A Large Scale Metagenomic Analysis of the Faecal Microbiota in Preterm Infants Developing Necrotising Enterocolitis

Ellaby, N
(2018) A Large Scale Metagenomic Analysis of the Faecal Microbiota in Preterm Infants Developing Necrotising Enterocolitis. PhD thesis, University of Liverpool.

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Necrotising enterocolitis (NEC) is an inflammatory intestinal disorder affecting premature infants. Despite the worldwide improvement of health care practices and facilities raising the survival rates of neonatal premature infants, there has not been any improvement in treatment options or mortality rates for NEC. There has been an extensive volume of research into NEC, though to date there has not been any evidence to directly associate a causal agent to this devastating disease, nor have there been any conclusive observations of NEC prior to birth. The only key prognostic signal for NEC is that onset and severity of the disease are significantly associated with the prematurity of the neonatal infant. During the process of birth, the infant transitions from the near sterile conditions of the womb to the outside environment teeming with bacteria. Only then does the infant develop a symbiotic relationship as a host to beneficial bacteria. Upon this transition the community of gut microbes develops and aids in a range of host functions, namely digestion and absorption of nutrients, as well as the immune response. It is also at this time that NEC can begin to develop in the lower gastrointestinal tract. This coinciding of factors implicates the colonisation of the gut with bacteria in the development of NEC, however studies to date have failed to provide consistent reports of a causative pathogen or a characteristic gut microbiome structure associated with NEC. The purpose of this study was to characterise the structure of the bacterial community in the gastrointestinal tract of infants with and without NEC in order to identify a community distinctive to infants with NEC. This was addressed using non-invasive faecal sampling of premature infants in a large, prospectively enrolled cohort from across England, sampled over a twenty-day window spanning ten days prior to and ten days following the onset of NEC. Using established V4 16S rRNA protocols, bacterial taxa within environmental samples could be characterised without relying on classic culture dependent methods. With this amplification technique it was possible to utilise small amounts of bacterial DNA isolated from infant faecal samples while at the same time mitigating the selection bias associated with culture-based techniques. Short read sequencing (illumina MiSeq) was performed on a total of 656 faecal samples from 132 infants spanning eight neonatal intensive care units across England. All infants had gestational durations of less than 35 weeks. 44 infants had NEC (225 samples) and 88 were assigned as controls (431 samples) according to key risk factors defined by medical practitioners. Taxonomic abundances assigned with the QIIME informatics pipeline were normalised using non-reductive negative binomial normalisation. Local contributions to beta-diversity (LCBD) scores were used to quantify taxonomic changes in the community structure through subset regression. Non-metric multidimensional scaling (NMDS) was used to establish risk factors that best described NEC and control samples. The Random Forest machine learning algorithm was used to establish taxa that best discriminated between NEC and control infants, as well as to identify any conserved pathogens. Subset regression identified feeding regime, mode of delivery and age at sampling as significant discriminating factors for the NEC status of infants based on sample LCBD values. However, NMDS plots of sample LCBD values showed no clear clustering of samples according to NEC status. Canonical correlation analysis (CCA) indicated that this variability was due to inter-individual differences. Of the risk factors that could be accounted for, feeding regime was the most effective in differentiating community structures of NEC and control infant samples. There was also evidence that initial communities were influenced by delivery method. Three subgroups of infants based on these influential risk factors and with sufficient sampling depth were established and analysed separately in addition to collective, population-scale analysis. Random Forest analysis demonstrated that reduced abundance of the genus Bifidobacterium was significantly associated with NEC across all sub-groups of infants. Additionally, this method of analysis indicated no clear pathogenic taxa that consistently spanned the population. For infants with NEC that were delivered by caesarean section and fed both formula and breast milk, there was increased abundance of the genus Dialister when sampled shortly after birth. Infants that did not develop NEC, who were delivered vaginally and fed both formula and breast milk were seen to have a significantly greater abundance of the genus Veillonella over time, relative to NEC subjects. There were no additional taxonomic differences that could be ascertained in the sub-group of infants who were vaginally delivered and fed breast milk exclusively. Overall, the unique nature of the microbiome and the high degree of inter-individual variation within the community made direct comparisons between NEC and non-NEC subjects difficult. However, by accounting for factors that were significantly associated with NEC status it was possible to observe consistent association of increased bifidobacterial abundance in infants that did not develop NEC. This highlights the importance of large scale studies and case-control assignment when analysing complex community structures such as that of the human gastrointestinal tract, as well as the powerful, deep analytical analysis provided by machine learning algorithms. Further work should look to establish the impact and role of Bifidobacteria in the human gut community to inform early interventions in healthcare settings for infants at risk of NEC, focussing specifically on encouraging the development of a community structure reflective of that observed in premature infants that do not develop NEC.

Item Type: Thesis (PhD)
Uncontrolled Keywords: Metagenomics, Necrotising Enterocolitis, Machine Learning, 16S rRNA Sequencing
Divisions: Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 28 Nov 2018 11:35
Last Modified: 19 Jan 2023 01:26
DOI: 10.17638/03025353
URI: https://livrepository.liverpool.ac.uk/id/eprint/3025353