biomedicines
Article
Different HCV Exposure Drives Specific miRNA Profile in
PBMCs of HIV Patients
Daniel Valle-Millares 1 , Óscar Brochado-Kith 1 , Luz Martín-Carbonero 2 , Lourdes Domínguez-Domínguez 3,
Pablo Ryan 4 , Ignacio De los Santos 5 , Sara De la Fuente 6, Juan M. Castro 2, María Lagarde 3,
Guillermo Cuevas 4, Mario Mayoral-Muñoz 2, Mariano Matarranz 3, Victorino Díez 4, Alicia Gómez-Sanz 1,
Paula Martínez-Román 1, Celia Crespo-Bermejo 7, Claudia Palladino 8, María Muñoz-Muñoz 9 ,
María A. Jiménez-Sousa 1, Salvador Resino 1 , Verónica Briz 7,† , Amanda Fernández-Rodríguez 1,10,*,†
and on Behalf of Multidisciplinary Group of Viral Coinfection HIV/Hepatitis (COVIHEP) ‡






Citation: Valle-Millares, D.;
Brochado-Kith, Ó.; Martín-Carbonero,
L.; Domínguez-Domínguez, L.; Ryan,
P.; De los Santos, I.; De la Fuente, S.;
Castro, J.M.; Lagarde, M.; Cuevas, G.;
et al. Different HCV Exposure Drives
Specific miRNA Profile in PBMCs of
HIV Patients. Biomedicines 2021, 9,
1627. https://doi.org/10.3390/
biomedicines9111627
Academic Editor:
Robert Schierwagen
Received: 16 September 2021
Accepted: 1 November 2021
Published: 5 November 2021
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Attribution (CC BY) license (https://
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4.0/).
1 Unit of Viral Infection and Immunity, National Center for Microbiology, Institute of Health Carlos III,
28220 Majadahonda, Madrid, Spain; da.valle@isciii.es (D.V.-M.); obrochado@isciii.es (Ó.B.-K.);
agsmm73@hotmail.com (A.G.-S.); paula.mroman@isciii.es (P.M.-R.); jimenezsousa@isciii.es (M.A.J.-S.);
sresino@isciii.es (S.R.)
2 Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain;
lmcarbonero@gmail.com (L.M.-C.); juanmi.castro@gmail.com (J.M.C.); mariocoslada@gmail.com (M.M.-M.)
3 VIH Servicio de Medicina Interna Research Institute Hospital 12 de Octubre (i+12), 28041 Madrid, Spain;
lourdes.dd@outlook.com (L.D.-D.); estudioshiv12@gmail.com (M.L.); marmatamo@gmail.com (M.M.)
4 Department of Infectious Diseases, Infanta Leonor Teaching Hospital, 28031 Madrid, Spain;
pabloryan@gmail.com (P.R.); guillermocuevastascon@gmail.com (G.C.); victorino.diez@gmail.com (V.D.)
5 Internal Medicine Servicie Hospital Universitario de La Princesa, 28006 Madrid, Spain;
isantosg@salud.madrid.org
6 Internal Medicine Service Hospital Puerta de Hierro, 28222 Madrid, Spain; sarafm28@hotmail.com
7 Laboratory of Reference and Research on Viral Hepatitis, National Center for Microbiology,
Institute of Health Carlos III, 28220 Majadahonda, Madrid, Spain; ccrespo@externos.isciii.es (C.C.-B.);
veronica.briz@isciii.es (V.B.)
8 Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa,
1649-003 Lisbon, Portugal; palladino.claudia@gmail.com
9 Department of Animal Genetics, Instituto Nacional de Investigación y Alimentación Agraria y
Alimentaria (INIA), 28040 Madrid, Spain; mariamm@inia.es
10 Faculty of Medicine, Universidad Alfonso X el Sabio, Avenida Universidad 1,
28691 Villanueva de la Cañada, Madrid, Spain
* Correspondence: amandafr@isciii.es; Tel.: +34-918-223-892
† These authors contributed equally to this work.
‡ The members can be seen in Acknowledgments part.
Abstract: Micro RNAs (miRNAs) are essential players in HIV and HCV infections, as both viruses
modulate cellular miRNAs and interact with the miRNA-mediated host response. We aim to analyze
the miRNA profile of HIV patients with different exposure to HCV to explore specific signatures in the
miRNA profile of PBMCs for each type of infection. We massively sequenced small RNAs of PBMCs
from 117 HIV+ infected patients: 45 HIV+ patients chronically infected with HCV (HIV/HCV+),
36 HIV+ that spontaneously clarified HCV after acute infection (HIV/HCV-) and 36 HIV+ patients
without previous HCV infection (HIV). Thirty-two healthy patients were used as healthy controls
(HC). Differential expression analysis showed significantly differentially expressed (SDE) miRNAs
in HIV/HCV+ (n = 153), HIV/HCV- (n = 169) and HIV (n = 153) patients. We found putative
dysregulated pathways, such as infectious-related and PI3K signaling pathways, common in all
contrasts. Specifically, putatively targeted genes involved in antifolate resistance (HIV/HV+), cancer-
related pathways (HIV/HCV-) and HIF-signaling (HIV) were identified, among others. Our findings
revealed that HCV strongly influences the expression profile of PBMCs from HIV patients through
the disruption of its miRNome. Thus, different HCV exposure can be identified by specific miRNA
signatures in PBMCs.
Keywords: HCV; HIV; microRNAs; spontaneous viral clearance; high throughput sequence
Biomedicines 2021, 9, 1627. https://doi.org/10.3390/biomedicines9111627 https://www.mdpi.com/journal/biomedicines
Biomedicines 2021, 9, 1627 2 of 18
1. Introduction
Approximately 2.75 million individuals worldwide are simultaneously coinfected
with hepatitis C virus (HCV) and HIV, as both share common transmission routes (sexual
and injection drug use (IDU) [1]. HIV has a negative impact on the progression of HCV
infection, and their natural history is altered in coinfection with HCV leading to a higher
rate of cirrhosis, liver failure, and hepatocellular carcinoma, among others [2].
HCV causes acute and chronic infections. Acute infection is usually asymptomatic
and 20–40% of infected persons spontaneously clear the virus within six months without
treatment [3]. A clearance occurs more frequently in young people and women [4], besides
a certain genetic predisposition, as people of African origin are less likely to clear HCV
than those of Caucasian origin, although the reasons for these differences are unclear.
HIV/HCV coinfection hinders spontaneous HCV clearance [5], with the frequency of
spontaneous clearance in coinfected individuals lower (between 5–10%) than monoinfected
patients (12–50%) [6]. Spontaneous HCV clearance can be predicted by the presence of
factors, such as high viral load, or the presence of certain alleles in the single nucleotide
polymorphisms (SNPs) rs12979860 [7,8] and rs8099917 [9] located at the IL28B gene en-
coding interleukin 28, also known as interferon (IFN) lambda type III. The rs12979860 CC
genotype has been associated with a higher frequency of spontaneous HCV resolution.
Additionally, spontaneous clarification of HCV is reflected in the transcriptional miRNA
profile of PBMCs, as it has been shown in HCV monoinfected patients [10].
MicroRNAs (miRNAs) are a type of non-coding RNAs that play an essential role in
the regulation of gene expression. These regulatory molecules have been shown to be
involved in the HIV and HCV infectious cycle, also regulating innate and adaptive immune
responses against viral infections. These miRNAs are susceptible to molecular alterations
due to viral infections, providing information on infectious dynamics and virus-host
relationships, as well as the consequences of the immune response to infection [11].
Despite hepatocytes being the target cells for HCV infection, peripheral blood mononu-
clear cells (PBMCs) constitute its main extrahepatic reservoir, being able to co-exist with
HIV in coinfection. PBMCs miRNA profile changes provide us information on innate and
adaptive immune responses against HIV and HCV infections or even viral hijacking. For
instance, the hsa-miR-122-5p, a liver-specific miRNA that modulates the liver homeostasis
and its HCV hijacking to favor the HCV viral cycle [12], while the hsa-miR-223 promotes
HIV latency in CD4+ T cells of HIV-1 patients by binding to the viral 3′ UTR [13]. Many
questions remain unanswered in miRNA profile characterization, especially in the context
of HIV/HCV coinfection, where scarce data have been published and none with a mas-
sive approach, which may be an additional key to elucidate virus-host relationships and
their consequences.
Thus, our study aims to massively characterize the miRNA profile alterations due
to chronic HCV infection or acute infection followed by HCV spontaneous elimination
in PBMCs of HIV patients. In addition, this study assesses specific miRNA signatures
in PBMCs entirely due to each infection, as well as the potential consequences of these
alterations at the molecular function level.
2. Materials and Methods
This cross-sectional study was conducted from 2016 to 2017. Patients were recruited
from five Public Spanish Hospitals in Madrid Autonomous Community: Hospital Uni-
versitario 12 de Octubre, Hospital Universitario La Paz, Hospital Universitario Infanta
Leonor (Madrid), Hospital Universitario La Princesa and Hospital Puerta de Hierro (see
Supplementary Material). Samples were processed at the National Center for Microbiology,
Institute of Health Carlos III, Madrid (Spain).
2.1. Patient Groups
A total of 149 patients, 117 HIV+ and 32 healthy controls (HC), all of them Europeans
of Caucasian origin, were enrolled in this study. Patients and controls were age and gender
Biomedicines 2021, 9, 1627 3 of 18
matched. All HIV+ patients were receiving suppressive antiretroviral treatment (ART) for
at least one year and had CD4+ T-cells counts ≥ 500 cel/mm3 for at least one year before
sample collection. HIV+ patients were classified into three groups: (1) HIV+/HCV+ group
(n = 45): HIV patients with active HCV-chronic infection naïve to any HCV treatment
(positive PCR and positive HCV antibodies); (2) HIV+/HCV- group (n = 36): HIV+ patients
who had been exposed to HCV but experienced spontaneous viral clearance during the first
6 months after HCV infection (negative PCR and positive HCV antibodies); (3) HIV+ group
(n = 36): HIV monoinfected patients that had never been infected with HCV (negative PCR,
negative HCV antibody and no previous HCV treatment).
2.2. High Throughput Sequencing of Small RNA
Total RNA, including miRNAs, was isolated from PBMCs within the first 4 h after
blood extraction with the miRNeasy Mini kit (Qiagen, Germantown, MD, USA). Small
RNA library synthesis was constructed with TruSeq Small RNA kit v.4 (Illumina, San Diego,
CA, USA) and sequencing were performed at the Centre for Genomic Regulation (CRG) in
Barcelona (Spain), as previously described [10].
2.3. Data Processing Pipeline
The miRNA sequencing data were processed and analyzed following a miRNA-specific
bioinformatics pipeline for the quality control, alignment, quantification and identification
of host miRNA sequences (see Supplementary Material for further information).
2.4. Statistical Analysis
Clinic and demographic data were summarized as medians for continuous variables
and frequency and percentage for categorical variables. Significant differences were calcu-
lated using the Kruskal–Wallis and Mann–Whitney U test for continuous variables and the
Fisher’s exact test for categorical variables.
2.4.1. Data Preprocessing
Firstly, we used the filterByExpr function to retain those miRNAs that have sufficiently
large counts according to the minimum expression criterion [14]. Second, we accounted
for sequencing depth normalization with the trimmed mean of M-values normalization
method (TMM) and the count per million (CPM) approach.
2.4.2. Data Exploratory Analysis
Partial least square discriminant analysis (PLS-DA) with the normalized miRNA expres-
sion profile was performed to evaluate the baseline performance of discriminant methods.
2.4.3. Differential Expression Analysis
Significant differentially expressed (SDE) miRNAs were obtained by a negative binomial
generalized linear regression model (NB-GLM) with the R package MASS v7.3-53 [15], where:
Yij = Tj + eij (1)
Y is the normalized count value for the ith miRNA in the dataset, T is the model’s
explanatory variable with levels j = 1 for HC and j = 2 for HIV, HIV/HCV+ or HIV/HCV-
groups, for each comparison and e is the random residual term. SDE miRNAs were identi-
fied by a statistically significant p-value < 0.05 adjusted by false discovery rate (FDR) using
Benjamin–Hochberg correction and a fold change (FC) higher than 1.5 (LogFC > 0.585).
We used the R software for statistical computing (v4.0.3) (www.r-project.org) for the
statistical analysis.
2.5. miRNA-Based Target Prediction and Pathway Enrichment Analysis of Target Genes
We explored target genes and potential altered molecular pathways for the SDE
miRNAs identified in the statistical analysis.
Biomedicines 2021, 9, 1627 4 of 18
The miRTarbase database was used to identify experimentally validated interactions
throughout the hypergeometric distribution approach. Statistically significant miRNA
gene-targets (enriched target genes) were found at p-value < 0.05 adjusted by FDR using
Benjamin–Hochberg correction and a minimum of two interactions.
The g:Profiler web-based server was used to conduct a functional enrichment analysis
on the list of significantly enriched gene targets [16]. We choose the KEGG (Kyoto Ency-
clopaedia of Genes and Genomes) database to retrieve statistically significantly enriched
pathways at the adjusted p-value < 0.05. The Gene Ontology (GO) annotation database
was used as a source of information for the enriched target genes identified by the SDE
miRNA uniquely found in each comparison.
3. Results
3.1. Clinical Characteristics of Each Group of Patients
Table 1 shows the epidemiological and clinical characteristics of each group of patients,
HIV+, HIV/HCV+, HIV/HCV- and HC. We only found significant differences for the HIV
transmission route (p < 0.001) and the genotype for rs12979860 polymorphism at the
IFNL4 gene, where HIV+/HCV- patients showed a higher proportion of the favorable
CC genotype (p = 0.001), as expected. There were no differences in CD4+ or CD8+ T-cell
parameters between any HIV+ patients.
We also explored the metabolic characteristics of the patients (Table 2), and we found
differences regarding weight (p = 0.015) and body mass index (p = 0.026), where HIV/HCV+
coinfected patients displayed lower levels. Regarding the liver profile parameters, we
found significant differences compared to HC in: total cholesterol (TC), high-density
lipoprotein (HDL) and atherogenic index of plasma (AIP) for patients in the HIV/HCV+
group; HDL, triglycerides (TG), atherogenic index (AI) and AIP in HIV/HCV- patients;
HDL, AI and AIP in HIV patients. Additionally, liver transaminases displayed significantly
higher levels in HIV/HCV+ coinfected patients relative to the HC group and to the rest of
the HIV-1 infected patients.
Biomedicines 2021, 9, 1627 5 of 18
Table 1. Clinical and epidemiological characteristics of the 149 patients enrolled in the study stratified by HCV infection status. Values are expressed as absolute numbers (%) and median
(percentile 25 –percentile 75). p-values were estimated with nonparametric Kruskal–Wallis test or Man–Whitney U test for continuous variables and Fisher’s exact test for categorical
variables: (A) comparison between HC and HIV/HCV+ groups; (B) comparison between HC and HIV/HCV- groups; (C) comparison between the HC group and HIV+ group; (D)
comparison between HIV-1 infected groups. Statistically significant differences are presented in bold.
HC HIV/HCV+ HIV/HCV- HIV+ p-Values
n 32 45 36 36 A B C D
Sex (%) 0.523 0.830 1.000 0.667
Male 16/32 (50.00%) 27/45 (60.00%) 20/36 (55.56%) 18/36 (50.00%)
Age (years) 49.00 (43.75–55.00) 50.00 (45.00–54.00) 52.00 (48.00–56.00) 49.50 (42.75–56.25) 0.592 0.376 0.619 0.250
Time of HIV Infection (Months) - 256.10 (115.12–324.76) 275.70 (142.72–335.41) 219.83 (88.64–265.33) - - - 0.187
Risk (%) - - - <0.001
IDUs 0/0 (0%) 26/40 (65.00%) 21/31 (67.74%) 0/30 (0.00%)
SEXUAL 0/0 (0%) 14/40 (35.00%) 10/31 (32.26%) 30/30 (100.00%)
HIV clinical status (%) - - - 0.169
A 0/0 (0%) 22/43 (51.16%) 13/32 (40.62%) 23/33 (69.70%)
B 0/0 (0%) 7/43 (16.28%) 8/32 (25.00%) 5/33 (15.15%)
C 0/0 (0%) 14/43 (32.56%) 11/32 (34.38%) 5/33 (15.15%)
HCV genotype (%) - - - -
1a 0/0 (0%) 16/39 (41.03%) 0/2 (0.00%) 0/0 (0%)
1b 0/0 (0%) 8/39 (20.51%) 1/2 (50.00%) 0/0 (0%)
2 0/0 (0%) 1/39 (2.56%) 0/2 (0.00%) 0/0 (0%)
3 0/0 (0%) 2/39 (5.13%) 0/2 (0.00%) 0/0 (0%)
4 0/0 (0%) 12/39 (30.77%) 1/2 (50.00%) 0/0 (0%)
IL28b (%) 0.192 0.085 0.730 0.001
CC 15/29 (51.72%) 14/45 (31.11%) 27/36 (75.00%) 19/36 (52.78%)
CT 12/29 (41.38%) 25/45 (55.56%) 6/36 (16.67%) 16/36 (44.44%)
TT 2/29 (6.90%) 6/45 (13.33%) 3/36 (8.33%) 1/36 (2.78%)
CD4+ T cells (cells/mm3) - 712.00 (530.00–1047.60) 732.70 (560.70–916.65) 833.00 (697.42–1062.00) - - - 0.101
CD4+ T cells (%) - 33.00 (27.00–41.50) 35.00 (31.00–40.00) 38.28 (31.50–44.75) - - - 0.460
CD8+ T cells (cells/mm3) - 790.00 (634.00–1048.00) 870.50 (636.50–1104.75) 905.50 (795.42–1244.50) - - - 0.750
CD8+ T cells (%) - 43.68 (12.35) 37.50 (9.40) 38.69 (8.44) - - - 0.158
CD4/CD8 (%) - 0.81 (0.56–1.00) 0.99 (0.75–1.16) 1.06 (0.69–1.27) - - - 0.269
Abbreviations: HIV: Human Immunodeficiency Virus; HCV: Hepatitis C Virus; IDU: Intravenous Drug Users.
Biomedicines 2021, 9, 1627 6 of 18
Table 2. Metabolic characteristics of patients enrolled in the study stratified by HCV infection status. Values are expressed as absolute numbers (percentage) and median (percentile
25–percentile 75). p-values were estimated by the nonparametric Kruskal–Wallis test and Mann–Whitney U test for continuous variables and by Fisher’s exact test for categorical variables.
The following comparisons were performed: (A) HC vs. HIV/HCV+ group; (B) HC vs. HIV/HCV- group; (C) HC group vs. HIV+ group; (D) HIV/HCV+ vs. HIV/HCV- vs. HIV+.
Statistically significant differences are shown in bold.
HC HIV/HCV+ HIV/HCV- HIV+ p-Value
n 32 45 36 36 A B C D
Weight (kg) 75.80 (64.60–88.00) 64.00 (56.25–73.40) 74.75 (62.62–80.75) 67.00 (61.10–79.00) 0.001 0.347 0.173 0.015
BMI 25.22 (22.78–28.58) 22.48 (20.81–25.72) 24.72 (22.07–27.89) 24.75 (22.44–27.12) 0.005 0.500 0.737 0.026
Lipid Profile
Glucose 90.50 (80.75–93.75) 88.00 (86.00–95.00) 96.50 (89.50–102.25) 92.50 (86.25–96.75) 0.368 0.051 0.282 0.403
Gluc > 110 2/26 (7.69%) 3/37 (8.11%) 5/28 (17.86%) 2/26 (7.69%) 1.000 0.480 1.000 0.377
Total cholesterol 207.50 (186.50–225.25) 186.00 (166.00–203.00) 192.00 (181.00–221.50) 185.00 (172.50–204.75) 0.010 0.178 0.278 0.343
Chol > 200 15/26 (57.69%) 10/35 (28.57%) 11/27 (40.74%) 10/26 (38.46%) 0.043 0.337 0.267 0.560
LDL - 108.00 (87.00–130.00) 115.00 (100.00–133.00) 115.50 (100.50–144.25) - - - 0.293
LDL > 130 - 8/23 (34.78%) 6/15 (40.00%) 9/18 (50.00%) - - - 0.614
HDL 67.50 (50.75–78.75) 50.00 (40.00–60.00) 51.00 (45.00–57.00) 46.00 (37.50–54.50) <0.001 0.002 0.001 0.878
TG 85.00 (70.00–134.50) 126.00 (80.00–167.00) 131.00 (118.00–182.00) 116.00 (97.25–178.75) 0.328 0.038 0.157 0.535
TG high 2/26 (7.69%) 4/35 (11.43%) 6/25 (24.00%) 5/24 (20.83%) 0.960 0.224 0.352 0.412
LDL/HDL - 2.23 (1.66–2.85) 2.42 (1.91–2.95) 2.43 (2.12–2.94) - - - 0.451
AI 3.00 (2.58–3.82) 3.83 (3.07–4.42) 4.00 (3.36–4.60) 3.95 (3.67–5.07) 0.085 0.011 0.005 0.447
AI low risk 23/26 (88.46%) 29/35 (82.86%) 17/21 (80.95%) 17/24 (70.83%) 0.806 0.759 0.229 0.519
AI moderate risk 3/26 (11.54%) 5/35 (14.29%) 4/21 (19.05%) 6/24 (25.00%) 1.000 0.759 0.385 0.584
AIP 0.18 (0.12–0.87) 0.97 (0.32–1.23) 0.92 (0.74–1.44) 0.89 (0.69–1.43) 0.019 0.004 0.005 0.568
AIP high risk 12/26 (46.15%) 29/35 (82.86%) 19/21 (90.48%) 23/24 (95.83%) 0.006 0.004 <0.001 0.289
LCI (×103) - 43.33 (27.17–75.66) 70.21 (44.18–81.30) 60.00 (35.84–122.80) - - - 0.731
Liver Biochemical Parameters
GOT (AST) 18.50 (15.00–20.00) 37.00 (29.00–46.00) 23.00 (19.00–26.00) 23.50 (20.50–26.75) <0.001 0.055 0.007 <0.001
GOT > 40 0/32 (0.00%) 12/36 (33.33%) 0/28 (0.00%) 2/25 (8.00%) 0.001 - 0.366 0.001
GPT (ALT) 15.00 (12.25–20.75) 45.00 (32.00–55.00) 21.50 (17.00–25.00) 27.50 (22.25–32.25) <0.001 0.675 0.012 <0.001
GPT > 40 2/26 (7.69%) 22/37 (59.46%) 0/28 (0.00%) 4/26 (15.38%) <0.001 0.439 0.664 <0.001
GGT 19.00 (12.00–28.00) 48.00 (32.25–86.75) 32.00 (26.75–39.50) 26.50 (21.00–41.50) 0.001 <0.001 0.014 0.003
GGT > 50 0/25 (0.00%) 17/34 (50.00%) 2/26 (7.69%) 4/24 (16.67%) <0.001 0.488 0.108 <0.001
APRI - 0.62 (0.50–0.67) 0.35 (0.30–0.44) - - - - -
FIB-4 - 1.56 (1.39–1.73) 1.22 (1.18–1.31) - - - - –
ALP – 85.00 (65.00–96.00) 94.50 (70.25–113.00) 77.00 (66.00–93.75) - - - 0.038
Albumin - 4.40 (4.03–4.40) 4.50 (4.43–4.68) 4.15 (4.00–4.60) - - - 0.133
Biomedicines 2021, 9, 1627 7 of 18
Table 2. Cont.
HC HIV/HCV+ HIV/HCV- HIV+ p-Value
Hemogram
Vitamin D - 22.00 (14.00–27.00) 15.50 (11.50–35.00) 24.00 (21.75–37.00) - - - 0.491
Calcium (Ca2+) 9.33 (9.21–9.65) 9.40 (9.00–9.70) 9.50 (9.25–9.70) 9.35 (9.10–9.80) 0.297 0.988 0.611 0.414
Phosphorous (P) 3.15 (2.60–3.55) 3.30 (2.90–3.60) 3.40 (3.15–3.80) 3.40 (3.15–3.90) 0.351 0.037 0.016 0.490
Iron (Fe2+) 86.50 (63.50–119.50) 79.00 (78.50–79.50) 83.00 (65.00–106.00) - 0.665 0.713 - -
Hemoglobin A1c - 5.30 (5.30–5.30) 5.60 (5.50–5.80) - - - - -
Leukocytes (×103) 6.02 (5.28–7.21) 6.91 (5.61–7.65) 7.31 (6.07–8.32) 6.90 (5.71–8.40) 0.313 0.023 0.137 0.297
Red blood cells 4.90 (4.49–5.12) 4.75 (4.24–5.09) 4.98 (4.90–5.16) 4.83 (4.77–4.96) 0.289 0.506 0.780 0.283
Hb 14.15 (12.75–15.45) 15.00 (14.50–15.90) 15.45 (13.67–16.12) 14.90 (13.80–15.67) 0.008 0.018 0.146 0.376
Hematocrit (%) 42.20 (38.62–45.77) 44.70 (43.00–47.50) 46.75 (45.90–47.73) 46.35 (45.63–49.38) 0.049 0.013 0.004 0.363
Platelets (×103) 247.00 (206.50–267.50) 219.00 (192.00–249.00) 224.50 (202.00–280.75) 234.50 (220.00–270.50) 0.069 0.962 0.702 0.090
LHD - 175.00 (167.00–195.50) 167.50 (143.00–187.00) 172.00 (162.50–203.50) - - - 0.466
Abbreviations: BMI, body mass index; TC, total cholesterol; LDL, low-density lipoprotein; TG, triglycerides; HDL, high-density lipoprotein; AI, atherogenic index, the cut-off values were: low risk <5% for men
and <4.5% for women, moderate risk 5–9 for men and 4.5–7 for women; AIP, atherogenic index for plasma considering high-risk AIP > 0.21; LCI, lipoprotein combine index defined as the ratio of TC*TG*LDL to
HDL-C; GOT, glutamate oxaloacetate transaminase; GPT, glutamic-pyruvic transaminase; GGT, gamma-glutamyltransferase; TB, total bilirubin; APRI, AST to platelet ratio index; FIB-4, fibrosis-4 index for liver
fibrosis; ALP, alkaline phosphatase; Hemoglobin A1c, hemoglobin glycosylated; LDH, lactate dehydrogenase.
Biomedicines 2021, 9, 1627 8 of 18
3.2. Differentially Expressed miRNAs between Study Groups
The raw sequencing data of the HIV-1 infected patients can be accessed at the Array-
Express repository (EMBL-EBI) under the accession number E-MTAB-8274 for HIV patients
and at E-MTAB-8023 for the HC individuals.
On average, 10.9 million reads per sample were obtained, which is an appropriate
depth for expression analysis. A total of 54% of the reads were mapped to the reference
genome, identifying 85.3% of the miRNAs in miRBase. A total of 1662 miRNAs were
identified; those with no informative expression were filtered out on each comparison.
Figure 1 shows a summary of the study and the significant differences identified.
Biomedicines 2021, 9, x FOR PEER REVIEW 10 of 21 
 
3.2. Differentially Expressed miRNAs between Study Groups 
The raw sequencing data of the HIV-1 infected patients can be accessed at the Ar-
rayExpress repository (EMBL-EBI) u der the ccession number E-MTAB-8274 for HIV pa-
tients and at E-MTAB-8023 for the HC individuals. 
On average, 10.9 million reads per sample were obtained, which is an appropriate 
depth for expression analysis. A total of 54% of the reads were mapped to the reference 
genome, identifying 85.3% of the miRNAs in miRBase. A total of 1662 miRNAs were iden-
tified; those with no informative expression were filtered out on each comparison. Figure 
1 shows a summary of the study and the significant differences identified.  
 
Figure 1. miRNA sequencing analysis and summary of results. Schematic representation of miRNA 
data analysis and results with respect to the healthy control group. The top grey square shows the 
total PBMC’s miRNAs in our raw expression matrix with annotations in miRBase. Minimum ex-
pression criterion was used to filter out miRNAs with no informative expression. The resulting miR-
NAs were subject to differential expression analysis (orange). Differentially expressed microRNAs 
of PBMCs in the HIV groups, their target genes and molecular pathways are represented by blue, 
yellow and green, respectively. 
The PLS-DA of normalized, log-transformed and scaled miRNA expression data 
show us a clear difference of HC control group with respect to the rest of the study groups, 
whilst HIV samples are all grouped together (Supplementary Figure S1). 
3.2.1. HIV/HCV+ Co-Infected Patients 
Overall, 535 miRNAs surpassed the filter of minimum expression criteria. Among 
them, 153 SDE miRNAs were identified in HIV/HCV+ patients compared to HC. After 
assessing the top expression changes (Log2FC < −2 or Log2FC > 2) we identified that the 
hsa-miR-1248, hsa-miR-3960, hsa-miR-4508 and hsa-miR-10401-3p were strongly down-
regulated in HIV/HCV+ patients (Figure 2A-left and Supplementary Table S1). 
Figure 1. miRNA sequencing analysis and summary of results. Schematic representation of miRNA data analysis and
results with respect to the healthy control group. The top grey square shows the total PBMC’s miRNAs in our raw expression
matrix with nnotations in miRBase. Minimum expression criterion was used to filter out miRNAs with no informative
expression. The resulting miRNAs we e subject to differential expression a alysis (orange). D fferentially expressed
microRNAs of PBMCs in the HIV groups, their target genes and molecul r pathways are represented by blue, yellow and
green, respectively.
The PLS-DA of normalized, log-transformed and scaled miRNA expression data show
us a clear difference of HC control group with respect to the rest of the study groups, whilst
HIV samples are all grouped together (Supplementary Figure S1).
3.2.1. HIV/HCV+ C -Infected Patients
Overall, 535 miRNAs surpassed the filter of minimum expression criteria. Among
them, 153 SDE miRNAs were identified in HIV/HCV+ patients compared to HC. After
assessing the top expression changes (Log2FC < −2 or Log2FC > 2) we identified that the
hsa-miR-1248, hsa-miR-3960, hsa-miR-4508 and hsa- iR-10401-3p were strongly downreg-
ulated in HIV/HCV+ patients (Figure 2A-left and Suppl mentary Table S1).
Biomedicines 2021, 9, 1627 9 of 18
Biomedicines 2021, 9, x FOR PEER REVIEW 11 of 21 
 
 
Figure 2. Analysis of differentially expressed miRNAs in HIV patients relative to healthy controls, potential target genes 
and molecular routes. (A) Volcano plots of SDE miRNAs in PBMCs of HIV/HCV+ (left), HIV/HCV- (middle) and HIV 
(right) patients compared to healthy controls. The expression level change (log2FC) and significance (−log10FDR) have been 
positioned on the x-axis and y-axis, respectively. The colored dots represent significantly differentially expressed miRNAs, 
indicating upregulation in green and downregulation in red. Those SDE miRNAs with strong up/downregulation (Log2FC 
> 2 or Log2FC < −2) are highlighted in wider triangle-shapes and labelled. (B) Chord diagram showing the enriched target 
genes for the SDE miRNAs in the HIV-infected groups (HIV/HCV+ in purple, HIV/HCV- in pink and HIV in blue). (C) 
Network map associating the enriched gene targets and their most significant biological process (KEGG pathways). 
The target enrichment analysis showed that these 153 miRNAs could modulate the 
expression of 66 genes (Figure 2B, dark blue; Supplementary Table S2), which were mainly 
involved in infectious-related pathways (toxoplasmosis, legionellosis and EBV), antifolate 
resistance and the PI3K-Akt signaling pathway (Figure 2C, purple; Supplementary Table 
S3). Further, the vast majority of enriched genes identified in this comparison were not 
shared with the HIV/HCV- spontaneous clearance and HIV analyses. 
3.2.2. HIV/HCV- Spontaneous Clearance 
We analyzed a total of 509 miRNAs that fulfilled the minimum expression criterion, 
identifying 169 SDE miRNAs in the HIV/HCV- group with respect to HC. Moreover, the 
hsa-miR-1246, hsa-miR-1248, hsa-miR-3960 and hsa-miR-4508 had the strongest downreg-
ulation (Log2FC < −2) (Figure 2A-middle and Supplementary Table S4). 
We explored the potential alteration at the molecular level, and we found a set of 27 
enriched genes whose expression could be modulated by the 169 SDE miRNAs in 
HIV/HCV- patients (Figure 2B, pink; Supplementary Table S2). These genes are signifi-
cantly involved in 11 functional pathways. Among them, several infection-related path-
ways were identified, such as HIV-1, toxoplasmosis, hepatitis B and Epstein–Barr. Addi-
Figure 2. nalysis of differentially expressed miRNAs in HIV patients relative to healthy controls, potential target genes
s f HCV+ (left), HIV/HCV- (middle) and HIV
( i i co pared to healthy controls. The expression level change (log2FC) and significance (−log10FDR) have
been positioned on the x-axis and y-axis, respectively. The colored dots represent significantly differentially expressed
miRNAs, indicating upregulation in green and downregulation in red. Those SDE miRNAs with strong up/downregulation
(Log2FC > 2 or Log2FC <−2) are highlighted in wider triangle-shapes and labelled. (B) Chord diagram showing the enriched
target genes for the SDE miRNAs in the HIV-infected groups (HIV/HCV+ in purple, HIV/HCV- in pink and HIV in blue).
(C) Network map associating the enriched gene targets and their most significant biological process (KEGG pathways).
The target enrichment analysis showed that these 153 miRNAs could modulate the
expression of 66 genes (Figure 2B, dark blue; Supplementary Table S2), which were mainly
involved in infectious-related pathways (toxoplasmosis, legionellosis and EBV), antifo-
late resistance and the PI3K-Akt signaling pathway (Figure 2C, purple; Supplementary
Table S3). Further, the vast majority of enriched genes identified in this comparison were
not shared with the HIV/HCV- spontaneous clearance and HIV analyses.
3.2.2. HIV/HCV- Spontaneous Clearance
We analyzed a total of 509 miRNAs that fulfilled the minimum expression criterion,
identifying 169 SDE miRNAs in the HIV/HCV- group with respect to HC. Moreover, the
hsa-miR-1246, hsa-miR-1248, hsa-miR-3960 and hsa-miR-4508 had the strongest downregu-
lation (Log2FC < −2) (Figure 2A-middle and Supplementary Table S4).
We explored the potential alteration at the molecular level, and we found a set of 27 en-
riched genes whose expression could be modulated by the 169 SDE miRNAs in HIV/HCV-
patients (Figure 2B, pink; Supplementary Table S2). These genes are significantly involved
in 11 functional pathways. Among them, several infection-related pathways were identi-
fied, such as HIV-1, toxoplasmosis, hepatitis B and Epstein–Barr. Additionally, the insulin-
Biomedicines 2021, 9, 1627 10 of 18
signaling pathway, miRNAs in cancer, chemokine and PI3K-AKt signaling pathways were
also represented, among others (Figure 2C, pink; Supplementary Table S3).
3.2.3. HIV+ Infected Group
We identified 153 SDE miRNAs (out of 521 analyzed) in the HIV+ group with respect
to the HC. As observed before, we found that the strongest alterations in the miRNA
profile of HIV+ patients occurred for the hsa-miR-1246, hsa-miR-1248, hsa-miR-3960 and
hsa-miR-4508 (Log2FC < −2) (Figure 2A-right, Supplementary Table S5).
The miRNA-gene interaction analysis showed 15 enriched target genes for the SDE
miRNAs in the HIV+ group (Figure 2B, light blue; Supplementary Table S2). According to
the pathway enrichment analysis, these genes are significantly enriched in seven functional
pathways (Figure 2C, blue; Supplementary Table S3) being the most significant the hypoxia-
inducible factor 1 (HIF-1) signaling pathway, and five related to different infections such
as the Epstein–Bar virus, the HIV, Chagas diseases toxoplasmosis and measles. The AGE-
RAGE signaling pathway in diabetic complications was also identified.
3.2.4. Specific Viral Infection Group Signatures Due to HIV+, HIV/HCV+ or HIV/HCV-
Despite 109 differentially expressed miRNAs commonly found in all the previous
analyses, hsa-miR-33, hsa-miR-122-5p and hsa-miR-125a-5p were the most relevant shared
miRNAs, which indicates the specific miRNA profile characteristic of HIV infection, we
focused on the non-shared SDE miRNAs to identify potential specific fingerprints for
each group of patients (HIV/HCV+, HIV/HCV- or HIV). Thus, we selected the unique
SDE miRNAs across each comparison with the HC group (N◦SDE miRNAs HIV/HCV+: 25;
N◦SDE miRNAs HIV/HCV-: 24; N◦SDE miRNAs HIV+: 13) (Figure 3A).
The analysis of the miRNA-target gene for each specific group of patients showed
us specific interactions that were further analyzed by up and downregulated miRNAs
separately (Figure 3B,C):
HIV/HCV+ group: We explored the putative target genes for the 25 unique SDE
miRNAs. Among the top 10 target genes, we found enriched the Spectrin Beta Non-
Erythrocytic 1 (SPTBN1) gene, targeted by the downregulated hsa-miR-589-3p, hsa-miR-
423-3p and hsa-miR-331-3p (Supplementary Table S6). The SPTBN1 gene plays a relevant
role in biological processes related to the regulation of SMAD proteins and cytoskeleton
movement guider. Additionally, the miRNA-target analysis revealed that autophagy-
related 5( ATG5), protein kinase AMP-activated catalytic subunit alpha 1(PRKAA1), MYCN,
integrin subunit beta 3 binding protein (ITGB3BP) and platelet-derived growth factor
receptor beta (PDGFRbeta) might be potential targets for upregulated hsa-miR-582, hsa-
miR-299, hsa-miR-34a and hsa-miR-9 in HIV/HCV+ patients.
HIV/HCV- group: From the unique 24 SDE miRNAs in HIV/HCV- patients, we found
the abraxas 2, BRISC complex subunit (ABRAXAS2) gene, targeted by: downregulated
hsa-miR-629-3p and hsa-miR-627-3p (Supplementary Table S7). Due to its polyubiquitin
modification function, ABRAXAS2 plays a key role in protein deubiquitination, cell division
and chromosome segregation, among others. Moreover, miRNA-target prediction of upreg-
ulated miRNAs showed significantly enriched target genes, such as claudin 1(CLDN1), that
could be repressed by hsa-miR-376c-3p, hsa-miR-1304-3p and hsa-miR-337-3p in PBMCs of
HIV/HCV- patients.
HIV+ group: The 13 unique SDE miRNAs showed a panel of 10 genes that interact with
downregulated hsa-miR-4516, hsa-miR-542-3p and hsa-miR-494-3p (Supplementary Table
S8). Among them, the zinc-binding alcohol dehydrogenase domain containing 2(ZADH2)
encodes for an oxidoreductase that negatively regulates fat cell differentiation.
Biomedicines 2021, 9, 1627 11 of 18
Biomedicines 2021, 9, x FOR PEER REVIEW 13 of 21 
 
 
Figure 3. Analysis of specific viral infection signature due to HIV/HCV+, HIV/HCV- or HIV infection: (A) Venn diagram 
representing the distribution of shared and non-shared significant differentially expressed SDE miRNAs among each HIV 
group (colorless and colored areas, respectively). (B) Bar plot of the fold change of non-shared SDE miRNAs (x-axis) in 
the log 2 scale (y-axis). Downregulated and upregulated miRNAs are presented as green and red bars, respectively. (C) 
Network graph of the top 10 target genes (black nodes) and their SDE miRNA interactor partners (green/red nodes) for 
each analysis. Down or upregulated miRNAs in HIV patients have been colored in green and red, respectively. The thicker 
Figure 3. Analysis of specific viral infection signature due to IV/ CV+, HIV/HCV- or HIV infection: (A) Venn diagram
representing the distribution of shared and non-shared significant differentially expressed SDE miRNAs among each HIV
group (colorless and colored areas, respectively). (B) Bar plot of the fold change of non-shared SDE miRNAs (x-axis) in
the log 2 scale (y-axis). Downregulated and upregulated miRNAs are presented as green and red bars, respectively. (C)
Network graph of the t p 10 target genes (black nodes) a d their SDE miRNA interactor partn rs (gre n/red nodes) for
each analysis. Down or upregulated miRNAs in HIV patients have been colored in green and red, respectively. The thicker
the line connecting SDE miRNAs to their target gene the more significant the interaction, and the wider the radius the
greater number of interactions for each target gene.
Biomedicines 2021, 9, 1627 12 of 18
4. Discussion
We have elucidated for the first time that the PBMCs miRNA profile of HIV patients
is specifically dysregulated according to the HCV exposure, being different for chronic
hepatitis C (CHC) infection, acute infection followed by spontaneous clearance or those
never infected by HCV. Moreover, here we report potential miRNAs and target genes
whose expression may be compromising, and thus the biological processes in which they
are involved.
To date, only scarce data have been published regarding the miRNA profile of
HIV/HCV patients [17,18], none of them with a massive approach and even less focused
on spontaneous clarifiers.
Analysis of clinic and metabolic characteristics showed significant deregulated levels
of lipid parameters of the three groups of HIV patients with respect to HC, while no
differences were found within HIV patients. The AIP, which is an index of dyslipidaemia
that accurately correlates with the size of HDL-C and LDL-C particles [19], was higher for all
HIV groups, especially for HIV/HCV+. Accordingly, we found dysregulation of key lipid
metabolism miRNAs, such as hsa-miR-33a, hsa-miR-122-5p and hsa-miR-125a-5p [20,21]
that were downregulated in all HIV patients. Both HIV and HCV infections disturb
host lipid metabolism generating abnormalities that could lead to severe pathologies [22].
Thus, the miRNA profile of PBMCs reflects common comorbidities in HIV/HCV patients
regarding lipid metabolism, such as an increased cardiometabolic risk, dyslipidaemia,
insulin resistance and hepatic steatosis [23].
Furthermore, all HIV patients showed a strong downregulation of miR-1248, miR-4508
and miR-3960. This feature gives us a common pattern between HIV groups highlighting
a shared signature of miRNA profile deregulation, probably due to immune response
against viral infections. Previously, the miR-1248 was positively correlated with immune
system-dependent processes, such as inflammation [24]. Thus far, little is known about
these miRNAs, and their relevance in HIV infection needs to be further explored.
4.1. HIV/HCV+ miRNA Profile
HIV/HCV coinfection leaves a fingerprint of 153 dysregulated miRNAs in PBMCs. We
found that these miRNAs might modulate the expression of genes involved in the PI3K-Akt
signaling pathway, which have been previously associated with the HCV infection and im-
mune response against other pathogens [25]. Among these putative miRNAs, we highlight
the miR-126-5p, hsa-miR-34a-5p and hsa-miR-148b-3p, whose expression levels were signif-
icantly higher in HIV/HCV coinfected PBMCs compared to HC. The miR-126-5p hepatic
expression has been associated with HCV viral load [26], the circulating hsa-miR-34a-5p
promotes liver fibrosis in CHC [27], and the hsa-miR-148b-3p has been previously identi-
fied as dysregulated in PBMCs of HIV patients [28]. Although these miRNAs were also
associated with the regulation of carcinogenesis and antitumor responses [29,30], we found
them involved in the PI3K-Akt signaling pathway. Both HIV and HCV infections promote
the activation of the PI3K-Akt signaling pathway, often resulting in other pathogenesis,
such as increasing the risk of viral carcinogenesis, and triggering immune-dependent
processes, such as inflammation [31,32]. Therefore, the enhanced expression that we have
observed of miR-126-5p, miR-34a-5p and miR-148b-3p in HIV/HCV patients—and their
putative target genes HOTAIR, SLC45A3, UGT8, VEGFA—could be associated with a higher
immune pro-inflammatory responses and deregulation of carcinogenic genes [30,33], which
is common in HIV/HCV patients.
Additionally, we also identified the downregulation of the well-known miR-125a/b-
3p and miR-122-5p in HIV/HCV+ patients, which were also previously observed in the
PBMCS of HCV chronic patients and HIV/HCV- patients, compared to HC [10]. The
miR-122 is a specific and highly abundant liver miRNA that favors HCV replication in
hepatocytes and other non-hepatic target cells due to HCV viral hijacking [34]. According
to our data set, miR-122-5p is downregulated in HIV/HCV coinfection and modulates the
expression of COPA and OSBP. Both genes are positively associated with the mechanism
Biomedicines 2021, 9, 1627 13 of 18
of replication of HCV within their target cells through the Golgi apparatus mechanism
allowing virogenic organelle formation and HCV release [35]. Therefore, miR-122-5p
downregulation might allow COPA and OSBP expression in HIV/HCV patients, promoting
HCV replication in PBMCs. In line with our results, Moghoofei et al. 2021 also reported that
hsa-miR-122-5p was differentially expressed in PBMCs of both HIV, HCV and HIV/HCV
patients [17]. These outcomes indicate that both HCV and HIV influence the expression of
hsa-miR-122-5p not only in liver cells but also in PBMCs.
On the one hand, miR-125a-5p has a dual role in blocking the expression of genes
involved in the lipid uptake and modulating anti-inflammatory processes [36], while
miR-125b-5p is mostly related to lipogenesis by targeting SCD-1 [37]. Therefore, hsa-miR-
125a/b-5p downregulation in the PBMCs of HIV/HCV+ may contribute to excessive lipid
accumulation, highlighting a worse prognosis of HIV/HCV patients.
On the other hand, we explored the specific HIV/HCV+ signature of PBMCs. Func-
tional analysis for up and downregulated miRNAs were separately performed to maximize
the identification of regulatory events related to phenotypic differences among a group of
patients [38]. The upregulation of hsa-miR-34a, hsa-miR-9, hsa-miR-299 and hsa-miR-582
suggest that they may be involved in the suppression of key target genes associated with
HCV-host interactions as the ATG5, whose inhibition may help to block the HCV replica-
tion [39]. Upregulated miRNAs potentially target genes related to liver-disease events, such
as PRKAA1, MYCN and ITGB3BP [40,41]. Interestingly, we also observed a potential target
of the PDGFRbeta, highly expressed by hepatic stellate cells (HSCs). The PDGFRbeta is a key
mediator in liver injury and fibrogenesis, contributing to human cirrhosis [42]. Therefore,
PBMCs, similar to HSCs, are susceptible to suffer alterations at the transcriptome level in
response to HCV infection. Thus, although most miRNAs involved in HCV infection have
been characterized in hepatocytes, in this study, we showed dysregulated miRNAs in HIV
patients chronically infected by HCV involved in liver-related diseases, thereby indicating
a similar response between PBMCs and hepatic cells against HCV infection.
Similarly, we also identified specific miRNAs exclusively downregulated in HIV/HCV+
patients (hsa-miR-589-3p, hsa-miR-423-3p and hsa-miR-331-3p), all of them being putative
modulators of SPTBN1 expression. This gene is involved in the TGF-β pathway, among oth-
ers, where it interacts with SMAD3/SMAD4. Within the liver, the loss or downregulation of
SPTBN1 expression promotes hepatocellular carcinoma—and other tumor progression—by
interrupting the TGF-β pathway and enhancing the Wnt signaling pathway in mice [43]
and humans [44]. Since both liver cells and PBMCs are host cells for HCV, they might
converge in the dysregulation of miRNA, gene or functional response [45].
4.2. HIV/HCV- Spontaneous Clearance Profile
We explored the long-term effects of HCV spontaneous clarification in PBMCs of HIV
patients, where 169 SDE miRNAs were observed. The top downregulated miRNAs (hsa-
miR-3960, hsa-miR-1248 and hsa-miR-4508) were previously identified in the HIV/HCV+
group. Additionally, we found that the expression levels of hsa-miR-1246 were also lower in
HIV/HCV- patients compared to healthy individuals. Hsa-miR-1246 upregulation in serum
has been considered a potential biomarker for the early detection of several cancers, such as
hepatocellular carcinoma, common in chronic HCV patients [46]. Hsa-miR-1246 was also
deeply downregulated in the PBMCs of HIV/HCV- patients, as previously observed by our
group in PBMCs after spontaneous clarification of HCV in HCV-monoinfected patients [10].
The downregulation of hsa-miR-1246 may be due to HIV infection [47], but further studies
are required to decipher whether a clear association between the spontaneous resolution of
HCV and deregulation of hsa-miR-1246 exists.
Despite more cancer-related diseases being reported during chronic HCV infection,
our results indicate that several cancer-related routes are highly represented for the 169 SDE
miRNAs identified in HIV/HCV- patients. Among these, we report 21 deregulated miR-
NAs in HIV/HCV- patients as potential targets of the Programmed Cell Death Ligand 1
(PD-L1) pathway in cancer. The PD-L1 (and its receptor PD-1) is an immune inhibitory
Biomedicines 2021, 9, 1627 14 of 18
factor that blocks cytokine secretion, and therefore, tumor cell apoptosis is the leading
cause for cancer escape. The blockage of PD-L1 and PD-1 is closely associated with immune
activation and HCV viremia reduction [48]. Therefore, our results indicate that miRNA
profile dysregulation highlights the molecular consequences due to HCV resolution, al-
though further investigations are needed to decipher the long-term consequences of HCV
spontaneous clearance and cancer-related events.
By exploring the specific footprint of HIV/HCV- patients, we exclusively found
24 SDE miRNAs. Regarding the upregulated miRNAs, we observed that hsa-miR-376c-
3p, hsa-miR-1304-3p and hsa-miR-337-3p are putative regulators of CLDN1. This gene
encodes for a key cellular co-receptor for HCV entry, which increases susceptibility to HCV
infection in the target cells where it is expressed [49]. The upregulation of the mentioned
miRNAs in HIV/HCV- patients will suppress CLDN1 expression, and thus, reduce PBMCs’
susceptibility to HCV infection.
On the other hand, we also analyzed those miRNAs specifically downregulated
in HIV/HCV- patients. We observed a specific downregulation of hsa-miR-627-3p and
hsa-miR-629-3p. These miRNAs are putative targets of ABRAXAS2, which is a part of
the deubiquitinating enzyme complex BRISC, whose deficiency resulted in the strong
attenuation of IFN response [50], which is stimulated by HCV infection [51]. The IFN-
signaling pathway has an essential role during the early stages of HCV infection by
enhancing and regulating the antiviral immune response [52], where hundreds of genes
are activated. Thus, hsa-miR-627-3p and hsa-miR-629-3p may be potential biomarkers that
highlight molecular alterations specifically due to HCV resolution in HIV-1 patients.
In addition, roughly 75% of HIV/HCV- patients enrolled in this study presented the
favorable interleukin-28B polymorphism (rs12979860) (CC genotype) at IFNL4, which is
part of type 1 IFNs that exhibit antiviral activity since it has been associated with a high
rate of HCV spontaneous resolution [7].
4.3. HIV miRNA Profile
Finally, we explored the miRNA expression profile of the HIV group without previous
HCV infection, where a total of 153 miRNAs were found dysregulated. Thus, the miRNA
profile in PBMCs of HIV patients lacks normalization, thereby indicating persistent host
immune activation and molecular pathway alterations in cellular metabolism, several
pathways related to host immune response against several pathogens stand out, such as
HIV, EBV, Chagas, toxoplasma and measles [53]. Although ART therapy has enormously
improved HIV suppression, it fails to eradicate HIV latently in target cells [54]. The HIF-1
signaling pathway was the most significant targeted pathway by the downregulated of
hsa-miR-143-3p (putative gene targets: BCL2, SERPINE1, HK2 and AKT1) among oth-
ers [55]. The HIF-1 pathway regulates the cellular response to low oxygen through HIFs
that control the expression of a wide range of genes involved in energy metabolism and
inflammation [56]. Hypoxia directly influences viral proliferation, promoting or suppress-
ing infectivity depending on the virus [57]. Similarly, Santanu et al. 2020 also reported an
alteration of the HIF-1 signaling pathway in the PBMCs of HIV-1 patients [28].
Moreover, we explored those specific miRNAs exclusively altered in HIV infection
compared to HC. Among the 13 HIV-specific upregulated miRNAs, we found the charac-
teristic miR-223. This microRNA is an important inhibitor of viral replication since it binds
to the 3’ UTR end of HIV RNA, inducing viral latency [13]. Thus, the suppression of this
miRNA increases the susceptibility of mononuclear cells to HIV-1 infection [13].
Additionally, we explored the specific downregulated miRNAs. Our results showed a
panel of three downregulated miRNAs (hsa-miR-4516, hsa-miR-494-3p and hsa-miR-542-
3p) involved in the regulation of genes associated with viral latency (SUPT16H, TFPI) [58],
HIV reactivation (PIM1) [59] and persistent metabolic and immune disorders (ZADH2,
CCL16). Taken all together, SDE miRNAs in suppressed HIV patients may indicate ongoing
abnormalities in PBMCs of HIV suppressed patients and potential biomarkers for HIV
latency. With respect to fatty acid metabolism, we also observed that the downregulated
Biomedicines 2021, 9, 1627 15 of 18
miRNAs hsa-miR-494-3p and hsa-miR-4516 also targeted the ZADH2 gene, a negative
regulator of fat cell differentiation. In this setting, HIV-infected patients showed significant
alterations of lipid profile (HDL, AI and AIP) and liver transaminases with respect to
non-infected patients. Even though HIV-infected individuals on ART reduced AIDS-
associated morbidities, one might expect a complete recovery of the metabolic equilibrium;
however, HIV-1 latency induces ongoing abnormalities in the lipid profile giving rise to
non-AIDS-associated comorbidities [60].
In conclusion, our study offers key insights into the molecular mechanisms of patho-
genesis of HCV infection in HIV patients, as well as the opportunity to define a charac-
teristic molecular fingerprint that can be a useful clinical tool for personalized patient
management and treatment.
To summarize, roughly 75% (109 SDE miRNAs) of deregulated miRNAs were shared
among the different HIV-1 groups (HIV/HCV+, HIV/HCV- and HIV+ vs. HC). HIV/HCV+
showed higher deregulation of infection and cancer-related pathways, HIV/HCV- mainly
displayed alteration of cancer-related pathways and HIV+ showed a lack of normalization
with remained dysregulation of infection-related pathways, such as HIV and HIF-signaling,
among others. Moreover, our findings show a specific miRNA profile dysregulation in
the PBMCs of HIV+, HIV/HCV+ and HIV patients after HCV spontaneous clearance. All
this together indicates that miRNAs are promising targets for biomarker analysis and may
also be a basic pillar of host immune response against viral infections by targeting and
regulating protein-coding genes expression.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10
.3390/biomedicines9111627/s1, Supplementary material and methods; Supplementary Figure S1:
Partial least squares discriminant analysis (PLS-DA) regression of miRNA normalized expression
data; Supplementary Table S1: Differentially expressed miRNAs in HIV/HCV+ patients; Supple-
mentary Table S2: MicroRNA-gene target enrichment analysis, Supplementary Table S3: Pathway
enrichment analysis; Supplementary Table S4: Differentially expressed miRNAs in HIV/HCV- pa-
tients; Supplementary Table S5: Differentially expressed miRNAs in HIV patients; Supplementary
Table S6: Top enriched target genes for specific miRNA deregulation signature in HIV/HCV+ pa-
tients; Supplementary Table S7: Top enriched target genes for specific miRNA deregulation signature
in HIV/HCV- patients; and Supplementary Table S8: Top enriched target genes for specific miRNA
deregulation signature in HIV patients.
Author Contributions: Conceptualization, methodology, supervision and funding acquisition, V.B.
and A.F.-R.; sample collection and clinical data acquisition, L.M.-C., L.D.-D., P.R., I.D.l.S., S.D.l.F.,
J.M.C., M.L., G.C., M.M.-M. (Mario Mayoral-Muñoz), M.M., V.D., A.G.-S., P.M.-R., C.C.-B., C.P.,
M.M.-M. (María Muñoz-Muñoz), P.M.-R. and M.A.J.-S.; data curation, D.V.-M. and Ó.B.-K.; formal
analysis, visualization and writing—original draft, D.V.-M. and A.F.-R.; writing—review and edit-
ing, D.V.-M., A.F.-R., V.B. and S.R. All authors have read and agreed to the published version of
the manuscript.
Funding: This work has been supported by grants from Institute of Health Carlos III, [PI15CIII/00031
and PI18CIII/00020/ to AFR and VB] and the Foundation Universidad Alfonso X el Sabio-Santander
[grant number 1.010.932 to AFR] and the Spanish AIDS Research Network (RD16CIII/0002/0002),
and Centro de Investigación Biomédica en Red (CIBER) en Enfermedades Infecciosas (CB21/13/00044).
AFR is supported by the Miguel Servet programme from Fondo de Investigación Sanitaria (ISCIII)
[CP14/CIII/00010 and CPII20CIII/0001].
Institutional Review Board Statement: The study was conducted according to the guidelines of
the Declaration of Helsinki, and approved by the Ethics Committee of Institute of Health Carlos III
(protocol code CEI PI 67_2015-V4 and approved on 2 September 2015).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Acknowledgments: We would like to thank all patients that participated in this study, the nursery
personal, and the Multidisciplinary Group of Viral Coinfection HIV/Hepatitis (COVIHEP). Institute
of Health Carlos III—Majadahonda (Madrid-Spain): José Alcamí; Verónica Briz; Oscar Brochado;
Mayte Coiras; Alicia Gómez-Sanz; Amanda Fernández-Rodríguez; Paula Martínez-Román; Salvado
Biomedicines 2021, 9, 1627 16 of 18
Resino; Marta Sánchez-Carrillo; Daniel Valle-Millares; Celia Crespo Bermejo; Violeta Lara; Doce
de Octubre Hospital (Madrid-Spain): Laura Bermejo-Plaza; Otilia Bisbal; Lourdes Domínguez-
Domínguez; María Lagarde; Mariano Matarranz; Federico Pulido; Rafa Rubio; Mireia Santacreu;
Infanta Leonor Hospital (Madrid-Spain): Guillermo Cuevas; Victorino Diez-Viñas; Pablo Ryan; Jesús
Troya; La Paz Hospital (Madrid-Spain): Juan Miguel Castro-Álvarez; Marta Gálvez-Charro; Luz
Martín-Carbonero; Mario Mayoral-Muñoz; La Princesa Hospital (Madrid-Spain): Ignacio de los
Santos; Lucio García-Fraile; Jesús Sanz-Sanz; Puerta de Hierro Hospital (Madrid-Spain): Alfonso
Ángel-Moreno; Sara de la Fuente Moral; Research Institute for Medicines (Lisboa-Portugal): Claudia
Palladino; Nuno Taveira; National Institute for Agricultural and Food Research and Technology
(INIA): María Muñoz-Muñoz.
Conflicts of Interest: The authors declare no conflict of interest.
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