Do Probiotics Modulate the Intestinal Microbiome of Extremely Preterm Infants?
Phase: PHASE3
Conditions: Microbial Colonization
Sponsor: Belal Alshaikh

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Do probiotics modulate the intestinal microbiome in extremely premature infants? 
Principle Investigator: Dr. Harish Amin 
REB16-0542 
October 11, 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

1 
 
Project Title: 
 
Do probiotics modulate the intestinal microbiome in extremely premature infants? 
 
Project Type:  
 
Primary – Research 
 
 
 
Secondary – Clinical care 
  
 
 
 
Principal Applicant (PA): 
Harish Amin, MD (Concept, literature review, study design, drafting application) 
Co-Principal Applicant:  
Belal Alshaikh, MD (Concept, study design and statistics, drafting application) 
 
Collaborators:   
 
Marie-Claire Arrieta, PhD (Microbiome Research Scientist) 
                                                          Richard Pon, PhD (ACHRI - Sample analysis) 
 
 
 
 
Jumana Samara, MD (MSc student in Microbiology) 
Joseph Vayalumkal, MD (Infection prevention and control) 
Amuchou Soraisham, MD (Epidemiology and data analysis)  
Deonne Dersch-Mills, PharmD (ACH/FMC pharmacists: product management)  
Project Summary:   
The gut microbiome plays a significant role in balancing the inflammatory system in the immature gut. A breakdown in this 
balance with altered colonization of the microbiota in very low birth weight (VLBW) preterm infants is associated with 
increased feeding intolerance, NEC and sepsis. Probiotics are proposed to normalize microbial populations and decrease 
intestinal disease in preterm infants. There is limited data linking clinical outcomes with the biology of probiotics. We aim to 
study the colonization of the GI tract with probiotic species contained in a specific probiotic blend – Florababy – in VLBW 
preterm infants.  
Stool microbiome will be analyzed at 4 time points in 2 groups (one given Florababy and the other no) of infants less than 
1000g birth weight and < 29 weeks gestation. A comparison of stool microbiome analysis and the incidence of feeding 
intolerance and time to reach full feeds in the two groups will be made. 
 
1. What you want to do.  
 
Research question 
Does Florababy change the gut colonization pattern in a measurable way in a population of very premature neonates?  
 
Hypothesis: 
Florababy probiotics can multiply in the gut and change the colonization pattern of the premature intestinal tract in a measurable 
way. 
 
Primary outcome: difference in stool microbiome after 4 weeks of Florababy or  compared to infants who didn’t receive 
Florababy. 
Secondary outcomes: i) difference in stool microbiome 1week after the study drug administration is stopped. 
ii) incidence of feeding intolerance and time to reach full feeds. 
 
3. Why this is important. 
At a glance: 
a) Probiotics are proposed as universal supplements in preterm neonates. Results of probiotic administration in neonates are 
conflicting. 
b) There is very little data linking clinical outcomes with the biology of probiotics. Gaps in current knowledge are the ecology 
of probiotics, their fate after administration of a specific product to a given infant, and the degree of influence on the pre-
existing and/or developing microbiome. 
c) It is essential that biological and mechanistic data are obtained and correlated with clinical data for a given probiotic.  
 
2.1 Microbiome and the neonate. 
There is growing recognition of the importance of the relationship between the human body and the microbial flora that coexist 
within it. The “human” body contains over 10 times more microbial cells than human cells, carrying 150 times as many genes 
as the human genome. The “microbiome” describes the collective genomes of these microorganisms.  Microbial populations 
residing in the GI tract are the largest and most complex microbiome in our body. Studies of the intestinal microbiome have 
revealed a wide and variable microbial population whose composition is affected by host factors such as genetics, the immune 
system and environmental factors. The microbiome has a myriad of roles locally and systemically. There has been an explosion 
of information about its effects on local and systemic immunity and inflammation, the nervous system, the endocrine 
environment and metabolic states, etc. with several reviews that have been published recently (1-5). 

2 
 
The fetal intestine was initially thought to be sterile. However, recent studies suggest that it may be exposed to microbes via 
the amniotic fluid (6-8). During and after birth, microorganisms from the maternal vagina and intestine, from the maternal skin 
surface, from maternal breastmilk and oral cavity, and from the environment colonize the neonatal GI tract. Many factors have 
been shown to alter the gut flora in neonates such as gestational age and feeding patterns. Data on the development of the 
normal microbiome in well term infants is still limited. It shows a very complex process where waves of microbial species get 
established in a careful orchestrated order with multiple interactions between them and the host (9-14). Many details of this 
process remain unknown.  
Furthermore, the intestinal microbiomes of infants admitted to NICUs have different patterns compared to infants not admitted 
to NICUs. This is due to exposure to a different environment in NICUs, and the use of antibiotics and sterile formulas. There 
is even less data about the establishment of the microbiome in very preterm infants, how that evolves with time, what constitutes 
a “good” profile and how it correlates with intestinal or systemic pathology. What we know shows that the microbiome in 
preterm infants demonstrates decreased diversity, altered species proportions and even different behavior of the same species 
when compared with the term infant (15-17).  
 
2.2 Microbiome and intestinal disease in the neonate. 
The microbiome plays an essential role in nutrition, mucosal immunity, and GI epithelial integrity. Current evidence suggests 
that interactions between the altered microbiome and the intestinal epithelium play an important role in the development of 
feeding intolerance, allergy, sepsis and necrotizing enterocolitis (NEC) (18-20).  
NEC is a multifactorial disease and one of the major pathologies affecting preterm infants. It is associated with significant 
mortality and morbidity, including long term neurologic disability. Systematic interventions to predict and prevent NEC have 
generally remained elusive. Feeding breastmilk and colostrum to preterm infants is helpful and use of probiotics has shown 
good results in a number of studies, though significant concerns remain. Concerns include: the limited safety data for infants 
born < 1000g and a domestically available probiotic formulation with demonstrated efficacy, safety, and regulatory approval. 
There remain concerns that the regulatory framework for approval of probiotics does not approach the rigor for drugs (21-22). 
Systematic reviews of randomized controlled trials concluded that probiotics as a group significantly reduce the incidence of 
severe NEC (23). The principle and methodology of such generalizations is controversial (24-26). Better tools are needed to 
aid in the understanding and monitoring of probiotic administration. 
The technology available to define the intestinal microbiome has been a sizable obstacle. The vast majority of species cannot 
be cultured and the genomic technologies have just only very recently reached a level where the ratio of cost and resolution 
allows for reasonably efficient research use.  
 
2.3 Techniques to study gut microbiota and microbiome. 
The composition of the gut microbiota has been classically analyzed using differential media to distinguish between populations 
of bacteria based on their metabolic requirements. The capability of these culture-based techniques to discriminate between 
different bacterial phylogenetic groups is limited. It is estimated that 80% of the gut microbiota cannot be cultured using these 
techniques. Recent reviews emphasized the role of genomic technology in mapping the microbiome, providing an 
unprecedented amount of detail on the constituent species (27-29). These genomic techniques are capable of identifying 
commercial probiotic strains. One of the leading techniques employed currently is 16S rDNA amplification and sequencing. It 
is available in Calgary through the Genomics facility at Alberta Children’s Hospital Research Institute (ACHRI). 
 
3.1 Probiotic administration results in preterm infants 
The subject of probiotic administration to preterm infants is important.  There are at least 20 meta-analyses and systematic 
reviews identified with a Pubmed search. This is a large number despite only 24 trials in the latest Cochrane review and a meta-
analysis from April 2014 (23). It is also important to note the contradictory statements in that review. It begins by stating: 
“Based on the available evidence for probiotics efficacy and safety in preterm infants, the number of infants enrolled, the 
narrow confidence intervals, and the probiotics safety profile, a change in practice is warranted at this stage. More studies to 
address the optimal preparation, dosing, and duration of therapy are still needed in head to head comparative studies rather than 
placebo controlled trials.” Immediately followed by: “Although all included trials evaluated probiotics use in preterm infants, 
the trials were highly variable with regard to enrolment criteria (that is birth weight and gestational age), baseline risk of NEC 
in the control groups, timing, dose, formulation of probiotic used, and feeding regimens.(23)The validity of our review’s results 
is potentially compromised as it included trials that utilized different preparations and dosing regimens of the intervention under 
study; and data on the highest risk population (ELBW infants) could not be retrieved.”  
 
Two sizeable recent studies were published or had outcome data released after the Cochrane review. The recent Probiotics in 
Preterm babies Study (PiPS) was done in Britain and enrolled 1315 infants of 30 weeks gestational age or less. It used a single 
bacterial strain – Bifidobacterium breve. This study did not find any significant clinical impact of that particular probiotic and 
the findings do not support the use of Bifidobacterium breve for the prevention of NEC (30). Another recent RCT studied 
Lactobacillus Reuteri in 400 VLBW infants of less than 32 weeks gestation and it concluded that there was no effect on NEC 

3 
 
or NEC and mortality though late onset sepsis was significantly reduced (31). The combined size of these 2 studies is large, 
over 1/3rd of all previous RCTs’ population in the 2014 Cochrane review.   
 
3.2 Florababy. 
Florababy is a 5 strain proprietary probiotic blend produced by Renew Life Canada. It contains 4 billion total CFU of 4 
Bifidobacterium species (Breve, bifidum, infantis and longum) together with Lactobacillus rhamnosus. The non-medicinal 
ingredients are maltodextrin and ascorbic acid. Florababy has been used for only a few years in neonates in Canada. A recently 
published retrospective cohort study compared data from 17 months before and after introduction of Florababy in a level III 
NICU for all infants less than 32 weeks gestational age at birth (32). It showed a positive impact on NEC stage 2 or more from 
a baseline of 10% in infants less than 32 weeks, double the current rate in Calgary. The amount of breastmilk feeding in that 
cohort is not specified. There was no impact on health care associated infections.  
 
3.3 Biology of probiotics and relation with effects – gaps in knowledge. 
The above underscores the need for specific product trials in this population as well as the pressing need for markers and 
biological understanding. A lot of the limited data concerning the fate of probiotics in preterm infants may not be applicable to 
current NICU care and feeding practices or was generated before latest genomics technology was available (33-43). The 
percentage of infants and interval of time in which specific probiotic strains are established in the gut, any differences between 
strains, differences within a population fed fresh breastmilk and correlation with clinical effects including tolerance of enteral 
feeds and time to reach full feeds are very important aspects. They need to be clarified for contemporary practices, with best 
methods, in order to assess and optimize the use of probiotics in the neonate. Using meta-analysis to advocate for an intervention 
when the biological pathways were not clear has been a source of error before in medicine (44). 
 
3.4 Definition of Feeding Intolerance. 
Feeding intolerance will be defined as the inability to digest enteral feedings associated with: i) gastric residual volumes of 
more than 50% of the volume of the previous feed, ii) abdominal distention and/or emesis, iii) disruption of the baby’s feeding 
plan (decrease, delay or discontinuation of enteral feedings). 
 
4. How you are going to do it. 
 
4.1 Study design 
This study will occur in a randomized controlled trial setup in the Calgary NICUs. Florababy probiotic will be administered to 
infants in the drug arm and clinical outcomes and gut microbiome data will be compared between the two groups.  
The projected starting time is Oct 2017. Study duration will be 18 months. 
 
4.2 Study Population 
Premature infants born less than 29 weeks gestation and < 1000g birth weight admitted to the NICU at Foothills Medical Centre 
in Calgary. 
a)  Inclusion Criteria: Preterm infants born at less than 29 weeks gestation and < 1000g birth weight. 
b) Exclusion Criteria: Infants with major congenital anomalies, hypoxic-ischemic injury and NEC or bowel perforation before 
72 hours of life. 
 
4.3 Sample Size and feasibility  
A total of 100 infants of birth weight <1000g are born annually and admitted to FMC NICU.  
Assuming that 80% of parents’ consent to their infant’s participation in the study, it will be feasible to complete enrollment of 
60 babies in the first year of the study. This study will randomize 30 infants to be given probiotics and 30 infants be in the 
control group where no probiotic given. The 2 groups will be matched by gender, gestational age +/- one week, birth weight 
+/- 100g, SGA vs AGA/LGA status and type of delivery.   
A sample size of 30 infants per group will allow for a power of 0.8 and an alpha of 0.05 for a two-tailed Wilcoxon-Mann-
Whitney test to detect a 0.7 effect size when comparing relative abundance of stool probiotic microbial species in the group 
given Florababy versus the one given placebo, conservatively assuming a background logistic distribution (G-Power 3.1 
software). 
 
4.4 Variables collected 
a) 
Demographic variables: Gestational age, birth weight, head circumference at birth, gender, mode of delivery, 
singleton/multiple and rank, maternal administration of corticosteroids and antibiotics. 
b) Clinical variables: Apgar scores, umbilical cord pH and base excess, first patient blood gas pH and lactate, day of life at 
start of feeds and at start of study, total days of study drug, antibiotic use during NICU, use of inotropes and vasoactive 
substances, steroids and medical and/or surgical treatment for PDA, type of feeds during NICU and at discharge. 

4 
 
c) Outcome variables: mortality; total days of TPN, total days NPO and number of separate NPO events; age, head 
circumference and weight at discharge home; diagnosis of: feeding intolerance, time taken to reach full enteral feeds (at 
least 150ml/kg/day), suspected NEC, NEC, cow milk protein allergy, late onset sepsis, BPD, ROP.  
 
4.5 Study protocol. 
Infants will be identified within 24 hours of birth and parents will be approached for informed consent.  
Once consent is obtained, subjects will be randomly assigned in blocks of 4 to receive either Florababy probiotics orno. 
Investigators will conduct the randomization using a computer-generated table of random numbers generated at the University 
of Calgary.  
 
Preparation and Administration of Study Drug 
The study probiotic will be started after the first stool sample is obtained and after informed parental consent. Very small 
amounts of breast milk/colostrum (0.1ml to 0.2ml every 4 hours) are usually given to all premature babies within the first 48 
hours of life.  Actual feeds of breast and/or human milk (donor milk) are initiated from 72 hours onwards (starting at 
10ml/kg/day and increasing slowly thereafter). The study probiotic (at a dose of 0.5g per day) will be started at or after 72 hours 
of life. It will be administered in 1 ml of sterile water prior to a feed.. No drug will be given to infants in the control group  If 
the infant is placed NPO, the study drug will be stopped and restarted together with refeeding.  
The study probiotic will be given until 34 weeks corrected gestational age.  
 
Sample collection 
Nurses will collect the stool samples at 4 time points: prior to, two weeks and four weeks after probiotic administration is 
commenced, and one week after it is stopped. Stool samples for the control group will be done at a matched feeding and 
gestational age time points. 
Stool will be collected directly from the infant’s diaper with a sterile spatula.  As soon as a sample is collected, the nurse will contact the 
investigators. The samples will then be placed in a laboratory freezer (-80°C) within 24 hours of collection. Batched samples 
will be transported to the ACH ACHRI Genomics laboratory for subsequent microbiome processing. 
 
4.6 Microbiome DNA extraction, amplification and species identification. 
 
 DNA extraction 
DNA will be extracted from ~200 mg of frozen (-80oc) stool using the Zymo ZR Fecal DNA Stool MiniPrep Kit (Zymo 
Research Corp, USA). 
 
Real-time quantitative PCR (qPCR) 
Quantification of target bacterial DNA will be measured using the SsoFast™ EvaGreen® Supermix (Biorad, Mississauga, 
Canada). Samples and standards (10 ng of DNA from known bacteria) will be prepared and run in duplicate in a final volume 
of 20 l on 96 well plates using the CFX96 detection system (Biorad,  Mississauga, Canada). All collected fecal samples will 
be quantified for Bacteroides, Prevotella, Clostridium coccoides and C. leptum, Enterococcus and Enterobacteriacae sp. as 
indicators of microbiome health. 45   
 
Library preparation and sequencing  
Sequencing of the V3-V4 region of the 16S rRNA gene will be performed on all cases and control samples. We will aim to 
recruit no more that 50% of our cases from VRE positive patients. Preparation of 16S metagenomics libraries and deep 
sequencing will be carried out by the University of Calgary's Core DNA Services. DNA libraries be prepared using the 16S 
Metagenomic sample preparation protocol (Illumina, San Diego, CA). The quality of the prepared library will be checked using 
Agilent TapeStation D1000 screen tape (Agilent Technologies, Santa Clara, CA), according to the manufacturer's instructions. 
Indexed DNA libraries are normalized to 4 nM and Illumina Experiment Manager used to build library plates and create sample 
sheet. Paired-end 300bp sequencing will be performed on the MiSeq instrument using the V3 600 cycle MiSeq cartridge and 
MiSeq v3 reagents. The completed run will be demultiplexed with Illumina's Casava software and stored in BaseSpace 
(Illumina) for downstream analysis. 
 
Bioinformatic analysis  
Sequences will be processed with the UPARSE pipeline as implemented in USEARCH v 8.0.1623.46  Details for each 
processing step are outlined. For quality filtering the forward and reverse 16S PCR primers along with any sequencing adapter 
will be trimmed off the raw reads using cutadapt v1.8. Following this the forward and reverse reads will be overlapped to 
reconstruct the full amplicon.  This will be done using the fastq_mergepairs option in USEARCH v 8.0.1623. Following this 
the reads will be quality filtered using the maximum expected error method and then truncated at a length of 173 base pairs. 
Sequences will be dereplicated, where identical reads are merged and counts of these reads are retained, with vsearch v1.1.3 

5 
 
hosted on GitHub (“https://github.com/torognes/vsearch”). Singleton reads will be filtered out with vsearch prior to clustering.  
The remaining sequences are then clustered into groups of 97% identity, designated as Operational Taxonomic Units (OTUs) 
with the cluster_otus command in USEARCH v 8.0.1623.  To construct an OTU table the filtered reads, with singletons 
included, were searched against the clustered OTU sequences with usearch_global in USEARCH v 8.0.1623. 
Taxonomy will be assigned to each of the representative sequences using the RDP Naïve Bayesian Classifer v 2.247 as 
implemented in Qiime 1.9.0 48 using the most recent version of the Greengenes database49. The RDP classifier uses a bootstrap 
procedure to assign confidence values to the taxonomic assignments at each level of the taxonomy.  The OTU sequences will 
be aligned with PyNAST, a flexible tool for aligning sequences to a template alignment50. Gaps in the alignment will be 
removed using Qiime’s ‘filter_alignment.py’, and a phylogeny constructed using FastTree, a tool for inferring phylogenies for 
alignments with up to hundreds of thousands of sequences51, using default parameters. All remaining analysis will be done in 
R, a programming language and software environment for statistical computing and graphics (R Core Team, 2015) using 
functions from phyloseq52 DESeq253  and vegan.  
The diversity within each sample, also known α diversity, will be measured using the Shannon index. This is a measure of both 
the organismal richness of a sample and the evenness of the organisms' abundance distribution54. A two-sided Mann-Whitney 
test (with False Discovery Rate multiple test correction) will be used to determine if there were significant differences in 
Shannon diversity between cases and controls.   
Diversity between samples (β -diversity) will be evaluated using weighted UniFrac, a rank-based ordination method and 
visualized with Non-metric Multidimensional Scaling (NMDS). β-diversity represents the explicit comparison of microbial (or 
other) communities based on their composition or relative abundance. β diversity metrics thus assess the differences between 
microbial communities. To test whether there are significant differences in community structure between groups the UniFrac 
distance matrix will be tested with permutational multivariate ANOVA (PERMANOVA) implemented in the adonis function 
in the vegan R package. Differential abundance of individual OTUs between groups will be assessed using generalized linear 
models with a negative binomial distribution as is used commonly for RNA-seq53.  The similarity in data structure between 
RNA-seq and 16S allow us to adapt already mature methods55. This analysis will be performed in DESeq2 v1.8.1 using default 
parameters.  
 
5. Why you/your group should do it (relevant prior experience and skills, collaborators for technical gaps, preliminary 
data showing feasibility): 
Dr. Amin has a lifelong interest and expertise as well as publications in neonatal GI pathophysiology and NEC.  
Dr. Alshaikh has completed a year of supplementary training in the study of the neonatal intestinal tract in Philadelphia. He is 
also skilled in study design and statistical analysis and has a Master degree in Epidemiology. 
The collaborators include Dr. Pon, the director of the ACHRI Genomics facility, Dr.  Arrieta is a research scientist at -University 
of Calgary and an expertise in microbiome, Dr Amuchou Soraisham (neonatologist and epidemiologist), and D. Dersch-Mills 
– Pediatric Pharmacists. 
 
6. Plans for dissemination or translation of results and plans for next steps in study of topic: The microbiome data will 
constitute a comprehensive clinical and biological study of probiotic administration in this population. Results will be presented 
at national and international meetings and submitted for publication.  
Findings from this study will also generate insight into the systemic effects of probiotics such that further hypotheses can be 
generated and tested and the best probiotic species and/or blends can be designed for use in preterm infants. 
 
Budget: 
1. Budget and considerations relating to budget 
 
Per sample cost 
Costing 
Consumables 
- 
$1000 
DNA extraction (Zymoresearch) 
Library preparation  
(Uof C sequencing centre) 
Sequencing 
$85 
$85 x 60 x4 = 
$20,400 
Equipment 
Pharmacy set up for Florababy for 60 patients in 1st year of 
study 
Phase III set up fee 
Dispensing fee for 60 patients ($30.00 per vial for each 
patient for 1 week) x 6 weeks = 60x30x6 
 
$1,500.00 
$1,000.00 
 
$10,800.00 
Total 
- 
$33,700.00 
 
 
 

6 
 
REFERENCES 
1. Sekirov I, Russell SL, Antunes LC, Finlay BB. Gut microbiota in health and disease. Physiological reviews. 
2010;90(3):859-904. Epub 2010/07/29. 
2. Group NHW, Peterson J, Garges S, Giovanni M, McInnes P, Wang L, et al. The NIH Human Microbiome Project. 
Genome research. 2009;19(12):2317-23. Epub 2009/10/13. 
3. Dethlefsen L, McFall-Ngai M, Relman DA. An ecological and evolutionary perspective on human-microbe mutualism 
and disease. Nature. 2007;449(7164):811-8. Epub 2007/10/19. 
4. Spor A, Koren O, Ley R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nature 
reviews Microbiology. 2011;9(4):279-90. 
5. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut 
microbiota. Nature 2012, Sep 13;489(7415):220-30. 
6. DiGiulio DB. Diversity of microbes in amniotic fluid. Semin Fetal Neonatal Med. 2012;17(1):2-11. Epub 2011/12/06. 
7. DiGiulio DB, Romero R, Kusanovic JP, Gomez R, Kim CJ, Seok KS, et al. Prevalence and diversity of microbes in the 
amniotic fluid, the fetal inflammatory response, and pregnancy outcome in women with preterm pre-labor rupture of 
membranes. Am J Reprod Immunol. 2010;64(1):38-57. Epub 2010/03/25. 
8. DiGiulio DB, Romero R, Amogan HP, Kusanovic JP, Bik EM, Gotsch F, et al. Microbial prevalence, diversity and 
abundance in amniotic fluid during preterm labor: a molecular and culture-based investigation. PloS one. 
2008;3(8):e3056. Epub 2008/08/30. 
9. Adlerberth I, Wold AE. Establishment of the gut microbiota in Western infants. Acta Paediatr. 2009;98(2):229-38. Epub 
2009/01/16. 
10. Jost T, Lacroix C, Braegger CP, Chassard C. New insights in gut microbiota establishment in healthy breast fed 
neonates. PloS one. 2012;7(8):e44595. Epub 2012/09/08. 
11. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factors influencing the composition of the 
intestinal microbiota in early infancy. Pediatrics. 2006;118(2):511-21. Epub 2006/08/03. 
12. Putignani L, Del Chierico F, Petrucca A, Vernocchi P, Dallapiccola B. The human gut microbiota: A dynamic interplay 
with the host from birth to senescence settled during childhood. Pediatr Res 2014, Jul;76(1):2-10. 
13. Neu J. The developing intestinal microbiome: probiotics and prebiotics. World review of nutrition and dietetics. 
2014;110:167-76. Epub 2014/04/23. 
14. Walker WA, ShubaIyengar R. Breastmilk, microbiota and intestinal immune homeostasis. Pediatr Res 2014,Oct 13. 
15. Barrett E, Kerr C, Murphy K, O'Sullivan O, Ryan CA, Dempsey EM, et al. The individual-specific and diverse nature of 
the preterm infant microbiota. Arch Dis Child Fetal Neonatal Ed 2013, Jul;98(4):F334-40. 
16. Unger S, Stintzi A, Shah P, Mack D, O'Connor DL. Gut microbiota of the very low birth weight infant. Pediatr Res 2014, 
Oct 13. 
17. Andriantsoanirina V, Teolis AC, Xin LX, Butel MJ, Aires J. Bifidobacterium longum and bifidobacterium breve isolates 
from preterm and full term neonates: Comparison of cell surface properties. Anae 
18. Canani RB, Di Costanzo M. Gut microbiota as potential therapeutic target for the treatment of cow's milk allergy. 
Nutrients. 2013;5(3):651-62. Epub 2013/03/05. 
19. Torrazza RM, Neu J. The altered gut microbiome and necrotizing enterocolitis. Clinics in perinatology. 2013;40(1):93-
108. Epub 2013/02/19. 
20. Robinson DT, Caplan MS. Linking fat intake, the intestinal microbiome and necrotizing enterocolitis in premature 
infants. Pediatr Res 2014, Oct 10. 
21. Neu J, Walker WA. Necrotizing enterocolitis. N Engl J Med 2011, Jan 20;364(3):255-64. 
22. Neu J. Necrotizing enterocolitis: The mystery goes on. Neonatology 2014;106(4):289-95. 
23. AlFaleh K, Anabrees J. Probiotics for prevention of necrotizing enterocolitis in preterm infants. Cochrane database of 
systematic reviews. 2014;4:CD005496. Epub 2014/04/12. 
24. Neu J. Probiotics and necrotizing enterocolitis. Clin Perinatol 2014, Dec;41(4):967-78. 
25. Millar M, Wilks M, Fleming P, Costeloe K. Should the use of probiotics in the preterm be routine? Arch Dis Child Fetal 
Neonatal Ed 2012, Jan;97(1):F70-4. 
26. Tarnow-Mordi WO, Wilkinson D, Trivedi A, Brok J. Probiotics reduce all-cause mortality and necrotizing enterocolitis: 
It is time to change practice. Pediatrics 2010, May;125(5):1068-70. 
27. Morgan XC, Huttenhower C. Meta'omic analytic techniques for studying the intestinal microbiome. Gastroenterology 
2014, May;146(6):1437-1448.e1. 
28. Norman JM, Handley SA, Virgin HW. Kingdom-agnostic metagenomics and the importance of complete 
characterization of enteric microbial communities. Gastroenterology 2014, May;146(6):1459-69. 
29. Davis C. Enumeration of probiotic strains: Review of culture-dependent and alternative techniques to quantify viable 
bacteria. J Microbiol Methods 2014, Aug;103:9-17. 
30. Costeloe K, Hardy P, Juszczak E, Wilks M, Millar MR, on behalf of The Probiotics in Preterm Infants Study 
Collaborative Group. The Lancet November 2015. http://dx.doi.org/10.1016/S0140-6736(15)01027-2. 

7 
 
31. Oncel MY, Sari FN, Arayici S, Guzoglu N, Erdeve O, Uras N, et al. Lactobacillus reuteri for the prevention of 
necrotising enterocolitis in very low birthweight infants: A randomised controlled trial. Arch Dis Child Fetal Neonatal 
Ed 2014, Mar; 99(2): F110-5. 
32. Janvier A, Malo J, Barrington KJ. Cohort study of probiotics in a north american neonatal intensive care unit. The 
Journal of pediatrics. 2014;164(5):980-5. Epub 2014/01/15Bezirtzoglou E, Stavropoulou E. Immunology and probiotic 
impact of the newborn and young children intestinal microflora. Anaerobe 2011, Dec;17(6):369-74. 
33. Akiyama K, Shimada M, Ishizeki S, Takigawa I, Imura S, Yamauchi K, et al. Effects of administration of 
Bifidobacterium in extremely premature infants; development of intestinal microflora by orally administered 
Bifidobacterium longum in comparison with Bifidobacterium breve. Acta Neonatol Japonica 1993;30:257-6 
34. Millar MR, Bacon C, Smith SL, Walker V, Hall MA. Enteral feeding of premature infants with lactobacillus GG. Arch 
Dis Child 1993, Nov;69(5 Spec No):483-7. 
35. Kitajima H, Sumida Y, Tanaka R, Yuki N, Takayama H, Fujimura M. Early administration of bifidobacterium breve to 
preterm infants: Randomised controlled trial. Arch Dis Child Fetal Neonatal Ed 1997, Mar 1;76(2):F101-7. 
36. Piano MD, Carmagnola S, Ballarè M, Balzarini M, Montino F, Pagliarulo M, et al. Comparison of the kinetics of 
intestinal colonization by associating 5 probiotic bacteria assumed either in a microencapsulated or in a traditional, 
uncoated form. J Clin Gastroenterol 2012, Oct;46 Suppl:S85-92. 
37. Underwood MA, Kalanetra KM, Bokulich NA, Lewis ZT, Mirmiran M, Tancredi DJ, Mills DA. A comparison of two 
probiotic strains of bifidobacteria in premature infants. J Pediatr 2013, Dec;163(6):1585-1591.e9.  
38. Tobin JM, Garland SM, Jacobs SE, Pirotta M, Tabrizi SN. Rapid assay to assess colonization patterns following in-vivo 
probiotic ingestion. BMC Res Notes 2013; 6:252. 
39. Li Y, Shimizu T, Hosaka A, Kaneko N, Ohtsuka Y, Yamashiro Y. Effects of bifidobacterium breve supplementation on 
intestinal flora of low birth weight infants. Pediatr Int 2004, Oct;46(5):509-15. 
40. Lee SJ, Cho SJ, Park EA. Effects of probiotics on enteric flora and feeding tolerance in preterm infants. Neonatology 
2007;91(3):174-9. 
41. Wang C, Shoji H, Sato H, Nagata S, Ohtsuka Y, Shimizu T, Yamashiro Y. Effects of oral administration of 
bifidobacterium breve on fecal lactic acid and short-chain fatty acids in low birth weight infants. J Pediatr Gastroenterol 
Nutr 2007, Feb;44(2):252-7. 
42. Arboleya S, Salazar N, Solís G, Fernández N, Hernández-Barranco AM, Cuesta I, et al. Assessment of intestinal 
microbiota modulation ability of bifidobacterium strains in in vitro fecal batch cultures from preterm neonates. Anaerobe 
2013, Feb;19:9-16. 
43. Patole S, Keil AD, Chang A, Nathan E, Doherty D, Simmer K, et al. Effect of bifidobacterium breve M-16V 
supplementation on fecal bifidobacteria in preterm neonates--a randomised double blind placebo controlled trial. PLoS 
ONE 2014;9(3):e89511. 
44. Borzak S, Ridker PM. Discordance between meta-analyses and large-scale randomized, controlled trials. Examples from 
the management of acute myocardial infarction. Ann Intern Med 1995, Dec 1;123(11):873-7.  
45. Louie TJ, Cannon K, Byrne B, et al. Fidaxomicin preserves the intestinal microbiome during and after treatment of   
Clostridium difficile infection (CDI) and reduces both toxin reexpression and recurrence of CDI. Clinical infectious 
diseases : an official publication of the Infectious Diseases Society of America. Aug 2012;55 Suppl 2:S132-142. 
46. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods. Oct 
2013;10(10):996-998. 
47. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the 
new bacterial taxonomy. Applied and environmental microbiology. Aug 2007;73(16):5261-5267. 
48. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. 
Nature methods. May 2010;7(5):335-336. 
49. DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench 
compatible with ARB. Applied and environmental microbiology. Jul 2006;72(7):5069-5072. 
50.  Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning 
sequences to a template alignment. Bioinformatics. Jan 15 2010;26(2):266-267. 
51. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PloS one. 
2010;5(3):e9490. 
52. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS computational 
biology. Apr 2014;10(4):e1003531. 
53. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. 
Genome biology. 2014;15(12):550. 
54. Morgan XC, Huttenhower C. Ch12: Human microbiome analysis. PLoS computational biology. 2012;8(12):e1002808. 
55. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome 
census data. PloS one. 2013;8(4):e61217. 
 

8