Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases

Specific Alternation of Gut Microbiota and the Role of Ruminococcus gnavus in the Development of Diabetic Nephropathy

In this study, we aim to investigate the precise alterations in the gut microbiota during the onset and advancement of diabetic nephropathy (DN) and examine the impact of Ruminococcus gnavus (R. gnavus) on DN. Eight-week-old male KK-Ay mice were administered antibiotic cocktails for a duration of two weeks, followed by oral administration of R. gnavus for an additional eight weeks. Our study revealed significant changes in the gut microbiota during both the initiation and progression of DN. Specifically, we observed a notable increase in the abundance of Clostridia at the class level, higher levels of Lachnospirales and Oscillospirales at the order level, and a marked decrease in Clostridia_UCG-014 in DN group. Additionally, there was a significant increase in the abundance of Lachnospiraceae, Oscillospiraceae, and Ruminococcaceae at the family level. Moreover, oral administration of R. gnavus effectively aggravated kidney pathology in DN mice, accompanied by elevated levels of urea nitrogen (UN), creatinine (Cr), and urine protein. Furthermore, R. gnavus administration resulted in down-regulation of tight junction proteins such as Claudin-1, Occludin, and ZO-1, as well as increased levels of uremic toxins in urine and serum samples. Additionally, our study demonstrated that orally administered R. gnavus up-regulated the expression of inflammatory factors, including nucleotide-binding oligomerization domain-like receptor pyrin domain-containing protein 3 (NLRP3) and Interleukin (IL)-6. These changes indicated the involvement of the gut-kidney axis in DN, and R. gnavus may worsen diabetic nephropathy by affecting uremic toxin levels and promoting inflammation in DN.

The curious case of Prevotella copri

Prevotella copri is an abundant member of the human gastrointestinal microbiome, whose relative abundance has curiously been associated with positive and negative impacts on diseases, such as Parkinson's disease and rheumatoid arthritis. Yet, the verdict is still out on the definitive role of P. copri in human health, and on the effect of different diets on its relative abundance in the gut microbiome. The puzzling discrepancies among P. copri studies have only recently been attributed to the diversity of its strains, which substantially differ in their encoded metabolic patterns from the commonly used reference strain. However, such strain differences cannot be resolved by common 16S rRNA amplicon profiling methods. Here, we scrutinize P. copri, its versatile metabolic potential, and the hypotheses behind the conflicting observations on its association with diet and human health. We also provide suggestions for designing studies and bioinformatics pipelines to better research P. copri.

Clostridia and Enteroviruses as Synergistic Triggers of Type 1 Diabetes Mellitus

What triggers type 1 diabetes mellitus (T1DM)? One common assumption is that triggers are individual microbes that mimic autoantibody targets such as insulin (INS). However, most microbes highly associated with T1DM pathogenesis, such as coxsackieviruses (COX), lack INS mimicry and have failed to induce T1DM in animal models. Using proteomic similarity search techniques, we found that COX actually mimicked the INS receptor (INSR). Clostridia were the best mimics of INS. Clostridia antibodies cross-reacted with INS in ELISA experiments, confirming mimicry. COX antibodies cross-reacted with INSR. Clostridia antibodies further bound to COX antibodies as idiotype-anti-idiotype pairs conserving INS-INSR complementarity. Ultraviolet spectrometry studies demonstrated that INS-like Clostridia peptides bound to INSR-like COX peptides. These complementary peptides were also recognized as antigens by T cell receptor sequences derived from T1DM patients. Finally, most sera from T1DM patients bound strongly to inactivated Clostridium sporogenes, while most sera from healthy individuals did not; T1DM sera also exhibited evidence of anti-idiotype antibodies against idiotypic INS, glutamic acid decarboxylase, and protein tyrosine phosphatase non-receptor (islet antigen-2) antibodies. These results suggest that T1DM is triggered by combined enterovirus-Clostridium (and possibly combined Epstein-Barr-virus-Streptococcal) infections, and the probable rate of such co-infections approximates the rate of new T1DM diagnoses.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Misidentification of Phocaeicola (Bacteroides) dorei in two patients with bacteremia

Phocaeicola (Bacteroides) dorei is a Gram-negative anaerobic bacillus that is rarely isolated from human specimens. Its accurate identification can be hampered by its close taxonomic relationship with Bacteroides vulgatus. We report on two patients with bacteremia due to P. (B.) dorei, which was initially identified as B. vulgatus by MALDI-TOF MS.