Author: Melinda Rodriguez, PharmD 2021 candidate, Lake Erie College of Osteopathic Medicine – L | E | C | O | M School of Pharmacy
Artificial intelligence traces fingerprints of the gut flora, connecting groups of microbiome species to the development of type 1 diabetes.
The healthy gut flora contains hundreds of thousands of microorganisms, constituting 150 times more genes than its human host. While a healthy and diverse gut microbial composition is essential for digestion, vitamin synthesis, and a healthy immune response, dysbiosis of the gastrointestinal tract can impair vital metabolic processes. In recent years, it has been proposed that changes in the gut microbiome may even play a role in autoimmunity, an important mechanism involved in the development of type 1 diabetes mellitus (T1D).
T1D, formerly known as juvenile diabetes or insulin-dependent diabetes, occurs when the pancreas produces little or no insulin due to the destruction of pancreatic β cells by the immune system. It is usually diagnosed in childhood or adolescence, although rarely in adults. Genetic and environmental factors are known to contribute to the development of T1D. However, the causal mechanisms are not fully understood and identifying the risks presents a challenge due to the practical, diagnostic and therapeutic implications. In a new study published in the Endocrine Society’s Journal of Clinical Endocrinology and Metabolism, researchers analyzed stool samples from pediatric patients to characterize the microbial imprint of people with T1D and identify groups of taxa that may be associated. to altered metabolic pathways.
Using a form of artificial intelligence called machine learning analysis, the researchers analyzed stool samples from 56 pediatric patients in Italy. All the children were first admitted to the emergency department and then sent to the pediatric inpatient clinic of the Regional Pediatric Diabetes Center. Thirty-one children newly diagnosed with T1D and 25 non-diabetic controls (healthy donors) were selected to participate in the study. Patients with gastrointestinal illness, recent antibiotic or probiotic use, or other forms of diabetes were excluded. Using next-generation sequencing, microbial DNA was extracted, amplified and sequenced. The hypervariable regions of the 16S ribosomal genes were used to determine the composition, relative abundance and biodiversity of each sample.
Two machine learning analyzes (Random Forest algorithms and l1l2), along with composition and biodiversity analyzes and statistical models, examined data regarding phylum, class, order, family, gender, species, relative abundance and diversity. The samples identified 1606 Operational Taxonomic Units (OTUs) in the study group and 1552 OTUs in healthy donors. After analysis, the researchers found significant differences in the composition of the gut microbiota of patients with T1D compared to healthy subjects. The most notable differences were higher relative abundances of B. stercoris, B. fragilis, B. intestinalis, B. bifidum, Gammaproteobacteria, Holdemania and Synergistetes species and lower abundances of B. vulgatus, Deltaproteobacteria, Parasutterella, Lactobacillus species. and Turicibacter. Additionally, healthy donors had a more diverse microbial flora than people with diabetes, although some analyzes attributed this to differences in geographic location. The genus Bacteroides (except B. vulgatus) and the Synergistetes subphylum have been identified as the most important organisms of interest in T1D.
Various metabolites produced by certain taxonomic groups have mechanisms that overlap with the pathology of metabolic diseases. The analysis of the genetic content made it possible to profile these metabolic pathways, linking them to their respective organisms. One hundred and seven of the 712 metabolic pathways found in diabetic patients were associated with glucose metabolism. The metabolism of fructose and mannose, the metabolism of starch and sucrose, the pentose phosphate pathway, and the metabolism of galactose were the most common. It is worth noting that reports have shown an increased risk of insulin resistance in patients with type 2 diabetes who had higher iron levels in the tissues. Several of these iron metabolic pathways have also been identified.
In recent reports, CD8 T cells, which are involved in autoimmunity, have been shown to cross-react with epitopes of B. stercoris. Interestingly, B. stercoris was more abundant in T1D patients than in healthy donors. Studies have suggested that the cross-reactivity of this bacterial strain can activate these cytotoxic lymphocytes leading to the destruction of pancreatic cells and the development of T1D. However, the impact of the microbiota on the function of local immune cells is not yet clear.
In summary, the study showed a significant change in the composition of the microbiota in children newly diagnosed with T1D. Species of the genus Bacteroides and the Synergistetes subphylum were significantly more abundant in the diabetic group. In addition, higher relative abundances of gammaproteobacterial and enterobacterial species were observed – a finding has also been reported in patients with type 2 diabetes and is believed to have been associated with impaired fasting blood sugar and intestinal permeability. . While other research reported data on two polymorphic regions of the 16S gene, this study included seven polymorphic regions adding to the strengths of this investigation. The variety of analyzes and approaches used in this study also add to the robustness and consistency of the results, indicating the need for further research into the role of the gut microbiome in diabetic patients.
- Significant changes in the composition of the microbiota have been observed in children newly diagnosed with T1D.
- Species of the genus Bacteroides (except B. vulgatus) and Synergistetes subphylum may be important indicators of metabolic disease.
- CD8 T lymphocytes, which are involved in autoimmunity, have been shown to cross-react with epitopes of B. stercoris.
References for “Gut Microbiome – Identifying Children With Type 1 Diabetes”:
Biassoni R, Di Marco E, Squillario M, et al. Gut microbiota in pediatric patients with T1D: machine learning algorithms to classify microorganisms as related to disease. J Clin Endocrinol Metab. 2020; 105 (9): dgaa407. doi: 10.1210 / clinem / dgaa407
Culina S, Lalanne AI, Afonso G, Cerosaletti K, et al. The frequencies of islet-reactive CD8 (+) T cells in the pancreas but not in the blood distinguish patients with type 1 diabetes from healthy donors. Sci Immunol. 2018; 3. PII: eaao4013.
Melinda Rodriguez, 2021 PhD Candidate, Lake Erie College of Osteopathic Medicine – L | E | C | O | M School of Pharmacy