The COVID-19 pandemic poses a great challenge to global public health. The extraordinary daily use of household disinfectants and cleaning products, social distancing and the loss of everyday situations that allow contact between individuals, have a direct impact on the transfer of microorganisms within the population. Together, these changes, in addition to those that occur in eating habits, can affect the composition and diversity of the gut microbiota. A two-time point analysis of the fecal microbiota of 23 Metropolitan Buenos Aires (BA) inhabitants was carried out, to compare pre-pandemic data and its variation during preventive and compulsory social isolation (PCSI) in 2020. To this end, 23 healthy subjects, who were previously studied by our group in 2016, were recruited for a second time during the COVID-19 pandemic, and stool samples were collected from each subject at each time point (n = 46). The hypervariable region V3-V4 of the 16S rRNA gene was high-throughput sequenced. We found significant differences in the estimated number of observed features (p < 0.001), Shannon entropy index (p = 0.026) and in Faith phylogenetic diversity (p < 0.001) between pre-pandemic group (PPG) vs. pandemic group (PG), being significantly lower in the PG. Although no strong change was observed in the core microbiota between the groups in this study, a significant decrease was observed during PCSI in the phylum Verrucomicrobia, which contributes to intestinal health and glucose homeostasis. Microbial community structure (beta diversity) was also compared between PPG and PG. The differences observed in the microbiota structure by unweighted UniFrac PCoA could be explained by six differential abundant genera that were absent during PCSI. Furthermore, putative functional genes prediction using PICRUSt infers a smaller predicted prevalence of genes in the intestinal tryptophan, glycine-betaine, taurine, benzoate degradation, as well as in the synthesis of vitamin B12 during PCSI. This data supports the hypothesis that the microbiome of the inhabitants of BA changed in the context of isolation during PCSI. Therefore, these results could increase the knowledge necessary to propose strategic nutraceutical, functional food, probiotics or similar interventions that contribute to improving public health in the post-pandemic era.
Background and Aims: Non-invasive biomarkers are urgently needed to identify patients with non-alcoholic fatty liver disease (NAFLD) especially those at risk of disease progression. This is particularly true in high prevalence areas such as Latin America. The gut microbiome and intestinal permeability may play a role in the risk of developing NAFLD and NASH, but the mechanism by which microbiota composition disruption (or dysbiosis) may affect NAFLD progression is still unknown. Targeted metabolomics is a powerful technology for discovering new associations between gut microbiome-derived metabolites and disease. Thus, we aimed to identify potential metabolomic biomarkers related to the NAFLD stage in Argentina, and to assess their relationship with clinical and host genetic factors.
Materials and methods: Adult healthy volunteers (HV) and biopsy-proven simple steatosis (SS) or non-alcoholic steatohepatitis (NASH) patients were recruited. Demographic, clinical and food frequency consumption data, as well as plasma and stool samples were collected. SNP rs738409 (PNPLA3 gene) was determined in all volunteers. HPLC and flow injection analysis with MS/MS in tandem was applied for metabolomic studies using the MxP Quant 500 Kit (Biocrates Life Sciences AG, Austria). Significantly different metabolites among groups were identified with MetaboAnalyst v4.0. Bivariate and multivariate analyses were used to identify variables that were independently related to NAFLD stage. Forward stepwise logistic regression models were constructed to design the best feature combination that could distinguish between study groups. Receiver Operating Characteristic (ROC) curves were used to evaluate models′ accuracy.
Results: A total of 53 volunteers were recruited: 19 HV, 12 SS and 22 NASH. Diet was similar between groups. The concentration of 33 out of 424 detected metabolites (25 in plasma and 8 in stool) was significantly different among study groups. Levels of triglycerides (TG) were higher among NAFLD patients, whereas levels of phosphatidylcholines (PC) and lysoPC were depleted relative to HV. The PNPLA3 risk genotype for NAFLD and NASH (GG) was related to higher plasma levels of eicosenoic acid FA(20:1) (p<0.001). Plasma metabolites showed a higher accuracy for diagnosis of NAFLD and NASH when compared to stool metabolites. Body mass index (BMI) and plasma levels of PC aa C24:0, FA(20:1) and TG(16:1_34:1) showed high accuracy for diagnosis of NAFLD; whereas the best AUROC for discriminating NASH from SS was that of plasma levels of PC aa C24:0 and PC ae C40:1.
Conclusion: A panel of plasma and stool biomarkers could distinguish between NAFLD and NASH in a cohort of patients from Argentina. Plasma biomarkers may be diagnostic in these patients and could be used to assess disease progression. Further validation studies including a larger number of patients are needed.
Interactions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi‐omics approach to unravel gut microbiome signatures from 32 biopsy‐proven patients (10 simple steatosis ‐SS‐ and 22 steatohepatitis ‐SH‐) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH.
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