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. Author manuscript; available in PMC: 2020 Jun 1.
Published in final edited form as: J Neuroimmune Pharmacol. 2018 Sep 21;14(2):200–214. doi: 10.1007/s11481-018-9805-6

Morphine potentiates dysbiotic microbial and metabolic shifts in acute SIV infection

Gregory M Sindberg 1, Shannon E Callen 2, Santanu Banerjee 3,4, Jingjing Meng 3,4, Vanessa L Hale 5, Ramakrishna Hegde 6, Paul D Cheney 6, Francois Villinger 7, Sabita Roy 3,4,*,, Shilpa Buch 2,*,
PMCID: PMC6917429  NIHMSID: NIHMS1524686  PMID: 30242614

Abstract

Background

Human Immunodeficiency Virus (HIV) pathogenesis has been closely linked with microbial translocation, which is believed to drive inflammation and HIV replication. Opioid drugs have been shown to worsen this symptom, leading to a faster progression of HIV infection to Acquired Immunodeficiency Syndrome (AIDS). The interaction of HIV and opioid drugs has not been studied at early stages of HIV, particularly in the gut microbiome where changes may precede translocation events.

Methods

This study modeled early HIV infection by examining Simian Immunodeficiency Virus (SIV)-infected primates at 21 days or less both independently and in the context of opioid use. Fecal samples were analyzed both for 16S analysis of microbial populations as well as metabolite profiles via mass spectrometry.

Results

Our results indicate that changes are minor in SIV treated animals in the time points examined, however animals treated with morphine and SIV had significant changes in their microbial communities and metabolic profiles. This occurred in a time-independent fashion with morphine regardless of how long the animal had morphine in its system.

Conclusions

Globally, the observed changes support that microbial dysbiosis is occurring in these animals at an early time, which likely contributes to the translocation events observed later in SIV/HIV pathogenesis. Additionally, metabolic changes were predictive of specific treatment groups, which could be further developed as a diagnostic tool or future intervention target to overcome and slow the progression of HIV infection to AIDS.

Keywords: HIV pathogenesis, opioids, intestine microbiome, intestine metabolism, Non-human primates, SIV

Background

Over the last two decades, with the ongoing development of more effective multi-drug cocktails for the treatment of human immunodeficiency virus (HIV), HIV has transitioned from a fatal, untreatable disease to a chronic, manageable disease in patients with access to these therapeutics. With therapy, patient plasma viral loads can be dramatically reduced and maintained below the level of detection. However, as treated patients continue to live longer, there has also been an increased prevalence of age-related co-morbidities including cardiovascular disease (Grunfeld et al., 2009; Lorenz et al., 2008; Sico et al., 2015), kidney disease (Kalayjian et al., 2012; Ryom et al., 2014), osteoporosis (Brown & Qaqish, 2006; Chitu-Tisu, Barbu, Lazar, Ion, & Badarau, 2016) and neurocognitive disorders (Antinori et al., 2007; Heaton et al., 2011; Tozzi et al., 2007). Underlying these pathologies is chronic immune activation attributed to a combination of sources including persistent, low-level viral replication, immune dysregulation, illicit drug abuse and microbial translocation (Lederman, Funderburg, Sekaly, Klatt, & Hunt, 2013). In fact, it has been recognized that the intensity of chronic immune activation, not the level of viral replication, is the primary factor in determining disease progression (Giorgi et al., 1999; Hazenberg et al., 2003; Jiang et al., 2009). A decade ago, Brenchley et al published a seminal perspective paper that put forward the idea that HIV infection leads to gut mucosal immune dysfunction and subsequent microbial translocation across the damaged epithelial barrier; postulating this as a key process in HIV infection driving chronic systemic immune activation which ultimately enhances disease progression(Brenchley et al., 2006). This proposition has been extensively studied and largely confirmed in recent years (Brenchley et al., 2006; Dandekar, George, & Bäumler, 2010; Mudd & Brenchley, 2016). However, mucosal immune dysfunction can also manifest in the context of other factors including illicit drug abuse (Meng, Sindberg, & Roy, 2015).

There is a strong correlation between the abuse of illicit drugs, particularly intravenous drugs including opiates, and the acquisition and progression of HIV disease (Ancuta et al., 2008; Bokhari et al., 2011; El-Hage et al., 2008; Gurwell et al., 2001). It is well established that opiates such as morphine have immunomodulatory functions (Mellon & Bayer, 1998; Roy et al., 2011). They inhibit cell-mediated immune responses by enhancing the expression of IL-4 while at the same time, suppressing the expression of IFN-y – two key cytokines regulating these responses (Roy, Wang, Charboneau, Loh, & Barke, 2005; J. Wang, Barke, Charboneau, Loh, & Roy, 2003). Therefore, it is not surprising that chronic morphine abuse in the context of HIV infection can exacerbate disease progression by attenuating the immune responses necessary for anti-viral protection. Furthermore, chronic morphine exposure alone has been shown to reduce pathogen clearance and induce microbial translocation across the gut barrier providing another possible avenue for morphine-mediated enhancement of HIV disease progression (S Banerjee et al., 2016; Hilburger, Adler, Truant, Meissler Jr., et al., 1997; Meng et al., 2013).

The gut is colonized with a diverse population of billions of microorganisms termed the gut microbiome which is essential for maintaining homeostasis within the gut (O’Hara & Shanahan, 2006). The gut microbiome serves several important functions including defense against colonization of pathogenic strains by displacement, production of bacteriocins and nutrient competition; maintenance of gut epithelial barrier; induction of mucosal secretory IgA to neutralize bacterial penetration; facilitating absorption of nutrients by microbial metabolism of indigestible carbohydrates and promoting systemic immune maturation and functions (Thaiss, Zmora, Levy, & Elinav, 2016; Turner, 2009). In light of these fundamental functions, the role of the gut microbiome in setting the stage for inflammatory manifestations is becoming increasingly recognized (Cabrera-Perez, Badovinac, & Griffith, 2016; Stecher, 2015). Microbial dysbiosis is a hallmark of interrupted gut homeostasis and has been observed in numerous diseases(Pflughoeft & Versalovic, 2012). Dysbiosis can occur as a direct effect of a pathogen outcompeting commensal bacteria, such as in C. difficile (Khoruts, Dicksved, Jansson, & Sadowsky, 2010) or indirectly as a result of altered host immunity, as in inflammatory bowel disease (Nell, Suerbaum, & Josenhans, 2010). Microbial dysbiosis in HIV/SIV infections have been shown to be correlated with chronic immune activation in late stages of disease progression (Dillon, Lee, Kotter, & Austin, 2014; McHardy et al., 2013; McKenna et al., 2008; Moeller et al., 2013; Mutlu et al., 2014; Vujkovic-Cvijin & Dunham, 2013). Furthermore, HIV patients treated with probiotics that restore non-pathogenic populations have demonstrated diminished inflammation and improved outcomes elucidating the integral role the microbiome exerts in the process of chronic inflammation (d’Ettorre et al., 2015). However, most studies examining changes in the gut microbiome involve cases of chronic SIV/HIV; this study seeks to determine if early changes are present and if so, could these changes predispose one to microbial translocation through immune dysregulation in gut mucosal tissues that ultimately accelerate disease progression.

Understanding the interplay between the gut microbiome, HIV and morphine is critical for the development of new therapies to ameliorate the chronic immune activation found in these patients. In this study, we utilize the SIV macaque model of HIV pathogenesis to assess for alterations in gut microbial composition and diversity as well as changes in bacterial associated metabolites in the presence or absence of acute SIV infection with or without chronic morphine dependence. Our results show that an acute 3-week SIV infection is sufficient to induce microbial metabolic changes even at this early time point. However, chronic morphine exposure produces more dramatic changes in the microbial community and related metabolites. Our data demonstrates that morphine and not SIV, at least at this early time point, has a greater impact on the induction of dysbiosis and associated changes in metabolites. Together, SIV infection in the context of chronic morphine dependence results in significant changes in the gut microbiome providing potential mechanisms to support the immune dysregulation and enhanced pathogenesis found in chronically infected patients.

Methods

Ethics Statement

All animal protocols were approved by the local animal care committee (IACUC, #2012-2070) at the University of Kansas in accordance with the Guide for the Care and Use of Laboratory Animals.

NHP housing and sampling

Animals used in this study were male, Indian-origin rhesus macaques between 3-4 years of age. All animals tested negative for tuberculosis, herpes B virus and simian retrovirus prior to enrollment in this study. Animals were individually housed in steel holding cages, in two dedicated rooms within the AAALAC-approved animal facility at the University of Kansas Medical Center. The NHPs were exposed, daily, to 12-hour light-dark cycles and given laboratory chow and water ad libitum. All animals had access to numerous environmental enrichments. All animal protocols were approved by the local animal care committee (IACUC) at the University of Kansas in accordance with the Guide for the Care and Use of Laboratory Animals. Feceal samples were collected at the time points indicated (S1 Fig). Pre-treatment samples were obtained from all 14 male rhesus macaques (Pre). Four animals were given morphine alone (Ms; blue), with collections at days 21, 64, and 84 in addition to a pre sample (S1 Fig, top panel). Four animals were treated solely with SIV (SIV; red), and samples were collected 3, 8, 15, and 22 days post infection in addition to a Pre sample (S1 Fig, middle panel). Finally, SIV+morphine animals were treated with morphine (Ms; blue) for 70 days prior to receiving SIV infection (SIVMs;green) (S1 Fig, bottom panel). For this final group, samples were collected at 21 and 64 days prior to SIV treatment, and were analyzed as morphine alone. Following SIV infection, samples were collected at 73 days morphine/3 days SIV, 78 days morphine/8 days SIV, 84days morphine/14 days SIV, and 92 days morphine/22 days SIV. The collections with SIV in these animals mirror the time points of fecal collections in SIV alone animals following infection.

Morphine treatment

NHPs were injected intramuscularly three times daily (8 hour interval) with morphine; the dose was ramped up during the first two weeks from 2mg/kg (week 1) to 3mg/kg (week 2) to the final dose of 4mg/kg which was continued for the duration of the study (D84 for Morphine alone, D92 for Morphine + SIV). Morphine administration was maintained throughout the study to minimize withdrawal effects. NHPs co-infected with SIV were pretreated with opioids for 70 days prior to SIV infection to establish a chronic use period. Animals not receiving morphine were intramuscularly injected with saline in parallel with morphine administration. Pharmaceutical grade morphine (50 mg/mL) was purchased from the University of Kansas Medical Center Pharmacy.

SIV Infection

SIVmacR71/17E (Raghavan et al., 1999), an SIVmac239-derived virus, was originally prepared from pooled brain homogenates from macaques infected with R17 and R71E both of which cause encephalitis. Virus stock was prepared in CD8+T cell depleted, ConA-activated macaque peripheral blood mononuclear cells (PBMC) and assayed for infectivity in CEMx174 cells and by plaque assay in GHOST Hi5 cells. Animals were inoculated intravenously via the saphenous vein with approximately 104 plaque forming units (PFU) of virus.

Necropsy

All monkeys were monitored daily for evidence of distress or disease including changes in attitude, appetite or behaviors suggestive of illness (lack of movement, withdrawn behavior, etc). At study endpoint (after 12 weeks of morphine or 3 weeks SIV infection) animals were euthanized. In preparation for necropsy, the animals were deeply anesthetized with ketamine (3mg/kg) and medetomidine (0.15mg/kg) given IM. Laparotomies were then performed, and the animals exsanguinated from the descending aorta and perfused transcardially with normal saline. A comprehensive sample collection was conducted throughout the body including the ileum. Samples were placed into cassettes and fixed in 4% paraformaldehyde or 10% formalin. These cassettes were then processed for routine histopathological analysis.

Viral loads in plasma

Viral loads in the plasma were assessed by Real-time RT-PCR. Briefly, plasma were centrifuged at 20,000g for 1 hour at 4°C and pellets were resuspended in PBS. RNA was isolated from the pellets using the QIAgen RNA mini-kit (Qiagen). Real-time PCR was performed using SIV gag-specific primers and a Taqman probe (Applied Biosystems) and analyzed on the ABI Prism 7700 sequence detection system. Thermal cycling conditions were as follows: 50°C (2 min), 60°C (30 min), 95°C (10 min), 95°C (15 sec; 44 cycles) and 60°C (30 sec; 44 cycles). Viral loads were calculated as Gag copies/million GAPDH copies. The minimum level of detection was 18 copies.

16S Sequencing

Feces were collected from animals and frozen in liquid nitrogen. The fecal matter was lysed using glass beads in MagnaLyser tissue disruptor (Roche) and total DNA isolated using Power-soil/fecal DNA isolation kit (Mo-Bio) as per manufacturer’s specifications. All samples were quantified via the Qubit® Quant-iT dsDNA Broad-Range Kit (Invitrogen, Life Technologies, Grand Island, NY) to ensure that they met minimum DNA concentration and mass requirements and submitted to the University of Minnesota Genomic Center for microbiome analysis as follows: To enrich the sample for the bacterial 16S V4 rDNA region, DNA was amplified utilizing fusion primers designed against the surrounding conserved regions which are tailed with sequences to incorporate Illumina (San Diego, CA) flow cell adapters and indexing barcodes. Each sample was PCR amplified with two differently bar coded V4 fusion primers and were advanced for pooling and sequencing. For each sample, amplified products were concentrated using a solid-phase reversible immobilization method for the purification of PCR products and quantified by electrophoresis using an Agilent 2100 Bioanalyzer®. The pooled 16S V4 enriched, amplified, barcoded samples were loaded into the MiSeq® reagent cartridge, and then onto the instrument along with the flow cell. After cluster formation on the MiSeq® instrument, the amplicons were sequenced for 250 cycles with custom primers designed for paired-end sequencing. Using QIIME(Caporaso et al., 2010), sequences were quality filtered and demultiplexed using exact matches to the supplied DNA barcodes. Resulting sequences were then searched against the Greengenes reference database of 16S sequences, clustered at 97% by UCLUST (closed-reference OTU picking).

Mass Spectrometry Analysis

Fecal samples were collected as for 16S DNA analysis. Samples were shipped on dry ice to Metabolon, Inc., where samples were prepared and analyzed using a 548 named biochemical compounds screen. Briefly, samples were divided into 4 fractions for analysis: one for UPLC-MS/MS with positive ion mode electrospray ionization, one for analysis by UPLC-MS/MS with negative ion mode electrospray ionization, one for analysis by UPLC-MS/MS polar platform (negative ionization), one for analysis by GC-MS. Raw data was analyzed with peak-identification and quality control, processed, and normalized using proprietary hardware and software, utilizing a library of authenticated standards.

16S Analysis and Statistics

OTU tables were rarefied to the sample containing the lowest number of sequences in each analysis. QIIME 1.8 was used to calculate alpha diversity (alpha_rarefaction.py), summarize taxa (summarize_taxa_through_plots.py), and compare variability within and between groups (compare_distance_boxplots.py). A repeated measures ANOVA was used to compare Observed OTU and Shannon Diversity Index values between groups and over time with subject included in the error term. PERMANOVA analysis was done using the pseudo-F statistic within each time point and group independently (time points: Ms, N=4; SIV, N=5; SIVMs=7) using 999 permutations. OTU table output was visualized and analyzed using Statistical Analysis of Metagenomic Profiles, or STAMP, software(Parks, Tyson, Hugenholtz, & Beiko, 2014). Principal Coordinate Analysis was done within this program using observation ID level. Heatmaps were generated using family level (L5) taxonomic data, which included microbes that were significantly changed (p<0.05) between groups compared: Pre vs Ms timepoints (Figure 2), Pre vs SIV timepoints (Figure 3), and Pre vs SIV+Ms timepoints (Figure 4). Indicator species analysis was performed in R using the IndicSpecies package with combined taxonomic output from the summarize taxa command above(Cáceres & Legendre, 2009). The relative abundances of all OTUs were compared across each group (Pre, Ms, SIV, SIVMs). A complete list is provided in S4 Table.

Figure 2. Morphine induces dysbiosis in nonhuman primates which is stable over time.

Figure 2

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(A) Weighted PCoA of Ms (various shades of blue) altered the microbial signature of animals from matched Pre (black) samples at the earliest (21D) time point, which was stable over time. (B) A heatmap of significantly changed families (p<.05) between time points showed that Pre (black) was distinctly clustered from Ms samples, aside from one animal at 84D morphine.

Figure 3. SIV does not significantly alter total microbial composition, but certain families are significantly altered at early timepoints.

Figure 3

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(A) Weighted PCoA of SIV (various shades of red) altered the microbial signature of animals from matched Pre (black) samples. However, the earliest (3D, dark red) time point was not changed greatly. (B) A heatmap of significantly changed families (p<.05) between time points showed disparity between Pre (black) samples. SIV samples that associated closely with Pre samples were from the same animal, implying that SIV had no induced dysbiosis at that point. Despite this, a large number of SIV samples clustered distinctly as a middle branch from Pre.

Figure 4. Morphine induces dysbiosis that is not altered further with SIV infection.

Figure 4

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(A) Weighted PCoA of NHP infected with SIV and co-treated with morphine. As with Ms animals, morphine (various shades of blue) altered the microbial signature of animals from their pretreatment (black) samples at the earliest (21D) time point, which was stable over time even after the animals were infected with SIV (various shades of green). (B) A heatmap of significantly changed families (p<.05) between time points showed that pre-treatment (black) was distinct from the majority of treatment samples (various shades of blue and green). Treatments (matched Ms vs SIVMs) or time points did not cluster distinctly.

Mass Spectrometry Analysis and Statistics

Metabolites were log transformed and ANOVA contrasts and paired t-tests examined metabolic differences between treatment groups and time points. Random forest analysis provides an unbiased and supervised classification based on decision trees, and identifies features (metabolites) that predict sample group membership. The top 30 biochemicals are presented.

Results

In order to look for short-term changes in the presence of SIV, animals were observed for 21 days post-infection with SIV. The infection paradigm was carried out in both control animals as well as animals that were given chronic morphine (S1 Figure) in order to detect whether opioids alter early pathogenesis of SIV, particularly on the microbiome of the intestine. SIV infection was validated with real-time PCR on plasma samples which all confirmed positive infection of SIV animals (S3 Table). Fecal samples were collected at regular intervals to assess changes in the microbiome over this early time period.

Microbial diversity is not altered by SIV and morphine

Microbial diversity did not differ significantly between groups (Fig 1A – Observed OTUs; F1,72=1.59, p=0.211; Shannon Diversity Index; F1,72=0.298, p=0.743).

Figure 1. Morphine and/or SIV does not change the number of species over time.

Figure 1

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The number of species (alpha diversity) was calculated up to 1710 sequences, which was the minimum sequence number in all samples. (A) No significant differences were observed in microbial diversity across groups. At 1710 sequences, there was no change in at any time point in Ms (B), SIV (C) or SIVMs (D) treated animals.

Each of the four treatment groups were then stratified over the time points collected to see if any changes occurred over time. There were no significant time-associated changes observed in Ms (Fig 1B), SIV (Fig 1C), or SIVMs (Fig 1D) samples compared to matched Pre samples in each group (SIV + Ms F6,35=1.044, p = 0.414; SIV only F4,15=0.63, p = 0.649; Morphine only F3,12=0.4, p = 0.755). This suggests that changes in microbial diversity are not a hallmark of ongoing exposure to infection or drug exposure at the early time points observed.

Morphine and SIV significantly alter gut microbial composition

Microbial composition was compared between groups and time points. Morphine caused a shift in microbial composition evident in beta-diversity (weighted UniFrac) plots (Fig 2A). PERMANOVA analysis indicated a significant difference in microbial composition pre and post morphine induction (pseudo-F: 4.027, p-value: 0.004). Post-morphine induction, microbial composition remained stable and did not change significantly over time (PERMANOVA pseudo-F: 0.537, p-value: 0.944). Differentially abundant taxa included Methanobacteriaceae of the domain Archaea which was enriched in many of the post-morphine induction Ms samples, and Streptococcaceae and Ruminococcaceae family microbes which were enriched in the pre-morphine samples (Fig 2B).

Microbial composition was not significantly altered by SIV infection alone, although many samples appeared to be trending away from Pre samples (Fig 3A). A PERMANOVA analysis of the five SIV sample time point groups (pseudo-F: 1.4354, p-value: 0.131) show that the microbial composition is not significantly altered between groups. Despite this, a heatmap of the significantly altered families shows clustering among many SIV samples apart from Pre samples, though the Pre samples themselves form two clusters (Fig 3B). Ruminococcacea family microbes were enriched post-SIV infection (Fig 3B). Notably, the SIV samples that cluster closely with Pre samples (black) are animal matched, suggesting that SIV did not induce early changes unilaterally, but may have greater effects in a subset of animals.

SIVMs (green) samples showed a shift in microbial composition similar to the microbial community shift seen in Ms (blue) time points (Fig 4A), again indicating that morphine has a greater effect on the microbiota than SIV at this early point infection. PERMANOVA analyses indicated that there was a significant change in the microbial community between Pre samples and all samples collected after morphine treatment began (pseudo-F: 6.839, p-value = 0.001). However, there was no significant difference in the microbial communities of monkeys treated with morphine and monkeys treated with morphine and SIV (pseudo-F = 0.956, p-value = 0.421. This was further explored by comparing weighted UniFrac distances within and between groups (S3 Fig. Distances within Pre samples were significantly shorter than distances between Pre and Ms samples and between Pre and SIVMs samples (Pre vs. Pre / Pre vs Ms Bonferroni-corrected p = 0.023, Pre vs. Pre / Pre vs. SIVMs Bonferroni-corrected p = 0.009, t-test). However, distances within Ms samples and between Ms samples and SIVMs samples was not significantly different (Bonferroni-corrected p = 1, t-test). These results indicate that the microbial community shifts after morphine introduction but does not change significantly when SIV is subsequently introduced. Differential abundance analysis revealed that, similar to morphine alone, Methanobacteriaceae family microbes were enriched and Streptococcaceae family microbes were diminished in morphine and SIV+morphine samples as compared to Pre samples (Fig 4B).

The species composition of all treatments was compared as a dendogram (S2 Fig). The dendogram corroborates evidence of shifts observed with morphine treatment in Ms (blue) and SIVMs (green), which cluster distinctly from matched Pre samples (black). SIV (red) clusters more closely with matched Pre samples than samples with morphine (blue/green), again showing that changes due to SIV at this early stage of infection are less significant than the dysbiosis induced by morphine.

Specific microbial taxa are linked with morphine treatment or SIV infection

To understand if particular taxa were driving the clustering in the species composition, an indicator species analysis was performed on the Pre and terminal collection time points (D84 morphine, D22 SIV, D92 morphine/D22 SIV) for each group (S1 Table). All three treatment groups showed a reduction in the Leuconostocaceae family compared with Pre samples. Interestingly, despite not showing significant beta diversity alterations, SIV was associated with increases in Veillonellaceae, Fibrobacteraceae fibrobacter, Veillonellaceae Megasphaera, Ersipelotrichaceae RFN20, and BS11. SIV was also associated with decreases in the TM7-3 order along with Paraprevotellaceae YRC22. Morphine was associated with decreases in Streptococcaceae streptococcus as well as Pasteurellaceae Aggregatibacter. Finally, SIVMs samples showed a decrease in Veillonellacea dialister, Pasteurellacea actinobacillus, Pasteurellacea haemophilus, and Methanobacteriacea methanosphaera.

Histological data from these each group show infiltration of immune cells into the lamina propria however no significant difference was observed between the SIV and the morphine or morphine+ HIV groups (S4 Figure). Overall, the species highlighted in this analysis are again supportive of microbial dysbiosis and contributing to the overall inflammatory environment observed in patients with SIV/HIV infections and chronic opioid abusers.

Microbial metabolites are significantly altered with SIV infection and more so when combined with chronic morphine

In order to capture the functions associated with each microbial community, we examined the metabolic profile of Pre samples and terminal points (D22 SIV, D64 morphine, and D92 morphine+SIV). We selected these time points because limited differences were observed at intermittent time points within a treatment group. A significant number of metabolites were altered in each treatment group (D64 morphine, D22 SIV, D92+D22 Morphine+SIV) compared with pre-treatment samples, indicating that the treatments drastically altered the metabolic profile. To this end, the metabolic profile for each treatment group was loaded into a forest prediction matrix and each had strong predictive capabilities: D64 morphine vs pre-samples (85% predictive accuracy, Fig 5A), D22 SIV vs pre-samples (87% predictive accuracy, Fig 5B), and D64 morphine vs D92+SIV vs pre-samples (89% predictive accuracy, Fig 5C). When all treatment groups were considered together, the model still maintained a 79% predictive accuracy amongst all treatment groups (Fig 5D). Metabolites that defined each prediction were prioritized to see which played a major role (S4 Fig).

Figure 5. Forest Matrix analysis of metabolic profile predicts treatment groups high accuracy against matched pre-samples.

Figure 5

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The forest matrix assessment was used to predict sample treatments based on their metabolic profile. A) There was a predictive accuracy of 85% between matched NHP treated with morphine (D64, n=10, class error= .2) vs pre-treated samples (n=10, class error = .1). B) There was a predictive accuracy of 87% between matched NHP infected with SIV (D22, n=4, class error = .25) vs pre-treated samples (n=4, class error = 0). C) There was a predictive accuracy of 89% between matched NHP treated with morphine (D64, n=6, class error = 0), NHP treated with morphine and infected with SIV (D92/22, n=6, class error = .167), and pre-treated samples (n=6, class error = .167). D) There was a predictive accuracy of 79% between NHP treated with morphine (D64, n=10, class error = .1), NHP treated with SIV alone (D22, n=4, class error = .25), NHP treated with morphine and infected with SIV (D92/22, n=6, class error = .333), and pre-treated samples (n=14, class error = .214).

The primates treated with morphine alone exhibited significant alterations in several fecal metabolites (S2 Table). The most notable alterations in this group involved biochemicals linked to microbiome function, oligosaccharide metabolism, and amino acid and lipid homeostasis. Compared with pre-treated controls, NHP treated with morphine showed a decrease in primary bile acids including cholate (0.48 fold change, p<.0031) and glycocholate (0.48 fold change, p<.0015); conversely, several secondary bile acids increased, including 7-ketolithocholate (1.35 fold change, p<.0166), 12-dehydrocholate (3.62 fold change, p<.0015), taurocholenate sulfate (6.33 fold change, p<.0388) and 3b-hydroxy-5-cholenoic acid (2.4 fold change, p<.0201). Additionally, morphine showed an increase in sphingolipid metabolites, including sphinganine (6.68 fold change, p<.0211) and sphingosine (3.8 fold change, p<.0056). These changes may correlate with differences in dietary intake and absorption or with changes in fatty acid and triglyceride metabolism within the morphine-treated subjects which will need to be examined in a future study.

Similar to microbiota analysis, the SIV-infected group displayed significantly fewer metabolite alterations than morphine when it was compared to its control group. Serotonin metabolites and microbial metabolites were among the biochemicals most strongly altered in NHP infected with SIV (S4B Fig). When the D22 SIV group was compared to the Pre SIV group, significant increases were observed in serotonin (4.03 fold change, p<.0002) and its acetylated derivative N-acetylserotonin (7.93 fold change, p<.0081).

The number of statistically significant metabolite differences arising from the SIV infection rose dramatically, however, when morphine was already on board (Table 1). When the day 92 Morphine+SIV group was compared to the day 64 Morphine+SIV group, significant alterations were observed in some of the biochemicals affected in the day 22 SIV-infected group. Significant examples included serotonin (SIV: 4.03 fold change, p<.0002; SIVMs: 10.49 fold change, p<6.86E-07), N-acetylserotonin (SIV: 7.93, p<.0081; SIVMs: 14.84 fold change, p<3.93E-06), N-acetylkynurenine (SIV: 3.92 fold change, p<.077; SIVMs: 7.99 fold change, p<.0006), tricarballylate (SIV: 7.26 fold change, p<0.359; SIVMs: 2.57 fold change, p<.0325), and secondary bile acids such as deoxycholate (SIV: 7.17 fold change, p<.0198; SIVMs: 7.46 fold change, p<.0027). These metabolites appear to track therefore with SIV infection status.

Table 1.

Significantly changed metabolites between treatment groups

Morphine Only SIV Only Morphine+SIV
Comparison Groups - Paired t-Test D64 Ms D22 SIV D92 Ms/D22 SIV
Pre Pre D64 Ms
Total Biochemicals
(p<0.05) 		163 49 164
Biochemicals
(p<0.05)
# up ∣ # down 		67 ∣ 96 29 ∣ 20 100 ∣ 64
Total Biochemicals
(0.05<p<0.10) 		67 46 53
Biochemicals
(0.05<p<0.10)
# up ∣ # down 		39 ∣ 28 20 ∣ 26 32 ∣ 21
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Discussion

This study shows for the first time that morphine induces microbial dysbiosis in NHP, and that dysbiosis associated with SIV begins developing within 3 weeks (22 days) of infection. Significant changes were observed in the bacterial composition of morphine- treated animals, particularly in partnership with SIV infection, compared with pre-samples. Changes in bacterial composition has been a hallmark for negative outcomes in other studies, including HIV(Gómez-Hurtado, Santacruz, & Peiró, 2011; Tamboli, Neut, Desreumaux, & Colombel, 2004; Vujkovic-Cvijin et al., 2013). This study also examined functional changes by characterizing fecal metabolic profiles, which were also significantly altered by both morphine and SIV infection. Combined, this study supports that SIV infection alters the gut microenironment beginning at very early time points of infection, and opioids like morphine are a dominant force that will exacerbate SIV-mediated alterations early in infection, which has important ramifications for opioid abusers who become infected with HIV.

The link between microbiota and immune activation and inflammation has been well established(Belkaid & Hand, 2014). The effects of morphine on the microbiota have not been examined in NHP or humans previously, but have been observed in mice using a slow-release pellet(Santanu Banerjee et al., 2016). The morphine-associated dysbiosis observed in the present study by morphine was consistent over the time course of drug administration (D21, D64, D84). While the mechanism for these changes was not assessed, opioid-induced immune modulation has previously been demonstrated in the gut, which results in bacterial translocation and altered tolerance to bacteria-mediated inflammation(Santanu Banerjee et al., 2013; Feng et al., 2006; Hilburger, Adler, Truant, Meissler, et al., 1997; Meng et al., 2013; Ninković & Roy, 2012). Another potential mechanism is through constipation, which has also been shown, independent of morphine, to alter intestinal microbiota(Zhu et al., 2014). It is likely that both contribute to an extent, and as this study did not directly control for constipation, it will need to be addressed in future work. Dysbiosis with morphine was maintained through long-term exposure to morphine, which suggests that the microbial shift is not limited by the development of tolerance. However, with this study design it is unclear how long these changes are stable and whether they would persist in the absence of escalating doses that are generally observed in chronic opioid users.

In this study, SIV infection was examined at time points earlier than previous studies of SIV- or HIV-induced microbial dysbiosis(Dillon et al., 2014; McHardy et al., 2013; McKenna et al., 2008; Moeller et al., 2013; Mutlu et al., 2014; Vujkovic-Cvijin & Dunham, 2013), During the infection window, a gradient is visible in beta diversity from pre-samples to D22, though the composition changes did not reach statistical significance. Specifically, SIV animals had an enrichment in the genera Megasphaera and family Erysipelotrichaceae which have been associated with immune activation in previous studies of HIV(Neff et al., 2018; Vazquez-Castellanos et al., 2015). Additionally, the metabolic profile of SIV-infected animals was altered. This suggests that microbial function changes before changes in microbial composition are observed in association with SIV.

Previous studies of HIV have shown a link between dysbiosis and bacterial translocation,(Dinh et al., 2015; Gómez-Hurtado et al., 2011; Wu, Li, & Xiong, 1998); therefore, dysbiosis and metabolic shifts may be a precursor to the bacterial translocation thought to drive HIV replication(Brenchley et al., 2006). Bacterial translocation or serum levels of LPS was not measured as part of this study design, and are not believed to occur early in infection(Brenchley et al., 2006). While it is clear that dysbiosis begins at this early stage of SIV infection, future studies with intermediate time points will be necessary to fully map the dysbiosis to the onset of bacterial translocation observed in late-stage SIV- or HIV-infections. This would allow for interventional therapies to be developed, as the microbial shift that occurs is likely to play a role in disrupting gut homeostasis that helps drive HIV replication.

NHP infected with SIV after treatment with morphine showed significantly altered bacterial composition and metabolite profiles, which likely underlie the relatively quick pathogenesis observed in HIV patients who abuse opioids(Meng et al., 2015; X. Wang, Zhang, & Ho, 2011). A specific example we observed was alterations of microbes within the Methanobacteriacea family in these samples, which have been shown to play an essential role in the production and adsorption of short-chain fatty acids(Pimentel, Gunsalus, Rao, & Zhang, 2012). We observed changes in sphingolipid metabolism in morphine samples, which may play a role in the hosts ability to function properly. Morphine has been shown to greatly increase HIV and SIV pathogenesis(Donahoe & Vlahov, 1998; Rivera-Amill, Silverstein, Noel, Kumar, & Kumar, 2010). Thus, we expected that SIV with morphine would induce dysbiosis earlier than SIV alone, but this was not supported by our data. Animals co-treated with SIV and morphine did not cluster distinctly from animals treated solely with morphine, suggesting that SIV has a relatively minor impact on the microbiome compared with morphine at these early time points of infection. However, future studies will be needed to investigate intermediate time points of SIV infection to see if dysbiosis occurs more overtly. Additionally, different paradigms of opioid use will need to be tested in the future. Infecting the animals prior to morphine exposure or looking at the effects of withdrawal within early time points of SIV infection would also be interesting avenues to pursue.

Consequences from microbial dysbiosis include functional shifts in the metabolic profile. Metabolite shifts can contribute to host pathologies and disruption of gut homeostasis, including further changes to the gut microbiome(Asselin & Gendron, 2014; Russell, Hoyles, Flint, & Dumas, 2013). Not surprisingly, altered metabolism of gut microbiota has been shown to be a factor in chronic immune activation of HIV patients(Vázquez-Castellanos et al., 2014). The metabolic environment is clearly shifting in early SIV infection as well as in the context of morphine. This was observed through the forest matrix analysis, which accurately predicted treatments of the samples at or above 79%. In addition, numerous individual metabolites were significantly shifted.

Of the metabolites shifted, bile acids were notable because of their established role in preventing bacterial overgrowth and translocation(Boesjes & Brufau, 2014; Hofmann & Eckmann, 2006; Lorenzo-Zúñiga & Bartoli, 2003). Select primary bile acids were decreased when morphine was present, whereas secondary bile acids were increased by both morphine and SIV. Thus, the deregulation of bile acid metabolism may contribute to microbial dysbiosis, and the eventual bacterial translocation observed in SIV and HIV infections. In previous experiments, we observed that morphine deregulated both primary and secondary bile acids in mice(Santanu Banerjee et al., 2016). As this study focused on bile acids in feces, future studies will need to examine the host contribution to the bile acid cycle, including liver production, to see how morphine influences the pathway in NHP. If it is an early step in bacterial translocation, which drives HIV replication and may occur earlier in HIV-infected individuals who abuse opioids, bile acids may be a therapeutic target to limit HIV replication.

Other metabolic shifts observed have functional potential as well. Samples treated with morphine, but not infected with SIV, displayed an increased number of sphingolipids. This is interesting, as sphingolipids have been shown in other studies to be increased with both morphine and HIV. These papers generally associated sphingolipids with negative effects on neurological symptoms(Bae et al., 2014; Bandaru, Patel, Ewaleifoh, & Haughey, 2011; Muscoli et al., 2010), which generally occurs chronically with HIV and faster when opioids are present(Bell, Brettle, Chiswick, & Simmonds, 1998; Donahoe & Vlahov, 1998; Hauser et al., 2007).

Additionally, serotonin was increased in association with SIV infection. This correlates with an increase in Clostridia, which emerged as a significant indicator of SIV-infected animals, and have been shown to produce serotonin(Yano et al., 2015). Aside from functioning as a neurotransmitter, serotonin also has roles in modulating gut motility and immune responses(Gershon, 2004; Khan & Ghia, 2010; Spiller, 2007). The increase in serotonin metabolites may correlate therefore with alterations in intestinal function and/or with alterations in the immune system of NHP infected with SIV. Interestingly, changes in serotonin receptors have been described in NHP infected with SIV are reported to express differential levels of the serotonin receptor SERT, which occurred as early as 7 days after infection in peripheral blood mononuclear cells(Yu et al., 2010).

This study shows that morphine induces microbial dysbiosis and functional shifts in the metabolic profile that can interact at early time points with SIV infection. In combination with SIV infection, morphine-induced significant microbial and metabolic dysbiosis, which supports the worsened pathogenesis observed in co-morbid patients. Altered metabolites, such as bile acids, sphingolipids, and serotonin, can have detrimental impacts on the host, particularly for immune responses that require tight regulation and ultimately drive HIV replication. This study was conducted using time points of SIV-infection much earlier than has previously been studied with microbial dysbiosis in SIV/HIV. Ultimately, this study highlights potential changes that may be advantageous to intervene with therapeutically as early as possible to delay or prevent the onset of AIDS, particularly in opioid abusing patients.

Conclusion

To our knowledge, this is the first time that the effects of opioids on the intestinal microbiome have been studied at early stages of HIV/SIV infection. In late stage HIV, it is well accepted that bacterial translocation results in circulating LPS that helps to drive HIV replication through systemic inflammation(Brenchley et al., 2006). Given the importance of bacterial translocation in the pathogenesis of HIV, we believe that the observed dysbiosis in bacterial communities and metabolites by morphine at early stages of SIV infection demonstrates an important role of opioids in promoting systemic LPS in HIV patients. Having an altered bacterial community, as well as a disrupted bacterial metabolite profile, contributes to increased or unregulated inflammation that can help drive HIV replication(Gómez-Hurtado et al., 2011). Given that opioids promote these changes, as observed in this study with early time points of SIV infection, we believe that this is a mechanism to help explain the increased progression of HIV to AIDS in the opiate-using population. By understanding the onset of HIV to AIDS in this vulnerable population, we can hopefully design better therapies to abate the disease and generate better long-term outcomes.

Supplementary Material

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Acknowledgments

Funding

Funding for the study was provided by the following grants from the National Institute on Drug Abuse (NIDA) division of the National Institute of Health (https://www.drugabuse.gov/): R01 DA043252 (SR), R01 DA037843 (SR), K05 DA033881 (SR), R01 DA031202 (SR), R01 DA035203 (ShB), R01 DA043138 (ShB), R01 DA040397 (ShB), R01 DA044586 (ShB). GS was supported under NIDA training grant T32 DA007097. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Ethics approval

All animal protocols were approved by the local animal care committee (IACUC, #2012-2070) at the University of Kansas in accordance with the Guide for the Care and Use of Laboratory Animals.

Consent for publication

Not applicable

Availability of data and material

NextGen sequencing files from the 16S microbiome analysis are available in the University of Minnesota “Data Repository for U of M” (DRUM) database and can be accessed at the following link: https://doi.org/10.13020/D6GC76

Metabolic data was processed and analyzed by Metabolon (http://www.metabolon.com/). The resulting data is presented in the S2_table.pdf attached with this submission. In addition, the final analysis provided by Metabolon can be requested from the authors on reasonable request.

Competing Interests

The authors declare that they have no competing interests in this section.

References

  1. Ancuta P, Kamat A, Kunstman KJ, Kim E-Y, Autissier P, Wurcel A, … Gabuzda D (2008). Microbial translocation is associated with increased monocyte activation and dementia in AIDS patients. PloS One, 3(6), e2516 10.1371/journal.pone.0002516 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Antinori A, Arendt G, Becker JT, Brew BJ, Byrd DA, Cherner M, … Wojna VE (2007). Updated research nosology for HIV-associated neurocognitive disorders. Neurology, 69(18), 1789–1799. https://doi.org/01.WNL.0000287431.88658.8b [pii] 10.1212/01.WNL.0000287431.88658.8b [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Asselin C, & Gendron F-P (2014). Shuttling of information between the mucosal and luminal environment drives intestinal homeostasis. FEBS Letters. 10.1016/j.febslet.2014.02.049 [DOI] [PubMed] [Google Scholar]
  4. Bae M, Patel N, Xu H, Lee M, Tominaga-Yamanaka K, Nath A, … Haughey NJ (2014). Activation of TRPML1 clears intraneuronal Aβ in preclinical models of HIV infection. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(34), 11485–11503. 10.1523/JNEUROSCI.0210-14.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bandaru VVR, Patel N, Ewaleifoh O, & Haughey NJ (2011). A failure to normalize biochemical and metabolic insults during morphine withdrawal disrupts synaptic repair in mice transgenic for HIV-gp120. Journal of Neuroimmune Pharmacology : The Official Journal of the Society on NeuroImmune Pharmacology, 6(4), 640–649. 10.1007/s11481-011-9289-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Banerjee S, Meng J, Das S, Krishnan A, Haworth J, Charboneau R, … Roy S (2013). Morphine induced exacerbation of sepsis is mediated by tempering endotoxin tolerance through modulation of miR-146a. Scientific Reports, 3, 1977 10.1038/srep01977 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Banerjee S, Sindberg GM, Wang F, Meng J, Sharma U, Zhang L, … Roy S (2016). Opioid-induced gut microbial disruption and bile dysregulation leads to gut barrier compromise and sustained systemic inflammation. Mucosal Immunology, In press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Banerjee S, Sindberg G, Wang F, Meng J, Sharma U, Zhang L, … Roy S (2016). Opioid-induced gut microbial disruption and bile dysregulation leads to gut barrier compromise and sustained systemic inflammation. Mucosal Immunol, 10.1038/mi.2016.9mi20169 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Belkaid Y, & Hand TW (2014). Role of the microbiota in immunity and inflammation. Cell. 10.1016/j.cell.2014.03.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bell JE, Brettle RP, Chiswick a, & Simmonds P (1998). HIV encephalitis, proviral load and dementia in drug users and homosexuals with AIDS. Effect of neocortical involvement. Brain : A Journal of Neurology, 121 (Pt 1, 2043–2052. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9827765 [DOI] [PubMed] [Google Scholar]
  11. Boesjes M, & Brufau G (2014). Metabolic effects of bile acids in the gut in health and disease. Current Medicinal Chemistry, 21(24), 2822–2829. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/24606522 [DOI] [PubMed] [Google Scholar]
  12. Bokhari SM, Hegde R, Callen S, Yao H, Adany I, Li Q, … others. (2011). Morphine Potentiates Neuropathogenesis of SIV Infection in Rhesus Macaques. Journal of Neuroimmune Pharmacology, 1–14. Retrieved from http://www.springerlink.com/index/H2K2873240R38004.pdf [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brenchley JM, Price D. a, Schacker TW, Asher TE, Silvestri G, Rao S, … Douek DC (2006). Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nature Medicine, 12(12), 1365–1371. 10.1038/nm1511 [DOI] [PubMed] [Google Scholar]
  14. Brown TT, & Qaqish RB (2006). Antiretroviral therapy and the prevalence of osteopenia and osteoporosis: a meta-analytic review. AIDS, 20(17), 2165–2174. 10.1097/QAD.0b013e32801022eb00002030-200611140-00005 [pii] [DOI] [PubMed] [Google Scholar]
  15. Cabrera-Perez J, Badovinac VP, & Griffith TS (2016). Enteric immunity, the gut microbiome, and sepsis: Rethinking the germ theory of disease. Exp Biol Med (Maywood). https://doi.org/1535370216669610 [pii] 10.1177/1535370216669610 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cáceres M De, & Legendre P (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574. 10.1890/08-1823.1 [DOI] [PubMed] [Google Scholar]
  17. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, … Knight R (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7(5), 335–336. 10.1038/nmeth.f.303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chitu-Tisu CE, Barbu EC, Lazar M, Ion DA, & Badarau IA (2016). Low bone mineral density and associated risk factors in HIV-infected patients. Germs, 6(2), 50–59. 10.11599/germs.2016.1089germs.2016.1089 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. d’Ettorre G, Ceccarelli G, Giustini N, Serafino S, Calantone N, De Girolamo G, … Vullo V (2015). Probiotics Reduce Inflammation in Antiretroviral Treated, HIV-Infected Individuals: Results of the “Probio-HIV” Clinical Trial. PLoS One, 10(9), e0137200 10.1371/journal.pone.0137200PONE-D-14-57636 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dandekar S, George MD, & Bäumler AJ (2010). Th17 cells, HIV and the gut mucosal barrier. Current Opinion in HIV and AIDS, 5(2), 173–178. 10.1097/COH.0b013e328335eda3 [DOI] [PubMed] [Google Scholar]
  21. Dillon S, Lee E, Kotter C, & Austin G (2014). An altered intestinal mucosal microbiome in HIV-1 infection is associated with mucosal and systemic immune activation and endotoxemia. Mucosal.… Retrieved from http://www.nature.com/mi/journal/vaop/ncurrent/full/mi2013116a.html [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dinh DM, Volpe GE, Duffalo C, Bhalchandra S, Tai AK, Kane AV, … Ward HD (2015). Intestinal Microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. In Journal of Infectious Diseases (Vol. 211). 10.1093/infdis/jiu409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Donahoe RM, & Vlahov D (1998). Opiates as potential cofactors in progression of HIV-1 infections to AIDS. Journal of Neuroimmunology, 83(1–2), 77–87. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9610676 [DOI] [PubMed] [Google Scholar]
  24. El-Hage N, Bruce-Keller AJ, Yakovleva T, Bazov I, Bakalkin G, Knapp PE, & Hauser KF (2008). Morphine exacerbates HIV-1 Tat-induced cytokine production in astrocytes through convergent effects on [Ca(2+)](i), NF-kappaB trafficking and transcription. PLoS One, 3(12), e4093 10.1371/journal.pone.0004093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Feng P, Truant AL, Meissler JJ, Gaughan JP, Adler MW, & Eisenstein TK (2006). Morphine withdrawal lowers host defense to enteric bacteria: spontaneous sepsis and increased sensitivity to oral Salmonella enterica serovar Typhimurium infection. Infection and Immunity, 74(9), 5221–5226. 10.1128/IAI.00208-06 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gershon MD (2004). Review article: serotonin receptors and transporters -- roles in normal and abnormal gastrointestinal motility. Alimentary Pharmacology & Therapeutics, 20 Suppl 7, 3–14. 10.1111/j.1365-2036.2004.02180.x [DOI] [PubMed] [Google Scholar]
  27. Giorgi JV, Hultin LE, McKeating JA, Johnson TD, Owens B, Jacobson LP, … Detels R (1999). Shorter survival in advanced human immunodeficiency virus type 1 infection is more closely associated with T lymphocyte activation than with plasma virus burden or virus chemokine coreceptor usage. J Infect Dis, 179(4), 859–870. https://doi.org/JID980617 [pii] 10.1086/314660 [DOI] [PubMed] [Google Scholar]
  28. Gómez-Hurtado I, Santacruz A, & Peiró G (2011). Gut microbiota dysbiosis is associated with inflammation and bacterial translocation in mice with CCI4-induced fibrosis. PLoS One. Retrieved from http://dx.plos.org/10.1371/journal.pone.0023037.g005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Grunfeld C, Delaney JA, Wanke C, Currier JS, Scherzer R, Biggs ML, … Kronmal RA (2009). Preclinical atherosclerosis due to HIV infection: carotid intima-medial thickness measurements from the FRAM study. AIDS, 23(14), 1841–1849. 10.1097/QAD.0b013e32832d3b85 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gurwell JA, Nath A, Sun Q, Zhang J, Martin KM, Chen Y, & Hauser KF (2001). Synergistic neurotoxicity of opioids and human immunodeficiency virus-1 Tat protein in striatal neurons in vitro. Neuroscience, 102(3), 555–563. https://doi.org/S0306-4522(00)00461-9 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hauser KF, El-Hage N, Stiene-Martin A, Maragos WF, Nath A, Persidsky Y, … Knapp PE (2007). HIV-1 neuropathogenesis: glial mechanisms revealed through substance abuse. Journal of Neurochemistry, 100(3), 567–586. 10.1111/j.1471-4159.2006.04227.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hazenberg MD, Otto SA, van Benthem BH, Roos MT, Coutinho RA, Lange JM, … Miedema F (2003). Persistent immune activation in HIV-1 infection is associated with progression to AIDS. AIDS, 77(13), 1881–1888. 10.1097/01.aids.0000076311.76477.6e [DOI] [PubMed] [Google Scholar]
  33. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, Leblanc S, … Grant I (2011). HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. J Neurovirol, 17(1), 3–16. 10.1007/s13365-010-0006-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hilburger ME, Adler MW, Truant a L., Meissler JJ, Satishchandran V, Rogers TJ, & Eisenstein TK (1997). Morphine induces sepsis in mice. The Journal of Infectious Diseases, 176(1), 183–188. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9207365 [DOI] [PubMed] [Google Scholar]
  35. Hilburger ME, Adler MW, Truant AL, Meissler JJ Jr., Satishchandran V, Rogers TJ, & Eisenstein TK (1997). Morphine induces sepsis in mice. J Infect Dis, 176(1), 183–188. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9207365 [DOI] [PubMed] [Google Scholar]
  36. Hofmann AF, & Eckmann L (2006). How bile acids confer gut mucosal protection against bacteria. Proceedings of the National Academy of Sciences of the United States of America, 103(12), 4333–4334. 10.1073/pnas.0600780103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Jiang W, Lederman MM, Hunt P, Sieg SF, Haley K, Rodriguez B, … Brenchley JM (2009). Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. J Infect Dis, 199(8), 1177–1185. 10.1086/597476 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kalayjian RC, Lau B, Mechekano RN, Crane HM, Rodriguez B, Salata RA, … Kitahata MM (2012). Risk factors for chronic kidney disease in a large cohort of HIV-1 infected individuals initiating antiretroviral therapy in routine care. AIDS, 26(15), 1907–1915. 10.1097/QAD.0b013e328357f5ed [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Khan WI, & Ghia JE (2010). Gut hormones: emerging role in immune activation and inflammation. Clinical and Experimental Immunology, 161(1), 19–27. 10.1111/j.1365-2249.2010.04150.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Khoruts A, Dicksved J, Jansson JK, & Sadowsky MJ (2010). Changes in the composition of the human fecal microbiome after bacteriotherapy for recurrent Clostridium difficile-associated diarrhea. J Clin Gastroenterol, 44(5), 354–360. 10.1097/MCG.0b013e3181c87e02 [DOI] [PubMed] [Google Scholar]
  41. Lederman MM, Funderburg NT, Sekaly RP, Klatt NR, & Hunt PW (2013). Residual immune dysregulation syndrome in treated HIV infection. Adv Immunol, 119, 51–83. 10.1016/B978-0-12-407707-2.00002-3B978-0-12-407707-2.00002-3 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lorenz MW, Stephan C, Harmjanz A, Staszewski S, Buehler A, Bickel M, … Sitzer M (2008). Both long-term HIV infection and highly active antiretroviral therapy are independent risk factors for early carotid atherosclerosis. Atherosclerosis, 196(2), 720–726. https://doi.org/S0021-9150(07)00031-7 [pii] 10.1016/j.atherosclerosis.2006.12.022 [DOI] [PubMed] [Google Scholar]
  43. Lorenzo-Zúñiga V, & Bartoli R (2003). Oral bile acids reduce bacterial overgrowth, bacterial translocation, and endotoxemia in cirrhotic rats. … Retrieved from http://onlinelibrary.wiley.com/doi/10.1053/jhep.2003.50116/abstract [DOI] [PubMed] [Google Scholar]
  44. McHardy IH, Li X, Tong M, Ruegger P, Jacobs J, Borneman J, … Braun J (2013). HIV Infection is associated with compositional and functional shifts in the rectal mucosal microbiota. Microbiome, 1(1), 26 10.1186/2049-2618-1-26 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. McKenna P, Hoffmann C, Minkah N, Aye PP, Lackner A, Liu Z, … Bushman FD (2008). The macaque gut microbiome in health, lentiviral infection, and chronic enterocolitis. PLoS Pathogens, 4(2), e20 10.1371/journal.ppat.0040020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Mellon RD, & Bayer BM (1998). Evidence for central opioid receptors in the immunomodulatory effects of morphine: review of potential mechanism(s) of action. J Neuroimmunol, 83(1–2), 19–28. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9610669 [DOI] [PubMed] [Google Scholar]
  47. Meng J, Sindberg GM, & Roy S (2015). Disruption of gut homeostasis by opioids accelerates HIV disease progression. Frontiers in Microbiology, 6, 643 10.3389/fmicb.2015.00643 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Meng J, Yu H, Ma J, Wang J, Banerjee S, Charboneau R, … Roy S (2013). Morphine Induces Bacterial Translocation in Mice by Compromising Intestinal Barrier Function in a TLR-Dependent Manner. PloS One, 8(1), e54040 10.1371/journal.pone.0054040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Moeller AH, Shilts M, Li Y, Rudicell RS, Lonsdorf EV, Pusey AE, … Ochman H (2013). SIV-induced instability of the chimpanzee gut microbiome. Cell Host & Microbe, 14(3), 340–345. 10.1016/j.chom.2013.08.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Mudd JC, & Brenchley JM (2016). Gut Mucosal Barrier Dysfunction, Microbial Dysbiosis, and Their Role in HIV-1 Disease Progression. J Infect Dis, 214 Suppl, S58–66. 10.1093/infdis/jiw258jiw258 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Muscoli C, Doyle T, Dagostino C, Bryant L, Chen Z, Watkins LR, … Salvemini D (2010). Counter-regulation of opioid analgesia by glial-derived bioactive sphingolipids. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 30(46), 15400–15408. 10.1523/JNEUROSCI.2391-10.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Mutlu EA, Keshavarzian A, Losurdo J, Swanson G, Siewe B, Forsyth C, … Landay A (2014). A Compositional Look at the Human Gastrointestinal Microbiome and Immune Activation Parameters in HIV Infected Subjects. PLoS Pathogens, 10(2). Retrieved from http://dx.plos.org/10.1371/journal.ppat.1003829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Neff CP, Krueger O, Xiong K, Arif S, Nusbacher N, Schneider JM, … Palmer BE (2018). Fecal Microbiota Composition Drives Immune Activation in HIV-infected Individuals. EBioMedicine, 30, 192–202. 10.1016/j.ebiom.2018.03.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Nell S, Suerbaum S, & Josenhans C (2010). The impact of the microbiota on the pathogenesis of IBD: lessons from mouse infection models. Nature Reviews. Microbiology, 8(8), 564–577. 10.1038/nrmicro2403 [DOI] [PubMed] [Google Scholar]
  55. Ninković J, & Roy S (2012). Morphine decreases bacterial phagocytosis by inhibiting actin polymerization through cAMP-, Rac-1-, and p38 MAPK-dependent mechanisms. The American Journal of Pathology, 180(3), 1068–1079. 10.1016/j.ajpath.2011.11.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. O’Hara AM, & Shanahan F (2006). The gut flora as a forgotten organ. EMBO Rep, 7(7), 688–693. https://doi.org/7400731 [pii] 10.1038/sj.embor.7400731 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Parks DH, Tyson GW, Hugenholtz P, & Beiko RG (2014). STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics (Oxford, England), 30(21), 3123–3124. 10.1093/bioinformatics/btu494 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Pflughoeft KJ, & Versalovic J (2012). Human Microbiome in Health and Disease. Retrieved from http://www.annualreviews.org/doi/abs/10.1146/annurev-pathol-011811-132421 [DOI] [PubMed] [Google Scholar]
  59. Pimentel M, Gunsalus RP, Rao SS, & Zhang H (2012). Methanogens in Human Health and Disease. The American Journal of Gastroenterology Supplements, 1(1), 28–33. 10.1038/ajgsup.2012.6 [DOI] [Google Scholar]
  60. Raghavan R, Cheney PD, Raymond LA, Joag SV, Stephens EB, Adany I, … Narayan O (1999). Morphological correlates of neurological dysfunction in macaques infected with neurovirulent simian immunodeficiency virus. Neuropathology and Applied Neurobiology, 25(4), 285–294. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10476045 [DOI] [PubMed] [Google Scholar]
  61. Rivera-Amill V, Silverstein PS, Noel RJ, Kumar S, & Kumar A (2010). Morphine and rapid disease progression in nonhuman primate model of AIDS: inverse correlation between disease progression and virus evolution. Journal of Neuroimmune Pharmacology : The Official Journal of the Society on NeuroImmune Pharmacology, 5(1), 122–132. 10.1007/s11481-009-9184-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Roy S, Ninkovic J, Banerjee S, Charboneau RG, Das S, Dutta R, … Barke RA (2011). Opioid drug abuse and modulation of immune function: consequences in the susceptibility to opportunistic infections. J Neuroimmune Pharmacol, 6(4), 442–465. 10.1007/s11481-011-9292-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Roy S, Wang J, Charboneau R, Loh HH, & Barke RA (2005). Morphine induces CD4+ T cell IL-4 expression through an adenylyl cyclase mechanism independent of the protein kinase A pathway. J Immunol, 175(10), 6361–6367. https://doi.org/175/10/6361 [pii] [DOI] [PubMed] [Google Scholar]
  64. Russell WR, Hoyles L, Flint HJ, & Dumas M-E (2013). Colonic bacterial metabolites and human health. Current Opinion In Microbiology, 16(3), 246–254. 10.1016/j.mib.2013.07.002 [DOI] [PubMed] [Google Scholar]
  65. Ryom L, Mocroft A, Kirk O, Ross M, Reiss P, Fux CA, … Lundgren JD (2014). Predictors of advanced chronic kidney disease and end-stage renal disease in HIV-positive persons. AIDS, 28(2), 187–199. 10.1097/QAD.0000000000000042 [DOI] [PubMed] [Google Scholar]
  66. Sico JJ, Chang CC, So-Armah K, Justice AC, Hylek E, Skanderson M, … Freiberg MS (2015). HIV status and the risk of ischemic stroke among men. Neurology, 84(19), 1933–1940. 10.1212/WNL.0000000000001560WNL.0000000000001560 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Spiller R (2007). Recent advances in understanding the role of serotonin in gastrointestinal motility in functional bowel disorders: alterations in 5-HT signalling and metabolism in human disease. Neurogastroenterology and Motility : The Official Journal of the European Gastrointestinal Motility Society, 19 Suppl 2, 25–31. 10.1111/j.1365-2982.2007.00965.x [DOI] [PubMed] [Google Scholar]
  68. Stecher B (2015). The Roles of Inflammation, Nutrient Availability and the Commensal Microbiota in Enteric Pathogen Infection. Microbiol Spectr, 3(3). 10.1128/microbiolspec.MBP-0008-2014 [DOI] [PubMed] [Google Scholar]
  69. Tamboli C, Neut C, Desreumaux P, & Colombel J (2004). Dysbiosis in inflammatory bowel disease. Gut. Retrieved from http://gut.bmj.com/content/53/1/1.1.short [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Thaiss CA, Zmora N, Levy M, & Elinav E (2016). The microbiome and innate immunity. Nature, 535(7610), 65–74. 10.1038/nature18847nature18847 [pii] [DOI] [PubMed] [Google Scholar]
  71. Tozzi V, Balestra P, Bellagamba R, Corpolongo A, Salvatori MF, Visco-Comandini U, … Narciso P (2007). Persistence of neuropsychologic deficits despite long-term highly active antiretroviral therapy in patients with HIV-related neurocognitive impairment: prevalence and risk factors. J Acquir Immune Defic Syndr, 45(2), 174–182. 10.1097/QAI.0b013e318042e1ee [DOI] [PubMed] [Google Scholar]
  72. Turner JR (2009). Intestinal mucosal barrier function in health and disease. Nat Rev Immunol, 9(11), 799–809. 10.1038/nri2653nri2653 [pii] [DOI] [PubMed] [Google Scholar]
  73. Vázquez-Castellanos JF, Serrano-Villar S, Latorre A, Artacho A, Ferrús ML, Madrid N, … Moya A (2014). Altered metabolism of gut microbiota contributes to chronic immune activation in HIV-infected individuals. Mucosal Immunology, 8(4). 10.1038/mi.2014.107 [DOI] [PubMed] [Google Scholar]
  74. Vázquez-Castellanos JF, Serrano-Villar S, Latorre A, Artacho A, Ferrús ML, Madrid N, … Moya A (2015). Altered metabolism of gut microbiota contributes to chronic immune activation in HIV-infected individuals. Mucosal Immunology, 8(4), 760–772. 10.1038/mi.2014.107 [DOI] [PubMed] [Google Scholar]
  75. Vujkovic-Cvijin I, & Dunham R (2013). Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Science Translational.… Retrieved from http://stm.sciencemag.org/content/5/193/193ra91.short [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Vujkovic-Cvijin I, Dunham RM, Iwai S, Maher MC, Albright RG, Broadhurst MJ, … McCune JM (2013). Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Scl Transl Med, 5(193), 193ra91 10.1126/scitranslmed.30064385/193/193ra91 [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wang J, Barke RA, Charboneau R, Loh HH, & Roy S (2003). Morphine negatively regulates interferon-gamma promoter activity in activated murine T cells through two distinct cyclic AMP-dependent pathways. J Biol Chem, 278(39), 37622–37631. 10.1074/jbc.M301224200M301224200 [pii] [DOI] [PubMed] [Google Scholar]
  78. Wang X, Zhang T, & Ho WZ (2011). Opioids and HIV/HCV infection. Journal of Neuroimmune Pharmacology, 1–13. Retrieved from http://www.springerlink.com/index/B3Q8364602316876.pdf [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Wu C, Li Z, & Xiong D (1998). Relationship between enteric microecologic dysbiosis and bacterial translocation in acute necrotizing pancreatitis. Group. Retrieved from http://www.wjgnet.com/1007-9327/full/v4/i3/242.htm [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Yano JM, Yu K, Donaldson GP, Shastri GG, Ann P, Ma L, … Hsiao EY (2015). Indigenous Bacteria from the Gut Microbiota Regulate Host Serotonin Biosynthesis. Cell, 163(1), 258 10.1016/j.cell.2015.09.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Yu K, Qiu C-L, Yang G-B, Zong C-M, Xing H, Shao Y, … Qin C (2010). Alteration of serotonin transporter messenger RNA level in the peripheral blood mononuclear cells from simian/human immunodeficiency virus infected Chinese rhesus macaques (Macaca mulatta). Brain, Behavior, and Immunity, 24(2), 298–305. 10.1016/j.bbi.2009.10.008 [DOI] [PubMed] [Google Scholar]
  82. Zhu L, Liu W, Alkhouri R, Baker RD, Bard JE, Quigley EM, & Baker SS (2014). Structural changes in the gut microbiome of constipated patients. Physiological Genomics. 10.1152/physiolgenomics.00082.2014 [DOI] [PubMed] [Google Scholar]

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