A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America

Abstract

HIV infection has a tremendous impact on the immune system's proper functioning. The mucosa-associated lymphoid tissue (MALT) is significantly disarrayed during HIV infection. Compositional changes in the gut microbiota might contribute to the mucosal barrier disruption, and consequently to microbial translocation. We performed an observational, cross-sectional study aimed at evaluating changes in the fecal microbiota of HIV-infected individuals from Colombia. We analyzed the fecal microbiota of 37 individuals via 16S rRNA gene sequencing; 25 HIV-infected patients and 12 control (non-infected) individuals, which were similar in body mass index, age, gender balance and socioeconomic status. To the best of our knowledge, no such studies have been conducted in Latin American countries. Given its compositional nature, microbiota data were normalized and transformed using Aitchison's Centered Log-Ratio. Overall, a change in the network structure in HIV-infected patients was revealed by using the SPIEC-EASI MB tool. Genera such as Blautia, Dorea, Yersinia, Escherichia-Shigella complex, Staphylococcus, and Bacteroides were highly relevant in HIV-infected individuals. Differential abundance analysis by both sparse Partial Least Square-Discriminant Analysis and Random Forest identified a greater abundance of Lachnospiraceae-OTU69, Blautia, Dorea, Roseburia, and Erysipelotrichaceae in HIV-infected individuals. We show here, for the first time, a predominantly Lachnospiraceae-based signature in HIV-infected individuals.

Figure 1

Network structure of the microbial communities found in HIV-infected and control individuals. (a) The fecal microbiota of HIV-infected individuals follows a pattern of universality as described by Bashan et al.. Each point represents the comparisons between the samples of two individuals. The fraction of shared bacteria (overlap) is plotted against the dissimilarity index, which was evaluated based on the square root of the Jensen–Shannon divergence. The dissimilarity index calculates the distance between pairs of individuals in terms of abundance of shared species. The dissimilarity-overlap curve (DOC) is represented by a pink curve and was calculated using the robust LOWESS method. The fraction of points beyond the inflection point (O_C) of the DOC curve – the point at which the curve shows a negative slope - was calculated by: ${\mathit{f}}_{\mathit{ns}}=\frac{\mathit{number}\mathit{of}\mathit{pair}\mathit{samples}\mathit{with}O>{\mathit{O}}_{\mathit{c}}}{\mathit{total}\mathit{number}\mathit{of}\mathit{sample}\mathit{pairs}}$. The P-values are finally calculated as the fraction of bootstrap runs resulting with non-negative slopes. (b) Heat map of Pearson Correlation of HIV-infected individuals and non-infected control group. We performed a Jennrich test to calculate the p-value associated to the correlation structures between HIV-infected patients control group. (c) Differences in the microbial networks of fecal samples obtained from HIV-infected and control individuals. Using the SPIEC-EASI MB method, networks were constructed from the table of clr-transformed OTUs.

Figure 2

Identification of an OTU signature associated with HIV-infected individuals. (a) PCA analysis of the sPLS-DA model fed with clr-transformed OTUs (left), and the resulting contribution of OTUs of component 1 (right). (b) OTU importance according to the random forest method. The Mean Gini Decrease of each OTU is plotted versus the delta of the CLR averages for each OTU (i.e., for each OTU, the ΔCLR was calculated subtracting the CLR mean of the HIV-infected group from the CLR mean of the control group).

Figure 3

Identification of OTU signatures associated with CD4(+) T cell activation status. (a) Subgroups of HIV-infected individuals according to their percentage of CD4(+) T cell activation. ANOVA-based analysis was performed to calculate statistical difference among groups. (b) Diversity indices, based on the intrinsic relationship of each one with respect to the transformation of Hill numbers, are plotted continuously on the x-axis. The Chao, Shannon and Simpson indices are shown when the value of the Hill numbers are -2, 0.5 and 2, respectively. The homogeneity with respect to the distribution of OTU abundance is an inverse function of the Simpson index. The richness of the OTUs is a direct function of the Shannon index. (c) PCA plot of β-diversity using the weighted UNIFRAC metric. PERMANOVA was used to estimate P-value. (d) Relative abundance of Firmicutes and Bacteroidetes in HIV-infected subgroups. Statistical difference among groups was estimated using ANOVA and P_Adjusted was calculated using false discovery rate and Bonferroni. Statistical difference between subgroups was estimated using Tukey least significance difference test with 95% confidence intervals (CIs) and P-value was adjusted by multiple comparisons.

Figure 4

MetagenomeSeq analysis. OTUs that were both identified as part of the signature by sPLS-DA/random forest and had a statistically significant P_adj value (Padj < 0.05) are shown. For better visualization, CLR values were used for plotting the abundance of each OTU. The P_adj values are shown in each respective panel. (a) Comparison of the control group with the HIV-infected group. (b) Comparison of the control group with the HIV-infected subgroup with high percentage of activated CD4(+) T cells.