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Fecal Metabolome Signature in the HIV-1 Elite Control Phenotype: Enrichment of Dipeptides Acts as an HIV-1 Antagonist but a Prevotella Agonist - PubMed

  • ️Fri Jan 01 2021

Fecal Metabolome Signature in the HIV-1 Elite Control Phenotype: Enrichment of Dipeptides Acts as an HIV-1 Antagonist but a Prevotella Agonist

Maike Sperk et al. J Virol. 2021.

Abstract

HIV-1 elite controllers (EC) are a rare group among HIV-1-infected individuals who can naturally control viral replication for a prolonged period. Due to their heterogeneous nature, no universal mechanism could be attributed to the EC status; instead, several host and viral factors have been discussed as playing a role. In this study, we investigated the fecal metabolome and microbiome in a Swedish cohort of EC (n = 14), treatment-naive viremic progressors (VP; n = 16), and HIV-negative individuals (HC; n = 12). Fecal untargeted metabolomics was performed by four ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Molecular docking and biochemical microscale thermophoresis (MST) were used to describe the peptide-metabolite interactions. Single-cycle infectivity assays were performed in TZM-Bl cell lines using CCR5- and CXCR4-tropic HIV-1 strains. The microbiome analysis was performed using 16S rRNA sequencing. Th effects of metabolites on bacterial species viability were determined using several clinical isolates. We observed an enrichment of dipeptides in EC compared to VP and HC (adjusted P < 0.05). In silico analysis by molecular docking, in vitro biochemical assays, and ex vivo infection assays identified anti-HIV-1 properties for two dipeptides (WG and VQ) that could bind to the HIV-1 gp120, of which WG was more potent. The microbiome analysis identified enrichment of the genus Prevotella in EC, and these dipeptides supported bacterial growth of the genus Prevotella in vitro. The enrichments of the dipeptides and higher abundance of Prevotella have a distinct mechanism of elite control status in HIV-1 infection that influences host metabolism. IMPORTANCE HIV-1 elite controllers (EC) are a rare group among HIV-1-infected individuals who can naturally control viral replication for a prolonged period. Due to their heterogeneous nature, no universal mechanism could be attributed to the EC status; instead, several host and viral factors have been discussed as playing a role. In this study, we investigated the fecal metabolome and microbiome in a Swedish cohort of EC, treatment-naive viremic progressors (VP), and HIV-negative individuals (HC). We observed an enrichment of dipeptides in EC compared to the other two study groups. In silico and in vitro analyses identified anti-HIV-1 properties for two dipeptides that could bind to the HIV-1 gp120 and act as an HIV-1 antagonist. Furthermore, these dipeptides supported bacterial growth of the genus Prevotella in vitro that was enriched in EC, which influences host metabolism. Thus, increased levels of both dipeptides and Prevotella could provide beneficial effects for EC.

Keywords: HIV-1 elite controller; antiviral agents; dipeptides; metabolomics.

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Figures

FIG 1
FIG 1

Distinct metabolite profile in EC and enrichment of dipeptides. (a) Stacked bar plots with proportions of each superpathway based on the total number of detected and identified metabolites in all groups (825 metabolites), based on the number of metabolites with significantly different levels between EC and HC (P < 0.05, q = 0.053, 333 metabolites), EC and VP (P < 0.05, q = 0.012, 533 metabolites), and VP and HC (P < 0.05, q = 0.187, 127 metabolites). The differential metabolite analysis is given in Table S1. (b) PLS-DA based on all metabolites detected (n = 825) shows clustering of all EC together and separated from HC and VP. Presented data include samples from HC (n = 12, green), EC (n = 12, orange), and VP (n = 16, red). (c and d) Network of the metabolites that were significantly different between EC and the other two groups, VP and HC. Rectangular node shapes represent the eight superpathways that are shown in different colors according to the legend. Circles are used for subpathways belonging to the eight superpathways. Octagonal node shapes show the single metabolites, where a gradient was applied depending on fold change from 0.01 = green (decreased metabolite level), to 0 = yellow (nonsignificant), to a fold change from 4.8 to 39 = red. The sizes of the octagons indicate P values: the larger the size, the lower the P value. Lines connect each metabolite to its respective subpathway and subpathways to their respective superpathways. (c) EC versus VP (533 metabolites). (d) EC versus HC (333 metabolites). (e) Heat map representing levels of metabolites that are part of dipeptides (upper part) or γ-glutamyl amino acids (lower part). Samples are grouped according to study group (EC, HC, and VP). Color depicts increasing log2 (1 + x) levels from blue via green to yellow. The data shown include samples of HC (n = 12), EC (n = 12), and VP (n = 16).

FIG 2
FIG 2

Dipeptides as an anti-HIV compound. (a) Bubble plot representing the binding energy of dipeptides to viral protein gp120 predicted by in silico analyses. The diameter of the bubble corresponds to the binding energy. (b to e) Binding curves of four dipeptide amides with viral protein gp120 revealed by microscale thermophoresis (MST). Normalized fluorescence (Fnorm) values are plotted against the dipeptide concentration in molarity (M) in a concentration-response curve. The dissociation constant (Kd) for the respective dipeptide is shown in each graph. (b) Valylglutamine amide (VQ-am; Kd = 6.03 × 10−7 M). (c) Tryptophylglycine amide (WG-am; Kd = 2.1 × 10−7 M). (d) Lysylleucine amide (KL-am; Kd = 7.5 × 10−7 M). (e) Tyrosylglycine amide (YG-am; Kd = 8.43 × 10−8 M). (f and g) In silico protein-peptide docking shows binding of WG (f) and VQ (g) in the same hydrophobic pocket of viral protein gp120. (h) A dose-response curve summarizes in vitro infection assays of TZM-bl that were preincubated with different concentrations of either WG-am (tryptophylglycine amide, blue and red curves) or valylglutamine amide (VQ-am; green and black curves) and subsequently infected with a CCR5-tropic (blue and green curves) or a CXCR4-tropic (red and black curves) HIV-1 strain. The percentage of inhibition of the infection is plotted against the log value of the dipeptide concentration in μM. WG-am is more potent at inhibiting both HIV-1 strains than VQ-am. The experiments were carried out in triplicate and at least three independent times. Data points show average value of the three experimental replicates and standard deviation.

FIG 3
FIG 3

Altered microbiome-related biochemicals in EC. (a) Heat map representing levels of metabolites that are partially or completely derived from the gut microbiome. Color depicts increasing log2 levels from blue via green to yellow. The data shown include samples of HC (n = 12), EC (n = 12), and VP (n = 16). (b) Schematic presentation of the tryptophan metabolism leading to indole and indole derivatives. Whereas tryptophan is catabolized into indoles by microbiota in the gut, indole metabolites are converted into indoxyl and indoxyl sulfate in the liver. Boxes in neighborhood of a metabolite indicate that the respective metabolite was detected and quantified in the samples (HC, n = 12; EC, n = 12; and VP, n = 16). Box 1 shows comparison between EC and HC, box 2 comparison between VP and HC, and box 3 comparison between EC and VP. A gray box means nonsignificant difference, a red box represents fold change greater than 1 (with P < 0.05), and a green box represents fold change smaller than 1 (with P < 0.05). (c) Box plots of selected metabolites that are derived by the gut microbiota. Log2-transformed values were used to create box plots. Different colors differentiate between the groups: EC in orange, HC in green, and VP in red. Median values and interquartile ranges are indicated by bars. P values were determined by Welch’s two-sample t test with levels of significance indicated as follows: *, P < 0.05 and q < 0.1; **, P < 0.05 and q < 0.05; and ***, P < 0.001 and q < 0.001. (d) Schematic presentation of the bile acid metabolism. Bile acids or salts are derived from cholesterol metabolism. The salts of cholic acid and chenodeoxycholic acid, the major primary bile acids synthesized in human livers, are conjugated in the liver with taurine or glycine for secretion into bile.

FIG 4
FIG 4

Differential bacterial abundance in EC. (a and b) Stacked bar plots showing the relative abundance of different bacterial genera in the patient samples. Bacteria genera are displayed in different colors, and samples are grouped according to study group (n = 11 for EC, HC, and VP). (a) Individual patient samples. (b) Intergroup comparison. The organisms with mean relative abundance are represented to compare the bacterial abundances between the three study groups. (c) Bar plot displaying results of permutational multivariate analysis of variance (PERMANOVA) for the contributions of different bacteria genera, detected in fecal samples, in separating EC from HC (n = 11 for EC and HC). (d and e) Bar plot illustrating growth of 17 different bacterial strains in CFU/ml when incubated with or without a dipeptide (concentration of 10 mM). Controls are depicted with a lighter color and dipeptide incubation with a darker color. Bar plots show mean and standard deviation from three independent experiments performed with biological triplicates. The level of significance is indicated by asterisks (*, P < 0.05), as determined by two-sided t test. (d) Incubation of bacteria with tryptophylglycine amide (WG-am), including a representative picture of bacterial growth on blood agar plates. (e) Incubation of bacteria with valylglutamine amide (VQ-am).

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