Abstract:Postoperative pancreatic fistula (POPF) is a common and dreaded complication after pancreaticoduodenectomy (PD). The gut microbiota has been considered as an crucial mediator of postoperative complications, however, the precise roles of gut microbiota in POPF are unclear. A prospective study was developed to explore the effects of somatostatin on gut microbiota and we aim to identify the microbial alterations in the process of POPF. A total of 45 patients were randomly divided into PD group or additional somat… Show more
“…3B , Kruskal–Wallis test, p = 2.17 × 10 −64 , E 2 = 0.102). Considering specifically the top 20 drugs (in terms of their mean impact on microbial taxa) that have not been screened in the original database, we found several compounds with reported in vivo anti-commensal activity such as the immunosuppressants Sirolimus 29 and Tacrolimus 30 and the antineoplastic agents Somatostatin 31 and Eribulin 32 . Fig.…”
Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species, and causing adverse effects. To guide personalized pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we develop a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predict drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map a large array of interactions between pharmaceuticals and human gut bacteria and demonstrate that medications’ anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.
“…3B , Kruskal–Wallis test, p = 2.17 × 10 −64 , E 2 = 0.102). Considering specifically the top 20 drugs (in terms of their mean impact on microbial taxa) that have not been screened in the original database, we found several compounds with reported in vivo anti-commensal activity such as the immunosuppressants Sirolimus 29 and Tacrolimus 30 and the antineoplastic agents Somatostatin 31 and Eribulin 32 . Fig.…”
Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species, and causing adverse effects. To guide personalized pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we develop a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predict drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map a large array of interactions between pharmaceuticals and human gut bacteria and demonstrate that medications’ anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.
“…In contrast, medications affecting the sensory, genitourinary, and respiratory systems show the lowest average activity (Figure 3B). Considering specifically the top 20 drugs (in term of their mean impact on microbial taxa) that have not been screened in the original database, we found several compounds with reported in-vivo anti-commensal activity such as, the immunosuppressants Sirolimus 29 and Tacrolimus 30 and the antineoplastic agents’ Somatostatin 31 and Eribulin 32 .…”
Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species and causing adverse effects. To determine individualized response to pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we developed a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predicts drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map all interactions between pharmaceuticals and bacteria and demonstrate that medications anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.
“…Moreover, LEfSe analysis ( Li et al, 2020 ) identified 42 signature bacterial taxa that were differentially altered, with an LDA score > 2, showing that there was a significant structural difference among the four groups ( Figure 3C ), in which 13 bacterial species were identified as key discriminants. All these results demonstrated that repeated stress-related diarrhea may be a key modulator of the microbiota composition, and the key bacteria may be able to identify repeated stress-related diarrhea disease.…”
Repeated stress-related diarrhea is a kind of functional bowel disorders (FBDs) that are mainly stemming from dysregulation of the microbiota–gut–brain axis mediated by a complex interplay of 5-hydroxytryptophan (5-HT). Intestinal content and intestinal mucosa microbiota belong to two different community systems, and the role of the two microbiota community systems in repeated stress-related diarrhea remains largely unknown. In order to ascertain the difference in composition and the potential function between intestinal content and intestinal mucosa microbiota response on repeated stress-related diarrhea, we collected intestinal contents and mucosa of mice with repeated stress-related diarrhea for 16S rRNA PacBio SMRT gene full-length sequencing, and with the digital modeling method of bacterial species abundance, the correlations among the two microbiota community systems and serum 5-HT concentration were analyzed. We found that the microbiotal composition differences both in intestinal contents and mucosa were consistent throughout all the phylogenetic ranks, with an increasing level of resolution. Compared with intestinal content microbiota, the diversity and composition of microbiota colonized in intestinal mucosa are more sensitive to repeated stress-related diarrhea. The PICRUSt2 of metagenomic function analysis found that repeated stress-related diarrhea is more likely to perturb the intestinal mucosa microbiota metagenomic functions involved in the neural response. We further found that the mucosal microbiota-based relative abundance model was more predictive on serum 5-HT concentration with the methods of machine-learning model established and multivariate dimensionality reduction (R2 = 0.876). These findings suggest that the intestinal mucosa microbiota might serve as a novel potential prediction model for the serum 5-HT concentration involvement in the repeated stress-related diarrhea, in addition to focusing on its mechanism in the gastrointestinal dysfunction.
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