Abstract

   Background: DNA methylation may be a link between HIV, aging, and the
   increased risk of lung comorbidities. We investigated whether
   bronchoalveolar lavage (BAL) cells of people living with HIV (PLWH)
   demonstrate epigenetic disruptions and advanced epigenetic aging.
   Methods: BAL cell DNA methylation from 25 PLWH and 16 HIV-uninfected
   individuals were tested for differential methylation of Alu and LINE-1
   sites, markers of aging. We used a weighted gene correlation network
   analysis to identify HIV- and age-associated co-methylation networks.
   We tested the effect of HIV on DNA methylation using a robust linear
   model (false discovery rate < 0.10). Results: The BAL cells of PLWH
   were marked by global hypomethylation in both Alu and LINE-1 elements.
   Six co-methylated CpG networks were identified that were significantly
   associated with age; of these, the red module was significantly
   differentially methylated in PLWH and enriched pathways (e.g., Ras
   signaling and T-cell receptors). We identified 6428 CpG sites
   associated with HIV. Conclusions: We have shown here for the first time
   that alterations in the DNA methylation of BAL cells in the lung with
   HIV show a pattern of advanced aging. This study strongly supports that
   HIV may contribute to an increased the risk of lung comorbidities
   through the epigenetics of aging.

   Keywords: BAL, HIV, DNA methylation, aging, PLWH

1. Introduction

   People living with human immunodeficiency virus (HIV, PLWH) are at high
   risk of developing chronic diseases, despite the therapeutic benefits
   of antiretroviral therapy (ART) [[50]1,[51]2,[52]3]. The use of ART has
   led to a decrease in the incidence of opportunistic lung infections in
   PLWH, nonetheless chronic lung conditions remain a considerable burden
   in this population [[53]4]. Although PLWH are living longer [[54]5],
   age-related comorbidities develop earlier compared to the general
   public, suggesting that the virus or its treatment accelerates
   biological aging [[55]6]. The lung is one of the organs most impacted
   by this phenomenon. HIV negatively affects aging mechanisms in the lung
   such as oxidative stress and cellular senescence [[56]7], which may
   ultimately place PLWH at higher risk for chronic obstructive lung
   disease (COPD) or lung cancer. The early targeting of these mechanisms
   may potentially reduce the risk of comorbidities and poor clinical
   outcomes in this population.

   One of the best indications of a cell’s biological age is its DNA
   methylation profile. DNA methylation is an epigenetic mechanism that
   involves the addition of a methyl group to a cytosine base located next
   to guanine base (CpG site). As a dynamic alteration, it can be affected
   by acquired infections such as HIV [[57]8]. DNA methylation is
   tissue-specific and at promoter regions is associated with changes in
   gene expression [[58]9], which can potentially affect downstream
   phenotypes. We have shown, for instance, that the blood and airway
   epithelial epigenomes are altered in PLWH compared to HIV-negative
   individuals [[59]8,[60]10], and that these epigenetic alterations are
   consistent with a pattern of accelerated aging [[61]10,[62]11].
   Previous findings by our laboratory have also suggested that the blood
   of PLWH with airflow obstruction is characterized by global
   hypomethylation [[63]12]. Global DNA methylation can be measured
   through the methylation of ubiquitous transposable elements Alu and
   LINE-1 that are abundant throughout the genome; hypomethylation along
   these elements is associated with advanced aging [[64]13] and disease
   [[65]14]. While we have reported these findings in airway epithelial
   and blood cells, whether HIV alters DNA methylation in bronchoalveolar
   lavage (BAL) fluid cells (predominantly alveolar macrophages) to
   promote accelerated aging has not yet been reported. Here, we
   hypothesized that HIV induces epigenetic disruptions in the BAL cells
   present in the airway along accelerated aging pathways.

2. Materials and Methods

2.1. Study Cohort

   Study participants included 41 adults, of which 25 were people living
   with HIV (PLWH), and 16 were HIV-uninfected participants. This cohort
   has been previously described [[66]10,[67]11]. Briefly, all
   participants underwent flexible bronchoscopy according to standard
   procedures previously published [[68]15]. BAL samples were obtained by
   instilling aliquots of sterile saline into the right middle lobe or
   lingula until a return volume of 30 mL or total instilled volume of 200
   mL was reached [[69]16]. All participants underwent pre-bronchodilator
   spirometry according to American Thoracic Society guidelines [[70]17].
   The samples were obtained between 2015 and 2019 at St. Paul’s Hospital
   in Vancouver, British Columbia, Canada. This study was approved by the
   University of British Columbia Research Ethics Board (Certificates
   H11-02713 and H15-02166). All participants provided written informed
   consent.

2.2. DNA Methylation Profiling

   DNA was extracted from BAL cells using the DNeasy Blood and Tissue Kit
   (Qiagen, Hilden, Germany) and the unmethylated cytosine residues were
   converted to uracil using the EZ DNA methylation kit (Zymo, Irvine, CA,
   USA). The extracts were profiled for DNA methylation using the Illumina
   Infinium MethylationEPICv1 BeadChip microarray which captures 863,904
   CpG sites. Samples were randomized by age, sex, and HIV status on the
   chips to minimize batch effects. The β-values for each CpG probe were
   calculated as the ratio of methylation intensity to the overall
   intensity. β-values range from 0 (fully unmethylated) to 1 (fully
   methylated) and were log transformed to M-values for the downstream
   analyses. Preprocessing and quality control steps for the DNA
   methylation data included background correction and normalization using
   the normal-exponential out-of-band [[71]18] and β-mixture quantile
   normalization [[72]19], respectively. No batch effects were detected.
   This pipeline has been previously standardized by our laboratory
   [[73]8,[74]12,[75]20,[76]21].

   The profiles were used to infer ancestry using the EPISTRUCTURE
   software (version GLINT_1.0.4) [[77]22] and the first 5 principal
   components were retained for downstream analyses. We later selected
   variables to be included in our downstream analyses as follows. The BAL
   cell DNA methylation profiles were used to conduct a principal
   component analysis (PCA). We then calculated the correlation between
   the first two DNA methylation principal components (PC1 and PC2) and
   demographic characteristics (smoking, age, sex, body mass index [BMI],
   BAL macrophage percentage, pre-bronchodilator forced expiratory volume
   in 1 s (FEV[1])% predicted, pre-bronchodilator FEV1/forced vital
   capacity (FVC) ratio, COPD status, and 5 PCs [EPISTRUCTURE]). Variables
   with significant (p < 0.05) correlations were included as covariates in
   the differential methylation analyses (age, sex, smoking status, and
   PC1 and PC2) ([78]Figure S1). BAL macrophage percentage was also
   included in the differential methylation analyses because cell type
   proportions may affect DNA methylation [[79]23].

2.3. Alu and LINE-1 Imputation

   Global hypomethylation is an accepted measure of biological age,
   measured by methylation at Alu and LINE-1 repetitive elements
   [[80]24,[81]25]. The repetitive element methylation prediction machine
   learning tool (R package REMP, version 4.4) [[82]26] was used to impute
   DNA methylation levels at Alu and LINE-1 sites based on the BAL DNA
   methylation profiles. We later conducted a robust linear model (rlm)
   using the MASS R package version 7.3-60.2 (M-estimation) to test the
   association between HIV status and global methylation. The resulting
   model was the following: M-value~HIV status + age + sex + smoking
   status + BAL macrophage percentage + PC1 + PC2. To determine whether
   COPD status was also associated with global hypomethylation, we
   repeated this test using the following model: M-value~COPD status + age
   + sex + smoking status + BAL macrophage percentage + PC1 + PC2.
   Significant effects were determined at a false discovery rate (FDR) <
   0.10.

2.4. Weighted Gene Correlation Network Analysis (WGCNA)

   Analytical approaches that treat each CpG as an independent unit of
   analysis may overlook coordinated CpGs that share a common regulatory
   mechanism or participate in similar biological processes and pathways.
   To identify co-methylated networks associated with aging and HIV, we
   used a weighted gene correlation network analysis (WGCNA) approach
   [[83]27]. This method has been previously used to identify
   co-methylated CpG sites [[84]28]. Briefly, we selected genome-wide CpGs
   annotated to promoter regions based on the Methylation Consortium
   ([85]https://clockfoundation.org/mammalianmethylationconsortium/
   accessed on 20 August 2023); in total, we retained 105,712 CpG sites.
   We used the WGCNA R package (version 1.72-5) to construct networks of
   co-methylated CpGs (modules) using M-values. Key measures obtained from
   this analysis included the module’s eigengene (summarized methylation
   for a group of co-methylated sites, i.e., the principal component of
   the module’s methylation); and the CpGs module membership (the
   correlation between methylation M-value and eigengene). We used
   Pearson’s correlations and univariate linear models to test the
   association between each of the modules’ eigengenes and age, HIV
   status, sex, macrophages percentage, CD4T count, smoking status, COPD,
   and FEV[1]% predicted (FDR < 0.10) in a hypothesis-free approach.
   First, we identified modules whose eigengenes were significantly
   correlated with age at FDR < 0.10. Next, we identified whether these
   modules were differentially methylated in PLWH compared to
   HIV-uninfected participants after adjustment for age, with significance
   set at p < 0.05.

2.5. Differential Methylation Analysis

   We conducted an epigenome-wide analysis to identify differentially
   methylated CpG positions or DMPs associated with HIV status using the
   MASS R package [[86]29]. The rlm used was adjusted for age, sex,
   smoking status, macrophage percentage, and the first two EPISTRUCTURE
   PCs. Significant DMPs were defined at an FDR < 0.10.

2.6. Pathway Enrichment Analysis

   We identified Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways
   that were enriched by genes that corresponded to CpG sites identified
   in the WGCNA and differential methylation analysis. The R package
   WebGestalt 2019 [[87]30,[88]31] was used for this analysis. Significant
   enrichment was defined at FDR < 0.10.

3. Results

3.1. Study Overview

   [89]Table 1 shows the demographic and clinical characteristics of our
   study cohort. PLWH were significantly younger, had a larger proportion
   of males, and lower lung function (based on pre-bronchodilator
   FEV[1]/FVC) than HIV-uninfected participants. BMI, smoking status, BAL
   macrophage percentage, and inhaled corticosteroid treatment were
   similar between the two groups, however, there was a higher proportion
   of physician-diagnosed COPD within PLWH. The majority of PLWH were on
   ART and had undetectable HIV viral loads. BAL cells were predominantly
   macrophages as expected.

Table 1.

   Study cohort characteristics.
   PLWH HIV-Uninfected p
   n 25 16 -
   Male Sex (%) 92 56 0.016
   Age, years 57.00 (53.00–62.00) 72.00 (70.00–75.00) 7.844 × 10^−4
   BMI, kg/m^2 24.54 (22.02–27.91) 26.07 (24.66–29.41) 0.279
   Pre-bronchodilator FEV[1], L 2.650 (2.06–3.06) 2.490 (2.08–2.65) 0.310
   Pre-bronchodilator FVC, L 4.07 (3.51–4.71) 3.24 (2.87–3.74) 0.010
   Pre-bronchodilator FEV[1]/FVC (%) 62.20 (54.93–74.94) 74.13
   (68.44–76.62) 0.048
   Pre-bronchodilator FEV[1]% predicted 77.00 (65.00–91.00) 87.00
   (74.25–96.75) 0.195
   Pre-bronchodilator FEV[1]/FVC % predicted 93.10 (81.00–105.00) 92.00
   (74.50–107.65) 0.820
   Smoking status 0.258
   Current smoker, (%) 36 12
   Former smoker, (%) 56 69
   Never smoker, (%) 8 19
   Smoking pack-years 30 (11.50–45.00) 30 (12.50–37.50) 0.450
   Physician-diagnosed COPD, (%) 76 25 0.003
   Bronchiectasis, (%) 4 0 ~1
   BAL neutrophils, (%) 2.00 (1.00–5.50) 3.63 (1.19–6.38) 0.462
   BAL lymphocytes, (%) 2.50 (1.50–5.50) 2.75 (1.00–3.75) 0.758
   BAL eosinophils, (%) 0.25 (0.00–1.00) 0.63 (0.23–4.06) 0.041
   BAL macrophage, (%) 92.50 (79.50–95.00) 91.25 (73.75–94.25) 0.679
   Inhaled corticosteroid use, (%) 12 19 0.662
   CD4 T-cell count, cell/mm^3 435.00 (355.00–590.00) - -
   HIV viral load undetectable, (%) 92 - -
   ART, (%) 96 - -
   Hypertension, (%) 20 44 0.161
   Diabetes, (%) 8 19 0.362
   [90]Open in a new tab

   Discrete variables are presented as percentages, and corresponding
   p-values were obtained using Fisher exact tests. Continuous variables
   are presented as median and interquartile range, p-values represent
   Kruskal–Wallis p-value. PLHW—people living with human immunodeficiency
   virus; BMI—body mass index; FVC—forced vital capacity; FEV[1]—forced
   expiratory volume in 1 s; COPD—chronic obstructive pulmonary disease;
   BAL—bronchoalveolar lavage; ART—antiretroviral therapy.

3.2. Global Hypomethylation Is Observed in the BAL Cells of PLWH, but Not in
COPD

   Our analyses on the retro-transposable elements Alu and LINE-1
   demonstrated a signature of global hypomethylation in BAL cells that
   was associated with HIV. [91]Figure 1A shows that the majority of the
   significant Alu sites were found to be hypomethylated in PLWH (2259 out
   of 2294 sites). A similar pattern of hypomethylation (although less
   evident) was identified in LINE-1 sites (21 out 31 sites) ([92]Figure
   1B). In contrast, we did not find an association between global
   hypomethylation and COPD. In total, 14 Alu sites were associated with
   COPD, of which only 7 demonstrated hypomethylation ([93]Figure S2),
   thus no pattern of hypomethylation was identified. A total of 11 LINE-1
   sites were associated with COPD, of which 10 showed hypermethylation
   ([94]Figure S3).

Figure 1.

   [95]Figure 1
   [96]Open in a new tab

   Global hypomethylation was observed in the bronchoalveolar lavage cells
   of PLWH. Markers of global methylation (A) Alu and (B) LINE-1
   identified in BAL cells at FDR < 0.10 (level of significance is
   represented by the horizontal grey dotted line). The x- and y-axes
   represent the beta difference effect size and level of significance,
   respectively, for the association between each Alu and LINE-1 site and
   HIV. Blue = hypomethylation in PLWH compared to HIV-uninfected
   individuals; Red = hypermethylation in PLWH compared to HIV-uninfected
   individuals. The horizontal dotted line represents the line of
   statistical significance.

3.3. Age-Associated Co-Methylation Networks Are Dysregulated in HIV

   We hypothesized that epigenetic networks involved in the progression of
   aging are altered in PLWH. We conducted a WGCNA analysis on CpGs within
   promoter regions of the genome, yielding 77 co-methylated modules
   ([97]Table S1). Through a univariate linear model, we determined that
   age was associated with the eigengene of six modules at FDR < 0.10.
   [98]Table 2 shows the modules that had significant correlations with
   age; no other co-methylation modules were associated with age. The
   yellow module demonstrated the strongest correlation with age (R =
   −0.61), while the pink had the weakest correlation out of the six
   modules (R = −0.45).

Table 2.

   Age- and HIV-associated modules.
   Module #CpGs #Genes Effect
   Age HIV
   p-Value FDR R p-Value FDR p-Value Age-Adj
   Skyblue 412 426 1.89 × 10^−5 0.001 0.56 0.001 0.002 0.056
   Red 2233 1150 1.44 × 10^−4 0.006 0.55 4.50 × 10^−4 0.002 0.044
   Yellow 2394 1900 2.29 × 10^−4 0.006 −0.61 0.001 0.002 0.127
   Palevioletred3 142 150 2.54 × 10^−3 0.043 −0.46 0.003 0.006 0.103
   Pink 1579 1346 3.38 × 10^−3 0.043 −0.45 0.007 0.007 0.151
   Floralwhite 210 163 2.84 × 10^−3 0.043 0.45 0.007 0.007 0.173
   [99]Open in a new tab

   We then hypothesized that epigenetic networks involved in the
   progression of aging are altered in PLWH. To investigate this
   hypothesis, we focused on the six age-associated modules and tested
   whether HIV has a significant effect on each module eigengene. We found
   that HIV was associated with all six age-associated modules (p < 0.05),
   of which the red module remained significant after adjusting for the
   effect of age (p = 0.044) ([100]Table 2). The linear relationship
   between age and the red module ([101]Figure 2A) had a correlation of R
   = 0.55 (p = 0.006), however, this relationship appears to be more
   evident in the HIV-uninfected group (R = 0.45, p = 0.079) compared to
   PLWH (R = 0.27, p = 0.193). Overall, PLWH were characterized by the
   hypomethylation of CpGs within the red module compared to
   HIV-uninfected participants ([102]Figure 2B).

Figure 2.

   [103]Figure 2
   [104]Open in a new tab

   Co-methylation network identified in BAL cells was associated with age
   and HIV. (A) Linear relationship between age and the red module
   co-methylation network eigengene is represented by the blue line with
   the grey area representing the 95% confidence interval for the linear
   relationship); black dots represent the module eigengene and
   chronological age values for each participant. (B) Heatmap of M-values
   of CpGs within the red module co-methylation network (y-axis) by HIV
   status (purple = PLWH, black = HIV-negative). The eigengene value is
   represented in the bottom bar. (C) Differentially methylated pathways
   (Kyoto Encyclopedia of Genes and Genomes) enriched by co-methylated
   CpGs (co-methylation network) associated with age and HIV (FDR < 0.10).

   This table shows the number of CpGs (co-methylated sites) and
   corresponding genes within each age- and HIV-associated co-methylation
   network (module). The level of significance (p-value and false
   discovery rate [FDR]) corresponds to the association between each
   module’s eigengene (or first principal component) and age and HIV. HIV
   tests were further adjusted for chronological age.

   Further exploration of the red module showed that its CpGs demonstrated
   a module membership (correlation between M-value and eigengene [i.e.,
   highly interconnected CpGs]) that ranged from 0.29 to 0.99. The top 10
   CpGs based on their module membership within the red module
   corresponded to the following protein coding genes: SH2D7, PKN1,
   PIK3CG, CD58, MYO9B, PTPN22, ARHGAP15, TESPA1, DCK, and MOB1B;
   ([105]Table S2). The top 10 CpGs located within a CpG island were
   ITGB4, FOXP4, RP11-750H9.5, S1PR4, ITGA4, ICAM1, and RASSF1. The red
   module significantly enriched 26 pathways (FDR < 0.10), including the
   human immunodeficiency virus 1 infection, Ras signaling, T-cell
   receptor, and bacterial infection of epithelial cells pathways, amongst
   others ([106]Figure 2C). No other demographic, disease, or laboratory
   phenotype demonstrated a significant association with the red module.

3.4. HIV Is Associated with DNA Methylation Alterations in BAL Cells

   Our epigenome-wide analysis yielded 6428 DMPs ([107]Table S3,
   [108]Figure 3A) in BAL cells that were associated with HIV (FDR <
   0.10). Their methylation beta difference ranged from −0.316 to 0.273 (a
   beta difference of 1 corresponds to 100% methylation in PLWH vs. 0%
   methylation in the HIV-uninfected group while a beta difference of −1
   corresponds to 0% methylation in PLWH vs. 100% methylation in the
   HIV-uninfected group). [109]Table 3 shows the top DMPs based on two
   criteria: smallest FDR and largest beta difference effect size. The
   6428 DMPs associated with HIV corresponded to 4713 DMGs. The top
   differentially methylated genes that corresponded to the DMPs with
   highest statistical significance included DHX32, CPN2, FUBP1, RGL3, and
   OTOG. The DMPs with the largest effect sizes corresponded to ABCB11,
   MYOM2, PARP12, and 7SK.

Figure 3.

   [110]Figure 3
   [111]Open in a new tab

   Differential DNA methylation in BAL cells associated with HIV. (A)
   Epigenome-wide differential methylation associated with HIV (FDR <
   0.10). The x- and y-axes represent the effect size and level of
   significance (represented by the horizontal grey dotted line),
   respectively, for the association between each methylation site and
   HIV. Blue = hypomethylation in PLWH compared to HIV-uninfected
   individuals; Red = hypermethylation in PLWH compared to HIV-uninfected
   individuals, horizontal dotted line represents the level of
   significance based on FDR < 0.10. (B) Differentially methylated
   pathways (Kyoto Encyclopedia of Genes and Genomes) associated with HIV
   (FDR < 0.10).

Table 3.

   Top 5 differentially methylated CpG sites (DMPs) associated with HIV in
   BAL cells.
   Probe Chr p FDR Beta Difference (Reference Group: HIV-Uninfected)
   Relation to CpG Island Gene Symbol
   Criteria: Lowest FDR
   cg26126053 10 1.43 × 10^−22 1.12 × 10^−16 −0.011 Open Sea DHX32
   cg00401660 3 8.05 × 10^−18 3.17 × 10^−12 0.018 Open Sea CPN2
   cg14118535 1 7.17 × 10^−17 1.88 × 10^−11 −0.013 Open Sea FUBP1
   cg08589141 19 2.00 × 10^−16 3.94 × 10^−11 0.029 North Shore RGL3
   cg16636316 11 3.52 × 10^−16 5.55 × 10^−11 0.034 Open Sea OTOG
   Criteria: Greatest Beta Difference
   cg09733528 2 3.40 × 10^−7 9.04 × 10^−4 −0.316 Open Sea ABCB11
   cg02630646 2 6.08 × 10^−5 2.35 × 10^−2 −0.314 Open Sea --
   cg11424828 8 4.02 × 10^−4 6.82 × 10^−2 0.273 Island MYOM2
   cg21015022 7 4.07 × 10^−5 1.87 × 10^−2 0.254 South Shore PARP12
   cg17975832 12 7.01 × 10^−4 9.21 × 10^−2 −0.250 Open Sea 7SK
   [112]Open in a new tab

   FDR—false discovery rate. Chr—chromosome.

   The DMGs significantly enriched (FDR < 0.10) 21 KEGG pathways including
   bacterial invasion of epithelial cells, central carbon metabolism in
   cancer, longevity regulating, Insulin resistance, apelin signaling,
   small cell lung cancer, Rap1 signaling, and pathways in cancer, amongst
   others ([113]Table S4, [114]Figure 3B). We compared our findings to
   previous methylation data in PLWH [[115]8,[116]10] and found that
   approximately 32% and 60% of DMGs were also identified in the blood
   immune cells and in the airway epithelial cells, respectively
   ([117]Table S5). While only 10% of HIV-associated pathways identified
   in BAL cells overlapped with those identified in blood, 76% of
   HIV-associated pathways identified in BAL cells were also found in the
   airway epithelial cells of PLWH ([118]Table S6). These include
   aging-related pathways such as Rap1 signaling, the longevity regulating
   pathway, and insulin resistance.

4. Discussion

   This is the first report to demonstrate that BAL cells in HIV have
   numerous epigenetic alterations in a pattern suggestive of accelerated
   aging. Our study revealed four main observations: first, these cells
   exhibited global hypomethylation as detected in Alu and LINE-1 elements
   along the genome, indicative of more advanced aging in the lungs of
   PLWH; second, we discovered numerous epigenetic networks of
   co-methylated CpGs in these cells that were associated with age, of
   which the red module was also significantly altered in PLWH; third, the
   inflammatory cells (mainly alveolar macrophages) in the airways of PLWH
   are characterized by epigenome-wide disruptions; and fourth, these
   epigenetic alterations are enriched along multiple biological processes
   that are known to be associated with aging and cancer, potentially
   linking DNA methylation with the known pulmonary complications
   associated with HIV. Together, these results demonstrate a pattern of
   epigenetic age alterations in BAL cells that complements what we have
   previously described in the blood and airway epithelium [[119]10].

   Our findings suggest that despite immune reconstitution and suppressed
   HIV viral loads, BAL cell methylation may still be impaired in PLWH.
   Although previous studies have shown that ART can deaccelerate
   HIV-associated aging through epigenetic mechanisms [[120]32], complete
   reversion to pre-seroconversion aging rates similar to those observed
   in uninfected populations may not yet be realized. These persistent
   changes, combined with the alterations we have recently observed in
   airway epithelial cells [[121]10], raise the possibility that they may
   drive the onset of age-related pulmonary comorbidities in PLWH such as
   COPD or lung cancer. Furthermore, blood senescence in PWLH demonstrated
   by advance blood epigenetic age [[122]8,[123]12], together with our
   present findings of BAL cells global hypomethylation, further support a
   widespread immunosenescence in PLWH. We propose that linking these BAL
   methylation changes in a larger cohort to disease outcomes in this
   population would be a fitting next step, allowing for a better
   understanding of how global accelerated aging across multiple lung
   compartments might result in injury.

   Our analyses highlighted genes with potential links to HIV
   pathogenesis. For instance, we found multiple hypomethylated DMPs
   within the promoter region of RUFY1, which are located within a CpG
   island region (regions of the genome rich in CpG sites that are often
   associated with changes in gene expression). Our previous methylation
   work in blood and airway epithelial cells of PLWH also yielded
   significant DMPs within RUFY1, suggesting that this particular gene may
   be systemically epigenetically disrupted in HIV [[124]8,[125]10]. The
   inactivation of RUFY1 inhibits efficient recycling of endocytosed
   transferrin [[126]33,[127]34,[128]35], one of the mechanisms that has
   been implicated in the internalization of HIV-1 [[129]36]. RUFY1 has
   been associated with time on ART in a previous trial investigating a
   therapeutic HIV-1 vaccine and romidepsin (a latency reversing agent) on
   HIV viral load, where a late ART start was correlated with blood DNA
   hypomethylation at RUFY1 [[130]37]. Although we are unable in our work
   to connect whether BAL cell hypomethylation in this gene was related to
   the timing of ART initiation, we speculate that epigenetic disruptions
   that occur in relation to sub-optimal ART timing may also occur in the
   lung.

   Our research also revealed other potential epigenetically regulated
   mechanisms of lung pathogenesis in HIV. We note, for example, the
   significant enrichment in HIV-associated DMPs for the bacterial
   invasion of epithelial cells pathway which may indicate that the innate
   immune defense of the lung may be compromised in PLWH through DNA
   methylation. Despite the advances of ART, bacterial pulmonary
   infections remain a common comorbidity in PLWH [[131]38,[132]39].
   Previous research has demonstrated that HIV can be recovered from BAL
   macrophages [[133]40,[134]41,[135]42], and that HIV impairs the
   alveolar macrophage defense against pneumococcal infection through
   impaired apoptosis and mitochondrial reactive oxygen species processes
   [[136]42]. We raise the possibility that these cellular impairments may
   in part be epigenetically regulated, although further work would be
   required to explore this hypothesis.

   Although our findings show important and novel observations for PLWH,
   this study is not without its limitations. First, although our
   bronchoscopy study cohort offers many unique advantages to investigate
   the lung-specific effects of HIV, our sample size was limited due to
   the invasive nature of the procedure and may not reflect the totality
   of this population. In addition, differences between PLWH and
   uninfected participants, such as smoking status and COPD prevalence,
   may have affected our conclusions; however, we adjusted our analysis
   for potential confounding to the best of our ability. The influence of
   other age-related comorbidities would also need to be tested in a
   larger cohort. Stratification by duration of HIV, duration of ART,
   viral control, and comorbidities was not able to be performed, but
   would yield greater insight into differential airway immune cell aging
   in the wider population of PLWH. Second, BAL samples contain multiple
   types of immune cells and the effect of HIV on specific cell types may
   vary. Although our analyses were controlled for cell proportions, our
   profiles reflect an average methylation across all cells. Future work
   using single cell sequencing or flow cytometry would help us understand
   the cell-specific aging process that occurs in HIV. Third, it remains
   unclear how HIV may affect the lung epigenome over time as our work was
   cross-sectional. Whether the BAL cells begin to age rapidly after HIV
   seroconversion or whether we are observing instead a gradual
   accumulation of epigenetic disruptions over time is still unclear.
   Future research should focus on the serial profiling of PLWH in order
   to investigate the longitudinal effects of HIV. Finally, we were not
   able to link our findings to specific clinical outcomes or downstream
   effects in PWLH. Cohorts followed longitudinally for lung function
   decline, incident pulmonary disease, and respiratory-related events
   would help us understand the clinical implications of accelerated BAL
   cell aging.

5. Conclusions

   Notwithstanding the limitations of this study, we have demonstrated
   that HIV may be linked with genome-wide epigenetic disruptions and
   aging in the lung’s BAL cell compartment, although further work will be
   needed to decode the contributions of smoking and COPD to these
   observations. Together with our previous findings in the airway
   epithelium of PLWH, these results give support to the idea of global
   lung aging in HIV. Efforts to understand the etiology of and strategies
   to mitigate this aging process are imperative to improving lung health
   in PLWH.

Supplementary Materials

   The following supporting information can be downloaded at:
   [137]https://www.mdpi.com/article/10.3390/biomedicines12061261/s1,
   Figure S1: Covariate selection; Figure S2: Volcano plot comparing Alu
   methylation in individuals with COPD to the reference group,
   individuals without COPD. There was no pattern of global Alu
   hypomethylation found in COPD. Blue = hypomethylation in individuals
   with COPD compared to individuals without COPD; Red = hypermethylation
   in individuals with COPD compared to individuals without COPD.
   Statistical significance was set at FDR < 0.10; Figure S3: Volcano plot
   comparing LINE-1 methylation in individuals with COPD to the reference
   group, individuals without COPD. There was no pattern of global LINE-1
   hypomethylation found in COPD. Blue = hypomethylation in individuals
   with COPD compared to individuals without COPD; Red = hypermethylation
   in individuals with COPD compared to individuals without COPD.
   Statistical significance was set at FDR < 0.10; Table S1:
   Co-methylation networks; Table S2: Co-methylation networks associated
   with age; Table S3: Differentially methylated CpGs associated with HIV;
   Table S4: Differentially methylated Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathways; Table S5: Differentially methylated genes
   overlapping between airway epithelial cells and BAL cells in PLWH;
   Table S6: Differentially methylated pathways that overlap between the
   airway epithelial cells and BAL cells of PLWH.
   [138]biomedicines-12-01261-s001.zip^ (4.2MB, zip)

Author Contributions

   Conceptualization: A.I.H.C., S.F.P.M. and J.M.L.; Manuscript writing:
   A.I.H.C. and J.M.L.; Data collection: J.Y., T.S., J.L.M., K.D., M.S.K.,
   J.M., M.H., S.G., S.F.P.M., D.D.S. and J.M.L.; Statistical analysis:
   A.I.H.C., X.L. and C.X.Y.; Manuscript editing: A.I.H.C., J.M.L., X.L.,
   J.Y., C.X.Y., T.S., J.L.M., K.D., M.S.K., J.M., M.H., S.G., S.F.P.M.,
   D.D.S. and J.M.L. All authors have read and agreed to the published
   version of the manuscript.

Institutional Review Board Statement

   This study was approved by the University of British Columbia Research
   Ethics Board (Certificates H11-02713 and H15-02166).

Informed Consent Statement

   All participants provided written informed consent.

Data Availability Statement

   DNA methylation data are deposited at the Gene Expression Omnibus
   repository ([139]GSE262656).

Conflicts of Interest

   JM is the Executive Director and Physician-in-Chief of the BC Centre
   for Excellence in HIV/AIDS, a provincial program serving all BC health
   authorities, and based at St. Paul’s Hospital-Providence Health Care.
   JM’s Treatment as Prevention^® (TasP^®) research, paid to his
   institution, has received support from the BC Ministry of Health,
   Health Canada, Canadian Institutes of Health Research, Public Health
   Agency of Canada, Genome Canada, Genome BC, Vancouver Coastal Health
   and VGH Foundation. Institutional grants have been provided by Gilead
   Sciences Inc, Janssen, Merck Sharp & Dohme LLC, and ViiV Healthcare.
   DDS receives support from the Canadian Institutes of Health Research.
   JML receives support from the Canadian Institutes of Health Research
   and the BC Lung Foundation. AIHC receives support from the BC Lung
   Foundation, the Canadian Institutes of Health Research, and Michael
   Smith Health Research BC. There are no conflicts of interest reported
   by the authors.

Funding Statement

   This work was supported by the Canadian Institutes of Health Research
   (SFPM, JML) and the BC Lung Foundation (AIHC, JML). AIHC is supported
   by the Canadian Institutes of Health Research REDI Award and the
   Michael Smith Health Research Trainee Award. MSK, DDS, and JML are
   supported by the Canada Research Chairs Program.

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References