Abstract

Background

   Marijuana’s putative anti-inflammatory properties may benefit
   HIV-associated comorbidities. How recreational marijuana use affects
   gene expression in peripheral blood cells (PBC) among youth with HIV-1
   (YWH) is unknown.

Approach

   YWH with defined substance use (n = 54) receiving similar
   antiretroviral therapy (ART) were assigned to one of four analysis
   groups: YWH with detectable plasma HIV-1 (> 50 RNA copies/ml) who did
   not use substances (H+V+S−), and YWH with undetectable plasma HIV-1 who
   did not use substances (H+V−S−), or used marijuana alone (H+V−S+[M]),
   or marijuana in combination with tobacco (H+V−S+[M/T]). Non-substance
   using youth without HIV infection (H−S−, n = 25) provided a reference
   group. PBC mRNA was profiled by Affymetrix GeneChip Human Genome U133
   Plus 2.0 Array. Differentially expressed genes (DEG) within outcome
   groups were identified by Significance Analysis of Microarrays and used
   for Hierarchical Clustering, Principal Component Analysis, and
   Ingenuity Pathways Analysis.

Results

   HIV-1 replication resulted in > 3000 DEG involving 27 perturbed
   pathways. Viral suppression reduced DEG to 313, normalized all 27
   pathways, and down-regulated two additional pathways, while marijuana
   use among virally suppressed YWH resulted in 434 DEG and no perturbed
   pathways. Relative to H+V−S−, multiple DEG normalized in H+V−S+[M]. In
   contrast, H+V−S+[M/T] had 1140 DEG and 10 dysregulated pathways,
   including multiple proinflammatory genes and six pathways shared by
   H+V+S−.

Conclusions

   YWH receiving ART display unique transcriptome bioprofiles based on
   viral replication and substance use. In the context of HIV suppression,
   marijuana use, alone or combined with tobacco, has opposing effects on
   inflammatory gene expression.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12977-022-00594-4.

   Keywords: Youth with HIV-1, Marijuana, Δ9-Tetrahydrocannabinol,
   Cannabinoids, Tobacco, Transcriptome, Biomarkers, Bioprofiles,
   Inflammation

Background

   Marijuana contains a complex variety of cannabinoids, including
   Δ^9-tetrahydrocannabinol (THC) and cannabidiol (CBD), known to impact
   inflammatory and lymphocyte activation pathways in lymphoid tissues and
   brain [[39]1–[40]4]. While marijuana continues to be classified under
   United States federal law as an illegal drug, medical use of cannabis
   is legal in at least 35 states, including 16 states and the District of
   Columbia where recreational marijuana is approved for individuals over
   the age of 21 [[41]5]. In many states, HIV infection is the sole
   indication for the use of medicinal marijuana as adjuvant therapy
   [[42]6]. Cannabis is effective in the treatment of HIV-associated
   peripheral neuropathic pain, improves appetite, and enhances overall
   quality of life in people with HIV [[43]6, [44]7]. There is emerging
   evidence that marijuana use attenuates pro-inflammatory pathways in
   HIV-infected adults, as well as in non-human primates infected with
   simian immunodeficiency virus [[45]8–[46]10]. However, few studies have
   examined the effect of recreational or medicinal cannabis use on
   specific inflammatory pathways in peripheral blood cells (PBC) among
   individuals with HIV [[47]8].

   More than 27% of new HIV infections in the United States occur among
   youth between the ages of 20 and 24 [[48]11]. HIV-associated chronic
   lymphocyte and macrophage activation drives many of the inflammatory
   consequences in YWH, including neurocognitive, metabolic, and vascular
   comorbidities, which persist even with sustained viral suppression by
   antiretroviral therapy [[49]12–[50]16]. Over a third of YWH regularly
   use recreational marijuana, both domestically and internationally
   [[51]17, [52]18], yet little is known about effects of marijuana use
   alone, or in combination with tobacco products, on overall health and
   long-term outcomes in YWH [[53]18]. The role of marijuana in modulating
   inflammatory pathways is particularly relevant to this population who
   face a lifetime of living with HIV infection and antiviral treatments.

   The hypothesis for this study is that recreational marijuana use by YWH
   modulates PBC transcriptional pathways associated with immune
   activation and inflammation. YWH have not developed the extent of
   comorbid conditions common in older adults with HIV-1 and are an ideal
   population to examine the interface between viral suppression,
   inflammation, and substance use. Using a computational biology
   approach, unique genetic and cellular pathways within the PBC
   transcriptome were defined in a population of virally suppressed YWH on
   ART who used marijuana, alone or in combination with tobacco, compared
   to a reference group of youth without HIV-1 (YWOH).

Results

Study population

   Based on substance use profiles, toxicology criteria, and viral load,
   88 individuals (34 YWOH and 54 YWH) were included in the analysis
   (Table [54]1). YWOH included 25 individuals who did not use any
   substance (Reference Group, H–S−) and 9 who used a combination of
   marijuana, tobacco and alcohol (Group I, H–S+[M/T/A]). At the time of
   study blood draw, 8 non-substance using YWH had detectable plasma HIV-1
   (Group II, H+V+S−). Among YWH, there were 46 YWH with undetectable
   plasma HIV-1 including: Group III, YWH who did not use any substance
   (n = 19) (H+V−S−); Group IV, YWH who used marijuana only (n = 8)
   (H+V−S+[M]); and Group V, YWH who used marijuana in combination with
   tobacco (n = 19) (H+V−S+[M/T]). Self-reported regular substance use by
   inhalation (smoking) in groups, IV and V was similar ranging from 3 to
   13 years. Plasma carboxy THC (THCA) and cotinine levels, as determine
   by toxicology, were also similar across the respective groups. There
   were inadequate numbers of YWH who used tobacco alone to include in the
   analysis. While YWOH were younger (median age 22 years) than YWH
   (median age 24 years) due to the study design, there were no
   significant age differences among YWH groups or YWOH using or not using
   substances. The study population was predominantly African American and
   male with no significant differences among study groups regardless of
   HIV infection and viral status, except that YWOH using substances
   (Group I) had a lower proportion of African American compared with
   other groups. All YWH received ART for a median of 2.7 years with no
   significant difference between study groups. Combination ART regimens
   included nucleoside reverse transcriptase inhibitors (NRTIs), primarily
   emtricitabine and tenofovir, in combination with protease inhibitors
   (ritonavir-boosted atazanavir) or a non-nucleoside reverse
   transcriptase inhibitor (efavirenz). The types of ART were balanced
   across the study groups. CD4 T cell counts at end of study were similar
   between YWH and YWOH [[55]13]. Nadir CD4 T cell counts were similar
   among YWH groups. Across all groups, median CD4 T cell counts at end of
   study were within the normal range [[56]19].

Table 1.

   Demographic and clinical characteristics of study groups (n = 88)
   Characteristics Youth without HIV-1 Youth with HIV-1 receiving ART
   VL > 50 VL ≤ 50^c
   Reference
   (n = 25) Group I
   (n = 9) Group II
   (n = 8) Group III
   (n = 19) Group IV
   (n = 8) Group V
   (n = 19)
   Substance use None Marijuana + Tobacco + Alcohol None None Marijuana
   Marijuana + Tobacco
   Group abbreviation H–S− H–S+[M/T/A] H+V+S− H+V−S− H+V−S+[M] H+V−S+[M/T]
   THCA conc. (ng/ml)^a  ≤ 1 118 [50, 336] ≤ 1 ≤ 1 122 [65, 242] 212 [167,
   322]
   Cotinine conc. (ng/ml)^a  ≤ 1 34 [13, 70] ≤ 1 ≤ 1 ≤ 1 40 [18, 45]
   Years of regular use^a
    Marijuana NA 4.5 [3.0, 6.0] NA NA 7.5 [5.8, 10.8] 7.0 [5.0, 8.0]
    Tobacco NA 4.5 [3.0, 6.0] NA NA NA 7.0 [5.5, 10.3]
   Age (years)^a, # 22 [20, 23] 22 [20, 23] 24.5 [23, 25] 24 [23, 26] 23
   [22, 25] 24 [22, 25]
   Male (%)^† 68 100 75 84 75 95
   African American (%)^‡ 80 33 88 79 100 63
   Years on ART^a, $ 0 0 2.6 [2.2, 2.9] 2.9 [2.4, 2.9] 2.6 [1.4, 2.8] 2.6
   [2.5, 2.9]
   ART regimen (%)^b, §
    PIs NA NA 88 63 63 79
    NRTIs NA NA 100 95 100 89
    NNRTIs NA NA 25 37 50 32

   HIV-1 RNA

   (copies/mL plasma)^a
   NA NA

   6959

   [127, 19655]
    ≤ 50  ≤ 50  ≤ 50
   CD4 Tcell count (cells/µL)^a, ¶
    End of study 751 [462, 864] 692 [447, 873] 574 [307, 818] 736 [581,
   964] 624 [522, 862] 688 [438, 850]
    Nadir NA NA 459 [269, 626] 451 [341, 490] 625 [416, 632] 360 [285,
   501]
   [57]Open in a new tab

   NA: not applicable. PIs: protease inhibitors. NRTIs: nucleoside reverse
   transcriptase inhibitors. NNRTIs: non-nucleoside reverse transcriptase
   inhibitors

   ^aMedian [25th and 75th quartile range]

   ^bPercent of YWH (n = 54)

   ^cVirally suppressed YWH, who used tobacco only or combinations of
   other substances, were excluded from the analyses due to small sample
   size

   ^#Ages were similar between Reference Group and Group I (p = 0.6107)
   and among YWH groups (p = 0.7947). YWOH was younger than YWH
   (p < 0.0001)

   ^†Gender proportions were similar among study groups (p = 0.1611)

   ^‡Proportions of African American were similar between YWOH and YWH
   (p = 0.3262) and within YWH groups (p = 0.1655) except that YWOH using
   substances had less proportion of African American in contrast to other
   groups (p = 0.0222)

   ^$Duration on ART was similar among YWH groups (p = 0.5069)

   ^§Types of ART were balanced across YWH groups (p = 0.9637)

   ^¶CD4 T cell counts at end of study were similar among all groups
   (p = 0.7927) and nadir CD4 T cell counts were similar among YWH groups
   (p = 0.4208)

Differentially expressed genes (DEG) by HIV-infection and substance use

   Genes perturbed by HIV-infection and substance use among YWH were
   globally assessed, with H–S− youth (n = 25) as the universal reference
   group (Fig. [58]1). Combined use of marijuana, tobacco, and alcohol in
   the absence of HIV infection (H–S+[M/T/A], Group I) failed to alter
   gene expression compared to the reference group. In contrast, almost
   5000 DEG were identified across groups of YWH. Non-substance using
   youth with persistent viral replication on ART (H+V+S−, Group II) had
   over 3000 DEG dysregulated compared to the reference group, indicating
   a clear effect of HIV replication on the PBC transcriptome.
   Non-substance using YWH virally suppressed on ART (H+V−S−, Group III)
   displayed about tenfold fewer DEG (n = 313), while marijuana use by
   virally suppressed YWH (H+V−S+[M], Group IV) resulted in 434 DEG.
   Down-regulation of DEG was most evident in Groups III and IV.
   Unexpectedly, combined use of marijuana and tobacco by suppressed YWH
   (H+V−S+ [M/T], Group V) resulted in 1,140 DEG, with a majority
   up-regulated.

Fig. 1.

   [59]Fig. 1
   [60]Open in a new tab

   Differentially expressed genes (DEG) stratified by HIV infection and
   substance use. DEG analysis was performed by a pairwise comparison of
   the transcriptome profile between each study group (Groups I to V) to a
   reference group of youth without HIV-1 (YWOH) who did not use
   substances (n = 25). Group I (n = 9) included YWOH who used a
   combination of marijuana, tobacco, and alcohol; Groups II to V included
   youth with HIV-1 (YWH) on antiretroviral therapy (ART): II (n = 8),
   detectable plasma virus (+) [VL > 50 copies/ml], no substance; III to
   V, undetectable plasma virus (−) [VL ≤ 50 copies/ml], with no substance
   use [III, n = 19], marijuana use only [IV, n = 8], or marijuana
   combined with tobacco use [V, n = 19]. Genes showing an absolute fold
   change |FC| ≥ 1.3 and a permutation false discovery rate (FDR) < 0.05
   were considered significantly altered in expression. Red: up-regulated
   DEG; Green: down-regulated DEG

Analysis by hierarchical clustering and distribution of principal components

   Two approaches were applied to visualize relatedness among all 88
   individuals based on normalized gene expression values for unique DEG.
   Hierarchical clustering of the top 40% of highly variable unique DEG
   (929 of 2322) revealed no clear clustering of gene expression patterns
   by HIV infection, viral replication, or substance use (Additional file
   [61]1: Fig. S1). Unsupervised PCA using all gene probes did not reveal
   any separations by demographic factors including age, gender, and
   ethnicity (Data not shown). In contrast, principal components (PC), in
   particular PC2 and PC3, best separated study groups into three clusters
   (Fig. [62]2). Cluster 1 consisted exclusively of YWOH, including about
   half of H–S− (Reference Group) and two-thirds of H–S+[M/T/A] (Group I).
   Cluster 2 included predominantly virally suppressed YWH, including 68%
   of H+V−S− (Group III) and 63% of H+V−S+[M] (Group IV). Cluster 3
   included 88% of H+V+S− (Group II), as well as 84% of H+V−S+[M/T] (Group
   V). Overall, YWOH were distinct from YWH; among YWH, viral suppression
   and marijuana use clustered together, while continued viral replication
   clustered with dual use of marijuana with tobacco.

Fig. 2.

   Fig. 2
   [63]Open in a new tab

   Principal component analysis (PCA). PCA based on the top 40% highly
   variable DEG probes (2634 of 6586) representing the top 40% highly
   variable unique DEG (929 of 2322) separated the 88 study participants
   into three clusters when using PC2 and PC3, which accounted for 12.1%
   or 6.1% of the total variance, respectively. Symbols: circles,
   participants; ellipses, participant clusters. Circle colors:
   gray-filled, Reference Group (non-substance using YWOH, n = 25);
   gray-open, Group I (YWOH using a combination of alcohol, marijuana and
   tobacco, n = 9); pink-filled, Group II [non-substance using YWH who had
   detectable plasma virus on ART (VL > 50 copies/ml), n = 8];
   blue-filled, Group III [non-substance using YWH who achieved viral
   suppression on ART (VL ≤ 50 copies/ml), n = 19]; blue-open: Group IV
   (virally suppressed YWH using marijuana alone, n = 8); pink-open: Group
   V (virally suppressed YWH using marijuana plus tobacco, n = 19)

Pathways perturbed by HIV-infection and substance use

   The sets of DEG from each group were evaluated by Ingenuity Pathway
   Analysis (IPA) software to identify canonical signaling pathways
   significantly enriched by the DEG. Among non-substance using YWH with
   active viral replication (Group II), a large number of DEG contributed
   to 27 perturbed pathways (18 activated and nine down-regulated)
   relative to the reference group (Fig. [64]3a). Control of viral
   replication among YWH (Groups III, IV, and V) reduced the total number
   of perturbed pathways to twelve (Fig. [65]3a). In the absence of
   substance use (Group III), all 27 signaling pathways perturbed by viral
   replication were normalized, while two additional pathways, NF-kB
   Signaling and PI3K Signaling in B lymphocytes, were uniquely
   suppressed. Marijuana use by virally suppressed YWH (Group IV) resulted
   in no significant perturbation of any pathways relative to the
   reference group. In contrast, marijuana used in combination with
   tobacco by virally suppressed YWH (Group V) was associated with ten
   dysregulated pathways, including four pathways uniquely dysregulated
   (three activated and one suppressed) and six pathways shared by YWH
   with viral replication but no substance use (Group II).

Fig. 3.

   [66]Fig. 3
   [67]Open in a new tab

   Significantly perturbed pathways in study groups and DEG within
   Interferon and ERK5 signaling pathways. a Significantly perturbed
   pathways were based on Ingenuity Pathway Analysis (IPA) using p ≤ 0.001
   and Z score ≥ + 1 for activation (blue-filled) or p ≤ 0.001 and Z
   score ≤ -1 for suppression (grey-filled). VL^+, > 50 viral copies per
   ml plasma; VL^−, ≤ 50 viral copies per ml plasma. Study groups: II,
   VL^+ no substance; III—V, VL^− with III, no substance; IV, marijuana
   only; and V, marijuana plus tobacco. ^a Pathway full name: #10, Role of
   Pattern Recognition Receptors in Recognition of Bacteria and Viruses.
   b, c DEG within the Interferon [#1] and the ERK5 [#9] Signaling
   Pathways (down-regulated, green-filled; up-regulated, red-filled).
   Boxes: blue, DEG specific for Group IV; brown, DEG specific for Group V

   Although viral suppression with or without substance use reduced the
   number of perturbed pathways classified by IPA, varying numbers of
   genes within a pathway might remain differentially expressed. For
   instance, the Interferon Signaling pathway, perturbed in Groups II and
   V with 19 and 12 DEG, respectively, was unperturbed in Groups III and
   IV, even though two genes were differentially expressed (Fig. [68]3b).
   Similarly, the ERK5 Signaling pathway, perturbed in Group V with 12
   DEG, was not perturbed in Group II even with 22 DEG (Fig. [69]3c).
   Within the groups, multiple DEG were identified even when the pathways
   were unperturbed by IPA (Fig. [70]3 and Additional file [71]1: Fig. S2A
   and S2B). Within each pathway, unique patterns of DEG were evident
   among viral and substance use groups.

DEG profiles affected by viral replication and substance use

   To establish transcriptome profiles of YWH based on viral status and
   substance use, DEG within 12 perturbed pathways were aggregated across
   groups of YWH and de-replicated, resulting in a list of 257 unique DEG,
   which were then reassigned to each study group (Fig. [72]4). Among 257
   DEG from the twelve pathways, over half (146/257) were specific to one
   of the groups of YWH (Groups II–V). Most DEG (118/146) were specific to
   Group II, YWH with active viral replication, while virally suppressed
   Groups III, IV, and V had only 2, 12, and 14 specific genes,
   respectively. The remaining 111/257 DEG were shared to varying extents
   among the study groups. Only eight DEG were shared among all four study
   groups. DEG sharing among three study groups varied but surprisingly 15
   DEG shared by Groups II, III, and V were not dysregulated in YWH who
   used marijuana alone (Group IV). A pairwise comparison between
   different study groups showed few shared DEG between Groups II and III,
   or II and IV. Groups IV and V shared up-regulated ACTB gene involved in
   cell motility, structure, integrity and intercellular signaling, while
   PPP2R2A, a Ser/Thr phosphatase implicated in control of cell growth and
   division, was dysregulated in opposite directions in these study
   groups. Groups II and V showed a striking high number of shared DEG.

Fig. 4.

   [73]Fig. 4
   [74]Open in a new tab

   DEG in 12 pathways unique or shared by study groups. Orange-coding
   classifies DEG unique to or shared by study groups with down-regulated
   DEG texted green, up-regulated DEG texted red and DEG expressed in
   opposite directions in two study groups texted blue. #: Additional
   genes not shown CREBBP, FOXO1, IL1A, MAP3K3, NFAT5, PDPK1 and TLR6 in 2
   pathways, and all others in a single pathway, including ACVR1B, ARID2,
   BCL6, BID, CFLAR, CSNK2A1, EP300, FOXP1, GAB2, IGF2R, IL4R, IL6R,
   LIMS1, LTB, MAP3K2, MDM2, MGAT5, MMP9, NAIP, PGF, POU2F1, PPP3CA,
   PTPRC, RAC1, RPS6KA2, SGK1, THEM4, TNFRSF21, TNFRSF25 and TNFSF14. $:
   Additional genes not shown NFATC2, STAT2, TGFBR3, TLR3 and YWHAQ in 2
   pathways, and all others in a single pathway, including ACTA2, ANAPC4,
   ANAPC10, ATF5, BATF, BIRC2, BMPR1A, CAMK2D, CD28, CD81, CDKN2A, CYCS,
   E2F7, E2F8, EIF2S1, EIF4E, FANCA, FANCF, FANCM, FGFR2, FYN, HLA-DQA1,
   HLTF, IFITM1, IL10RA, IL12RB1, ILK, INPP5F, IRF3, IRF7, KIF11, LCK,
   MRE11, MX1, PARP11, PARP15, PARP2, PARP3, PDCD1, PLEKHA3, PLK4, PPP3CB,
   PRKAB2, PTTG1, RBBP8, RFC5, RPS6KC1, SH2D2A, SMAD3, SMARCE1, SOS1,
   STAT4, TAB3, TAP1 and TBX21. ‡: Additional genes not shown ATF3, ATR,
   AZI2, BARD1, BRIP1, C1QB, CASP3, CASP7, CCNB1, CCNB2, CDC7, CDC20,
   CDK1, CTLA4, DDX58, EOMES, FANCL, FBXO5, FZD1, HLA-DPA1, IFI35, IFI6,
   IFIH1, IFIT1, IFIT3, IRF1, ISG15, ITGA4, ITGB1, MEF2A, NBN, OAS2, OAS3,
   PARP12, PARP14, PDIA3, PIK3AP1, PRC1, PRDM1, PRKCH, RAD50, RFC3, ROCK1,
   SLK and UBE2N in a single pathway. *: Same gene which was up (↑)- and
   down (↓)-regulated in different groups. a: ↓ in II, ↑in V. b: ↑in II,
   ↓in V. c: ↓ in IV, ↑ in V

   The effects of substance use on cellular and inflammatory pathways in
   YWH were evident when examining individual patterns DEG unique to Group
   III, Group IV, and Group V (Fig. [75]4). Non-substance using youth who
   achieved long-term viral suppression (Group III) showed only two unique
   DEG. In contrast, marijuana users (Group IV) had 12 unique DEG
   including down-regulated genes involved in cell death and apoptosis
   (FADD, TNFRSF10B and XIAP) and up-regulated genes involved in cellular
   proliferation (KRAS and TP53), B cell signaling (BLK and CD79A), and
   TNF signaling (LTBR). Similarly, YWH who used marijuana in combination
   with tobacco (Group V) displayed down-regulated DEG involved in
   negative control of cell growth (PDK1 and PPP2R1B) and up-regulated
   genes involved in cellular proliferation (MAPK8 and PTPN11), DNA repair
   (MSH6), as well as DEG in complement, prostaglandins, innate immunity,
   and inflammation (C1QA, CLEC7A, PTGS2, and TNFAIP3).

   Examining DEG shared by groups also revealed the effect of substance
   use among virally suppressed YWH. The most striking observation is the
   vast number of pro-inflammatory genes shared exclusively by Group II
   (H+V+S−) and Group V (H+V−S+[M/T]). Comparing suppressed YWH who only
   used marijuana (Group IV) to all other groups (Group II, III, and V)
   revealed normalization of unique DEG associated with marijuana use
   alone. These genes include down-regulated genes involved in cellular
   proliferation and ontogenesis (PIK3CB, PTEN and TNFRSF10A) and several
   genes known to be up-regulated by HIV infection including CCL5, CD274
   (PD-L1), FAS, IFNG, IL-15 and STAT1. These results support a specific
   effect of marijuana on the PBC transcriptome in the context of viral
   suppression. There were five genes BMPR2, BRD7, PRKAG2, PRKAR1A and
   PPP2R2A dysregulated in opposite directions between Group V
   (H+V−S+[M/T]) and Group II or Group IV suggesting an association with
   tobacco use.

   Figure [76]5 is a network display of the 257 unique DEG outlined in
   Fig. [77]4 showing gene expression profiles unique to study groups,
   including DEG specific to each group or those shared with other one or
   more groups. Distinct gene expression profiles among H+V+S− (Group II),
   H+V−S− (Group III) and H+V−S+[M] (Group IV) indicate remarkable gene
   regulation by viral suppression alone or in combination with marijuana
   use. Massive gene sharing between H+V+S− (Group II) and H+V−S+[M/T]
   (Group V) point to inflammatory effect of tobacco used in combination
   with marijuana in virally suppressed YWH.

Fig. 5.

   [78]Fig. 5
   [79]Open in a new tab

   Relationship of unique DEG in 12 pathways across study groups. DEG in
   each of 12 pathways including 6 pathways perturbed in both Group II and
   Group V, two perturbed uniquely in Group III, and 4 perturbed
   specifically in Group V, were collected, deduplicated and assigned back
   to study groups. The relationships of DEG across groups were displayed
   as networks to display DEG unique to each group or shared between/among
   groups. Red dot/line, an up-regulated DEG; green dot/line, a
   down-regulated DEG; purple dot with green and red lines, differentially
   up- or down-regulated between two groups

Discussion

   Putative anti-inflammatory properties of cannabinoids have led to
   increased medicinal marijuana use among the general public [[80]8,
   [81]20–[82]22]. Many studies of substance use effects in HIV-associated
   inflammatory pathways involve older, chronically infected adults using
   multiple substances, including cocaine, opioids, tobacco, alcohol, and
   methamphetamine, in combination with marijuana [[83]23–[84]27]. While
   there is evidence supporting the anti-inflammatory effects by marijuana
   use, polysubstance use presents challenges in identifying
   marijuana-specific effects on immune pathways [[85]9, [86]27–[87]29].

   Our study focused on a unique population of YWH with few comorbidities
   who initiated ART prior to CD4 T cell decline, displayed sustained
   viral suppression for 3 years on ART, and showed evidence of chronic
   inflammation even with sustained viral suppression [[88]12–[89]14]. YWH
   who used substances self-reported regular use over the previous 3 to
   13 years, with recent use validated by toxicology. The study design
   enables a detailed assessment of the global inflammatory transcriptome
   in YWH with or without substance use compared to a non-substance using
   reference group of YWOH, balanced for age, gender, and race [[90]13,
   [91]14, [92]17, [93]18, [94]30]. Importantly, the study population,
   composed primarily of young African American males who use no substance
   or use recreational marijuana alone or in combination with tobacco
   [[95]18, [96]31], reflects the predominant demographic of current new
   infections in the United States.

   Unsupervised analysis of PBC transcriptome profiles revealed
   segregation between YWOH and YWH groups based on HIV replication and
   substance use profiles. Persistent low level viral replication while
   receiving ART significantly altered multiple DEG, while viral
   suppression to undetectable levels modulated this effect. More
   importantly, marijuana used alone or in combination with tobacco had
   substantial effects on the transcriptome profile even when viral
   replication was suppressed by ART. Compared to the reference group of
   non-substance using YWOH, virally suppressed YWH using marijuana alone
   displayed an anti-inflammatory profile, while use of marijuana plus
   tobacco resulted in a distinct pro-inflammatory profile. The
   pro-inflammatory profile displayed by YWH using marijuana combined with
   tobacco may stem from interactions between marijuana and tobacco,
   and/or a dominant modifying effect by tobacco on inflammatory pathways.
   Unfortunately, our cohort did not include adequate numbers of YWH who
   used tobacco alone to examine a tobacco-only effect on the
   transcriptome. Nonetheless, a key finding of our study is the profile
   associated with marijuana and tobacco co-use by virally suppressed YWH,
   which resembled, in part, the profile of non-substance using YWH with
   persistent viral replication. This observation may have grave
   implications for YWH who continue to regularly use tobacco with or
   without concomitant marijuana use and face a lifetime of living with
   HIV. Tobacco use among YWH may increase the long-term metabolic,
   cardiovascular, and neurocognitive complications of living with HIV
   [[97]14–[98]16, [99]32, [100]33]. These results suggest that
   significant efforts are needed to mitigate tobacco use among YWH who
   otherwise display viral control and maintain normal CD4 T cell counts.
   In contrast, the anti-inflammatory effects by marijuana use alone could
   limit viral replication and sustain viral latency, perhaps by
   inhibition of T cell activation or attenuation of macrophage infection
   [[101]27, [102]34, [103]35]. Combined substance use including tobacco,
   marijuana, and alcohol by YWOH produced no discernable perturbations of
   gene expression. Ex vivo studies using human PBMC and transcriptome
   analysis from adults without HIV who heavily used tobacco (> 10
   cigarettes per day) revealed upwards of 100 DEG [[104]36, [105]37]. In
   contrast, our studies of younger individuals without HIV, who did not
   use tobacco heavily, displayed no effect on the PBC transcriptome by
   marijuana, tobacco, and alcohol, perhaps due to lower cumulative
   tobacco exposure or the polysubstance use of marijuana plus tobacco and
   alcohol.

   Assessment of multiple genes and relevant canonical pathways among
   various YWH outcome groups provides mechanistic insights of the effects
   by HIV replication and substance use on the global PBC transcriptome.
   Most of the 27 pathways perturbed by HIV replication involve immune
   response and inflammatory pathways, with metabolic and cell cycle
   pathways affected as well. These results support previous gene set
   enrichment studies using either whole blood or selected cell
   populations in people with HIV, which all revealed perturbed gene
   signatures associated with HIV replication, disease progression, and
   clinical outcomes [[106]38–[107]41]. In contrast, viral suppression by
   ART among non-substance using YWH significantly reduced gene
   perturbations [[108]42], providing an opportunity to examine how
   marijuana or marijuana used in combination with tobacco affects the
   transcriptome in the absence of viral replication. Marijuana use alone
   resulted in altered regulation of multiple unique genes not observed in
   the other groups of virally suppressed YWH. Unique DEG, particularly
   downregulation of FADD and upregulation of KRAS (RAS) and TP53 have
   been implicated in cannabinoid signaling in animal models and ex vivo
   cultured cells lines indicating a cannabis effect on cellular
   proliferation and apoptotic pathways [[109]43–[110]49]. Furthermore,
   comparing genes perturbed among non-substance using virally suppressed
   and unsuppressed YWH, as well as suppressed YWH using marijuana plus
   tobacco to the group of virally suppressed YWH using only marijuana,
   revealed marijuana specific effects.

   Unique normalized DEG seen solely in the marijuana group included CCL5,
   CD274 (PDL-1), FAS, IFNγ, IL-15 and STAT1, genes in pro-inflammatory
   pathways known to be perturbed by HIV infection [[111]50–[112]56]. THC
   has an anti-CCL5 effect in SIV-infected rhesus macaques and Cannabis
   sativa is postulated to block signaling through Jak/Stat
   [[113]57–[114]59]. Our study is the first observation that marijuana
   may affect signaling through IL-15 and CD274 (PD-L1) in individuals
   with HIV. While IL-15 has been shown to increase HIV replication, use
   of IL-15 as a possible therapy to reverse latency for HIV infection has
   also been suggested [[115]60–[116]63]. THC mediates anti-inflammatory
   effects on IL-15 and IFNγ in macrophages in vitro [[117]64], but
   marijuana effects on these pathways in the context of HIV have not been
   explored. PD-1 (CD274) has long been known to be upregulated on T cells
   of HIV-infected individuals and is positively correlated with viral
   load to induce cellular anergy with associated declines in cellular
   proliferation, cytotoxic function, and cytokine secretion
   [[118]65–[119]67]. If THC inhibits CD274 (PD-L1) expression in HIV,
   similar to pancreatic cancer studies [[120]68], then inhibition of
   PD-1/PD-L1 by marijuana might reverse HIV-mediated T cell exhaustion
   [[121]65–[122]67].

   Among the DEG perturbed in suppressed YWH who used marijuana in
   combination with tobacco, multiple pro-inflammatory genes associated
   with tobacco use were identified. For example, BMPR2, BRD7, PRKAG2,
   PRKAR1A and PPP2R2A (PP2A Bα subunit B55α), which are differentially
   expressed between group V (H+V−S+[M/T]) and groups II/IV
   (H+V+S−/H+V−S+[M]), play roles in tobacco-mediated lung diseases and
   lung cancers [[123]69–[124]76]. Group V-specific genes PTPN11 (Shp2) is
   involved in acute cigarette smoke-mediated lung inflammation by
   increasing IL-8 production [[125]75], while MAPK8 (JNK1) plays a key
   role in cigarette smoke exposure-mediated cell death [[126]76].
   Transcriptome analysis alone is insufficient to determine the precise
   impact of substance use on inflammation. However, the blood
   transcriptome derived primarily from leukocytes offers an opportunity
   to examine the effects of HIV in vivo [[127]77]. Marijuana contains
   multiple cannabinoids that primarily influence signaling through G
   protein-coupled receptors involving diverse pathways which ultimately
   results in the wide array of DEG, and pathways perturbed by tobacco use
   in YWH [[128]78, [129]79].

   Although the Affymetrix microarray provides a restricted survey of
   expressed genes in PBC, our results show clear effects by control of
   viral replication and substance use. In general, unsupervised analysis
   exposed differences between YWOH and YWH that could reflect an effect
   of viral infection or ART on the blood transcriptome. The regimen and
   percent of YWH receiving PIs, NRTIs and NNRTIs were similar across YWH
   groups to balance out the potential influence of ART on gene
   expression. One omics approach examining the effect pre-exposure
   prophylaxis (PrEP) in individuals without HIV found limited global
   effects on host gene expression with no differentially expressed genes
   in blood [[130]80]. Our study was not designed to evaluate
   anti-inflammatory effects of marijuana in the context of a controlled
   clinical trial. While the routes of drug administration and total
   exposure to marijuana and tobacco were similar within the study
   cohorts, the effect of total exposure to drug, drug metabolites, or
   combustion products were beyond the scope of the study design.
   Controlled studies with clear clinical endpoints using
   self-administered or prescribed marijuana or isolated compounds
   including the use or THC or CBD are in progress [[131]34]. However, our
   results do provide insight into “real life” use of recreational
   marijuana among YWH. The current national climate to legalize
   recreational and medical use of cannabinoids highlights the need to
   evaluate the effects of marijuana in people with HIV as these
   substances could have effects on inflammatory and immune pathways, as
   well as HIV latency. Even with optimal viral suppression, YWH will have
   chronic inflammation across their life span with its associated
   comorbidities. If it can be demonstrated that marijuana attenuates
   pro-inflammatory cytokines in chronic infections such as HIV, or even
   COVID-19 [[132]81], our results may have implications for management of
   other inflammatory conditions.

Conclusions

   YWH receiving ART display unique transcriptome bioprofiles based on
   viral replication and substance use. Multiple DEG and pathways are
   perturbed by uncontrolled viral replication. In the context of HIV
   suppression, marijuana use alone by YWH normalizes multiple genes and
   pathways resulting in distinct DEG patterns. In contrast, marijuana use
   in combination with tobacco has opposing effects on inflammatory
   pathways and gene expression, as many DEG modulated by HIV replication
   are also perturbed by the use of marijuana plus tobacco. Substance use
   has an overall effect on YWH receiving ART that is likely to have
   long-term implications in a population who faces a lifetime of living
   with HIV infection and treatment.

Materials and methods

Demographics, clinical characteristics, and substance use among study
participants

   The study cohort consisted of 129 YWH infected via sexual transmission,
   ages 18 to 28 years, enrolled in Adolescent Medicine Trials Network
   (ATN) for HIV/AIDS Interventions protocol 071/101, Assessment of
   Inflammatory Markers Associated with Neurocognitive Impairment in
   HIV-infected Adolescents. This 3-year longitudinal study took place at
   22 metropolitan sites throughout the United States and Puerto Rico
   (ClinicalTrials.gov Identifier [133]NCT00683579). Completed in 2013,
   the primary outcome results of this 3-year study have been reported
   [[134]13, [135]14, [136]18, [137]82]. Participants were monitored at
   regular intervals, at entry, and weeks 24, 48, 96 and 144 for viral
   load (VL), CD4 T cell counts, clinical status, antiretroviral
   medications, and substance use. All YWH received ART, including 64.8%
   using protease inhibitor-based ART, 27.8% non-nucleotide reverse
   transcriptase inhibitor-based ART, 1.9% Integrase Strand Transfer
   Inhibitor, and 5.6% received non-nucleotide reverse transcriptase
   inhibitor plus protease inhibitor ART. The classes and combinations of
   ART were similar among YWH study groups. All participants provided
   consent for a single blood draw obtained at the end of study (week 144)
   for transcriptome analysis. In addition, consent was obtained for a
   reference group of 55 YWOH balanced for gender and race with YWH as
   previously described [[138]13]. This study was performed under
   protocol, Consequences of Marijuana Use on Inflammatory pathways in
   HIV-Infected Youth, reviewed and approved by the institutional review
   board (IRB) of Duke University and Molecular HIV and Host Interaction
   Section (MHHIS) at National Institute of Allergy and Infectious
   Diseases (NIAID), National Institutes of Health (NIH).

   Use of alcohol, marijuana, tobacco products, and other substances was
   evaluated in two ways: Audio Computer-assisted Self-administered
   Interviewing (ACASI) with Alcohol, Smoking and Substance Involvement
   Screening Test (ASSIT) and plasma toxicology screening. ASSIST was
   developed for WHO [[139]83] and was validated for adults [[140]84] as
   well as adolescents [[141]85]. ACASI reports captured the age of the
   participants when they first began using substances as well as the
   numbers of years of regular use defined as daily, weekly, or monthly
   use. There was a significant concordance between the self-reported
   substance use and toxicology results [[142]18]. Toxicology screening
   was performed on plasma samples by Immunalysis Corporation (Pomona, CA;
   Immunalysis.com) using enzyme-linked immunosorbent assays (ELISA) for
   27 analytes, including over the counter, prescription, and illicit
   drugs. The ELISA for marijuana detects > 1 ng/ml of plasma carboxy THC
   (THCA), a precursor of THC, within 1 day (acute) as well as up to
   30 days of marijuana use (chronic) [[143]86–[144]88], while the assay
   for tobacco detects > 1 ng/ml of plasma cotinine, a nicotine
   metabolite, up to 7 days after use. Substance use was defined as
   detectable plasma THCA or cotinine with self-reported regular use in
   the past 3 months.

Sample collection, RNA isolation, amplification, and microarray

   Whole blood samples were collected in PAXgene Blood RNA Tubes (Becton,
   Dickinson and Company, Franklin Lakes, New Jersey, US) and stored at
   − 80 °C [[145]77]. Intracellular total RNA was isolated using PAXgene
   Blood RNA Kit (PreAnalytiX, Hombrechtikon, Switzerland). Globin mRNAs
   were depleted using GLOBINclear Kit (Ambion, Waltham, MA, USA). RNA
   (100 ng) was amplified and labeled using GeneChip 3’ IVT Express Kit
   (Affymetrix, Waltham, MA, USA) and hybridized to GeneChip Human Genome
   U133 Plus 2.0 Array with 54,675 probes (Affymetrix, Waltham, MA, USA)
   in the Interdisciplinary Center for Biotechnology Research at
   University of Florida. Raw images in CEL format were generated with
   Affymetrix GeneChip Operating Software. The quality of each array was
   determined by manually checking mean values, variances, and paired
   scatter plots. All arrays passed the quality check. For downstream
   computational analysis statistical environment of R version 3.6 was
   used [[146]89].

Analysis plan

   Study groups with a minimum of eight individuals per group to ensure
   reliability of analysis were defined based on HIV-1 status, VL, and
   substance use profile. The reference group included 25 YWOH using no
   substance. An additional group of 9 YWOH using a combination of
   alcohol, marijuana and tobacco were also included as a comparison group
   (Group I). There were insufficient numbers of YWOH who only used
   tobacco or marijuana to include in the analysis. Four study groups
   included ART-treated YWH: Group II, 8 non-substance users with
   detectable virus; Group III, 19 virally suppressed non-substance users;
   Group IV, 8 suppressed marijuana users; and Group V, 19 virally
   suppressed marijuana plus tobacco users (Table [147]1). YWH who used
   substances other than tobacco or marijuana were excluded from the
   analysis, as well as groups using tobacco or alcohol alone due to small
   sample size.

Microarray expression normalization

   Microarray raw probe signal values were corrected for background,
   normalized by quantile, and summarized using the Robust Multi-Array
   Averaging (RMA) algorithm [[148]90]. All expression values were
   converted to a log[2] scale for downstream analysis.

Differential expression analysis

   Differentially expressed genes (DEG) were identified in reference to
   H–S− (Reference Group, n = 25) by Significance Analysis for Microarrays
   (SAM), which used one-thousand permutations of the repeated
   measurements to estimate the percentage of genes identified by chance,
   the false discovery rate (FDR) [[149]91]. Fold change (FC) was
   calculated for each gene by first calculating the average expression
   value for each study group, and then taking the anti-log[2] of the
   difference between a study group and the reference group. Genes showing
   an absolute FC ≥ 1.3 and FDR < 0.05 were classified as DEG and used in
   hierarchical clustering and principal component analysis to predict the
   relatedness of study groups among the study cohort [[150]39, [151]92].
   For FDR controlled microarray analysis based on two-class unpaired test
   required to detect a 1.3-fold change in expression level, a samples
   size of 8 could provide adequate statistical power [[152]93, [153]94].

Hierarchical clustering analysis (HCA) and principal component analysis (PCA)

   Both unsupervised HCA and PCA were performed using probes associated
   with DEG. The coefficient of variation (CV) was computed from the
   normalized gene expression values. The probes for each individual were
   ranked by CV, and the top 40% (2634 of 6586) of probes representing the
   top 40% of highly variable unique DEG (929 of 2322) were used for both
   HCA and PCA (Additional file [154]2). Ward.D2 method was used for HCA
   and heatmap was plotted using pheatmap [[155]95]. PCA was carried out
   using R package, and Plotly [[156]96] was used for graphical display
   and interpretation [[157]97, [158]98]. Collectively, the first three
   principal components (PC1, PC2 and PC3) included 40.7% of the total
   variance with PC1 accounting for 22.5% of variance, and PC2 and PC3
   accounting for 12.1% and 6.1%, respectively. To address influences by
   confounding demographic differences including age, gender, and
   ethnicity, unsupervised PCA were performed using the top 10% ranking
   normalized expression values of the total 54,675 gene probes.

Pathway enrichment analysis

   DEG in each study group were imported into Ingenuity Pathway Analysis
   (IPA) software (Qiagen; Bioinformatics, Redwood City, CA, USA) to
   identify canonical signaling pathways associated with genes. In IPA,
   canonical pathways were named by the IPA content team based on the
   compendium of information used to describe the functions. Fisher’s
   Exact Test was used to calculate the p-value by comparing the
   proportion of study genes associated with a particular pathway to the
   proportion of genes expected with p ≤ 0.0001 defining a significantly
   enriched pathway. The z-score was calculated by IPA to infer the
   activity pattern (activation or inhibition) of the canonical pathway
   based on the direction of the expression pattern (up-regulation or
   down-regulation) of the study DEG in comparison to the expected
   directions of same genes in the Ingenuity Knowledge Base [[159]99]. In
   this study, pathways with p ≤ 0.0001 and Z score ≥ 1 were classified as
   activated, and those with p ≤ 0.0001 and Z score ≤ -1 were defined as
   suppressed [[160]100].

Specific DEG profile within perturbed pathways effected by viral replication
and substance use

   DEG in each of 12 pathways, including six pathways perturbed in both
   Group II and Group V, two pathways specifically perturbed in Group III,
   and four pathways specifically perturbed in Group V, were aggregated,
   de-duplicated and assigned back to each study group to identify DEG
   specific for each study group, or shared between or among groups. The
   gene relationships among groups were displayed as a network constructed
   using Cytoscape v3.8.0, an open source software for visualizing complex
   networks and integrating these with attribute data [[161]101]. GeneCard
   served as the source of gene function(s) [[162]102].

Statistical analysis

   The t test was used to compare data between two groups. One-way ANOVA
   was used to compare plasma concentrations of THCA and cotinine, age,
   ART duration, CD4 count and nadir CD4 count among groups. Chi-square
   test was used to predict influence of gender and race on HIV-infection
   or viral load status in HIV-infected individuals as well as balance in
   percent use of ART among study groups in YWH. GraphPad Prism 7.04
   (GraphPad Software, San Diego, CA, USA) was used for all statistical
   analyses. Statistical significance was defined as p < 0.05.

Supplementary Information

   [163]12977_2022_594_MOESM1_ESM.pdf^ (323.6KB, pdf)

   Additional file 1. Additional Figures S1 and S2.
   [164]12977_2022_594_MOESM2_ESM.xlsx^ (1.6MB, xlsx)

   Additional file 2. Log[2] expression values of top 40% DEG used in
   hierarchical clustering analysis in Figure S1.

Acknowledgements