Dataset Information

Fetal exposures and perinatal influences on the stool microbiota of premature infants.

ABSTRACT: To test the hypothesis that maternal complications significantly affect gut colonization patterns in very low birth weight infants.Forty-nine serial stool samples were obtained weekly from nine extremely premature infants enrolled in a prospective longitudinal study. Sequencing of the bacterial 16S rRNA gene from stool samples was performed to approximate the intestinal microbiome. Linear mixed effects models were used to evaluate relationships between perinatal complications and intestinal microbiome development.Subjects with prenatal exposure to a non-sterile intrauterine environment, i.e. prolonged preterm premature rupture of membranes (PPPROM) and chorioamnionitis exposure, were found to have a relatively higher abundance of potentially pathogenic bacteria in the stool across all time points compared to subjects without those exposures, irrespective of exposure to postnatal antibiotics. Compared with those delivered by Caesarean section, vaginally delivered subjects were found to have significantly lower diversity of stool microbiota across all time points, with lower abundance of many genera, most in the family Enterobacteriaceae.We identified persistently increased potential pathogen abundance in the developing stool microbiota of subjects exposed to a non-sterile uterine environment. Maternal complications appear to significantly influence the diversity and bacterial composition of the stool microbiota of premature infants, with findings persisting over time.

SUBMITTER: Chernikova DA

PROVIDER: S-EPMC4476945 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fetal exposures and perinatal influences on the stool microbiota of premature infants.

Chernikova Diana A DA Koestler Devin C DC Hoen Anne Gatewood AG Housman Molly L ML Hibberd Patricia L PL Moore Jason H JH Morrison Hilary G HG Sogin Mitchell L ML Zain-Ul-Abideen Muhammad M Madan Juliette C JC

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians 20150904 1

<h4>Objective</h4>To test the hypothesis that maternal complications significantly affect gut colonization patterns in very low birth weight infants.<h4>Methods</h4>Forty-nine serial stool samples were obtained weekly from nine extremely premature infants enrolled in a prospective longitudinal study. Sequencing of the bacterial 16S rRNA gene from stool samples was performed to approximate the intestinal microbiome. Linear mixed effects models were used to evaluate relationships between perinatal ...[more]

PMID: 25394613

Similar Datasets

Project description:BackgroundNecrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient's risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL).ResultsWe used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the "growing bag" analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or "shotgun" whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula.ConclusionsWe have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota.

Project description:BackgroundThe gastrointestinal (GI) microbiota has been linked to health consequences throughout life, from early life illnesses (e.g. sepsis and necrotising enterocolitis) to lifelong chronic conditions such as obesity and inflammatory bowel disease. It has also been observed that events in early life can lead to shifts in the microbiota, with some of these changes having been documented to persist into adulthood. A particularly extreme example of a divergent early GI microbiota occurs in premature neonates, who display a very different GI community to term infants. Certain characteristic patterns have been associated with negative health outcomes during the neonatal period, and these patterns may prove to have continual damaging effects if not resolved.ResultsIn this study we compared a set of premature infants with a paired set of term infants (n = 37 pairs) at 6 weeks of age and at 2 years of age. In the samples taken at 6 weeks of age we found microbial communities differing in both diversity and specific bacterial groups between the two infant cohorts. We identified clinical factors associated with over-abundance of potentially pathogenic organisms (e.g. Enterobacteriaceae) and reduced abundances of some beneficial organisms (e.g. Bifidobacterium). We contrasted these findings with samples taken at 2 years of age, which indicated that despite a very different initial gut microbiota, the two infant groups converged to a similar, more adult-like state. We identified clinical factors, including both prematurity and delivery method, which remain associated with components of the gut microbiota. Both clinical factors and microbial characteristics are compared to the occurrence of childhood wheeze and eczema, revealing associations between components of the GI microbiota and the development of these allergic conditions.ConclusionsThe faecal microbiota differs greatly between infants born at term and those born prematurely during early life, yet it converges over time. Despite this, early clinical factors remain significantly associated with the abundance of some bacterial groups at 2 years of age. Given the associations made between health conditions and the microbiota, factors that alter the makeup of the gut microbiota, and potentially its trajectory through life, could have important lifelong consequences.

Dataset Information

Fetal exposures and perinatal influences on the stool microbiota of premature infants.

Publications

Fetal exposures and perinatal influences on the stool microbiota of premature infants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets