Host genetics and gut microbiota influence lipid metabolism and inflammation: potential implications for ALS pathophysiology in SOD1G93A mice

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by the progressive loss of motor neurons, with genetic and environmental factors contributing to its complex pathogenesis. Dysregulated immune responses and altered energetic metabolism are key features, with emerging evidence implicating the gut microbiota (GM) in disease progression. We investigated the interplay among genetic background, GM composition, metabolism, and immune response in two distinct ALS mouse models: 129Sv_G93A and C57Ola_G93A, representing rapid and slow disease progression, respectively.Using 16 S rRNA sequencing and fecal metabolite analysis, we characterized the GM composition and metabolite profiles in non-transgenic (Ntg) and SOD1^G93A mutant mice of both strains. Our results revealed strain-specific differences in GM composition and functions, particularly in the abundance of taxa belonging to Erysipelotrichaceae and the levels of short and medium-chain fatty acids in fecal samples. The SOD1 mutation induces significant shifts in GM colonization in both strains, with C57Ola_G93A mice showing changes resembling those in 129 Sv mice, potentially affecting disease pathogenesis. ALS symptom progression does not significantly alter microbiota composition, suggesting stability.Additionally, we assessed systemic immunity and inflammatory responses revealing strain-specific differences in immune cell populations and cytokine levels.Our findings underscore the substantial influence of genetic background on GM composition, metabolism, and immune response in ALS mouse models. These strain-specific variations may contribute to differences in disease susceptibility and progression rates. Further elucidating the mechanisms underlying these interactions could offer novel insights into ALS pathogenesis and potential therapeutic targets.

Keywords: Amyotrophic lateral sclerosis; Cytokines; Fatty acids; Genetic background; Immune response; MCFA; Microbiome; SOD1.

Fig. 1

Comparative Analysis of Microbiota Composition and Metabolic Profiles in C57Ola_Ntg and 129Sv_Ntg Mice at 12 weeks’ age. (A) Boxplots showcasing alpha diversity indices (Observed Richness, Shannon index, Evenness) in fecal samples. Statistical differences were evaluated using a paired Wilcoxon signed-rank test. P-values less than 0.05 were considered statistically significant. (B) Principal coordinates analysis (PCoA) according to the Hellinger distance computed on genera abundances. Results of the permutational multivariate analysis of variance (PERMANOVA) are also shown based on the first two coordinates. (C) Circular heatmap representing the differentially abundant taxa in C57Ola versus 129 Sv mice samples: concentric circles represent taxonomic ranks from phylum (P) to genus (G); yellow shades indicate positive logFC values, whereas blue shades indicate negative logFC values correlations; the intensity of colors is proportional to logFC values. (D) Box plot reporting the statistically significant different free fatty acids among the two Ntg strains. (E) Computed Linear Discriminant Analysis (LDA) scores representing significantly differential KEGG Pathway Expression (LDA > 2.5) between 129Sv_Ntg and C57Ola-Ntg mice

Fig. 2

Microbiota Differences and Diversity Analyses in C57Ola Strain, Comparing Non-Transgenic (Ntg) and G93A Transgenic Mice at 12 Weeks of Age. (A) Principal coordinates analysis (PCoA) according to Hellinger distance computed on genera abundances. (B) Boxplots with the differentially abundant taxa. (C) Computed Linear Discriminant Analysis (LDA) scores representing significantly differential KEGG Pathway Expression (LDA > 2.5). (D) Boxplots of the significantly different free fatty acids. (E) Heatmap of correlations between MCFA levels and the relative abundance of the Erysipelotrichaceae, Dubosiella and Fecalibaculum taxa MCFA = medium chain fatty acids. *p-value < 0.05

Fig. 3

Microbiota Differences and Diversity Analyses in 129 Sv Strain, Comparing Non-Transgenic (Ntg) and G93A Transgenic Mice at 12 Weeks of Age. (A) Principal coordinates analysis (PCoA) according to Hellinger distance computed on genera abundances. (B) Boxplots with the differentially abundant taxa. (C) Computed Linear Discriminant Analysis (LDA) scores representing significantly differential KEGG Pathway Expression (LDA > 2.5)

Fig. 4

Temporal analysis of microbiota between C57Ola_G93A and 129Sv_G93A mice. Beta diversity, as well as DESeq2 and PICRUSt2 analysis, were conducted at the pre-symptomatic stage (PS) in panels A, B, and C; at disease onset (OS) in panels D, E, and F; and during the symptomatic stage (SY) in panels G, H, and I. LDA = linear discriminant analysis

Fig. 5

Analysis of circulating monocyte populations across different time points and mouse strains. Proinflammatory monocytes are identified as CD45 + CD11b + Ly6C + cells, while patrolling monocytes are defined as CD45 + CD11b + Ly6C- cells. Data are expressed as mean ± SEM of n = 3–4 mice per experimental group. °p < 0.05, °°p < 0.01 (129 Sv Ntg Vs 129 Sv G93A, C57 Ntg and C57 G93A); *p < 0.05, **p < 0.01 (129 Sv G93A Vs 129 Sv Ntg, C57 Ntg and C57 G93A) by two way-ANOVA with Fisher post-analysis

Fig. 6

Serum cytokine levels across different time points and mouse strains. Boxplot showing the serum cytokines levels. Comparison between time points and strains were performed by two-way ANOVA using Tukey’s multiple comparisons post-test. * adjusted p value < 0.05