceted control by miRNAs
influenced by HIV-1 infection. The regulatory significance of these
nodes was further explored by calculating disease node scores, which
considered network parameters such as degree, betweenness centrality
(BC), clustering coefficient (CC), and average shortest path length
(ASPL) [126][73], normalized using specific formulas. Therefore,
disease node scores were calculated following the normalization of
network parameters value using the following formula in the combined
target network.
[MATH: Disease node score=Degreenorm+BCnormCCnorm+ASPLnorm :MATH]
Where the normalization was represented as
[MATH: Degreenorm=Degreenode−Degreemin
Degreemax
−Degreemin
:MATH]
[MATH: BCnorm=BCnode−BCminBCmax
−BCmin :MATH]
[MATH: CCnorm=CCnode−CCminCCmax
−CCmin :MATH]
[MATH: ASPLnorm=ASPLnode−ASPLminASPLmax−ASPLmin :MATH]
The interaction network of miRNA targets was constructed to visualize
the broader impact of HIV-1-induced miRNA dysregulation on cellular
processes. The network analysis identified several highly
interconnected nodes, which are indicative of critical regulatory hubs
within the network. These hubs, or disease nodes, are particularly
significant as they may represent key points of vulnerability or
control within the cell's response to HIV-1 infection. To further
refine our analysis, the disease nodes were categorized based on a
calculated score. These top-ranked disease nodes ([127]Table 2) provide
a clear view of the most influential targets within the network
([128]Fig. 3), representing potential therapeutic targets, as their
dysregulation could have widespread effects on cellular function.
Table 2.
Top 20 genes in the combined network ranked according to the disease
score.
Sl. No. Gene Disease node Score Targeting miRNA Expression
1 ZRANB1 4.700070283 Downregulated
2 KRAS 3.35972315 Both
3 APP 3.199559451 Upregulated
4 CUL3 2.77976849 Both
5 MYC 2.749710376 Upregulated
6 TP53 2.552078059 Both
7 HSPA8 1.989861198 Downregulated
8 ESR2 1.932012165 Upregulated
9 ESR1 1.88556316 Both
10 TGOLN2 1.439907778 Downregulated
11 HSPA5 1.426698995 Downregulated
12 RAF1 1.183360563 Upregulated
13 HIST1H4A 1.177426293 Upregulated
14 HSP90AA1 1.061589466 Upregulated
15 EZH2 1.059684953 Upregulated
16 CFTR 1.054236071 Upregulated
17 BRD4 1.025858166 Downregulated
18 PRNP 0.910649715 Downregulated
19 SOX2 0.892597337 Upregulated
20 YWHAZ 0.872974495 Downregulated
[129]Open in a new tab
Fig. 3.
[130]Fig. 3
[131]Open in a new tab
Interaction network of miRNA targets and their first interactors as per
interaction data obtained from BIOGRID 4.4.229. This network
visualization represents upregulated miRNA targets with red color,
downregulated miRNA targets with green color, both up and down miRNA
targets with orange color, and other human targets with blue color
nodes. The size of the node is arranged as per their relative degree
value in the interaction network.
The next phase of our analysis focused on the interaction network
specific to the upregulated miRNA target genes. This network, formed by
these targets and their first interactors, reveals the immediate
downstream effects of miRNA upregulation in HIV-1-infected macrophages.
This network sheds light on the potential pathways and processes that
are most affected by the upregulated miRNAs. To prioritize the most
significant nodes within this network, we observed the degree of the
nodes, which highlights the key regulatory elements that might be
driving the observed changes in cellular function ([132]Fig. 4,
[133]Table 3). Similarly, the interaction network for downregulated
miRNA target genes was constructed to understand the consequences of
reduced miRNA expression in the context of HIV-1 infection. This
network illustrates how the downregulated miRNAs influence their
immediate interactors and potentially disrupt normal cellular
functions. The identification of key nodes within this downregulated
miRNA target network was achieved through the same system used for the
upregulated targets, highlighting the most critical targets affected by
the downregulated miRNAs. These targets are particularly important as
they may represent areas where loss of regulation contributes to HIV-1
pathology ([134]Fig. 5, [135]Table 4).
Fig. 4.
[136]Fig. 4
[137]Open in a new tab
Interaction network of upregulated miRNA targets and their first
interactors as per interaction data obtained from BIOGRID 4.4.229. This
network visualization represents upregulated miRNA targets with red
color, downregulated miRNA targets with green color, both up and down
miRNA targets with orange color, and other human targets with blue
color nodes. The size of the node is arranged as per their relative
degree value in the interaction network. Upregulated miRNA targets also
display interaction with other miRNAs targets and therefore present
different color nodes in interaction network.
Table 3.
Most 20 genes that are the targets of up regulated miRNA and its First
interactor.
Sl. No. Gene Degree Targeting miRNA Expression
1 MYC 3189 Upregulated
2 KRAS 2919 Both
3 TP53 2721 Both
4 APP 2638 Upregulated
5 ESR1 2479 Both
6 ESR2 2316 Upregulated
7 HSPA8 2305 Downregulated
8 HIST1H4A 1805 Upregulated
9 RAF1 1710 Upregulated
10 BRD4 1675 Downregulated
11 EZH2 1614 Upregulated
12 HSPA5 1579 Downregulated
13 NR2C2 1423 Upregulated
14 PARP1 1412 Both
15 HSP90AA1 1409 Upregulated
16 MYCN 1392 Upregulated
17 SOX2 1381 Upregulated
18 FBXW7 1286 Upregulated
19 NPM1 1242 Upregulated
20 YWHAZ 1236 Downregulated
[138]Open in a new tab
Fig. 5.
[139]Fig. 5
[140]Open in a new tab
Interaction network of downregulated miRNA targets and their first
interactors as per interaction data obtained from BIOGRID 4.4.229. This
network visualization represents upregulated miRNA targets with red
color, downregulated miRNA targets with green color, both up and down
miRNA targets with orange color, and other human targets with blue
color nodes. The size of the node is arranged as per their relative
degree value in the interaction network. Downregulated miRNA targets
also display interaction with other miRNAs targets and therefore
present different color nodes in interaction network.
Table 4.
Most 20 genes that are the targets of down regulated miRNA and its
First interactor.
Sl. No. Gene Degree Targeting miRNA Expression
1 ZRANB1 4172 Downregulated
2 KRAS 2919 Both
3 HSPA8 2305 Downregulated
4 BRD4 1675 Downregulated
5 EZH2 1614 Upregulated
6 TGOLN2 1584 Downregulated
7 HSPA5 1579 Downregulated
8 PARP1 1412 Both
9 HSP90AA1 1409 Upregulated
10 EGLN3 1315 Downregulated
11 NPM1 1242 Upregulated
12 YWHAZ 1236 Downregulated
13 YWHAQ 1190 Downregulated
14 PRNP 1125 Downregulated
15 RAC1 1034 Upregulated
16 HNRNPU 1023 Downregulated
17 HSPD1 987 Upregulated
18 CUL1 976 Upregulated
19 CSNK2A1 974 Upregulated
20 GSK3B 912 Both
[141]Open in a new tab
Finally, to capture the full scope of HIV-1-induced miRNA
dysregulation, we analyzed the network of miRNA targets that are
concurrently regulated by both upregulated and downregulated miRNAs.
This network showcases the complex interplay between various miRNA
targets and their interactors, providing a holistic view of the
cellular landscape under HIV-1 infection. The network highlights
potential areas of crosstalk and interaction between different miRNA
pathways. The presence of common targets across both upregulated and
downregulated networks underscores the complexity of HIV-1's impact on
macrophage function and suggests potential points of intersection where
therapeutic interventions might be most effective ([142]Fig. 6,
[143]Table 5).
Fig. 6.
[144]Fig. 6
[145]Open in a new tab
Interaction network of targets that are concurrently regulated by both
upregulated and downregulated miRNAs and their first interactors as per
interaction data obtained from BIOGRID 4.4.229. This network
visualization represents upregulated miRNA targets with red color,
downregulated miRNA targets with green color, both up and down miRNA
targets with orange color, and other human targets with blue color
nodes. The size of the node is arranged as per their relative degree
value in the interaction network. Targets of both up and downregulated
miRNA also display interaction with other miRNAs targets and therefore
present different color nodes in interaction network.
Table 5.
Most 20 genes that are the targets of both up and down regulated miRNA
and its First interactor.
Sl. No. Gene Degree Targeting miRNA Expression
1 ZRANB1 4172 Downregulated
2 MYC 3189 Upregulated
3 KRAS 2919 Both
4 CUL3 2798 Both
5 TP53 2721 Both
6 APP 2638 Upregulated
7 ESR1 2479 Both
8 HSPA8 2305 Downregulated
9 RAF1 1710 Upregulated
10 HSPA5 1579 Downregulated
11 PARP1 1412 Both
12 HSP90AA1 1409 Upregulated
13 FBXW7 1286 Upregulated
14 NPM1 1242 Upregulated
15 YWHAZ 1236 Downregulated
16 CFTR 1210 Upregulated
17 YWHAQ 1190 Downregulated
18 RAC1 1034 Upregulated
19 HNRNPU 1023 Downregulated
20 HSPD1 987 Upregulated
[146]Open in a new tab
3.5. Gene ontology analysis reveals enrichment of significant molecular
functions dysregulated by HIV-1-induced miRNA changes
To gain further insights into the molecular functions impacted by the
dysregulated miRNAs, we performed the Gene Ontology (GO) analysis. The
analysis revealed significant enrichment of several critical molecular
functions associated with the targets of both upregulated miRNAs
([147]Fig. 7) and downregulated miRNAs ([148]Fig. 8).
Fig. 7.
[149]Fig. 7
[150]Open in a new tab
Upregulated miRNA target and their first interactors analysis for Gene
ontology: Molecular function using BINGO and visualized through
Cytoscape 3.10.1. The colour of the nodes represents the significance
of the over-representation as indicated in the scale bar, where colour
towards yellow to orange indicate increasing significance.
Fig. 8.
[151]Fig. 8
[152]Open in a new tab
Downregulated miRNA target and their first interactors analysis for
Gene ontology: Molecular function using BINGO and visualized through
Cytoscape 3.10.1. The colour of the nodes represents the significance
of the over-representation as indicated in the scale bar, where colour
towards yellow to orange indicate increasing significance.
Binding activities emerged as one of the most significantly affected
categories. Specifically, the most enriched terms included protein
binding, RNA binding, purine ribonucleotide binding, ribonucleotide
binding, and structural components of ribosomes. Other highly enriched
functions included nucleotide binding, purine nucleotide binding, ATP
binding, adenyl ribonucleotide binding, nucleoside binding, adenyl
nucleotide binding, and nucleoside-triphosphatase activity. Additional
enriched functions were related to purine nucleoside binding,
pyrophosphatase activity, hydrolase activity on acid anhydrides, ATPase
activity, coupled ATPase activity, and RNA polymerase activity. These
findings suggest that HIV-1-induced miRNA dysregulation in macrophages
could have far-reaching consequences, potentially disrupting a broad
range of cellular processes, including protein-protein interactions,
RNA metabolism, nucleotide binding and hydrolysis, and ribosome
structure and function. The enriched functions are visually represented
for the upregulated miRNA targets ([153]Fig. 7), downregulated miRNA
targets ([154]Fig. 8), and the combined set of targets for both
upregulated and downregulated miRNAs along with their first interactors
([155]Fig. 9), showcasing the top molecular functions sorted by
enrichment scores.
Fig. 9.
[156]Fig. 9
[157]Open in a new tab
Both up and downregulated miRNA target and their first interactors
analysis for Gene ontology: molecular function using BINGO and
visualized through Cytoscape 3.10.1. The colour of the nodes represents
the significance of the over-representation as indicated in the scale
bar, where colour towards yellow to orange indicate increasing
significance.
3.6. Pathway enrichment analysis revealed the key pathways influenced by the
most dysregulated miRNAs
The DAVID analysis revealed several enriched pathways impacted by the
genes predicted to be regulated by the dysregulated miRNAs during HIV-1
infection in monocyte-derived macrophages (MDMs). Pathway enrichment
analysis was conducted using KEGG, Reactome, and WikiPathways
databases, revealing a total of 210, 549, and 209 enriched pathways,
respectively study ([158]Supplementary Data - Combined Analysis: Sheet
17–19). KEGG analysis revealed dysregulations in critical pathways,
including Pathways in Cancer, MAPK signaling, PI3K-Akt signaling, mTOR
signaling, NF-κB signaling, cellular senescence and p53 signaling,
which are integral to cell survival, proliferation, immune response,
and apoptosis Pathways related to autophagy and cellular senescence
were also notably affected, emphasizing the potential role of miRNA
dysregulation in cellular homeostasis during HIV-1 infection. The
reactome analysis provided complementary findings, including
significant enrichment in pathways regulating the immune system, such
as cytokine signaling, antigen presentation, and apoptosis,
highlighting dysregulation of immune defense mechanisms. WikiPathways
analysis identified similar critical pathways, including cancer
pathways, MAPK signaling, and PI3K AKT mTOR signaling. The other
pathways that are affected include VEGFR2 signaling, and Alzheimer’s
disease, alongside the critically important B cell receptor signaling
and TNF alpha signaling. The top 20 pathways enriched in DAVID
functional annotation according to –Log10 (p-Value) is represented in
[159]Fig. 10. Collectively, these findings demonstrate the extensive
influence of HIV-1-associated miRNA dysregulation on fundamental
biological processes, signaling pathways, and host-pathogen dynamics,
providing valuable insights into the molecular framework of HIV
pathogenesis.
Fig. 10.
[160]Fig. 10
[161]Open in a new tab
DAVID pathway functional annotation analysis of targets against pathway
databases: (A) KEGG, (B) WikiPathways, and (C) Reactome Pathways. The
top 20 pathways are presented based on their significance, measured as
– Log10 (p-value). The size of the data points represents the gene
count, indicating the minimum number of genes associated with the
corresponding pathway. The color gradient represents the gene ratio,
defined as the proportion of genes in the given pathway relative to the
total list. Darker shades indicate higher gene ratios. Scale bars are
included for size (gene count) and color (gene ratio) to provide a
clear visual reference for interpretation.
4. Discussion
MicroRNAs serve as molecular orchestrators that regulate a diverse
array of functions. Their dynamic expression determines the fate of
cellular activities, adapting to changes in the cellular environment,
such as viral infections. This study aimed to elucidate the impact of
HIV-1 infection on miRNA expression in macrophages and to explore the
downstream effects of miRNA dysregulation on protein-protein
interactions and molecular functions within these cells. Our
investigation revealed that HIV-1 infection led to the dysregulation of
23 miRNAs associated with inflammatory responses, with 15 miRNAs
downregulated and 8 miRNAs upregulated. However, it should be
acknowledged that the observed miRNA expression profiles could be
influenced not only by direct HIV-1 infection but also by other
cellular mechanisms such as immune response variability, cellular
stress responses, and epigenetic modifications. Future studies
integrating transcriptomic, proteomic, and epigenomic data may offer a
more holistic understanding of the underlying regulatory dynamics. The
miRNAs identified in this study, which have been previously implicated
in various cellular processes, including immune regulation, apoptosis,
and cellular metabolism [162][42], suggest that their dysregulation
could have far-reaching consequences for macrophage function.
Target identification analysis using miRTarBase revealed that the 10
most significantly dysregulated miRNAs were mapped to over 2000
experimentally validated unique gene targets. This extensive targeting
emphasizes the potential of miRNAs to modulate a broad array of
cellular functions. The identification of multiple genes targeted by
both upregulated and downregulated miRNAs highlights the complex
regulatory nature of miRNAs, where they may exert both activating and
inhibitory effects on the same pathways, depending on their level of
expression.
Protein-protein interaction analysis showed that approximately 31.7 %
of human protein interactions involve targets of the most dysregulated
miRNAs, according to the BIOGRID database. Although no database is
complete, and information about interactions is still growing, the
large number of interactions with miRNA targets indicates an extensive
network influenced by HIV-1-induced miRNA dysregulation. Analyzing the
node based on BC, ASPL, and CC, in addition to degree, revealed several
significant nodes. The presence of key cellular regulators, such as
MYC, TP53, and KRAS, as central hubs in our analysis, suggests that
HIV-1-induced miRNA dysregulation may have extensive effects on
fundamental cellular processes, including cell cycle control,
apoptosis, and signal transduction. This finding is consistent with
previous reports that have investigated and confirmed the deregulation
of these cellular processes in HIV-1 infection [163][74], [164][75],
[165][76].
One of the genes identified with the highest degree is ZRANB1, which
encodes the TRABID protein and is targeted by the downregulated miRNA
hsa-miR-410–3p. Recent studies have highlighted this protein's role in
regulating autophagy and mitosis in cancers, which could have
implications for HIV-1 pathogenesis [166][77]. Inhibition of TRABID has
been reported to activate cGAS/STING, induce type I interferon genes,
and subsequently upregulate interferon-stimulatory genes (ISGs)
[167][78]. However, as identified in our study, ZRANB1 is targeted by a
downregulated miRNA, suggesting that its expression may be elevated
compared to uninfected macrophages, potentially suppressing the
activation of the cGAS/STING pathway. This finding could explain the
absence of cGAS-mediated responses upon HIV-1 infection, as reported in
previous studies [168][79], [169][80].
Another significant hub gene identified in this study is MYC, a direct
target of the upregulated miRNA hsa-miR-34c-5p. This interaction
suggests a potential downregulation of MYC expression upon HIV-1
infection of macrophages. MYC, a member of the basic helix-loop-helix
leucine zipper (bHLH-Zip) transcription factor family, is essential for
cellular growth, proliferation, and differentiation [170][81]. Previous
studies have shown that ectopic expression of c-Myc results in
decreased HIV-1 expression [171][82]. Conversely, it can be
hypothesized that the reduced expression of MYC during HIV-1 infection
may attenuate the repression of HIV-1, potentially facilitating viral
replication and propagation.
Some central hub genes, like KRAS, TP53, and CUL3, are controlled by
both upregulated and downregulated miRNAs. This dual regulation may
exist because these genes control many pathways, and any skewed changes
from single-sided regulation could potentially disrupt multiple
cellular processes. It is possible that this bidirectional control
allows protein levels to remain in dynamic states, though the exact
reasons for this complex regulatory mechanism are not fully understood.
Gene ontology analysis revealed that binding activities were among the
most significantly affected categories, with significant enrichment of
molecular functions related to binding activities, particularly those
associated with protein binding, RNA binding, and nucleotide binding.
These changes in binding functions may influence various stages of the
viral lifecycle, including entry, reverse transcription, integration,
and assembly. Altered protein binding could affect viral-host
interactions crucial for infection and replication [172][83]. Changes
in RNA binding might impact viral RNA processing and trafficking, while
differences in nucleotide binding could influence reverse transcription
and integration processes [173][84], [174][85]. The enrichment of
ATPase activity suggests impacts on energy metabolism, which is central
to many cellular activities. Altered ATPase activity could affect
energy-dependent processes critical for viral replication, such as
reverse transcription, nuclear import of the viral genome, and virion
assembly [175][86]. Moreover, changes in ATPase activity might
influence cellular processes like immune signaling and antigen
presentation, which are vital for the macrophage's role in immune
responses [176][87].
As a retrovirus, HIV heavily depends on the host cell's transcriptional
machinery, particularly RNA polymerase activity, to replicate its
genome and produce viral proteins. GO analysis also highlighted RNA
polymerase activities that may alter the transcriptional regulation in
HIV-1-infected macrophages, impacting both host and viral gene
expression. In protein synthesis, ribosomes play a central role crucial
for both host cellular functions and viral replication, and the study
revealed that the structural components of the ribosome were
significantly enriched, suggesting that HIV-1-induced miRNA changes
might have a more direct impact on ribosomal function than previously
thought. This indicates alterations in the translation machinery,
potentially affecting the efficiency or specificity of protein
production. Such changes might influence the balance between host and
viral protein synthesis, impacting viral replication rates, the
production of immune mediators, and the overall cellular response to
infection. These findings align with the well-established understanding
that HIV-1 exploits and modifies the host cell's translation mechanisms
to facilitate its replication and survival [177][88]. Another critical
molecular function enriched by GO is hydrolase activity. Hydrolases
catalyze the breakdown of various biological molecules and play crucial
roles in numerous cellular functions, including protein degradation,
lipid metabolism, and signal transduction [178][89]. Their enrichment
could be related to changes in these processes, potentially affecting
viral protein processing, cellular defense mechanisms, or the breakdown
of viral components. Collectively, these alterations could either
facilitate or hinder HIV-1 infection and replication in macrophages,
potentially affecting viral latency and the cell's immune responses.
However, further experimental investigation is required to determine
whether these enriched binding activities ultimately promote or inhibit
HIV-1 infection in macrophages.
The DAVID functional enrichment analysis revealed the potential
dysregulation of multiple critical pathways mediated by miRNA
alterations during HIV-1 infection in macrophages. The analysis
highlighted that the HIV-1 pathogenesis is a multifaceted process
driven by huge interactions between the virus and host cellular
pathways, with dysregulated miRNAs emerging as critical regulators.
Among the enriched pathways, "Pathways in Cancer" was identified as a
significantly enriched term. Cancer pathways encompass numerous
signaling pathway networks involved in cell cycle regulation,
apoptosis, and immune evasion – the networks frequently co-opted by
HIV-1 to sustain its replication and persistence. This overlap suggests
that HIV-1 infection may potentially elevate the risk of malignancies
in long-term infections through the involvement of miRNAs, aligning
with established evidence linking chronic HIV-1 infection to a higher
susceptibility to cancer [179][90], [180][91], [181][92], [182][93].
Other significantly enriched pathways included the MAPK signaling
pathway, which governs cell survival, inflammation, stress response and
apoptosis. MAPK is also involved in HIV-1 progression, where viral
proteins, such as Tat and Nef, are known to exploit this pathway to
induce pro-inflammatory cytokine production, thereby enhancing viral
replication and perpetuating chronic inflammation [183][94], [184][95],
[185][96]. The present study suggests that miRNAs further modulate this
pathway, amplifying its dysregulation. Similarly, central signaling
pathways like PI3K-Akt and mTOR, which are well-documented targets of
HIV-1 manipulation, were predicted to be influenced by miRNA
dysregulation in this study [186][97], [187][98]. Analysis also
enriched the pathways involved in apoptosis, such as the p53 signaling
axis, underscoring the role of miRNAs in determining infected cell
survival. Additionally, the study highlights that neuroinflammation
pathways, already known to be dysregulated and contribute to
HIV-associated neurocognitive disorders through inflammatory damage in
the central nervous system, are also influenced by miRNAs, as revealed
by the current analysis [188][99], [189][100]. Adding to this, the
analysis also enriched the Alzheimer’s disease pathway, underscoring
the possible role of miRNA in disease progression. The analysis also
revealed the B cell receptor (BCR) signaling dysregulation during HIV-1
infection of MDM, which may have profound significance, as it
contributes to immune dysfunction and impaired humoral responses.
Dysregulated BCR signaling leads to hyperactivation of B cells, which,
paradoxically, results in their functional exhaustion and loss of
antigen-specific responses. This impairs the generation of effective
antibodies against HIV-1, enabling viral persistence and immune
evasion. Moreover, altered BCR signaling disrupts normal B cell
differentiation, skewing the balance toward short-lived plasma cells
and reducing the generation of long-lived memory B cells, which are
critical for sustained immunity. These mechanisms collectively weaken
the host's ability to control HIV-1 infection and may contribute to the
progression of acquired immunodeficiency syndrome (AIDS) [190][101].
Apart from the aforementioned pathways, many other pathways were also
enriched, highlighting significant molecular dynamics induced by HIV-1
infection. While the dysregulation of several of these pathways has
been previously documented, a comprehensive understanding remains
elusive. This study attempted to showcase how these pathways, some
already known to be disrupted during HIV-1 infection, may be regulated
through miRNA-mediated mechanisms. Such insights may have significant
practical implications and reveal additional therapeutic targets that
have yet to be explored.
The identified dysregulated miRNAs and affected pathways could serve as
valuable biomarkers for early HIV infection diagnosis, disease
progression monitoring, and treatment response evaluation[191][67],
[192][102]. Additionally, targeted interventions using miRNA mimics or
inhibitors present promising new avenues for HIV cure research,
particularly for modulating viral latency and manipulating macrophage
reservoirs[193][103]. However, translating miRNA-based therapies to
clinical applications faces substantial hurdles, most notably
developing delivery systems that can effectively and safely target
infected macrophages while avoiding off-target effects elsewhere in the
body [194][104], [195][105]. Additional challenges include the inherent
instability of miRNAs, as they are rapidly degraded by nucleases in
biological fluids, necessitating the use of advanced carriers such as
lipid-based or inorganic nanoparticles to protect them during systemic
circulation [196][106], [197][107]. Furthermore, some delivery vehicles
may provoke unintended immune responses, especially when viral vectors
or certain lipid formulations are used, which can lead to systemic
inflammation [198][108], [199][109]. Achieving precise cell-type
specificity is also complex, as macrophage subpopulations exhibit
heterogeneous surface markers, making it difficult to ensure that
therapeutic miRNAs are delivered exclusively to the intended cells
[200][110]. Finally, dose optimization poses a significant challenge
because miRNAs often regulate multiple downstream targets, increasing
the risk of off-target effects even with localized delivery [201][111].
While recent advances such as CRISPR/Cas systems and stimuli-responsive
nanocarriers show promise in addressing these issues, scalability and
long-term safety remain to be established in clinical settings
[202][112], [203][113]
While these results offered important insights, critical limitations
must be acknowledged. The experimental approach using
PMA-differentiated THP-1 macrophages, though widely accepted,
represents a simplified model that may not fully capture the complexity
of HIV-1 infection in primary cells or in vivo environments [204][114],
[205][115]. Addressing this limitation requires further research to
validate these findings using primary human macrophages and clinical
samples. Additionally, it is important to note that the miRNA target
interactions identified in this study were based on bioinformatic
predictions and validated databases, which, while valuable, may not
fully replicate the biological interactions occurring in vivo.
Therefore, experimental validation of key miRNA-target relationships
will be necessary to confirm their functional relevance [206][116].
Future studies may employ RNA-seq for higher-resolution analysis, and
develop functional assays that directly measure how miRNA manipulation
affects HIV-1 replication and latency in macrophages. Investigating
synergies between miRNA interventions and current antiretroviral
therapies or latency-reversing agents would further strengthen
translational potential. This comprehensive examination of miRNA
dysregulation during HIV-1 infection not only expands understanding of
host-pathogen interactions but also lays crucial groundwork for
developing next-generation therapeutic strategies targeting persistent
viral reservoirs.
5. Conclusions
The study provides a comprehensive examination of the profound impact
of HIV-1 infection on miRNA expression in macrophages, revealing
significant dysregulation of both upregulated and downregulated miRNAs.
This dysregulation influences key pathways, leading to notable
alterations in protein-protein interactions and molecular functions
within these cells ([207]Fig. 11). The identified miRNAs are pivotal in
regulating essential cellular processes, and their altered expression
may play a crucial role in the persistence and pathogenesis of HIV-1.
By highlighting the regulatory role of miRNAs in HIV-1 infection, this
study offers new insights that could pave the way for the development
of targeted RNAi-based therapies and enhance our understanding of
viral-host interactions. Further research is needed to validate these
findings in clinical settings and explore the therapeutic potential of
targeting these miRNA-mediated mechanisms for combating HIV-1 infection
and its associated complications.
Fig. 11.
[208]Fig. 11
[209]Open in a new tab
Overview of HIV-1 infection stages, deregulated miRNAs, targeted hub
genes, and principal enriched signaling pathways in macrophages. The
figure illustrates the major stages of the HIV-1 replication cycle in
macrophages: (1) Binding of HIV-1 to CD4 and CCR5 receptors, (2) Fusion
of viral and host membranes, (3) Initiation of reverse transcription,
(4) Completion of reverse transcription, (5) Uncoating of the viral
capsid, (6) Integration of viral DNA into the host genome, (7)
Translation of viral proteins, (8) Assembly and budding of new viral
particles, and (9) Release and maturation of virions. Identified
deregulated miRNAs following HIV-1 infection are shown, with
upregulated miRNAs indicated in red, and downregulated miRNAs shown in
green. Key hub genes targeted by these miRNAs are represented in three
groups, comprising genes targeted by upregulated miRNAs (red box),
genes targeted by downregulated miRNAs (green box), and genes targeted
by both up- and downregulated miRNAs (brown box).The lower panel
depicts the principal signaling pathways enriched among the miRNA
targets, including the MAPK, PI3K-Akt, mTOR, NF-κB, and p53 signaling
pathways, highlighting their major biological roles such as
proliferation, apoptosis, immune regulation, cellular metabolism, and
inflammation.
Funding
This research was funded by the Department of Health Research
(WSS/2020/000023/AP), India and the Researchers Supporting Project
(RSPD2025R1115), King Saud University, Riyadh, Saudi Arabia. The APC
was funded by Indian Council of Medical Research and ICMR- National
Institute of Translational Virology and AIDS Research, Pune.
CRediT authorship contribution statement
Harshithkumar R.: Writing – original draft, Visualization, Validation,
Software, Methodology, Investigation, Formal analysis, Data curation,
Conceptualization. Kaul Mollina: Methodology, Investigation, Formal
analysis. Chandane-Tak Madhuri: Methodology, Investigation. Siddiqi
Nikhat J.: Writing – review & editing, Software, Investigation and
Funding acquisition. Malik Abdul: Writing – review & editing, Software,
Funding acquisition. Khan Abdul Arif: Writing – review & editing,
Writing – original draft, Visualization, Validation, Supervision,
Software, Resources, Project administration, Investigation, Formal
analysis, Data curation, Conceptualization. Mukherjee Anupam: Writing –
review & editing, Writing – original draft, Visualization, Validation,
Supervision, Resources, Project administration, Funding acquisition,
Formal analysis, Data curation, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgments