This webinar slidedeck will focus on the acquisition and development of the preterm gut microbiome from birth and following discharge from intensive care. Specifically, the discussion will be around the association of the gut microbiome with necrotizing enterocolitis (NEC) and late onset sepsis (LOS), as well as the impact of birth mode. The other discussion points will be the analysis of multi-omic datasets, including the analysis of the airway microbiome and metabolome in infants hospitalized with bronchiolitis.

1. Christopher J. Stewart, Ph.D.
Post Doctoral Associate
Baylor College of Medicine, Petrosino Lab

2. Agenda
1. Introduc=on to microbiome research at BCM
2. Normal microbiome development through infancy
3. Preterm microbiome development
4. Preterm microbiome in health and disease

3. TMC – 69 entities
21 renowned hospitals
14 support organizations
10 academic institutions
8 academic and research institutions
7 nursing programs
3 public health organizations
3 medical schools
2 pharmacy schools
1 dental school
Texas Medical Centre

4. Phenotype
of Interest
Controls
Samples
& Metadata Sequencing
Common
features
Dis=nguishing
features
Model
Targets
Large Scale Studies & Trials
Bioinforma=c Analyses
Cell Culture
Enteroids
Mice
GermFree mice
Bioreactors
C. elegans
In silico modeling
Taxa
Metabolites
Gene of Interest
Gene Clusters
Single taxa
Microbial Communi=es
Iden=fica=on & Isola=on
Culture
Dielectrophoresis
Ab-capture
Bacteria
Viruses
Fungi
Parasites
Taxonomic classifica=on, genome assemblies and annota=on,
sta=s=cal modeling, cluster, network, and compara=ve analyses,
and machine learning
​ 𝑑 𝑥/𝑑𝑡
=α 𝑥
− 𝐵𝑥𝑦
Observed Phenotype
Microbiome experimental design

5. DNA Extraction and Sequencing
Primary samples
Microbial DNA
Extraction Kits
16S – V4 PCR
Amplification
Illumina MiSeq
2x250bp
Raw – Pair End
sequences
Alpha Diversity
(Richness)
CMMR-16S
Pipeline
Quality Filtering
Demultiplexing
Mapping
Beta Diversity
(Community Analysis)
Taxonomic Abundance
(Phylum-Genus)
SILVA db. – v4 slice,
97% identity
Unique 12-mer
barcodes
Trim at first Q5
Merging
>50bp overlap, 0bp
mismatch
Error Filtering Filter cutoff 0.05
expected error
Automated
Manual
Biological
Environmental
Industrial

6. Agenda
1. Introduc=on to Microbiome research at BCM
2. Normal microbiome development through infancy
3. Preterm microbiome development
4. Preterm microbiome in health and disease

7. Role of the microbiome in humans
Laukens et al., 2015. FEMS

8. Factors influencing the microbiome
Aagaard, Stewart, Chu. 2016. EMBO Reports

9. Microbiome development from birth
Bokulich et al. Sci Trans Med (2016) Yassour et al. Sci Trans Med (2016)

10. Birth mode differences in year 1?
Yassour et al. Sci Trans Med (2016) Bokulich et al. Sci Trans Med (2016)

11. No birth mode association after 6
weeks?
Chu et al. Nature Medicine (2017)
P < 0.001
R2 = 0.189
P = 0.057
R2 = 0.007

12. CS increases later life disease risk
Sevelsted et al., Pediatrics (2015)

13. Pannaraj et al. 2017. JAMA
Breast feeding slows maturation of
the microbiome

14. Dogaru et al., Am J Epidemiology (2013)
117 study meta-analysis
Breast milk reduces risk of asthma

15. Breast milk reduces risk of obesity
Davis et al., Diabetes Care (2006)
15,253 children age 9-14 years old

16. Agenda
1. Introduc=on to Microbiome research at BCM
2. Normal microbiome development through infancy
3. Preterm microbiome development
4. Preterm microbiome in health and disease

17. Preterm Microbiome
Preterm microbiome is poten=ally altered due to:
• Increased C-sec=on
• Limited environmental exposure
• Increased an=bio=cs / an=fungals
• Reduced breast feeding

18. Key differences in microbiome
acquisition and development
Term infantPreterm infant
Child
?
Reduced:
Diversity
Stability
Bifidobacterium sp.
Lactobacillus sp.
Bacteroides sp.
Increased:
Klebsiella sp.
Staphylococcus sp.
Escherichia sp.
Enterococcus sp.
1-3 Years
of age
Full term Preterm
Stewart and Cummings, Taylor & Francis (In Press)

19. Birth Mode Cohort
Stewart, CJ. et al. 2017. FronDers in Microbiology

20. Comparable microbiome profiles
based on weighted UniFrac
Stewart, CJ. et al. 2017. FronDers in Microbiology
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−0.2
0.0
0.2
0.4
−0.5 0.0 0.5
PC1 (49.7% variation explained)
PC2(11.9%variationexplained)
Deliverymode_simple
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CS
V
P−Value: 0.925; R−Squared: 0.0114; F−Statistic: 0.346
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−0.4
−0.2
0.0
0.2
−0.4 0.0 0.4
PC1 (58.7% variation explained)
PC2(13.2%variationexplained)
Deliverymode_simple
●
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CS
V
P−Value: 0.646; R−Squared: 0.0137; F−Statistic: 0.556
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−0.4
−0.2
0.0
0.2
0.4
−0.25 0.00 0.25
PC1 (28.9% variation explained)
PC2(20.7%variationexplained)
Deliverymode_simple
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CS
V
P−Value: 0.795; R−Squared: 0.016; F−Statistic: 0.584
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−0.6
−0.4
−0.2
0.0
0.2
−0.50 −0.25 0.00 0.25 0.50
PC1 (32.3% variation explained)
PC2(17.9%variationexplained)
Deliverymode_simple
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CS
V
P−Value: 0.344; R−Squared: 0.0365; F−Statistic: 1.1
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−0.2
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−0.6 −0.3 0.0 0.3
PC1 (41.9% variation explained)
PC2(21.9%variationexplained)
Deliverymode_simple
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CS
V
P−Value: 0.45; R−Squared: 0.061; F−Statistic: 0.91
Cesarian
Vaginal
Week 1 Week 3
Week 5 Week 8
Post Discharge
P = 0.925 P = 0.646
P = 0.795 P = 0.344
P = 0.45
BA
DC
E

21. No difference in longitudinal alpha-
and beta- diversity
Stewart, CJ. et al. 2017. FronDers in Microbiology
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Age in Days
ObservedOTUs
Deliverymode_simple CS V
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1.0
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Age in Days
ShannonDiversity
Deliverymode_simple CS V
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Age in Days
WeightedUniFrac
Deliverymode_simple CS V
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0.4
0.6
0.8
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Age in Days
UnweightedUniFrac
Deliverymode_simple CS V
A B
C D
Observed OTUS Shannon Diversity
Weighted UniFrac Unweighted UniFrac
Age in days Age in days
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Age in Days
ObservedOTUs
Deliverymode_simple CS V
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20
30
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0 25 50 75 100
Age in Days
ObservedOTUs
Deliverymode_simple CS V
Cesarean
Vaginal

22. Vaginal infants ‘kept’ more OTUs
Stewart, CJ. et al. 2017. FronDers in Microbiology
Age in days Age in days
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5
10
15
0 25 50 75 100
Age in Days
OTUsKept
Deliverymode_simple CS V
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0 25 50 75 100
Age in Days
OTUsLost
Deliverymode_simple CS V
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6
9
0 25 50 75 100
Age in Days
OTUsRegained
Deliverymode_simple CS V
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15
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0 25 50 75 100
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NewOTUsGained
Deliverymode_simple CS V
C DOTUs Regained New OTUs Gained
A BOTUs Kept OTUs Lost
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ObservedOTUs
Deliverymode_simple CS V
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30
40
0 25 50 75
Age in Days
ObservedOTUs
Deliverymode_simple CS V
Cesarean
Vaginal

23. Comparable temporal development
of abundant taxa
Stewart, CJ. et al. 2017. FronDers in Microbiology

24. Post Discharge Cohort
Stewart, CJ. et al. 2016. Nature ScienDfic Reports

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DOL
AlphaDiversity
Disease
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Control
LOS
NEC
DOL
Shannon Diversity
4.0
PD
1 – 3 Yr
Stewart, CJ. et al., Nature ScienDfic Reports (2016)
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0.0
0.5
1.0
1.5
2.0
0 25 50 75 100
DOL
Disease
●
●
●
Control
LOS
NEC
DOL
Preterm infants restore diversity
post-discharge from NICU
Term infant Shannon diversity
Day of life

26. Diversity increased post discharge
NICU NICU
Stewart, CJ. et al. 2016. Nature ScienDfic Reports

27. Agenda
1. Introduc=on to Microbiome research at BCM
2. Normal microbiome development through infancy
3. Preterm microbiome development
4. Preterm microbiome in health and disease

28. Preterm Disease
empowher.com/files/ebsco/images/infant_sepsis.jpg
Necro=sing Enterocoli=s (NEC) and Late onset sepsis (LOS)
• Leading cause of death in preterm infants
• Prematurity of infant is the major risk factor
• Abnormal bacterial colonisa=on is a prerequisite
NEC LOS

29. Altered microbiome predicts NEC?
Warner, BB. et al. 2016. Lancet
• Increased Gammaproteobacteria in infants diagnosed with NEC acer day 30 of life only
• Most NEC is diagnosed prior to day 30 of life
• Shannon diversity increased in controls but remains consistent in infants later
diagnosed with NEC
• Findings driven by differences in infants under 27 weeks gesta=on

30. Bacterial load comparable
Abdulkadir, B. et al. 2016. Early Human Development

31. NEC/LOS vs Control Cohort
Stewart, CJ. et al. 2016. Microbiome
Stewart, CJ. et al. 2017. Microbiome (In Press)

32. Exis=ng data
Results

33. Bacterial profiles in NEC and LOS
Day of life
1
6
3
1
6
1
1
9
9
1
7
1
1
3
9
7 11 12 13 14 15 18 22 23
9 10 11 12 13 14 15 16 19 21 29 36 44 48 49 51 52 54 57 60 67 69
13 15 16 17 19 20 32 33 35 37 47 48 50 52 60 74
9 24 27 35 40 50 58 63 68 73 78 95 98
7 8 10 11 12 14 16 18 19 20 21 22 23 24 27 36 38 41 45 49 53 58 62 66 74 81 86 92
1
8
0
S.epidermidis
S.hominis
Day of life
1
3
0
2
5
1
1
7
2
1
7
3
1
6
6
5 6 8 9 11 12 14 16 18 20 23 26 29 32 38 41
8 9 10 11 12 13 14 18 19 21 22 26 28 30 34 37 39 45 41 54 58 67 72 83 87
8 9 11 14 16 17 18 22 45 48 49 50 51 54 58
2 8 12 14 15 17 18 19 20 24 26 32 33 34
4 7 9 14 15 20 22 27
S.aureus
E.faecalis
S.agalactiae
S.aureus
11 12 31 36 40 44 46 48
1
7
4
4 6 8 9 10 11 13 14 16 17 18 19 20 21 23 24 25 25 27 29 30 36 37 39 42
8 12 14 17 20 21 23 25 27 30 36 42 48 52 54 66 72 78 80 88
E.coli
1
8
1
11 13 14 24 25 26 27 28
1
7
8
S.epidermidis
S.epidermidis
A B

34. PGCT clustering heatmap (PAM)
●
●
●
●
●●
●●
●●
●●
●
●
A
Obs
0
10
20
30
40
50
A B
AlphaDiversity
1 2
A B
C
Status
PGCT
Stewart, CJ. et al. 2016. Microbiome

35. Alpha diversity increased in PGCT 6
●
●
●
●
●●
●●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
Adj. P = 1.7e−13
● ●
●●●●
●
Adj. P = 2.9e−61
Observed OTUs Shannon
0
10
20
30
40
50
0
1
2
A B C D E F A B C D E F
AlphaDiversity
PGSTletter
A
B
C
D
E
F
1 2 3 4 5 6 1 2 3 4 5 6
PGCT PGCT
** ****** *** ****** ***
B
Stewart, CJ. et al. 2016. Microbiome

36. PGCTs in NEC and LOS
Control_117
Control_131
Control_143
Control_152
Control_153
Control_156
Control_159
Control_167
Control_168
Control_176
Control_182
Control_186
Control_188
Control_203
Control_206
Control_207
Control_208
Control_209
Control_215
Control_222
Control_223
Control_224
Control_228
Control_229
Control_232
Control_234
Control_241
Control_253
LOS_130
LOS_166
LOS_172
LOS_173
LOS_178
LOS_181
LOS_251
NEC_139
NEC_161
NEC_163
NEC_171
NEC_174
NEC_180
NEC_199
0 10 20 30 40 50
DOL
SubjectOrder
PGST
1
2
3
4
5
6
Fraction PreLOS
0.0 0.5 1.0
1
2
3
4
5
6
Fraction PreNEC
0.0 0.5 1.0
1
2
3
4
5
6
A
B
C
Control_117
Control_131
Control_143
Control_152
Control_153
Control_156
Control_159
Control_167
Control_168
Control_176
Control_182
Control_186
Control_188
Control_203
Control_206
Control_207
Control_208
Control_209
Control_215
Control_222
Control_223
Control_224
Control_228
Control_229
Control_232
Control_234
Control_241
Control_253
LOS_130
LOS_166
LOS_172
LOS_173
LOS_178
LOS_181
LOS_251
NEC_139
NEC_161
NEC_163
NEC_171
NEC_174
NEC_180
NEC_199
0 10 20 30 40 50
DOL
SubjectOrder
PGST
1
2
3
4
5
6
Control_117
Control_131
Control_143
Control_152
Control_153
Control_156
Control_159
Control_167
Control_168
Control_176
Control_182
Control_186
Control_188
Control_203
Control_206
Control_207
Control_208
Control_209
Control_215
Control_222
Control_223
Control_224
Control_228
Control_229
Control_232
Control_234
Control_241
Control_253
LOS_130
LOS_166
LOS_172
LOS_173
LOS_178
LOS_181
LOS_251
NEC_139
NEC_161
NEC_163
NEC_171
NEC_174
NEC_180
NEC_199
0 10 20 30 40 50
DOL
SubjectOrder
PGST
1
2
3
4
5
6
PGCT
Stewart, CJ. et al. 2016. Microbiome

37. Increased stability in controls
1
2
3
4
5
6
PGCT
Vazquez-Baeza Y, et al (2013), Gigascience

38. Metabolic pathways associated with NEC
Stewart, CJ. et al. 2016. Microbiome
A
B
C
D
E
F
Control
NEC

39. Metabolic pathways associated with NEC
Stewart, CJ. et al. 2017. Microbiome (In Press)
Escherichia11beta,21-Dihydroxy-5beta
-pregnane-3,20-dione
Klebsiella
18-Hydroxycortisol
Raffinose
18-Oxocortisol
Bifidobacterium
Perillaldehyde
15-keto-PGE1Urocortisone
Galactan
PGE2-1-glyceryl ester
vitamin K
hydroquinone
10,11-dihydro-12R-
hydroxy-leukotriene E4
2-Phenyl-1,3-propanediol
monocarbamate
Ascorbic acid
Veillonella
10,11-dihydro-
leukotriene B4
3-Oxooctadecanoyl
-CoA
D-Glucosamine
Ribose
5-phosphate
Acetic acid
Pseudomonas
L-alpha-Acetyl-
N-normethadol
Enterococcus
Morganella
Bacteroides
Staphylococcus
Dihydroneopterin
Streptococcus
Significantly increased in LOS
Significantly increased in Controls
Not significant
−0.8 −0.2 0.8
Color key
Pos246.2177
Pos377.1921
Pos362.1
Pos497.2686
Pos287.0869
Pos292.1658
Pos506.2349
Pos167.1153
Pos159.0628
Pos477.1837
Pos242.1864
Pos47
Pos232.202
Pos4
Pos47
Pos2
Pos232.2021
Pos477.1832
Pos364.0988
Pos274.2126
Pos337.1687
Pos362.1559
Neg277.0878
Neg360.1433
Neg237.015
Klebsiella
Enterococcus
Bifido
Veillonella
Bacteroides
StreptocM
-0.8 0.8 0

40. Summary
• Longitudinal 16S rRNA gene sequencing studies useful to survey bacterial community
in clinical samples
• Limited to non-invasive stool sampling
• Addi=onal ‘omic technologies (e.g., transcriptomics, proteomics, and metabolomics)
facilitate func=onal analysis
• Correla=ons are important but causality remains elusive
• Discovery research requires further valida=on in animal models and ex vivo cell culture
• Valida=on and mechanis=c understanding of preterm research is especially challenging
due to unique phenotype of immature human gut
• Enteroids1 and organoids2 may pave a new fron=er in preterm research, allowing
ex vivo co-culture of microbiome, primary human cells, and leukocytes
1 – Zachos, NC. et al. 2016. JBC
2 – Hill, DR. et al. 2017. BioRxiv preprint

41. Andrew Nelson, PhD
Janet Berrington, MD
Nicholas Embleton, MD
Tom Skeath, MD
Stefan Zalewski, MD
John Perry, PhD
Joe Petrosino, PhD
Nadim Ajami, PhD
Daniel Smith, PhD
Ta=ana Fofanova, BSc
Majhew Wong, BSc
Kjers= Aagaard, MD PHD
Derrick Chu, BSc
Acknowledgements
Stephen Cummings, PhD
Caroline Orr, PhD
Elizabeth Clements, BSc
Sophie Hambleton, PhD
John Kirby, PhD
Chris Lamb, PhD
Carlos Camargo, MD
Kohei Hasegawa, MD
Janice Espinola, MPH
Jonathan Mansbach, MD, MPH
Amy Hair, MD
Roxana Fatemizadeh, MD

42. @CJStewart7
www.neonatalresearch.net
cs12@bcm.edu
Learn more about and apply for Microbiome Awards:
hYps://mobio.com/microbiome
Ques=ons? qiawebinars@qiagen.com
Visit QIAGEN’s microbiome solu=on: hYps://www.qiagen.com/products/life-
science-research/microbiology-research/
Contact QIAGEN: www.qiagen.com/about-us/contact/global-contacts/