Phenylketonuria (PKU) is an inborn error of metabolism associated with high blood levels of phenylalanine (Phe). A Phe-restricted diet supplemented with L-amino acids is the main treatment strategy for this disease; if started early, most neurological abnormalities can be prevented. The healthy human gut contains trillions of commensal bacteria, often referred to as the gut microbiota. The composition of the gut microbiota is known to be modulated by environmental factors, including diet. In this study, we compared the gut microbiota of 8 PKU patients on Phe-restricted dietary treatment with that of 10 healthy individuals. The microbiota were characterized by 16S rRNA sequencing using the Ion Torrent™ platform. The most dominant phyla detected in both groups were Bacteroidetes and Firmicutes. PKU patients showed reduced abundance of the Clostridiaceae, Erysipelotrichaceae, and Lachnospiraceae families, Clostridiales class, Coprococcus, Dorea, Lachnospira, Odoribacter, Ruminococcus and Veillonella genera, and enrichment of Prevotella, Akkermansia, and Peptostreptococcaceae. Microbial function prediction suggested significant differences in starch/glucose and amino acid metabolism between PKU patients and controls. Together, our results suggest the presence of distinct taxonomic groups within the gut microbiome of PKU patients, which may be modulated by their plasma Phe concentration. Whether our findings represent an effect of the disease itself, or a consequence of the modified diet is unclear.

Despite increased efforts, the diverse etiologies of Necrotizing Enterocolitis (NEC) have remained largely elusive. Clinical predictors of NEC remain ill-defined and currently lack sufficient specificity. The development of a thorough understanding of initial gut microbiota colonization pattern in preterm infants might help to improve early detection or prediction of NEC and its associated morbidities. Here we compared the fecal microbiota successions, microbial diversity, abundance and structure of newborns that developed NEC with preterm controls. A 16S rRNA based microbiota analysis was conducted in a total of 132 fecal samples that included the first stool (meconium) up until the 5th week of life or NEC diagnosis from 40 preterm babies (29 controls and 11 NEC cases). A single phylotype matching closest to the Enterobacteriaceae family correlated strongly with NEC. In DNA from the sample with the greatest abundance of this phylotype additional shotgun metagenomic sequencing revealed Citrobacter koseri and Klebsiella pneumoniae as the dominating taxa. These two taxa might represent suitable microbial biomarker targets for early diagnosis of NEC. In NEC cases, we further detected lower microbial diversity and an abnormal succession of the microbial community before NEC diagnosis. Finally, we also detected a disruption in anaerobic microorganisms in the co-occurrence network of meconium samples from NEC cases. Our data suggest that a strong dominance of Citrobacter koseri and/or Klebsiella pneumoniae, low diversity, low abundance of Lactobacillus, as well as an altered microbial-network structure during the first days of life, correlate with NEC risk in preterm infants. Confirmation of these findings in other hospitals might facilitate the development of a microbiota based screening approach for early detection of NEC.

The data used for profiling microbial communities is usually sparse with some microbes having high abundance in a few samples and being nearly absent in others. However, current bioinformatics tools able to deal with this sparsity are lacking. pime (Prevalence Interval for Microbiome Evaluation) was designed to remove those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of pime were compared against existing methods and tested using 16S rRNA independent data sets. pime filters microbial taxa not shared in a per treatment prevalence interval started at 5% prevalence with increasing increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees were calculated to predict the likelihood of detecting differences in treatments. The best prevalence‐filtered data set was user‐selected by choosing the prevalence interval that kept a large portion of the 16S rRNA sequences in the data set while also showing the lowest error rate. To obtain the likelihood of introducing type I error while building prevalence‐filtered data sets, an error detection step based was also included. A pime reanalysis of published data sets uncovered other expected microbial associations than previously reported, which may be masked when only relative abundance was considered.

BackgroundAdministering intravenous antibiotics during labor to women at risk for transmitting Group B Streptococcus (GBS) can prevent infections in newborns. However, the impact of intrapartum antibiotic prophylaxis on mothers’ microbial community composition is largely unknown. We compared vaginal microbial composition in pregnant women experiencing preterm birth at ≤ 32 weeks gestation that received intrapartum antibiotic prophylaxis with that in controls.MethodsMicrobiota in vaginal swabs collected shortly before delivery from GBS positive women that received penicillin intravenously during labor or after premature rupture of membranes was compared to controls. Microbiota was analyzed by 16S rRNA sequencing using the PGM Ion Torrent to determine the effects of penicillin use during hospitalization and GBS status on its composition.ResultsPenicillin administration was associated with an altered vaginal microbial community composition characterized by increased microbial diversity. Lactobacillus sp. contributed only 13.1% of the total community in the women that received penicillin compared to 88.1% in the controls. Streptococcus sp. were present in higher abundance in GBS positive woman compared to controls, with 60% of the total vaginal microbiota in severe cases identified as Streptococcus sp.ConclusionsVaginal communities of healthy pregnant women were dominated by Lactobacillus sp. and contained low diversity, while Group B Streptococcus positive women receiving intrapartum antibiotic prophylaxis had a modified vaginal microbiota composition with low abundance of Lactobacillus but higher microbial diversity.

Introduction The gut microbiome has been related to several features present in Glycogen Storage Diseases (GSD) patients including obesity, inflammatory bowel disease (IBD) and liver disease. Objectives The primary objective of this study was to investigate associations between GSD and the gut microbiota. Methods Twenty-four GSD patients on treatment with uncooked cornstarch (UCCS), and 16 healthy controls had their faecal microbiota evaluated through 16S rRNA gene sequencing. Patients and controls were ≥3 years of age and not on antibiotics. Faecal pH, calprotectin, mean daily nutrient intake and current medications were recorded and correlated with gut microbiome. Results Patients’ group presented higher intake of UCCS, higher prevalence of IBD (n = 04/24) and obesity/overweight (n = 18/24) compared to controls (n = 0 and 06/16, respectively). Both groups differed regarding diet (in patients, the calories’ source was mainly the UCSS, and the intake of fat, calcium, sodium, and vitamins was lower than in controls), use of angiotensin-converting enzyme inhibitors (patients = 11, controls = 0; p-value = 0.001) multivitamins (patients = 22, controls = 01; p-value = 0.001), and mean faecal pH (patients = 6.23; controls = 7.41; p = 0.001). The GSD microbiome was characterized by low diversity and distinct microbial structure. The operational taxonomic unit (OTU) abundance was significantly influenced by faecal pH (r = 0.77; p = 6.8e-09), total carbohydrate (r = -0.6; p = 4.8e-05) and sugar (r = 0.057; p = 0.00013) intakes. Conclusions GSD patients presented intestinal dysbiosis, showing low faecal microbial diversity in comparison with healthy controls. Those findings might be due to the disease per se , and/or to the different diets, use of UCSS and of medicines, and obesity rate found in patients. Although the main driver of these differences is unknown, this study might help to understand how the nutritional management affects GSD patients.