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Background: The gastrointestinal tract is a major site of HIV transmission and early replication, and this change in the mucosal environment contributes to the progression
of the disease.
Persistent mucosal inflammation during HIV infection leads to microbial metabolic disorders and translocations, and indicators of microbial translocation have been repeatedly associated with
systemic inflammation in clinical studies.
However, the time course and drivers of dysregulation of microbial metabolism in HIV are not fully understood
.
In many studies, changes in the composition of the gut microbiome or biological dysregulation have been linked to
HIV.
The most common reports include an increase in potentially pathogenic Proteus, an increase in the inflammatory Commona species, and a decrease in commensal bacteria such as Bacteroides and nonbacillus
.
Despite a large number of high-quality studies, the generalizability of microbiome data is limited
by differences in sample collection, cohorts, and unknown effects of multiple confounders.
There are many factors that affect the composition of the microbiome, including age, geographic location, diet, antibiotics, and behavior
.
Recent studies have shown that men who have sex with men (MSM) have a characteristic microbiome composition that is different from that of men who have sex with women, with some microbiome changes similar to those
attributed to HIV in other cross-sectional studies.
So far, few studies have examined the microbiome composition of individuals before and after HIV infection in a longitudinal manner, possibly due to the rarity of such samples
.
A recent study by Chen et al.
examined stool samples from MSM in a multicenter AIDS cohort study (MACS) and found that HIV-1 seroconversions had significant microbiome changes, including decreased Bacteroides, prior to HIV-1 infection, which were associated
with an increase in plasma inflammatory biomarkers.
After seroconversion, an increase in Prevoteco and Vikviaco was associated
with faster disease progression.
This highlights the need for further examination of the microbiome before HIV acquisition and in early infection to better understand its role
in acquisition risk and disease trajectory.
Changes in the microbiome in HIV are associated
with changes in host metabolites and immune biomarkers.
Changes in plasma metabolites associated with bile acid metabolism and amino acid metabolism are associated with HIV, and metabolic changes are further associated
with the progression of the disease in several studies.
Serrano-Villar et al.
showed that HIV also causes specific changes
in gut bacterial metabolites associated with amino acid metabolism compared to other inflammatory diseases.
The timing of these metabolic changes and how they might be related to HIV infection is unclear
.
Methods: To address this issue, we performed longitudinal measurements
of the gut microbiome, serum metabolome, and cytokines of 27 individuals before and during acute HIV infection using samples collected from several ongoing cohort studies.
Matched control participants (n=28) from the same cohort study but with similar behavioral risks were used for comparison
.
Results: There was little change in the microbiota during acute HIV infection, but metabolites of secondary bile acids (lithocholic acid sulfate, glycocholic acid) and amino acid metabolism (3-methyl-2-valeric acid, serine, cysteine, N-acetyramine) in serum did change
.
When comparing pre-HIV visits with matched high-risk controls, larger microbiome differences can be seen, including a decrease in Bacteroides and an increase in
Megabacterium egrei.
Compared with matched controls, patients infected with HIV also had elevated
inflammatory cytokines (tumor necrosis factor-a, B cell activator, IL-8) and biologically active lipids (palmitoyl-sphingosine-phosphoethanolamine and glycerol-phosphoinositol) before HIV infection.
Figure 1.
Comparison
of the gut microbiome between HIV cases and controls.
(A) In HIV cases (top) and control populations (bottom), microbiome composition is shown as relative abundance by genus over time
.
The month is relative to the month in which the HIV test was positive, and for HIV cases, time = 0 months
.
(b) Comparison
of alpha diversity between control visits and pre- and post-HIV cases.
The box line represents the median value and the minimum to maximum
of the beard.
The value of P determined by
Wilcoxon's symbolic rank test.
(C) Principal coordinate analysis
of HIV case groups before HIV (blue) and after HIV (red).
(D) Master coordinate analysis of HIV cases, pre-HIV samples (blue) and control group (green).
(E) A heat map showing the relative abundance between control, pre-HIV and post-HIV samples, where at least 10% of the study samples have a relative abundance of at least 1%
of all bacterial species.
(For an explanation of the references to colors in this legend, see the web version of
this article.
) )
Figure 2.
Changes in
gut bacteria before and after early HIV seroconversion.
Bacteria
were identified by comparing pre-HIV infection and post-HIV infection samples from HIV case groups using a linear mixed-effects model.
Only those with adjusted p-values (Q) < 0.
25 are displayed, and the error bars represent standard errors
.
(A) showed the entire study population and (B) only the US cohort
.
(C) Relative abundance of important bacteria before and after HIV infection
.
The boxes represent the mean and the error bars represent the standard deviation
.
Figure 3.
Distinguish between bacteria
that control human and HIV cases.
Bacteria
were identified by comparing a linear mixed-effects model of pre-HIV cases and control samples.
Only the adjusted p-value (Q) < 0.
25 is displayed, while * represents the value
of Q<0.
1.
Error bars indicate standard errors
.
(A) showed the entire study population and (B) only the US cohort
.
(C) Relative abundance of significant bacteria between pre-HIV cases and controls
.
The boxes represent the mean and the error bars represent the standard deviation
.
(D) Bacterial characteristics determined by random forests, distinguishing pre-HIV cases from control cases, ranked by
relative importance in the model.
The color indicates the genus of bacteria, and the error bars indicate the standard deviation
.
(For an explanation of the references to colors in this legend, see the web version of
this article.
) )
Figure 4.
Metabolites
different from HIV infection and seroconversion.
(A) Metabolite characteristics determined by random forests, distinguishing pre- and post-HIV samples from HIV case groups, ranked by
relative importance in the model.
The color indicates the metabolic hyperpathway, and the error bars indicate the standard deviation
.
(B) Relative abundance of metabolites before and after HIV infection in HIV case groups
.
The boxes represent the mean and the error bars represent the standard deviation
.
(C) Metabolite characteristics determined by random forests, distinguishing pre-HIV cases from controls, ranked by
relative importance in the model.
The color indicates the metabolic hyperpathway, and the error bars indicate the standard deviation
.
(D) Relative abundance of metabolites between pre-HIV cases and controls
.
The boxes represent the mean and the error bars represent the standard deviation
.
(For an explanation of the colors in this legend, see the web version
of this article.
) )
Conclusion: Longitudinal sampling determined pre-existing microbiome differences
in acute HIV patients compared to matched control participants observed during the same period.
These data highlight the importance
of increasing understanding of the role of the microbiome in HIV susceptibility.
Original source:
Fulcher JA, Li F, Tobin NH, Zabih S, et al.
Gut dysbiosis and inflammatory blood markers precede HIV with limited changes after early seroconversion.
EBioMedicine 2022 Sep 27; 84