Metagenomic profiles of the early life microbiome of Indonesian inpatient neonates and their influence on clinical characteristics.
Radhian AmanditoAmarila MalikRinawati RohsiswatmoPublished in: Scientific reports (2022)
Determining the initial normal neonatal gut microbiome is challenging. The debate regarding the sterile fetal environment is still ongoing. Therefore, studying and comparing normal and dysbiotic microbiomes requires the elucidation of both the fetal and infant microbiomes. Factors influencing the normal microbiome also include regional and genetic factors specific to different countries. Determining the normal microbiome population in our center and their association with the clinical conditions of infants is helpful as a tool for both the prevention and treatment of related diseases during neonatal care. Here, we employed metagenomic sequencing to characterize meconium and the subsequent early-life gut microbiome of preterm neonates in Jakarta, Indonesia. Microbiome diversity and complexity was higher in the meconium and on day 4 than on day 7. At the genus level, the most abundant genus overall was unidentified Enterobacteriaceae, with meconium samples dominated by Ureaplasma, day 4 fecal samples dominated by Staphylococcus, and day 7 samples dominated by Clostridiales, while at the phylum level the most abundant was Proteobacteria and Firmicutes. Perinatal factors of PROM and mother's diet influenced the meconium microbiome, while day 4 and day 7 microbiome was associated with bacteremia and early administration of antibiotics. One of our sample sets was derived from triplets, and they had varying diversity despite being triplets. These data are valuable for understanding the formation of a healthy microbiome specific to neonates and devising a strategy to improve both the gut health and related clinical outcomes of the neonate.
Keyphrases
- early life
- low birth weight
- healthcare
- mental health
- palliative care
- public health
- physical activity
- multidrug resistant
- electronic health record
- pregnant women
- gene expression
- preterm infants
- weight loss
- machine learning
- risk assessment
- cystic fibrosis
- chronic pain
- quality improvement
- health information
- gram negative
- data analysis
- human health