The metabolic consequences of HIV/TB co-infection

Abstract

Background: The synergy between the human immunodeficiency virus (HIV) and Mycobacterium tuberculosis during co-infection of a host is well known. While this synergy is known to be driven by immunological deterioration, the metabolic mechanisms that contribute to the associated disease burden experienced during HIV/tuberculosis (TB) co-infection remain poorly understood. Furthermore, while anti-HIV treatments suppress viral replication, these therapeutics give rise to host metabolic disruption and adaptations beyond that induced by only infection or disease.

Methods: In this study, the serum metabolic profiles of healthy controls, untreated HIV-negative TB-positive patients, untreated HIV/TB co-infected patients, and HIV/TB co-infected patients on antiretroviral therapy (ART), were measured using two-dimensional gas chromatography time-of-flight mass spectrometry. Since no global metabolic profile for HIV/TB co-infection and the effect of ART has been published to date, this pilot study aimed to elucidate the general areas of metabolism affected during such conditions.

Results: HIV/TB co-infection induced significant changes to the host's lipid and protein metabolism, with additional microbial product translocation from the gut to the blood. The results suggest that HIV augments TB synergistically, at least in part, contributing to increased inflammation, oxidative stress, ART-induced mitochondrial damage, and its detrimental effects on gut health, which in turn, affects energy availability. ART reverses these trends to some extent in HIV/TB co-infected patients but not to that of healthy controls.

Conclusion: This study generated several new hypotheses that could direct future metabolic studies, which could be combined with other research techniques or methodologies to further elucidate the underlying mechanisms of these changes.

Keywords: GCxGC-TOFMS; Gut microbiome; HIV/AIDS; HIV/TB co-infection; Metabolism; Metabolomics; Tuberculosis.

Fig. 1

Comparisons pertinent to the aims of this investigation include; Aim 1: Determine the effect of untreated HIV/TB co-infection on the host metabolism by comparing a healthy control group to an untreated co-infected group; Aim 2: Determine the effect of untreated HIV infection on patients with TB by comparing an untreated co-infected group to an untreated group with TB only (no HIV infection); Aim 3: Determine the effect of ART on HIV/TB co-infected patients by comparing an untreated co-infected group to a co-infected group receiving ART. Groups: HIV-/TB-/Tn-: healthy controls; HIV-/TB + /Tn-: untreated HIV-negative patients with active pulmonary TB; HIV + /TB + /Tn-: untreated HIV/TB co-infected patients; HIV + /TB + /Tn + : HIV/TB co-infected patients on ART

Fig. 2

Pearson’s dendrograms (A) of the healthy control and untreated HIV/TB co-infected samples (n = 38); (B) after the exclusion of untreated HIV/TB co-infected samples with CD4 T-cell counts above 600 cells/mm³ (n = 36); (C) after the exclusion of five healthy controls (n = 31); (D) of the untreated HIV/TB co-infected samples after stratification based on CD4 T-cell count before sample exclusion (LCD: lower CD4 count, < 100 cells/mm³, HCD: higher CD4 count, > 100 cells/mm³, n = 9); and (E) the healthy control, untreated TB-positive and untreated HIV/TB co-infected samples after the exclusion of the seven samples (n = 53). The distance measure used in these dendrograms was Pearson’s and the clustering algorithm, Ward’s D

Fig. 3

Box plots with overlaid strip plots showing the distribution of the data for the metabolites significantly altered in the t-tests comparing the HCD (n = 5) and LCD (n = 4) groups within the untreated HIV/TB co-infected group before exclusion of samples with high CD4 T-cell counts. Analysis of the role of CD4 in the treated co-infected group was not done due to the large heterogeneity displayed by this group. These results were not statistically significant after multiple testing, but as this is an explorative study, p-values before correction are reported here: (A) 0.032, (B) 0.037, (C) 0.038, and (D) 0.041

Fig. 4

A PCA and (B) PLS-DA scores plots indicating the distribution of samples (healthy controls and all untreated patient samples, n = 53). Groups mainly overlapped at (A) but were more homogenous and could be better distinguished from each other at (B) mainly because of changes linked to inflammation and the breakdown of molecules as fuel during chronic inflammatory diseases such as this. While the PLS-DA model (B) did not perform optimally in cross-validation for a one-component model (accuracy = 0.54), it did validate during permutation testing (p-value =  < 0.05)

Fig. 5

(A) PCA and (B) PLS-DA scores plots indicating the distribution of all samples (healthy controls and all untreated and treated patient samples) after sample exclusions (n = 65). Post sample exclusion, the groups were more homogenous and could be better distinguished from each other, more so with PLS-DA analysis and those variables important in separating the groups, identified. The PLS-DA model (B) did not validate (cross-validation for a one-component model: Q² = 0.40, R² = 0.60, accuracy = 0.60; permutation testing p-value = 0.241) and was used solely in an explorative, as opposed to predictive manner

Fig. 6

A circle packing plot showing the distribution of compound classes within the metabolites identified as significant by ANOVA. The radius of the circle is proportional to the number of metabolites of that specific class (also indicated in brackets). The plot shows lipid-like molecules as well as organic acid intermediates to be key contributors explaining the variance between the groups in this cohort. Contributing the least was the organic nitrogen compounds (ONC)

Fig. 7

Boxplots with overlaid strip plots showing the distribution of the data for the metabolites significantly altered in the following comparisons: HIV-/TB + /Tn- (TB) versus HIV + /TB + /Tn- (untreated HIV/TB) and HIV + /TB + /Tn- (untreated HIV/TB) versus HIV + /TB + /Tn + (ART-treated HIV/TB). (A) Metabolites significantly altered by HIV infection during an existing TB infection, of which this trend was reversed to some extent by ART (although not with statistical significance in all cases). (B) 3-Hydroxyisovaleric acid was the only metabolite not significantly altered by HIV infection in those with untreated HIV/TB co-infection but was significantly altered because of ART in co-infected individuals

Fig. 8

A visual summary of the potential effects of HIV infection and its treatment on patients with TB, as elucidated by this metabolomics investigation. A Shown are the implications of the metabolites found to be significantly different between the untreated TB and untreated HIV/TB co-infected groups, representing the effect of HIV infection on an individual who already has TB. B The implications of the metabolites found to be significantly different between the untreated and ART-treated HIV/TB co-infected groups, representing the effect of ART on HIV/TB co-infection. Although ART improved the effects of HIV infection to some extent, implying that some metabolites returned to a value closer to, but not equal to, those of healthy controls, many metabolites were unaffected by ART